## Main.ApplicationWebinars History

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<br>June 17, 2014<br>11 AM Eastern</a>

<br>May 27, 2014<br>11 AM Eastern</a>

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M4SM1B8Z07E957OOW8FIDPB4EU-18BV'>Reduced Order Modeling of Residential Buildings from Smart Meter Data</a>

</td></tr><tr><td>3 Minute Speed Networking

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<br>Apr 15, 2014<br>11 AM Eastern</a> </td><td nowrap><a href='/wiki/uploads/Main/2014_04_Krystian_Perez.pdf'>Krystian Perez</a> </td><td>University of Texas at Austin

</td></tr><tr><td>3 Minute Speed Networking

<br>Apr 22, 2014<br>11 AM Eastern</a> </td><td nowrap><a href='https://docs.google.com/spreadsheet/ccc?key=0AiFb4B-lVvk-dHNHQXEyaHk0Zk9LNFI3aklxZ1o1YWc&usp=sharing'>Sign up here</a> </td><td>

</td></tr><tr><td>Title TBD

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<span class="_start">27-05-2014 09:00:00</span> <span class="_end">27-05-2014 10:00:00</span>

<br>Apr 22, 2014<br>11 AM Eastern</a> </td><td nowrap><a href='https://docs.google.com/spreadsheet/ccc?key=0AiFb4B-lVvk-dHNHQXEyaHk0Zk9LNFI3aklxZ1o1YWc&usp=sharing'>Sign up here</a> </td><td>

<br>June 17, 2014<br>11 AM Eastern</a> </td><td nowrap>John Siirola </td><td>Sandia National Laboratory

</td></tr><tr><td>Meters to Models: Using Smart Meter Data to Predict and Control Home Energy Use </td><td><a href='http://youtu.be/ttyGCkgLWwM'><img src='http://img.youtube.com/vi/ttyGCkgLWwM/default.jpg'></a> </td><td>Apr 15, 2014 </td><td nowrap><a href='/wiki/uploads/Main/2014_04_Krystian_Perez.pdf'>Krystian Perez</a> </td><td>University of Texas at Austin

</td><td nowrap><a href='/wiki/upload/Main/2014_03_Krystian_Perez.pdf'>Krystian Perez</a>

</td><td nowrap><a href='/wiki/uploads/Main/2014_04_Krystian_Perez.pdf'>Krystian Perez</a>

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=MAXZ4UWO079ZXVCV7SFJUES7Y1-18BV'>Multi‐echelon Supply Chain Inventory Optimization</a>

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M4SM1B8Z07E957OOW8FIDPB4EU-18BV'>Reduced Order Modeling of Residential Buildings from Smart Meter Data</a>

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<br>Mar 25, 2014<br>11 AM Eastern</a> </td><td nowrap><a href='/wiki/uploads/Main/2014_03_Anshul_Agarwal_DOW.pdf'>Anshul Agarwal</a> </td><td>Dow Chemical

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

<br>Apr 15, 2014<br>11 AM Eastern</a> </td><td nowrap><a href='/wiki/upload/Main/2014_03_Krystian_Perez.pdf'>Krystian Perez</a> </td><td>University of Texas at Austin

</td></tr><tr><td>3 Minute Speed Networking

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<br>Apr 15, 2014<br>11 AM Eastern</a> </td><td nowrap>Krystian Perez </td><td>University of Texas at Austin

</td></tr><tr><td>3 Minute Speed Networking

<br>Apr 22, 2014<br>11 AM Eastern</a> </td><td nowrap><a href='https://docs.google.com/spreadsheet/ccc?key=0AiFb4B-lVvk-dHNHQXEyaHk0Zk9LNFI3aklxZ1o1YWc&usp=sharing'>Sign up here</a> </td><td>

</td></tr><tr><td>Title TBD

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<span class="_start">17-06-2014 09:00:00</span> <span class="_end">17-06-2014 10:00:00</span>

</td></tr><tr><td>Title TBD

<br>June 17, 2014<br>11 AM Eastern</a> </td><td nowrap>Urmila Diwekar </td><td>Vishwamitra Research Institute

</td></tr><tr><td>Control of Artificial Pancreas Systems

<span class="_start">17-06-2014 09:00:00</span> <span class="_end">17-06-2014 10:00:00</span>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<br>June 17, 2014<br>11 AM Eastern</a> </td><td nowrap>Urmila Diwekar </td><td>Vishwamitra Research Institute

</td></tr><tr><td>Control of Artificial Pancreas Systems </td><td nowrap>

<br>Sept 16, 2014<br>11 AM Eastern</a> </td><td nowrap>Ali Cinar </td><td>Illinois Institute of Technology

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling </td><td>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<span class="_start">30-09-2014 09:00:00</span> <span class="_end">30-09-2014 10:00:00</span>

<span class="_date_format">DD/MM/YYYY</span>

<br>Sept 16, 2014<br>11 AM Eastern</a> </td><td nowrap>Ali Cinar </td><td>Illinois Institute of Technology

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling </td><td>

<br>Sept 30, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS </td><td nowrap>

<span class="_start">30-09-2014 09:00:00</span> <span class="_end">30-09-2014 10:00:00</span>

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

<br>Sept 30, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS

<span class="_date_format">DD/MM/YYYY</span>

<br>Oct 14, 2014<br>11 AM Eastern</a> </td><td nowrap>Pieter Schmal </td><td><a href='http://www.psenterprise.com'>PSE</a> </td></tr>

</td></tr><tr><td>Topic TBD

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

<span class="_start">21-10-2014 09:00:00</span> <span class="_end">21-10-2014 10:00:00</span>

<br>Oct 14, 2014<br>11 AM Eastern</a> </td><td nowrap>Pieter Schmal </td><td><a href='http://www.psenterprise.com'>PSE</a> </td></tr>

</td></tr><tr><td>Topic TBD </td><td nowrap> <a href="http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars" title="Add to Calendar" class="addthisevent">

Add to Calendar <span class="_start">21-10-2014 09:00:00</span> <span class="_end">21-10-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Multi-echelon Supply Chain Inventory Optimization </td><td><a href='http://youtu.be/MKfgF3wzsdc'><img src='http://img.youtube.com/vi/MKfgF3wzsdc/default.jpg'></a> </td><td>Mar 25, 2014 </td><td nowrap><a href='/wiki/uploads/Main/2014_03_Anshul_Agarwal_DOW.pdf'>Anshul Agarwal</a> </td><td>Dow Chemical

</td></tr><tr><td>Title TBD </td><td nowrap> <a href="http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars" title="Add to Calendar" class="addthisevent">

Add to Calendar <span class="_start">17-06-2014 09:00:00</span> <span class="_end">17-06-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

<br>June 17, 2014<br>11 AM Eastern</a> </td><td nowrap>Urmila Diwekar </td><td>Vishwamitra Research Institute

</td><td nowrap><a href='2014_03_Anshul_Agarwal_DOW.pdf'>Anshul Agarwal</a>

</td><td nowrap><a href='/wiki/uploads/Main/2014_03_Anshul_Agarwal_DOW.pdf'>Anshul Agarwal</a>

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=MAXZ4UWO079ZXVCV7SFJUES7Y1-18BV'>Multi‐echelon Supply Chain Inventory Optimization</a>

</td><td nowrap>Anshul Agarwal<br>John Wassick

</td><td nowrap><a href='2014_03_Anshul_Agarwal_DOW.pdf'>Anshul Agarwal</a>

</td><td>Mar 11, 2014<br>11 AM Eastern</a>

</td><td>Mar 11, 2014

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=MBK6L1F1NCKLISQQ1PBRQVEC6E-18BV'>Parallel Computing and Opportunities in Nonlinear Optimization</a> </td><td>

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical </td><td nowrap>

<span class="_start">11-03-2014 09:00:00</span> <span class="_end">11-03-2014 10:00:00</span>

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<br>Mar 11, 2014<br>11 AM Eastern</a> </td><td><a href='/wiki/uploads/Main/2014_03_Carl_Laird.pdf'>Carl Laird</a> </td><td>Purdue University

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical

<br>Mar 25, 2014<br>11 AM Eastern</a> </td><td nowrap>Anshul Agarwal<br>John Wassick </td><td>Dow Chemical

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<br>Mar 25, 2014<br>11 AM Eastern</a> </td><td nowrap>Anshul Agarwal<br>John Wassick </td><td>Dow Chemical

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

<br>Apr 15, 2014<br>11 AM Eastern</a> </td><td nowrap>Krystian Perez </td><td>University of Texas at Austin

</td></tr><tr><td>3 Minute Speed Networking

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<br>Apr 15, 2014<br>11 AM Eastern</a> </td><td nowrap>Krystian Perez </td><td>University of Texas at Austin

</td></tr><tr><td>3 Minute Speed Networking

</td></tr><tr><td>Control of Artificial Pancreas Systems

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<br>Apr 22, 2014<br>11 AM Eastern</a> </td><td nowrap><a href='https://docs.google.com/spreadsheet/ccc?key=0AiFb4B-lVvk-dHNHQXEyaHk0Zk9LNFI3aklxZ1o1YWc&usp=sharing'>Sign up here</a>

<br>Sept 16, 2014<br>11 AM Eastern</a> </td><td nowrap>Ali Cinar </td><td>Illinois Institute of Technology

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling

</td></tr><tr><td>Control of Artificial Pancreas Systems </td><td nowrap>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<span class="_start">30-09-2014 09:00:00</span> <span class="_end">30-09-2014 10:00:00</span>

<span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling </td><td>

<br>Sept 30, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS </td><td nowrap>

<span class="_start">30-09-2014 09:00:00</span> <span class="_end">30-09-2014 10:00:00</span>

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

<br>Sept 30, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS

<span class="_date_format">DD/MM/YYYY</span>

<br>Oct 14, 2014<br>11 AM Eastern</a> </td><td nowrap>Pieter Schmal </td><td><a href='http://www.psenterprise.com'>PSE</a> </td></tr>

</td></tr><tr><td>Topic TBD

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

<span class="_start">21-10-2014 09:00:00</span> <span class="_end">21-10-2014 10:00:00</span>

</td></tr><tr><td>Topic TBD </td><td nowrap> <a href="http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars" title="Add to Calendar" class="addthisevent">

Add to Calendar <span class="_start">21-10-2014 09:00:00</span> <span class="_end">21-10-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Parallel Computing and Opportunities in Nonlinear Optimization </td><td><a href='http://youtu.be/0WnZhoeUYkk'><img src='http://img.youtube.com/vi/0WnZhoeUYkk/default.jpg'></a> </td><td>Mar 11, 2014<br>11 AM Eastern</a> </td><td><a href='/wiki/uploads/Main/2014_03_Carl_Laird.pdf'>Carl Laird</a> </td><td>Purdue University

</td></tr><tr><td>Parallel Optimization

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=MBK6L1F1NCKLISQQ1PBRQVEC6E-18BV'>Parallel Computing and Opportunities in Nonlinear Optimization</a>

</td><td>Carl Laird

</td><td><a href='/wiki/uploads/Main/2014_03_Carl_Laird.pdf'>Carl Laird</a>

<a href='/wiki/uploads/Main/2014_02_Martin_Schlueter_MIDACO.pdf'>Martin Schlueter</a>

</td><td><a href='/wiki/uploads/Main/2014_02_Martin_Schlueter_MIDACO.pdf'>Martin Schlueter</a>

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M8JSQZNIK7WKY6J9L5HEIQ4Q0E-18BV'>Global Optimization of MINLP by Evolutionary Algorithms</a>

</td></tr><tr><td>Parallel Optimization

<span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span>

<span class="_start">11-03-2014 09:00:00</span> <span class="_end">11-03-2014 10:00:00</span>

<br>Feb 25, 2014<br>5 PM Eastern</a> </td><td><a href='/wiki/uploads/Main/2014_02_Martin_Schlueter_MIDACO.pdf'>Martin Schlueter</a> </td><td>Hokkaido University, Japan (<a href='http://www.midaco-solver.com'>MIDACO</a>)

</td></tr><tr><td>Parallel Optimization </td><td>

<br>Mar 11, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Purdue University

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical </td><td nowrap>

<span class="_start">11-03-2014 09:00:00</span> <span class="_end">11-03-2014 10:00:00</span>

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<br>Mar 11, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Purdue University

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical

<br>Mar 25, 2014<br>11 AM Eastern</a> </td><td nowrap>Anshul Agarwal<br>John Wassick </td><td>Dow Chemical

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

</td></tr><tr><td>3 Minute Speed Networking

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

</td></tr><tr><td>3 Minute Speed Networking

</td></tr><tr><td>Control of Artificial Pancreas Systems

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<br>Apr 22, 2014<br>11 AM Eastern</a> </td><td nowrap><a href='https://docs.google.com/spreadsheet/ccc?key=0AiFb4B-lVvk-dHNHQXEyaHk0Zk9LNFI3aklxZ1o1YWc&usp=sharing'>Sign up here</a>

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling

</td></tr><tr><td>Control of Artificial Pancreas Systems </td><td nowrap>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<span class="_start">30-09-2014 09:00:00</span> <span class="_end">30-09-2014 10:00:00</span>

<span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling </td><td>

<br>Sept 30, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS </td><td nowrap>

<span class="_start">30-09-2014 09:00:00</span> <span class="_end">30-09-2014 10:00:00</span>

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

<br>Sept 30, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS

<span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Topic TBD

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

<span class="_start">21-10-2014 09:00:00</span> <span class="_end">21-10-2014 10:00:00</span>

</td></tr><tr><td>Topic TBD </td><td nowrap> <a href="http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars" title="Add to Calendar" class="addthisevent">

Add to Calendar <span class="_start">21-10-2014 09:00:00</span> <span class="_end">21-10-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Global Optimization of MINLP by Evolutionary Algorithms </td><td><a href='http://youtu.be/oCVib-sRbjY'><img src='http://img.youtube.com/vi/oCVib-sRbjY/default.jpg'></a> </td><td>Feb 25, 2014 <a href='/wiki/uploads/Main/2014_02_Martin_Schlueter_MIDACO.pdf'>Martin Schlueter</a> </td><td>Hokkaido University, Japan (<a href='http://www.midaco-solver.com'>MIDACO</a>)

</td><td>Martin Schlueter

</td><td><a href='/wiki/uploads/Main/2014_02_Martin_Schlueter_MIDACO.pdf'>Martin Schlueter</a>

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling </td><td>

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical </td><td nowrap>

<span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span>

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<br>Mar 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical

<span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

</td></tr><tr><td>3 Minute Speed Networking

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

</td></tr><tr><td>3 Minute Speed Networking

</td></tr><tr><td>Control of Artificial Pancreas Systems

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<br>Apr 22, 2014<br>11 AM Eastern</a> </td><td nowrap><a href='https://docs.google.com/spreadsheet/ccc?key=0AiFb4B-lVvk-dHNHQXEyaHk0Zk9LNFI3aklxZ1o1YWc&usp=sharing'>Sign up here</a>

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling

</td></tr><tr><td>Control of Artificial Pancreas Systems </td><td nowrap>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<span class="_start">30-09-2014 09:00:00</span> <span class="_end">30-09-2014 10:00:00</span>

<span class="_date_format">DD/MM/YYYY</span>

<br>Sept 30, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td><td><a href='http://youtu.be/ZR0ac1ZOuIo'><img src='http://img.youtube.com/vi/ZR0ac1ZOuIo/default.jpg'></a>

</td><td><a href='http://youtu.be/LbdbhVhDDbc'><img src='http://img.youtube.com/vi/LbdbhVhDDbc/default.jpg'></a>

<a href='/wiki/uploads/Main/2014_02_Jeffrey_Kelly.pdf'>Jeffrey Kelly</a>

</td><td><a href='/wiki/uploads/Main/2014_02_Jeffrey_Kelly.pdf'>Jeffrey Kelly</a>

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M57C7RRF9ZS2BPWXE7EWGQN7FB-18BV'>Industrial Flowsheet Optimization and Estimation using IMPL</a>

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M8JSQZNIK7WKY6J9L5HEIQ4Q0E-18BV'>Global Optimization of MINLP by Evolutionary Algorithms</a>

<span class="_start">18-02-2014 09:00:00</span> <span class="_end">18-02-2014 10:00:00</span>

<span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span>

<br>Feb 18, 2014<br>11 AM Eastern</a> </td><td><a href='/wiki/uploads/Main/2014_02_Jeffrey_Kelly.pdf'>Jeffrey Kelly</a> </td><td>Industrial Algorithms

</td></tr><tr><td>Global Optimization of MINLP by Evolutionary Algorithms

<br>Feb 25, 2014<br>5 PM Eastern</a> </td><td>Martin Schlueter </td><td>Hokkaido University, Japan (<a href='http://www.midaco-solver.com'>MIDACO</a>)

</td></tr><tr><td>Parallel Optimization

<span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span>

<span class="_start">11-03-2014 09:00:00</span> <span class="_end">11-03-2014 10:00:00</span>

<br>Feb 25, 2014<br>5 PM Eastern</a> </td><td>Martin Schlueter </td><td>Hokkaido University, Japan (<a href='http://www.midaco-solver.com'>MIDACO</a>)

</td></tr><tr><td>Parallel Optimization

<br>Mar 11, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Purdue University

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling

<span class="_start">11-03-2014 09:00:00</span> <span class="_end">11-03-2014 10:00:00</span>

<span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span>

<span class="_date_format">DD/MM/YYYY</span>

<br>Mar 11, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Purdue University

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling </td><td>

<br>Mar 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical </td><td nowrap>

<span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span>

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<br>Mar 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical

<span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

</td></tr><tr><td>3 Minute Speed Networking

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

</td></tr><tr><td>3 Minute Speed Networking

</td></tr><tr><td>Control of Artificial Pancreas Systems

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

</td></tr><tr><td>Control of Artificial Pancreas Systems

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS

</td></tr><tr><td>Topic TBD

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

<span class="_start">21-10-2014 09:00:00</span> <span class="_end">21-10-2014 10:00:00</span>

</td></tr><tr><td>Industrial Flowsheet Optimization and Estimation using IMPL </td><td><a href='http://youtu.be/ZR0ac1ZOuIo'><img src='http://img.youtube.com/vi/ZR0ac1ZOuIo/default.jpg'></a> </td><td>Feb 18, 2014 <a href='/wiki/uploads/Main/2014_02_Jeffrey_Kelly.pdf'>Jeffrey Kelly</a> </td><td>Industrial Algorithms

</td></tr><tr><td>Industrial Flowsheet Optimization and Estimation using IMPL

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M57C7RRF9ZS2BPWXE7EWGQN7FB-18BV'>Industrial Flowsheet Optimization and Estimation using IMPL</a>

</td><td>Jeff Kelly

</td><td><a href='/wiki/uploads/Main/2014_02_Jeffrey_Kelly.pdf'>Jeffrey Kelly</a>

</td></tr><tr><td>Industrial Flowsheet Optimization and Estimation using IMPL (Industrial Modeling & Programming Language)

</td></tr><tr><td>Industrial Flowsheet Optimization and Estimation using IMPL

</td><td nowrap>Antonio Flores Tlacuahuac

</td><td>Antonio Flores Tlacuahuac

<br>Oct 21, 2014<br>11 AM Eastern</a> </td><td nowrap>Antonio Flores Tlacuahuac </td><td>Universidad Iberoamericana </td></tr>

</td><td nowrap>Feb 11, 2014

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M10CMN75ACBW9KHDWJM3QYNUN1-18BV'>Feedback Control of <br>Stochastic Self-Assembly</a>

</td></tr><tr><td>Industrial Flowsheet Optimization and Estimation using IMPL (Industrial Modeling & Programming Language)

<span class="_start">11-02-2014 09:00:00</span> <span class="_end">11-02-2014 10:00:00</span>

<span class="_start">18-02-2014 09:00:00</span> <span class="_end">18-02-2014 10:00:00</span>

<br>Feb 11, 2014<br>11 AM Eastern</a> </td><td><a href='/wiki/uploads/Main/2014_02_Martha_Grover.pdf'>Martha Grover</a> </td><td>Georgia Tech

</td></tr><tr><td>Industrial Flowsheet Optimization and Estimation using IMPL (Industrial Modeling & Programming Language)

<br>Feb 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Kelly </td><td>Industrial Algorithms

</td></tr><tr><td>Global Optimization of MINLP by Evolutionary Algorithms

<span class="_start">18-02-2014 09:00:00</span> <span class="_end">18-02-2014 10:00:00</span>

<span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span>

<br>Feb 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Kelly </td><td>Industrial Algorithms

</td></tr><tr><td>Global Optimization of MINLP by Evolutionary Algorithms

<br>Feb 25, 2014<br>5 PM Eastern</a> </td><td>Martin Schlueter </td><td>Hokkaido University, Japan (<a href='http://www.midaco-solver.com'>MIDACO</a>)

</td></tr><tr><td>Parallel Optimization

<span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span>

<span class="_start">11-03-2014 09:00:00</span> <span class="_end">11-03-2014 10:00:00</span>

</td></tr><tr><td>Parallel Optimization

<br>Mar 11, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Purdue University

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling

<span class="_start">11-03-2014 09:00:00</span> <span class="_end">11-03-2014 10:00:00</span>

<span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span>

<span class="_date_format">DD/MM/YYYY</span>

<br>Mar 11, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Purdue University

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling </td><td>

<br>Mar 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical </td><td nowrap>

<span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span>

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<br>Mar 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical

<span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

</td></tr><tr><td>3 Minute Speed Networking

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

</td></tr><tr><td>3 Minute Speed Networking

</td></tr><tr><td>Control of Artificial Pancreas Systems

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

</td></tr><tr><td>Control of Artificial Pancreas Systems

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS </td><td nowrap> <a href="http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars" title="Add to Calendar" class="addthisevent">

Add to Calendar <span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Feedback Control of <br>Stochastic Self-Assembly </td><td><a href='http://youtu.be/PkvtPEjqLNU'><img src='http://img.youtube.com/vi/PkvtPEjqLNU/default.jpg'></a> </td><td><a href='/wiki/uploads/Main/2014_02_Martha_Grover.pdf'>Martha Grover</a> </td><td>Georgia Tech

</td><td><a href='/wiki/uploads/Main/2014_02_Martha_Grover.pdf>Martha Grover</a>

</td><td><a href='/wiki/uploads/Main/2014_02_Martha_Grover.pdf'>Martha Grover</a>

</td></tr><tr><td>Feedback Control of <br>Stochastic Self-Assembly

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M10CMN75ACBW9KHDWJM3QYNUN1-18BV'>Feedback Control of <br>Stochastic Self-Assembly</a>

</td><td>Martha Grover

</td><td><a href='/wiki/uploads/Main/2014_02_Martha_Grover.pdf>Martha Grover</a>

</td><td>Texas A&M

</td><td>Purdue University

<span class="_start">04-03-2014 09:00:00</span> <span class="_end">04-03-2014 10:00:00</span>

<span class="_start">11-03-2014 09:00:00</span> <span class="_end">11-03-2014 10:00:00</span>

<br>Mar 4, 2014<br>11 AM Eastern</a>

<br>Mar 11, 2014<br>11 AM Eastern</a>

</td><td nowrap>Jan 28, 2014

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=MCGPSBB7BJGA55TH6JVZMDZLQU-18BV'>New Interface Developments <br>with the AMPL Modeling Language & System</a> </td><td nowrap>

</td></tr><tr><td>Feedback Control of <br>Stochastic Self-Assembly </td><td>

<span class="_start">28-01-2014 09:00:00</span> <span class="_end">28-01-2014 10:00:00</span>

<span class="_start">11-02-2014 09:00:00</span> <span class="_end">11-02-2014 10:00:00</span>

<br>Jan 28, 2014<br>11 AM Eastern</a> </td><td nowrap><a href='/wiki/uploads/Main/2014_01_Robert_Fourer_AMPL.pdf'>Robert Fourer</a> </td><td>Northwestern University

</td></tr><tr><td>Feedback Control of <br>Stochastic Self-Assembly

<br>Feb 11, 2014<br>11 AM Eastern</a> </td><td>Martha Grover </td><td>Georgia Tech

<span class="_start">11-02-2014 09:00:00</span> <span class="_end">11-02-2014 10:00:00</span>

<span class="_start">18-02-2014 09:00:00</span> <span class="_end">18-02-2014 10:00:00</span>

<br>Feb 11, 2014<br>11 AM Eastern</a> </td><td>Martha Grover </td><td>Georgia Tech

<br>Feb 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Kelly </td><td>Industrial Algorithms

</td></tr><tr><td>Global Optimization of MINLP by Evolutionary Algorithms

<span class="_start">18-02-2014 09:00:00</span> <span class="_end">18-02-2014 10:00:00</span>

<span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span>

<br>Feb 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Kelly </td><td>Industrial Algorithms

</td></tr><tr><td>Global Optimization of MINLP by Evolutionary Algorithms

</td></tr><tr><td>Parallel Optimization

<span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span>

<span class="_start">04-03-2014 09:00:00</span> <span class="_end">04-03-2014 10:00:00</span>

</td></tr><tr><td>Parallel Optimization

<br>Mar 4, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Texas A&M

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling

<span class="_start">04-03-2014 09:00:00</span> <span class="_end">04-03-2014 10:00:00</span>

<span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span>

<span class="_date_format">DD/MM/YYYY</span>

<br>Mar 4, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Texas A&M

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling </td><td>

<br>Mar 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical </td><td nowrap>

<span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span>

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<br>Mar 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical

<span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

</td></tr><tr><td>3 Minute Speed Networking

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

</td></tr><tr><td>3 Minute Speed Networking

</td></tr><tr><td>Control of Artificial Pancreas Systems

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

</td></tr><tr><td>Control of Artificial Pancreas Systems

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS </td><td nowrap> <a href="http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars" title="Add to Calendar" class="addthisevent">

Add to Calendar <span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>New Interface Developments <br>with the AMPL Modeling Language & System </td><td nowrap><a href='http://youtu.be/8n1pd_cnRlw'><img src='http://img.youtube.com/vi/8n1pd_cnRlw/default.jpg'></a> </td><td nowrap><a href='/wiki/uploads/Main/2014_01_Robert_Fourer_AMPL.pdf'>Robert Fourer</a> </td><td>Northwestern University

</td><td>Pending Approval

</td><td><a href='http://youtu.be/pG0jQkwEivc'><img src='http://img.youtube.com/vi/pG0jQkwEivc/default.jpg'></a><br><a href='/wiki/uploads/Main/2014_01_Garcia_Hybrid_Energy.pdf'>Presentation</a>

</td><td nowrap>Robert Fourer

</td><td nowrap><a href='/wiki/uploads/Main/2014_01_Robert_Fourer_AMPL.pdf'>Robert Fourer</a>

</td></tr><tr><td>New Interface Developments <br>with the AMPL Modeling Language & System

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=MCGPSBB7BJGA55TH6JVZMDZLQU-18BV'>New Interface Developments <br>with the AMPL Modeling Language & System</a>

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M601J50Q2N45WNOSSBU03LJSIB-18BV'>Stochastic Optimal Control for Gas Networks</a>

</td></tr><tr><td>New Interface Developments <br>with the AMPL Modeling Language & System

<span class="_start">21-01-2014 09:00:00</span> <span class="_end">21-01-2014 10:00:00</span>

<span class="_start">28-01-2014 09:00:00</span> <span class="_end">28-01-2014 10:00:00</span>

<br>Jan 21, 2014<br>11 AM Eastern </a> </td><td><a href='/wiki/uploads/Main/WebCAST_Victor_Zavala.pdf'>Victor Zavala</a> </td><td>Argonne National Laboratory

</td></tr><tr><td>New Interface Developments <br>with the AMPL Modeling Language & System </td><td nowrap>

<br>Jan 28, 2014<br>11 AM Eastern</a> </td><td nowrap>Robert Fourer </td><td>Northwestern University

</td></tr><tr><td>Feedback Control of <br>Stochastic Self-Assembly </td><td>

<span class="_start">28-01-2014 09:00:00</span> <span class="_end">28-01-2014 10:00:00</span>

<span class="_start">11-02-2014 09:00:00</span> <span class="_end">11-02-2014 10:00:00</span>

<br>Jan 28, 2014<br>11 AM Eastern</a> </td><td nowrap>Robert Fourer </td><td>Northwestern University

</td></tr><tr><td>Feedback Control of <br>Stochastic Self-Assembly

<br>Feb 11, 2014<br>11 AM Eastern</a> </td><td>Martha Grover </td><td>Georgia Tech

<span class="_start">11-02-2014 09:00:00</span> <span class="_end">11-02-2014 10:00:00</span>

<span class="_start">18-02-2014 09:00:00</span> <span class="_end">18-02-2014 10:00:00</span>

<br>Feb 11, 2014<br>11 AM Eastern</a> </td><td>Martha Grover </td><td>Georgia Tech

<br>Feb 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Kelly </td><td>Industrial Algorithms

</td></tr><tr><td>Global Optimization of MINLP by Evolutionary Algorithms

<span class="_start">18-02-2014 09:00:00</span> <span class="_end">18-02-2014 10:00:00</span>

<span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span>

<br>Feb 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Kelly </td><td>Industrial Algorithms

</td></tr><tr><td>Global Optimization of MINLP by Evolutionary Algorithms

</td></tr><tr><td>Parallel Optimization

<span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span>

<span class="_start">04-03-2014 09:00:00</span> <span class="_end">04-03-2014 10:00:00</span>

</td></tr><tr><td>Parallel Optimization

<br>Mar 4, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Texas A&M

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling

<span class="_start">04-03-2014 09:00:00</span> <span class="_end">04-03-2014 10:00:00</span>

<span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span>

<span class="_date_format">DD/MM/YYYY</span>

<br>Mar 4, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Texas A&M

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling </td><td>

<br>Mar 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical </td><td nowrap>

<span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span>

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<br>Mar 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical

<span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

</td></tr><tr><td>3 Minute Speed Networking

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

</td></tr><tr><td>3 Minute Speed Networking

</td></tr><tr><td>Control of Artificial Pancreas Systems

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

</td></tr><tr><td>Control of Artificial Pancreas Systems

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS </td><td nowrap> <a href="http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars" title="Add to Calendar" class="addthisevent">

Add to Calendar <span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Stochastic Optimal Control for Gas Networks </td><td nowrap><a href='http://youtu.be/JYni77V1Nt8'><img src='http://img.youtube.com/vi/JYni77V1Nt8/default.jpg'></a> </td><td nowrap>Jan 21, 2014 </td><td nowrap><a href='/wiki/uploads/Main/WebCAST_Victor_Zavala.pdf'>Victor Zavala</a> </td><td>Argonne National Laboratory

</td><td>

</td><td>Pending Approval

</td><td><a href='/uploads/Main/WebCAST_Victor_Zavala.pdf'>Victor Zavala</a>

</td><td><a href='/wiki/uploads/Main/WebCAST_Victor_Zavala.pdf'>Victor Zavala</a>

</td><td><a href='WebCAST_Victor_Zavala.pdf'>Victor Zavala</a>

</td><td><a href='/uploads/Main/WebCAST_Victor_Zavala.pdf'>Victor Zavala</a>

</td><td>Victor Zavala

</td><td><a href='WebCAST_Victor_Zavala.pdf'>Victor Zavala</a>

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=MBELJJTFW1ANQTW4RP8C7URO59-18BV'>Process Integration & Optimization Using Dynamic Systems Models</a>

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M601J50Q2N45WNOSSBU03LJSIB-18BV'>Stochastic Optimal Control for Gas Networks</a>

<span class="_start">14-01-2014 09:00:00</span> <span class="_end">14-01-2014 10:00:00</span>

<span class="_start">21-01-2014 09:00:00</span> <span class="_end">21-01-2014 10:00:00</span>

<br>Jan 14, 2014<br>11 AM Eastern</a> </td><td nowrap>Richard Boardman<br>Humberto Garcia </td><td>Idaho National Laboratory

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M601J50Q2N45WNOSSBU03LJSIB-18BV'>Stochastic Optimal Control for Gas Networks</a>

<br>Jan 21, 2014<br>11 AM Eastern </a> </td><td>Victor Zavala </td><td>Argonne National Laboratory

</td></tr><tr><td>New Interface Developments <br>with the AMPL Modeling Language & System

<span class="_start">21-01-2014 09:00:00</span> <span class="_end">21-01-2014 10:00:00</span>

<span class="_start">28-01-2014 09:00:00</span> <span class="_end">28-01-2014 10:00:00</span>

<br>Jan 21, 2014<br>11 AM Eastern </a> </td><td>Victor Zavala </td><td>Argonne National Laboratory

</td></tr><tr><td>New Interface Developments <br>with the AMPL Modeling Language & System </td><td nowrap>

<br>Jan 28, 2014<br>11 AM Eastern</a> </td><td nowrap>Robert Fourer </td><td>Northwestern University

</td></tr><tr><td>Feedback Control of <br>Stochastic Self-Assembly </td><td>

<span class="_start">28-01-2014 09:00:00</span> <span class="_end">28-01-2014 10:00:00</span>

<span class="_start">11-02-2014 09:00:00</span> <span class="_end">11-02-2014 10:00:00</span>

<br>Jan 28, 2014<br>11 AM Eastern</a> </td><td nowrap>Robert Fourer </td><td>Northwestern University

</td></tr><tr><td>Feedback Control of <br>Stochastic Self-Assembly

<br>Feb 11, 2014<br>11 AM Eastern</a> </td><td>Martha Grover </td><td>Georgia Tech

<span class="_start">11-02-2014 09:00:00</span> <span class="_end">11-02-2014 10:00:00</span>

<span class="_start">18-02-2014 09:00:00</span> <span class="_end">18-02-2014 10:00:00</span>

<br>Feb 11, 2014<br>11 AM Eastern</a> </td><td>Martha Grover </td><td>Georgia Tech

<br>Feb 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Kelly </td><td>Industrial Algorithms

</td></tr><tr><td>Global Optimization of MINLP by Evolutionary Algorithms

<span class="_start">18-02-2014 09:00:00</span> <span class="_end">18-02-2014 10:00:00</span>

<span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span>

<br>Feb 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Kelly </td><td>Industrial Algorithms

</td></tr><tr><td>Global Optimization of MINLP by Evolutionary Algorithms

</td></tr><tr><td>Parallel Optimization

<span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span>

<span class="_start">04-03-2014 09:00:00</span> <span class="_end">04-03-2014 10:00:00</span>

</td></tr><tr><td>Parallel Optimization

<br>Mar 4, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Texas A&M

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling

<span class="_start">04-03-2014 09:00:00</span> <span class="_end">04-03-2014 10:00:00</span>

<span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span>

<span class="_date_format">DD/MM/YYYY</span>

<br>Mar 4, 2014<br>11 AM Eastern</a> </td><td>Carl Laird </td><td>Texas A&M

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling </td><td>

<br>Mar 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical </td><td nowrap>

<span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span>

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<br>Mar 18, 2014<br>11 AM Eastern</a> </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical

<span class="_date_format">DD/MM/YYYY</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

<span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span>

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data

</td></tr><tr><td>3 Minute Speed Networking

<span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span>

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

</td></tr><tr><td>3 Minute Speed Networking

</td></tr><tr><td>Control of Artificial Pancreas Systems

<span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span>

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

</td></tr><tr><td>Control of Artificial Pancreas Systems

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS

<span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span>

<span class="_start">14-10-2014 09:00:00</span> <span class="_end">14-10-2014 10:00:00</span>

</td></tr><tr><td>Process Integration & Optimization Using Dynamic Systems Models </td><td> </td><td nowrap>Jan 14, 2014 </td><td nowrap>Richard Boardman<br>Humberto Garcia </td><td>Idaho National Laboratory

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gProms

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gPROMS

<br>Sept 16, 2014<br>11 AM Eastern</a>

<br>Oct 14, 2014<br>11 AM Eastern</a>

</td></tr><tr><td>Dynamic Modeling and Optimization Advances with gProms </td><td nowrap> <a href="http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars" title="Add to Calendar" class="addthisevent">

<br>Sept 16, 2014<br>11 AM Eastern</a> </td><td nowrap>Pieter Schmal </td><td><a href='http://www.psenterprise.com'>PSE</a>

</td></tr><tr><td>3 Minute Speed Networking </td><td nowrap> <a href="http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars" title="Add to Calendar" class="addthisevent">

Add to Calendar <span class="_start">22-04-2014 09:00:00</span> <span class="_end">22-04-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

</td><td><a href='/wiki/uploads/Main/apm_advanced_deepwater_monitoring_7Nov13.pdf'>Presentation</a>

</td><td><a href='http://youtu.be/qaHAscHdQTI'><img src='http://img.youtube.com/vi/qaHAscHdQTI/default.jpg'></a><br><a href='/wiki/uploads/Main/apm_advanced_deepwater_monitoring_7Nov13.pdf'>Presentation</a>

<br>Apr 15, 2014<br>11 AM Eastern</a>

<br>Sept 16, 2014<br>11 AM Eastern</a>

</td></tr><tr><td>Control of Artificial Pancreas Systems </td><td nowrap> <a href="http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars" title="Add to Calendar" class="addthisevent">

Add to Calendar <span class="_start">16-09-2014 09:00:00</span> <span class="_end">16-09-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

<br>Apr 15, 2014<br>11 AM Eastern</a> </td><td nowrap>Ali Cinar </td><td>Illinois Institute of Technology

(:cell:)

(:cell:)

Webinars consist of applications and tutorials in mathematical modeling, estimation, and optimization. The sessions are hosted with WebEx with interactive chat, video conferencing, and remote computer desktop sharing. Each of the sessions is recorded and later posted online.

Webinars consist of applications and tutorials in mathematical modeling, estimation, and optimization. The sessions are hosted with WebEx with interactive chat, video conferencing, and remote computer desktop sharing. Each of the sessions is recorded and later posted online. Sessions are sponsored by the Computing and Systems Technology (CAST) division of the American Institute of Chemical Engineers (AIChE) as the WebCAST series.

(:cell:)

- Nominate a seminar speaker

<span class="_date_format">DD/MM/YYYY</span>

</td><td nowrap>Anand Govindarajan<br>Upasana Manimegalai-Sridhar<br>R. Russell Rhinehart

</td><td nowrap>Anand Govindarajan<br>Upasana<br>Manimegalai-Sridhar<br>R. Russell Rhinehart

</td><td>Anand Govindarajan<br>Upasana Manimegalai-Sridhar<br>R. Russell Rhinehart

</td><td nowrap>Anand Govindarajan<br>Upasana Manimegalai-Sridhar<br>R. Russell Rhinehart

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</td><td nowrap>Anand Govindarajan<br>Upasana Manimegalai-Sridhar<br>R. Russell Rhinehart

</td><td>Anand Govindarajan<br>Upasana Manimegalai-Sridhar<br>R. Russell Rhinehart

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</td><td nowrap><a href='http://ate.so/?0PHkr0T'>Jan 14, 2014<br>9 AM MST</a> </td><td nowrap>Richard Boardman<br>Humberto Garcia </td><td>Idaho National Laboratory

<span class="_start">21-01-2014 09:00:00</span> <span class="_end">21-01-2014 10:00:00</span>

<span class="_start">14-01-2014 09:00:00</span> <span class="_end">14-01-2014 10:00:00</span>

<span class="_description">Webinar</span>

<span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span>

<br>Jan 21, 2014<br>9 AM MST

<br>Jan 14, 2014<br>11 AM Eastern</a> </td><td nowrap>Richard Boardman<br>Humberto Garcia </td><td>Idaho National Laboratory

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M601J50Q2N45WNOSSBU03LJSIB-18BV'>Stochastic Optimal Control for Gas Networks</a> </td><td nowrap> <a href="http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars" title="Add to Calendar" class="addthisevent">

Add to Calendar <span class="_start">21-01-2014 09:00:00</span> <span class="_end">21-01-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

<br>Jan 21, 2014<br>11 AM Eastern

</td><td nowrap>Jan 28, 2014<br>9 AM MST

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Add to Calendar <span class="_start">28-01-2014 09:00:00</span> <span class="_end">28-01-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

<br>Jan 28, 2014<br>11 AM Eastern</a>

</td><td>Feb 11, 2014<br>9 AM MST

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Add to Calendar <span class="_start">11-02-2014 09:00:00</span> <span class="_end">11-02-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

<br>Feb 11, 2014<br>11 AM Eastern</a>

</td><td>Feb 18, 2014<br>9 AM MST

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Add to Calendar <span class="_start">18-02-2014 09:00:00</span> <span class="_end">18-02-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

<br>Feb 18, 2014<br>11 AM Eastern</a>

</td><td>Feb 25, 2014<br>3 PM MST

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Add to Calendar <span class="_start">25-02-2014 15:00:00</span> <span class="_end">25-02-2014 16:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

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</td><td>Mar 4, 2014<br>9 AM MST

Add to Calendar <span class="_start">04-03-2014 09:00:00</span> <span class="_end">04-03-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

<br>Mar 4, 2014<br>11 AM Eastern</a>

</td><td>Mar 18, 2014<br>9 AM MDT

Add to Calendar <span class="_start">18-03-2014 09:00:00</span> <span class="_end">18-03-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

<br>Mar 18, 2014<br>11 AM Eastern</a>

</td><td nowrap>Mar 25, 2014<br>9 AM MDT

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Add to Calendar <span class="_start">25-03-2014 09:00:00</span> <span class="_end">25-03-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

<br>Mar 25, 2014<br>11 AM Eastern</a>

</td><td nowrap>Apr 15, 2014<br>9 AM MDT

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Add to Calendar <span class="_start">15-04-2014 09:00:00</span> <span class="_end">15-04-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Join Webinar at http://apmonitor.com/wiki/index.php/Main/ApplicationWebinars</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

<br>Apr 15, 2014<br>11 AM Eastern</a>

Jan 21, 2014<br>9 AM MST

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</td><td nowrap>Jan 21, 2014<br>9 AM MST

Add to Calendar <span class="_start">21-01-2014 09:00:00</span> <span class="_end">21-01-2014 10:00:00</span> <span class="_zonecode">9</span> <span class="_summary">Symposium on Modeling and Optimization</span> <span class="_description">Webinar</span> <span class="_location">WebEx</span> <span class="_organizer">John Hedengren</span> <span class="_organizer_email">support@apmonitor.com</span> <span class="_all_day_event">false</span> <span class="_date_format">DD/MM/YYYY</span>

Jan 21, 2014<br>9 AM MST </a>

</td><td nowrap><a href='http://ate.so/?JUD3H2r'>Jan 14, 2014<br>9 AM MST</a>

</td><td nowrap><a href='http://ate.so/?0PHkr0T'>Jan 14, 2014<br>9 AM MST</a>

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</td><td nowrap><a href='http://ate.so/?JUD3H2r'>Jan 14, 2014<br>9 AM MST</a>

</td><td nowrap>Jan 14, 2014<br>9 AM MST

</td><td nowrap><a href=''http://ate.so/?JUD3H2r>Jan 14, 2014<br>9 AM MST</a>

</td><td nowrap>Jan 14, 2013<br>9 AM MST

</td><td nowrap>Jan 14, 2014<br>9 AM MST

</td><td nowrap>Jan 21, 2013<br>9 AM MST

</td><td nowrap>Jan 21, 2014<br>9 AM MST

</td><td><b>Date</b> </td><td><b>Time</b>

</td><td><b>Date / Time</b>

</td><td nowrap>Jan 14, 2013 </td><td nowrap>9 AM MST

</td><td nowrap>Jan 14, 2013<br>9 AM MST

</td><td nowrap>Jan 21, 2013 </td><td nowrap>9 AM MST

</td><td nowrap>Jan 21, 2013<br>9 AM MST

</td><td nowrap>Jan 28, 2014 </td><td nowrap>9 AM MST

</td><td nowrap>Jan 28, 2014<br>9 AM MST

</td><td>Feb 11, 2014 </td><td nowrap>9 AM MST

</td><td>Feb 11, 2014<br>9 AM MST

</td><td>Feb 18, 2014 </td><td nowrap>9 AM MST

</td><td>Feb 18, 2014<br>9 AM MST

</td><td>Feb 25, 2014 </td><td nowrap>3 PM MST

</td><td>Feb 25, 2014<br>3 PM MST

</td><td>Mar 4, 2014 </td><td nowrap>9 AM MST

</td><td>Mar 4, 2014<br>9 AM MST

</td><td>Mar 18, 2014 </td><td nowrap>9 AM MDT

</td><td>Mar 18, 2014<br>9 AM MDT

</td><td nowrap>Mar 25, 2014 </td><td nowrap>9 AM MDT

</td><td nowrap>Mar 25, 2014<br>9 AM MDT

</td><td nowrap>Apr 15, 2014 </td><td nowrap>9 AM MDT

</td><td nowrap>Apr 15, 2014<br>9 AM MDT

</td><td>BYU.

</td><td>BYU

</td><td><a href='http://youtu.be/M_5OIOZcoBc'><img src='http://img.youtube.com/vi/M_5OIOZcoBc/default.jpg'></a><br><br><a href='/wiki/uploads/Main/apm_minlp_tes.pdf'>Slides #1</a><br><hr><br><a href='http://youtu.be/yNeTF8_XSyw'><img src='http://img.youtube.com/vi/yNeTF8_XSyw/default.jpg'></a><br><br><a href='/wiki/uploads/Main/apm_minlp_mpc.pdf'>Slides #2</a>

</td><td><a href='http://youtu.be/M_5OIOZcoBc'><img src='http://img.youtube.com/vi/M_5OIOZcoBc/default.jpg'></a><br><br><a href='http://youtu.be/yNeTF8_XSyw'><img src='http://img.youtube.com/vi/yNeTF8_XSyw/default.jpg'></a>

<br><br><a href='/wiki/uploads/Main/mars_rover_curiosity.pdf'>Slides</a>

</td><td><a href='/wiki/uploads/Main/apm_reactive_distillation_17July12.pdf'>Presentation</a><br><a href='http://youtu.be/1hnBFU-rFBY'><img src='http://img.youtube.com/vi/1hnBFU-rFBY/default.jpg'></a>

</td><td><a href='http://youtu.be/1hnBFU-rFBY'><img src='http://img.youtube.com/vi/1hnBFU-rFBY/default.jpg'></a>

</td><td><b>Description</b>

</td><td><b>Affiliation</b>

</td><td>This presentation details environmental and economic progress on co-generation or multi-generation systems with SOFCs, nuclear, coal, natural gas power generation.

</td><td>McMaster University

</td><td nowrap>Hugh Hales, BYU </td><td>Hugh Hales shares thoughts on careers, leadership, and reservoir engineering research.

</td><td nowrap>Hugh Hales </td><td>Brigham Young University

</td><td>Reza Asgharzadeh Shishivan, BYU </td><td>PhD prospectus defense on monitoring and control of energy systems, particularly with oil and gas drilling and production systems

</td><td>Reza Asgharzadeh Shishivan </td><td>Brigham Young University

</td><td><a href="/wiki/uploads/Main/2013_SOFC_Jacobsen_webinar.pdf">Improving Solid Oxide Fuel Cell performance through dynamic optimization.</a>

</td><td>ExxonMobil Chemical<br>BYU

</td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations.

</td><td>Itasca Houston, Inc

</td><td>John Eason and Selen Cremaschi<br>Univ of Tulsa </td><td>Surrogate models, by constructing simpler functional approximations of complex and computationally expensive models, allow the use of traditional optimization algorithms for otherwise intractable problems. This talk discusses three possible algorithms for efficient surrogate model building.

</td><td>John Eason and Selen Cremaschi </td><td>Univ of Tulsa

</td><td>Milan Vukov and Moritz Diehl, KU Leuven </td><td>Fast estimation and control techniques are critical for real-time applications. Researchers at KU Leuven will share cutting-edge methods to apply Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) for real-time applications.

</td><td>Milan Vukov<br>Moritz Diehl </td><td>KU Leuven

</td><td nowrap>Cara R. Touretzky<br>Michael Baldea<br>UT Austin </td><td>Buildings are dynamic systems defined by fluctuations in weather, occupancy and energy prices. A nonlinear model-based optimal energy management strategy demonstrates significant energy savings compared to setpoint tracking strategies.

</td><td nowrap>Cara R. Touretzky<br>Michael Baldea </td><td>UT Austin

</td><td>Wesley Cole, UT Austin <br><br>Jeremy Castagno, BYU </td><td>Many design, engineering, and scientific applications include a mixture of continuous and discrete decision variables and nonlinear relationships. Mixed Integer Nonlinear Programming (MINLP) solvers are reviewed and compared for application to a scheduling problem for UT Austin central campus cooling.

</td><td>Wesley Cole<br><br>Jeremy Castagno </td><td>UT Austin<br><br>BYU

</td><td>Juan Ruiz, Visual MESA </td><td>Even though there has been a significant increase in the use of MIP models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP).

</td><td>Juan Ruiz </td><td>Visual MESA

</td><td>Kody Powell, UT Austin </td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant requires the use of conditional arguments that change the system dynamics. New formulations of <a href='http://apmonitor.com/wiki/index.php/Apps/MpecExamples'>Mathematical Programs with Equilibrium Constraints (MPECs)</a> have improved convergence properties to enable more realistic dynamic simulations.

</td><td>Kody Powell </td><td>UT Austin

</td><td>Todd Barber, NASA JPL </td><td>The Curiosity robot is equipped with a nuclear-powered lab capable of vaporizing rocks and ingesting soil, measuring habitability, and potentially paving the way for human exploration. Todd will review interesting and unusual experiences in his career as a NASA engineer. He will present audio-visual highlights and recent findings of the Mars Rover Missions. (Spirit, Opportunity, and Curiosity).

</td><td>Todd Barber </td><td>NASA JPL

</td><td>Liang Sun, BYU </td><td>Moving Horizon Estimation (MHE) is used to update a mathematical model during real-time flight tests. Results from the flight tests demonstrate that large-scale models can be applied in real-time applications for estimation and control.

</td><td>Liang Sun </td><td>BYU

</td><td>The equations that simulate separation processes naturally involve higher index Differential and Algebraic Equations (DAEs). Other popular techniques for DAE simulation can often only handle up to index-1 DAEs. New techniques for large-scale DAEs allow efficient solution for bifurcation analysis, simulation of complex systems, and stability analysis.

</td><td>BASF

</td><td>Prediction of protein structures is a rapidly advancing area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems.

</td></tr><tr><td>Optimization of Design Under Uncertainty

</td><td>BYU

</td></tr><tr><td>Optimization of Design Under Uncertainty<br><a href='/wiki/uploads/Main/apm_uncertain_params.zip'>Download example application</a>

</td><td>Process design has a number of unknown or uncertain parameters that influence the optimal design, desired operational conditions, and project profitability. Optimization of design maximizes the expected NPV (Net Present Value) considering uncertain design parameters. <a href='/wiki/uploads/Main/apm_uncertain_params.zip'>Download example application</a>

</td></tr><tr><td>SBML Models in APM

</td><td>BYU

</td></tr><tr><td><a href='/wiki/index.php/Main/SBML'>SBML Models in APM</a>

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/wiki/index.php/Main/SBML'>APM conversion utility</a> and solving SBML example problems.

</td><td>BYU.

</td><td>Trevor Slade and Reza Asgharzadeh </td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

</td></tr><tr><td>Optimal Boiler Control

</td><td>Trevor Slade<br>Reza Asgharzadeh </td><td>BYU

</td></tr><tr><td>Optimal Boiler Control - <a href='/wiki/uploads/Main/apm_mpc_demo.zip'>Demo Files</a>

</td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers. <a href='/wiki/uploads/Main/apm_mpc_demo.zip'>Download example application</a>

</td><td>BYU

</td><td>To identify models for MPC applications in chemical plants and refineries, the process is perturbed through increasingly automated step testing. Mark will give a tutorial overview of how to apply practical control engineering and design experiments for model development of dynamic systems.

</td><td>CMiD Solutions

</td><td>Reza Asgharzadeh, Chris Lyden, and Jim Huff </td><td>Thermal oxidizers are used in chemical plants and refineries to combust waste streams with low concentrations of reactants. The design and operation of the thermal oxidizer is of crucial importance for safety, environmental, and economic reasons.

</td></tr><tr><td>Nonlinear Programming with APM MATLAB

</td><td>Reza Asgharzadeh<br>Chris Lyden<br>Jim Huff </td><td>BYU / PAS

</td></tr><tr><td>Nonlinear Programming with <a href='http://apmonitor.com/wiki/index.php/Main/MATLAB'>APM MATLAB</a>

</td><td>The APM interface extends MATLAB to be used for parameter estimation, nonlinear control, and optimization. Participants can <a href='/wiki/uploads/Main/apm_demo.zip'>download a few example applications</a> to run prior to the meeting that will be covered as part of the tutorial.

</td></tr><tr><td>Nonlinear Programming with APM Python

</td><td>BYU

</td></tr><tr><td>Nonlinear Programming with <a href='http://apmonitor.com/wiki/index.php/Main/PythonApp'>APM Python</a>

</td><td>The APM interface extends Python to be used a variety of optimization applications. Dynamic optimization programming applications are demonstrated with Python.

</td><td>BYU

</td><td>Solid Oxide Fuel Cells (SOFCs) can be damaged by load changes that shorten life through delamination and micro-cracking. Application of SOFCs in load following applications is discussed to improve lifecycle costs.

</td><td>BYU

</td><td>Path optimization of an Unmanned Aerial Vehicle (UAV) becomes more challenging with select constraints. Application of a UAV application is discussed from the MAGICC Lab group at BYU.

</td><td>BYU

</td><td>Kody Powell, UT Austin </td><td>Dynamic thermal energy storage with weather forecasting has the potential to improve solar energy capture by up to 64%. Results from a recent study are discussed to demonstrate the benefits of dynamic optimization.

</td><td>Kody Powell </td><td>UT Austin

</td><td>Large scale biological models are important to gain an understanding of complex pathways to improve treatments such as anti-viral application. Simulated clinical trial data is aligned to an available model of HIV infection.

</td><td>BYU

</td><td>Friction Stir Welding (FSW) temperature control is important to achieve uniform and consistent weld properties. This application is using a PDAE model for estimation and control. The model validation, control, and experimental data testing are discussed.

</td><td>BYU

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=MBELJJTFW1ANQTW4RP8C7URO59-18BV'>Process Integration & Optimization Using Dynamic Systems Models</a> </td><td nowrap>Jan 14, 2013 </td><td nowrap>9 AM MST </td><td nowrap>Richard Boardman<br>Humberto Garcia </td><td>Idaho National Laboratory

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M31LFRF7ZY64F6TPZ0WYELKQV1-18BV'>A Dynamic Optimization Framework with Economic MPC for Energy Capacity Planning</a> </td><td nowrap>Dec 3, 2013

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M601J50Q2N45WNOSSBU03LJSIB-18BV'>Stochastic Optimal Control for Gas Networks</a> </td><td nowrap>Jan 21, 2013

</td><td>Jose Mojica </td><td>Brigham Young University

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M601J50Q2N45WNOSSBU03LJSIB-18BV'>Stochastic Optimal Control for Gas Networks</a> </td><td nowrap>Jan 21, 2013 </td><td nowrap>9 AM MST

### 5 Steps to Start a Technical Presentation

### Past Presentations

<iframe width="560" height="315" src="http://www.youtube.com/embed/lWBr7ploeNM" frameborder="0" allowfullscreen></iframe> (:htmlend:)

### Past Presentations

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<br> <iframe width="560" height="315" src="//www.youtube.com/embed/videoseries?list=PLLBUgWXdTBDjhBUNyTPIYCJsH4CAkpKzP" frameborder="0" allowfullscreen></iframe> <br>

</td></tr><tr><td>A Dynamic Optimization Framework with Economic MPC for Energy Capacity Planning </td><td><a href='http://youtu.be/0PbPhGcVtAw'><img src='http://img.youtube.com/vi/0PbPhGcVtAw/default.jpg'></a> </td><td nowrap>Dec 3, 2013 </td><td>Jose Mojica </td><td>Brigham Young University

</td><td><a href='http://youtu.be/xq3ku8drlXA'>Presentation (62 min)</a>

</td><td><a href='http://youtu.be/xq3ku8drlXA'><img src='http://img.youtube.com/vi/xq3ku8drlXA/default.jpg'></a>

</td><td><a href='http://youtu.be/HAhXf9RBmhI'>Panel Discussion (61 min)</a>

</td><td><a href='http://youtu.be/HAhXf9RBmhI'><img src='http://img.youtube.com/vi/HAhXf9RBmhI/default.jpg'></a>

</td><td><a href='http://youtu.be/B_4DZ5UNiCo'>Presentation (51 min)</a>

</td><td><a href='http://youtu.be/B_4DZ5UNiCo'><img src='http://img.youtube.com/vi/B_4DZ5UNiCo/default.jpg'></a>

</td><td><a href='http://youtu.be/Sq5USrBOt-E'>Presentation (55 min)</a>

</td><td><a href='http://youtu.be/Sq5USrBOt-E'><img src='http://img.youtube.com/vi/Sq5USrBOt-E/default.jpg'></a>

</td><td><a href='http://youtu.be/20n_KtEy6Y4'>Presentation (40 min)</a>

</td><td><a href='http://youtu.be/20n_KtEy6Y4'><img src='http://img.youtube.com/vi/20n_KtEy6Y4/default.jpg'></a>

</td><td><a href='http://youtu.be/Dbk69Rh_ovY'>Presentation (35 min)</a>

</td><td><a href='http://youtu.be/Dbk69Rh_ovY'><img src='http://img.youtube.com/vi/Dbk69Rh_ovY/default.jpg'></a>

</td><td><a href='http://youtu.be/_7rHP9-mPj0'>Presentation (55 min)</a>

</td><td><a href='http://youtu.be/_7rHP9-mPj0'><img src='http://img.youtube.com/vi/_7rHP9-mPj0/default.jpg'></a>

</td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations. These techniques will allow the industry to gain greater insight and understanding of how natural fracture geometry and intensity, fracture hydromechanical properties, and stress field affect hydraulic fracturing performance in the presence of different operational parameters.

</td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations.

</td><td><a href='http://youtu.be/bDa5isKRS-4'>Presentation (43 min)</a>

</td><td><a href='http://youtu.be/bDa5isKRS-4'><img src='http://img.youtube.com/vi/bDa5isKRS-4/default.jpg'></a>

</td><td><a href='http://youtu.be/zMu1bSOlOAA'>Presentation (57 min)</a>

</td><td><a href='http://youtu.be/zMu1bSOlOAA'><img src='http://img.youtube.com/vi/zMu1bSOlOAA/default.jpg'></a>

</td><td><a href='http://youtu.be/wQ0SsfzGo6I'>Presentation (34 min)</a>

</td><td><a href='http://youtu.be/wQ0SsfzGo6I'><img src='http://img.youtube.com/vi/wQ0SsfzGo6I/default.jpg'></a>

</td><td><a href='http://youtu.be/M_5OIOZcoBc'>Presentation #1 (28 min)</a><br><br><a href='/wiki/uploads/Main/apm_minlp_tes.pdf'>Slides #1</a><br><hr><br><a href='http://youtu.be/yNeTF8_XSyw'>Presentation #2 (14 min)</a><br><br><a href='/wiki/uploads/Main/apm_minlp_mpc.pdf'>Slides #2</a>

</td><td><a href='http://youtu.be/M_5OIOZcoBc'><img src='http://img.youtube.com/vi/M_5OIOZcoBc/default.jpg'></a><br><br><a href='/wiki/uploads/Main/apm_minlp_tes.pdf'>Slides #1</a><br><hr><br><a href='http://youtu.be/yNeTF8_XSyw'><img src='http://img.youtube.com/vi/yNeTF8_XSyw/default.jpg'></a><br><br><a href='/wiki/uploads/Main/apm_minlp_mpc.pdf'>Slides #2</a>

</td><td><a href='http://youtu.be/dfgzaVd8gLg'>Presentation (34 min)</a>

</td><td><a href='http://youtu.be/dfgzaVd8gLg'><img src='http://img.youtube.com/vi/dfgzaVd8gLg/default.jpg'></a>

</td><td>Mixed-Integer Programming (MIP) provides a framework for mathematically modeling many optimization problems that involve discrete and continuous variables. Even though there has been a significant increase in the use of these models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP), a modeling framework that aims at tackling the above question.

</td><td>Even though there has been a significant increase in the use of MIP models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP).

<br><a href='http://youtu.be/wS8ru1IcVBs'>Intro_1<br>(2.5 min)</a> <br><br><a href='http://youtu.be/OG2TAYiHz6s'>Intro_2<br>(3 min)</a> <br><br><a href='http://youtu.be/5DuNvTWg62Q'>Presentation (30 min)</a>

<br><a href='http://youtu.be/wS8ru1IcVBs'><img src='http://img.youtube.com/vi/wS8ru1IcVBs/default.jpg'></a> <br><br><a href='http://youtu.be/OG2TAYiHz6s'><img src='http://img.youtube.com/vi/OG2TAYiHz6s/default.jpg'></a> <br><br><a href='http://youtu.be/5DuNvTWg62Q'><img src='http://img.youtube.com/vi/5DuNvTWg62Q/default.jpg'></a>

</td><td><a href='http://youtu.be/N9hXqzkH7YA'>Introduction (2.5 min)</a> <br><br><a href='http://youtu.be/cfku_J4Ectk'>Presentation (59 min)</a>

</td><td><a href='http://youtu.be/N9hXqzkH7YA'><img src='http://img.youtube.com/vi/N9hXqzkH7YA/default.jpg'></a> <br><br><a href='http://youtu.be/cfku_J4Ectk'><img src='http://img.youtube.com/vi/cfku_J4Ectk/default.jpg'></a>

</td><td><a href='http://youtu.be/j2w66_yC7Jk'>Introduction (3 min)</a> <br><br><a href='http://youtu.be/toJHFdG4N2A'>Presentation (28 min)</a>

</td><td><a href='http://youtu.be/j2w66_yC7Jk'><img src='http://img.youtube.com/vi/j2w66_yC7Jk/default.jpg'></a> <br><br><a href='http://youtu.be/toJHFdG4N2A'><img src='http://img.youtube.com/vi/toJHFdG4N2A/default.jpg'></a>

</td><td><a href='/wiki/uploads/Main/apm_reactive_distillation_17July12.pdf'>Presentation</a><br><a href='http://youtu.be/1hnBFU-rFBY'>Webinar Video</a>

</td><td><a href='/wiki/uploads/Main/apm_reactive_distillation_17July12.pdf'>Presentation</a><br><a href='http://youtu.be/1hnBFU-rFBY'><img src='http://img.youtube.com/vi/1hnBFU-rFBY/default.jpg'></a>

</td><td>Prediction of protein structures is a rapidly advancing area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems. Methods to align experimental results with simulation are discussed.

</td><td>Prediction of protein structures is a rapidly advancing area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems.

</td><td><a href='http://www.youtube.com/watch?v=86W71ocI8Ww&feature=share&list=UU2GuY-AxnNxIJFAVfEW0QFA'>Presentation (59 min)</a>

</td><td><a href='http://youtu.be/86W71ocI8Ww'><img src='http://img.youtube.com/vi/86W71ocI8Ww/default.jpg'></a>

</td><td><a href='http://youtu.be/1E7uDNAWp5U'><img src='http://img.youtube.com/vi/vSSR3qvX_os/default.jpg'></a>

</td><td><a href='http://youtu.be/vSSR3qvX_os'><img src='http://img.youtube.com/vi/vSSR3qvX_os/default.jpg'></a>

</td><td><a href='http://youtu.be/1E7uDNAWp5U'>Presentation (45 min)</a>

</td><td><a href='http://youtu.be/1E7uDNAWp5U'><img src='http://img.youtube.com/vi/1E7uDNAWp5U/default.jpg'></a>

</td><td><a href='http://youtu.be/SpK-fhpI-Ic'>Presentation (42 min)</a>

</td><td><a href='http://youtu.be/SpK-fhpI-Ic'><img src='http://img.youtube.com/vi/SpK-fhpI-Ic/default.jpg'></a>

</td><td><a href='http://youtu.be/1E7uDNAWp5U'><img href='http://img.youtube.com/vi/vSSR3qvX_os/default.jpg'></a>

</td><td><a href='http://youtu.be/1E7uDNAWp5U'><img src='http://img.youtube.com/vi/vSSR3qvX_os/default.jpg'></a>

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=MB8BYLEHUE7W9NPTI2YFXTMF5Y-18BV'>Multicomponent Prediction Methods</a> </td><td nowrap>Nov 19, 2013

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M31LFRF7ZY64F6TPZ0WYELKQV1-18BV'>A Dynamic Optimization Framework with Economic MPC for Energy Capacity Planning</a> </td><td nowrap>Dec 3, 2013

</td><td>Jacob Reynolds </td><td>Washington River Protection Solutions

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M31LFRF7ZY64F6TPZ0WYELKQV1-18BV'>A Dynamic Optimization Framework with Economic MPC for Energy Capacity Planning</a> </td><td nowrap>Dec 3, 2013 </td><td nowrap>9 AM MST

</td></tr><tr><td>Multicomponent Prediction Methods for Hanford Nuclear Waste </td><td><a href='http://youtu.be/1E7uDNAWp5U'><img href='http://img.youtube.com/vi/vSSR3qvX_os/default.jpg'></a> </td><td nowrap>Nov 19, 2013 </td><td>Jacob Reynolds </td><td>Washington River Protection Solutions

</td></tr><tr><td>Stochastic Optimal Control for Gas Networks

</td></tr><tr><td>A Dynamic Optimization Framework with Economic MPC for Energy Capacity Planning

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=M31LFRF7ZY64F6TPZ0WYELKQV1-18BV'>A Dynamic Optimization Framework with Economic MPC for Energy Capacity Planning</a>

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</td></tr><tr><td>Multicomponent Prediction Methods

</td></tr><tr><td><a href='https://meetings.webex.com/collabs/meetings/join?uuid=MB8BYLEHUE7W9NPTI2YFXTMF5Y-18BV'>Multicomponent Prediction Methods</a>

</td><td>Dow Chemical

</td><td>University of Texas at Austin

</td><td>Dow Chemical

</td></tr><tr><td>Reduced Order Modeling of Residential Buildings from Smart Meter Data </td><td nowrap>Apr 15, 2014 </td><td nowrap>9 AM MDT </td><td nowrap>Krystian Perez

</td></tr><tr><td>A Dynamic Optimization Framework with Economic MPC for Energy Capacity Planning </td><td nowrap>Dec 3, 2013 </td><td nowrap>9 AM MST </td><td>Jose Mojica </td><td>Brigham Young University

</td><td>Nov 19, 2013

</td><td nowrap>Nov 19, 2013

</td><td>Jan 21, 2013

</td><td nowrap>Jan 21, 2013

</td><td>Jan 28, 2014

</td><td nowrap>Jan 28, 2014

</td><td>Mar 25, 2014

</td><td nowrap>Mar 25, 2014

</td></tr><tr><td>UAVs for Infrastructure <br>Monitoring </td><td>Dec 5, 2013 </td><td nowrap>4 PM MST </td><td>Kevin Franke </td><td>BYU Civil Engineering

</td><td>January 28, 2014

</td><td>Jan 28, 2014

</td><td>February 11, 2014

</td><td>Feb 11, 2014

</td><td>February 18, 2014

</td><td>Feb 18, 2014

</td><td>February 25, 2014

</td><td>Feb 25, 2014

</td><td>March 4, 2014

</td><td>Mar 4, 2014

</td><td>March 18, 2014

</td><td>Mar 18, 2014

</td><td>March 25, 2014

</td><td>Mar 25, 2014

</td></tr><tr><td>Stochastic Optimal Control for Gas Networks </td><td>Jan 21, 2013 </td><td nowrap>9 AM MST </td><td>Victor Zavala </td><td>Argonne National Laboratory

</td><td>Hokkaido University, Japan

</td><td>Hokkaido University, Japan (<a href='http://www.midaco-solver.com'>MIDACO</a>)

</td></tr><tr><td><a href='/wiki/uploads/Main/2013_Wesley_Cole.pdf'>Improved Grid Management through Predictive Residential Air Conditioning Control</a> </td><td>Oct 31, 2013 </td><td nowrap>4 PM MDT </td><td>Wesley Cole </td><td>The University of Texas at Austin

</td></tr><tr><td>Advanced Deep Water <br>Monitoring Systems </td><td>Nov 7, 2013 </td><td nowrap>4 PM MST </td><td>David Brower </td><td>Astro Technology, Inc.

</td></tr><tr><td>Advanced Deep Water <br>Monitoring Systems </td><td><a href='/wiki/uploads/Main/apm_advanced_deepwater_monitoring_7Nov13.pdf'>Presentation</a> </td><td>Nov 7, 2013 </td><td>David Brower </td><td>Astro Technology, Inc.

</td></tr><tr><td><a href='/wiki/uploads/Main/2013_Wesley_Cole.pdf'>Improved Grid Management through Predictive Residential Air Conditioning Control</a> </td><td><a href='http://youtu.be/1E7uDNAWp5U'>Presentation (45 min)</a> </td><td>Oct 31, 2013 </td><td>Wesley Cole </td><td>The University of Texas at Austin

</td></tr><tr><td>Global Optimization of MINLP by Evolutionary Algorithms </td><td>February 25, 2014 </td><td nowrap>3 PM MST </td><td>Martin Schlueter </td><td>Hokkaido University, Japan

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</td></tr><tr><td>Improved Grid Management through Predictive Residential Air Conditioning Control

</td></tr><tr><td><a href='/wiki/uploads/Main/2013_Wesley_Cole.pdf'>Improved Grid Management through Predictive Residential Air Conditioning Control</a>

</td><td>Robert Fourer

</td><td nowrap>Robert Fourer

</td><td>Anshul Agarwal<br>John Wassick

</td><td nowrap>Anshul Agarwal<br>John Wassick

</td></tr><tr><td>Supply Chain Optimization in Dow Chemical </td><td>March 25, 2014 </td><td nowrap>9 AM MDT </td><td>Anshul Agarwal<br>John Wassick </td><td>Dow Chemical

To participate in an upcoming presentation, click the link above to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

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To participate in an upcoming presentation, click the link above to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

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Webinars are held about every two-weeks. These seminars consist of applications and tutorials in mathematical modeling, estimation, and optimization.

(:keywords webinar, modeling, optimization, technical:) (:description Browse sessions for the Symposium on Modeling and Optimization, an online forum for sharing the latest applications and technological developments in process systems engineering.:)

Webinars consist of applications and tutorials in mathematical modeling, estimation, and optimization. The sessions are hosted with WebEx with interactive chat, video conferencing, and remote computer desktop sharing. Each of the sessions is recorded and later posted online.

</td></tr><tr><td>Tuning the Static and Dynamic Performance of Microchannel Reactors </td><td>Oct 22, 2013 </td><td nowrap>10 AM MDT </td><td nowrap>Richard Pattison<br>Michael Baldea </td><td>The University of Texas at Austin

</td><td>Oct 29, 2013 </td><td nowrap>9 AM MDT

</td><td>Oct 31, 2013 </td><td nowrap>4 PM MDT

</td></tr><tr><td>Tuning the Static and Dynamic Performance of Microchannel Reactors </td><td><a href='http://youtu.be/SpK-fhpI-Ic'>Presentation (42 min)</a> </td><td>Oct 22, 2013 </td><td nowrap>Richard Pattison<br>Michael Baldea </td><td>The University of Texas at Austin

</td><td nowrap>Michael Baldea

</td><td nowrap>Richard Pattison<br>Michael Baldea

</td></tr><tr><td>Leapfrog Applications in APC and Plant Optimization </td><td>Oct 15, 2013 </td><td nowrap>9 AM MDT </td><td nowrap>Anand Govindarajan<br>Upasana Manimegalai-Sridhar<br>R. Russell Rhinehart </td><td>Oklahoma State University

</td></tr><tr><td>Leapfrog Applications in APC and Plant Optimization </td><td><a href='http://www.youtube.com/watch?v=86W71ocI8Ww&feature=share&list=UU2GuY-AxnNxIJFAVfEW0QFA'>Presentation (59 min)</a> </td><td>Oct 15, 2013 </td><td nowrap>Anand Govindarajan<br>Upasana Manimegalai-Sridhar<br>R. Russell Rhinehart </td><td>Oklahoma State University

</td><td nowrap>9 AM MST

</td><td nowrap>9 AM MDT

</td></tr><tr><td>Parallel Optimization </td><td>March 4, 2014

</td></tr><tr><td>Industrial Flowsheet Optimization and Estimation using IMPL (Industrial Modeling & Programming Language) </td><td>February 18, 2014

</td><td>Jeff Kelly </td><td>Industrial Algorithms

</td></tr><tr><td>Parallel Optimization </td><td>March 4, 2014 </td><td nowrap>9 AM MST

</td></tr><tr><td>Progress and Challenges in Equation Oriented Dynamic Modeling </td><td>March 18, 2014 </td><td nowrap>9 AM MST </td><td>Jeff Renfro </td><td>Honeywell Process Solutions

</td></tr><tr><td>Tutorial on Bayesian Approaches for State and Parameter Estimation </td><td>Oct 8, 2013

</td></tr><tr><td>Leapfrog Applications in APC and Plant Optimization </td><td>Oct 15, 2013

</td><td nowrap>Bhushan Gopaluni </td><td>University of British Columbia, Vancouver

</td></tr><tr><td>Leapfrog Applications in APC and Plant Optimization </td><td>Oct 15, 2013 </td><td nowrap>9 AM MDT

</td></tr><tr><td>Tutorial on Bayesian Approaches for State and Parameter Estimation </td><td><a href='http://youtu.be/xq3ku8drlXA'>Presentation (62 min)</a> </td><td>Oct 8, 2013 </td><td nowrap>Bhushan Gopaluni </td><td>University of British Columbia, Vancouver

</td></tr><tr><td>New Interface Developments <br>with the AMPL Modeling Language & System </td><td>January 28, 2014 </td><td nowrap>9 AM MST </td><td>Robert Fourer </td><td>Northwestern University

- See http://www.youtube.com/playlist?list=PLLBUgWXdTBDjhBUNyTPIYCJsH4CAkpKzP for recordings

- See YouTube Playlist for recordings

- See APMonitorCom YouTube Channel for recordings

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</td></tr><tr><td>Tutorial on Methods for State and Parameter Estimation

</td></tr><tr><td>Tutorial on Bayesian Approaches for State and Parameter Estimation

</td></tr><tr><td><a href="/wiki/uploads/Main/olefin_optimization.pdf">Prospects and Challenges<br> for Olefins Production</a><br><br><a href="/wiki/uploads/Main/olefin_speakers.pdf">Speaker Introduction</a><br><br><a href="/wiki/uploads/Main/olefin_presentation.pdf">Discussion Topics</a> </td><td>Oct 3, 2013

</td></tr><tr><td>Tutorial on Methods for State and Parameter Estimation </td><td>Oct 8, 2013

</td><td nowrap>Mark Darby<br>Doug Nicholson<br>Judson Wooters<br>Richard Hughes </td><td nowrap>CMiD Solutions<br>APEX Optimization<br>Chevron Phillips<br>SABIC UK

</td></tr><tr><td>Tutorial on Methods for State and Parameter Estimation </td><td>Oct 8, 2013 </td><td nowrap>9 AM MDT

</td></tr><tr><td><a href="/wiki/uploads/Main/olefin_optimization.pdf">Prospects and Challenges<br> for Olefins Production</a><br><br><a href="/wiki/uploads/Main/olefin_speakers.pdf">Speaker Introduction</a><br><br><a href="/wiki/uploads/Main/olefin_presentation.pdf">Discussion Topics</a> </td><td><a href='http://youtu.be/HAhXf9RBmhI'>Panel Discussion (61 min)</a> </td><td>Oct 3, 2013 </td><td nowrap>Mark Darby<br>Doug Nicholson<br>Judson Wooters<br>Richard Hughes </td><td nowrap>CMiD Solutions<br>APEX Optimization<br>Chevron Phillips<br>SABIC UK

</td></tr><tr><td><a href="/wiki/uploads/Main/olefin_optimization.pdf">Prospects and Challenges<br> for Olefins Production</a><br><a href="/wiki/uploads/Main/olefin_presentation.pdf">Discussion Topics</a>

</td></tr><tr><td><a href="/wiki/uploads/Main/olefin_optimization.pdf">Prospects and Challenges<br> for Olefins Production</a><br><br><a href="/wiki/uploads/Main/olefin_speakers.pdf">Speaker Introduction</a><br><br><a href="/wiki/uploads/Main/olefin_presentation.pdf">Discussion Topics</a>

</td></tr><tr><td><a href="/wiki/uploads/Main/olefin_optimization.pdf">Prospects and Challenges<br> for Olefins Production</a>

</td></tr><tr><td><a href="/wiki/uploads/Main/olefin_optimization.pdf">Prospects and Challenges<br> for Olefins Production</a><br><a href="/wiki/uploads/Main/olefin_presentation.pdf">Discussion Topics</a>

</td></tr><tr><td>Future Polygeneration Systems: <br>Environmental and Economic Progress

</td></tr><tr><td>Future Polygeneration Systems

</td><td>

</td><td>This presentation details environmental and economic progress on co-generation or multi-generation systems with SOFCs, nuclear, coal, natural gas power generation.

</td></tr><tr><td>Future Polygeneration Systems: <br>Environmental and Economic Progress </td><td>Sept 24, 2013 </td><td nowrap>10 AM MDT </td><td nowrap>Thomas Adams, II </td><td>McMaster University

</td></tr><tr><td>Future Polygeneration Systems: <br>Environmental and Economic Progress </td><td><a href='http://youtu.be/B_4DZ5UNiCo'>Presentation (51 min)</a> </td><td>Sept 24, 2013 </td><td nowrap>Thomas Adams, II </td><td>

</td><td nowrap>9 AM MDT

</td><td nowrap>10 AM MDT

</td></tr><tr><td>Tuning the Static and Dynamic Performance of Microchannel Reactors </td><td>Oct 22, 2013 </td><td nowrap>9 AM MDT </td><td nowrap>Michael Baldea </td><td>The University of Texas at Austin

</td></tr><tr><td>Research and Careers in <br>Reservoir Engineering </td><td>Sept 10, 2013 </td><td nowrap>3 PM MDT </td><td nowrap>Hugh Hales </td><td>Brigham Young University

</td></tr><tr><td>Research and Careers in Reservoir Engineering </td><td><a href='http://youtu.be/Sq5USrBOt-E'>Presentation (55 min)</a> </td><td>Sept 10, 2013 </td><td nowrap>Hugh Hales, BYU </td><td>Hugh Hales shares thoughts on careers, leadership, and reservoir engineering research.

</td></tr><tr><td>BYU Research and Careers in <br>Reservoir Engineering

</td></tr><tr><td>Research and Careers in <br>Reservoir Engineering

</td><td nowrap>9 AM MDT

</td><td nowrap>10 AM MDT

</td></tr><tr><td>TBD

</td></tr><tr><td>Parallel Optimization

</td></tr><tr><td>TBD

</td></tr><tr><td>Future Polygeneration Systems: <br>Environmental and Economic Progress

</td></tr><tr><td><a href="/wiki/uploads/Main/olefin_optimization_webinar.pdf">Prospects and Challenges<br> for Olefins Production</a>

</td></tr><tr><td><a href="/wiki/uploads/Main/olefin_optimization.pdf">Prospects and Challenges<br> for Olefins Production</a>

</td></tr><tr><td>BYU Research and Careers in <br>Reservoir Engineering </td><td>Sept 10, 2013 </td><td nowrap>3 PM MDT </td><td nowrap>Hugh Hales </td><td>Brigham Young University

</td><td nowrap>4 PM MDT

</td><td nowrap>4 PM MST

</td><td nowrap>9 AM MDT

</td><td nowrap>9 AM MST

</td><td nowrap>9 AM MDT

</td><td nowrap>9 AM MST

</td></tr><tr><td>Feedback Control of <br>Stochastic Self-Assembly </td><td>February 11, 2014 </td><td nowrap>9 AM MDT </td><td>Martha Grover </td><td>Georgia Tech

</td><td nowrap>CMiD Solutions<br>APEX Optimization<br>Chevron Phillips<br>SABIC UK<br>Dow

</td><td nowrap>CMiD Solutions<br>APEX Optimization<br>Chevron Phillips<br>SABIC UK

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</td></tr><tr><td>Tutorial on Methods for State and Parameter Estimation </td><td>Sept 10, 2013

</td></tr><tr><td>TBD </td><td>Sept 24, 2013

</td><td nowrap>Bhushan Gopaluni </td><td>University of British Columbia, Vancouver

</td></tr><tr><td>TBD </td><td>Sept 24, 2013 </td><td nowrap>9 AM MDT

</td></tr><tr><td>Tutorial on Methods for State and Parameter Estimation </td><td>Oct 8, 2013 </td><td nowrap>9 AM MDT </td><td nowrap>Bhushan Gopaluni </td><td>University of British Columbia, Vancouver

</td></tr><tr><td>TBD </td><td>March 4, 2014 </td><td nowrap>9 AM MDT </td><td>Carl Laird </td><td>Texas A&M

</td><td>TBD

</td><td>Oct 15, 2013

</td></tr><tr><td>Leap Frog Applications in APC and Plant Optimization

</td></tr><tr><td>Leapfrog Applications in APC and Plant Optimization

</td><td>Anand Govindarajan<br>Upasana Manimegalai-Sridhar<br>R. Russell Rhinehart

</td><td nowrap>Anand Govindarajan<br>Upasana Manimegalai-Sridhar<br>R. Russell Rhinehart

</td></tr><tr><td>Leap Frog Applications in APC and Plant Optimization </td><td>TBD </td><td nowrap>9 AM MDT </td><td>Anand Govindarajan<br>Upasana Manimegalai-Sridhar<br>R. Russell Rhinehart </td><td>Oklahoma State University

</td><td nowrap>9 AM MST

</td><td nowrap>9 AM MDT

</td></tr><tr><td>Improved Grid Management through Predictive Residential Air Conditioning Control </td><td>Oct 29, 2013 </td><td nowrap>9 AM MST </td><td>Wesley Cole </td><td>The University of Texas at Austin

</td><td nowrap>Mark Darby<br>Doug Nicholson<br>Judson Wooters<br>Richard Hughes<br>Brian Ashcraft

</td><td nowrap>Mark Darby<br>Doug Nicholson<br>Judson Wooters<br>Richard Hughes

</td></tr><tr><td>Monitoring, Control, and Optimization of Energy Infrastructure </td><td>Aug 26, 2013

</td></tr><tr><td>Tutorial on Methods for State and Parameter Estimation </td><td>Sept 10, 2013

</td><td nowrap>Reza Asgharzadeh Shishivan </td><td>Brigham Young University

</td></tr><tr><td>Tutorial on Methods for State and Parameter Estimation </td><td>Sept 10, 2013 </td><td nowrap>9 AM MDT

</td></tr><tr><td>Monitoring, Control, and Optimization of Energy Infrastructure </td><td><a href='http://youtu.be/20n_KtEy6Y4'>Presentation (40 min)</a> </td><td>Aug 26, 2013 </td><td>Reza Asgharzadeh Shishivan, BYU </td><td>PhD prospectus defense on monitoring and control of energy systems, particularly with oil and gas drilling and production systems

</td></tr><tr><td>Monitoring, Control, and Optimization of Energy Infrastructure </td><td>Aug 26, 2013 </td><td nowrap>9 AM MDT </td><td nowrap>Reza Asgharzadeh Shishivan </td><td>Brigham Young University

- Visit http://byu.webex.com

</td></tr><tr><td>Multicomponent Prediction Methods </td><td>Nov 19, 2013 </td><td nowrap>9 AM MST </td><td>Jacob Reynolds </td><td>Washington River Protection Solutions

</td></tr><tr><td>TBD </td><td>Sept 24, 2013 </td><td nowrap>9 AM MDT </td><td nowrap>Thomas Adams, II </td><td>McMaster University

</td></tr><tr><td><a href="/wiki/uploads/Main/2013_SOFC_Jacobsen_webinar.pdf">Dynamic Optimization of <br>a Solid Oxide Fuel Cell</a> </td><td>July 2, 2013 </td><td nowrap>10 AM MDT </td><td>Lee Jacobsen </td><td>See <a href="http://www.linkedin.com/pub/lee-jacobsen/44/b92/148">LinkedIn Profile</a>

</td></tr><tr><td><a href="/wiki/uploads/Main/2013_SOFC_Jacobsen_webinar.pdf">Dynamic Optimization of <br>a Solid Oxide Fuel Cell</a> </td><td><a href='http://youtu.be/Dbk69Rh_ovY'>Presentation (35 min)</a> </td><td>July 2, 2013 </td><td><a href="http://www.linkedin.com/pub/lee-jacobsen/44/b92/148">Lee Jacobsen</a> </td><td><a href="/wiki/uploads/Main/2013_SOFC_Jacobsen_webinar.pdf">Improving Solid Oxide Fuel Cell performance through dynamic optimization.</a>

</td></tr><tr><td>Tutorial on Estimation Methods for State and Parameter Estimation

</td></tr><tr><td>Tutorial on Methods for State and Parameter Estimation

</td></tr><tr><td>Tutorial on Estimation Methods <br>for State and Parameter Estimation

</td></tr><tr><td>Tutorial on Estimation Methods for State and Parameter Estimation

</td><td>Bhushan Gopaluni

</td><td nowrap>Bhushan Gopaluni

</td></tr><tr><td>Tutorial on Estimation Methods <br>for State and Parameter Estimation </td><td>Sept 10, 2013 </td><td nowrap>9 AM MDT </td><td>Bhushan Gopaluni </td><td>University of British Columbia, Vancouver

</td></tr><tr><td>UAVs for Infrastructure Monitoring

</td></tr><tr><td>UAVs for Infrastructure <br>Monitoring

</td></tr><tr><td><a href="/wiki/uploads/Main/olefin_optimization_webinar.pdf">Prospects and Challenges for Olefins Production</a>

</td></tr><tr><td><a href="/wiki/uploads/Main/olefin_optimization_webinar.pdf">Prospects and Challenges<br> for Olefins Production</a>

</td></tr><tr><td>Advanced Deep Water Monitoring Systems

</td></tr><tr><td>Advanced Deep Water <br>Monitoring Systems

</td></tr><tr><td><a href="/wiki/uploads/Main/2013_SOFC_Jacobsen_webinar.pdf">Dynamic Optimization of a Solid Oxide Fuel Cell</a>

</td></tr><tr><td><a href="/wiki/uploads/Main/2013_SOFC_Jacobsen_webinar.pdf">Dynamic Optimization of <br>a Solid Oxide Fuel Cell</a>

</td></tr><tr><td>Nonlinear MPC of a Solid Oxide Fuel Cell

</td></tr><tr><td><a href="/wiki/uploads/Main/2013_SOFC_Jacobsen_webinar.pdf">Dynamic Optimization of a Solid Oxide Fuel Cell</a>

</td><td nowrap>12 PM MDT

</td><td nowrap>10 AM MDT

</td><td>BYU / ExxonMobil Chemical

</td><td>See <a href="http://www.linkedin.com/pub/lee-jacobsen/44/b92/148">LinkedIn Profile</a>

</td></tr><tr><td>Process Control in the Upstream Industry </td><td>TBD </td><td nowrap>4 PM MDT </td><td>Michael Nikolaou </td><td>University of Houston

</td></tr><tr><td>Nonlinear MPC of a Solid Oxide Fuel Cell </td><td>July 2, 2013 </td><td nowrap>12 PM MDT </td><td>Lee Jacobsen </td><td>BYU / ExxonMobil Chemical

</td><td>Mark Darby<br>Doug Nicholson<br>Judson Wooters<br>Richard Hughes<br>Brian Ashcraft </td><td>CMiD Solutions<br>APEX Optimization<br>Chevron Phillips<br>SABIC UK<br>Dow

</td><td nowrap>Mark Darby<br>Doug Nicholson<br>Judson Wooters<br>Richard Hughes<br>Brian Ashcraft </td><td nowrap>CMiD Solutions<br>APEX Optimization<br>Chevron Phillips<br>SABIC UK<br>Dow

</td></tr><tr><td>New Opportunities in Olefins Production

</td></tr><tr><td><a href="/wiki/uploads/Main/olefin_optimization_webinar.pdf">Prospects and Challenges for Olefins Production</a>

</td></tr><tr><td>New Challenges and Opportunities in the Optimization of Olefins Production

</td></tr><tr><td>New Opportunities in Olefins Production

</td></tr><tr><td>Optimization of Olefins Production

</td></tr><tr><td>New Challenges and Opportunities in the Optimization of Olefins Production

</td><td>CMiD Solutions

</td><td>CMiD Solutions<br>APEX Optimization<br>Chevron Phillips<br>SABIC UK<br>Dow

</td><td>Mark Darby

</td><td>Mark Darby<br>Doug Nicholson<br>Judson Wooters<br>Richard Hughes<br>Brian Ashcraft

</td></tr><tr><td>Advanced Deep Water Monitoring Systems </td><td>Nov 7, 2013 </td><td nowrap>4 PM MST </td><td>David Brower </td><td>Astro Technology, Inc.

</td><td>Oct 31, 2013

</td><td>Dec 5, 2013

</td></tr><tr><td>Advanced Deep Water Monitoring Systems </td><td>Nov 7, 2013 </td><td nowrap>4 PM MST </td><td>David Brower </td><td>Astro Technology, Inc.

</td><td nowrap>4 PM MDT

</td><td nowrap>9 AM MDT

</td><td>Oct 17, 2013

</td><td>Oct 3, 2013

</td></tr><tr><td>UAVs for Infrastructure Monitoring </td><td>Sept 19, 2013

</td></tr><tr><td>Optimization of Olefins Production </td><td>Oct 17, 2013

</td><td>Kevin Franke </td><td>BYU Civil Engineering

</td></tr><tr><td>Optimization of Olefins Production </td><td>Oct 10, 2013 </td><td nowrap>4 PM MDT

</td></tr><tr><td>UAVs for Infrastructure Monitoring </td><td>Oct 31, 2013 </td><td nowrap>4 PM MDT </td><td>Kevin Franke </td><td>BYU Civil Engineering

</td><td>Sept 12, 2013

</td><td>Sept 19, 2013

</td><td nowrap>4 PM MDT

</td><td nowrap>4 PM MST

</td></tr><tr><td>Advanced Deep Water Monitoring Systems </td><td>Sept 26, 2013

</td></tr><tr><td>Optimization of Olefins Production </td><td>Oct 10, 2013

</td><td>David Brower </td><td>Astro Technology, Inc.

</td></tr><tr><td>Optimization of Olefins Production </td><td>Oct 10, 2013 </td><td nowrap>4 PM MDT

</td><td>Oct 24, 2013

</td><td>TBD

</td></tr><tr><td>Advanced Deep Water Monitoring Systems </td><td>Nov 7, 2013 </td><td nowrap>4 PM MDT </td><td>David Brower </td><td>Astro Technology, Inc.

</td><td><a href='/wiki/uploads/Main/apmug_fsw_15Nov11.pdf'>Presentation</a>

</td><td><a href='/wiki/uploads/Main/apm_fsw_15Nov11.pdf'>Presentation</a>

</td><td>Presentation

</td><td><a href='/wiki/uploads/Main/apmug_fsw_15Nov11.pdf'>Presentation</a>

</td><td><b>Description</b>

</td></tr><tr><td>Adaptive Model Reduction </td><td>Jan 29, 2013 </td><td nowrap>9 AM MST </td><td>Sivakumar Pitchaiah </td><td>The seminar will cover the concepts and applications of adaptive model reduction in control of distributed processes (processes modeled by PDEs).

</td><td><b>Affiliation</b>

</td></tr><tr><td>UAVs for Infrastructure Monitoring </td><td>Sept 12, 2013 </td><td nowrap>4 PM MDT </td><td>Kevin Franke </td><td>BYU Civil Engineering

</td></tr><tr><td>Advanced Deep Water Monitoring Systems </td><td>Sept 26, 2013 </td><td nowrap>4 PM MDT </td><td>David Brower </td><td>Astro Technology, Inc.

</td></tr><tr><td>Optimization of Olefins Production </td><td>Oct 10, 2013 </td><td nowrap>4 PM MDT </td><td>Mark Darby </td><td>CMiD Solutions

</td></tr><tr><td>Process Control in the Upstream Industry </td><td>Oct 24, 2013 </td><td nowrap>4 PM MDT </td><td>Michael Nikolaou </td><td>University of Houston

To participate in an upcoming presentation, click the link to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

To participate in an upcoming presentation, click the link above to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

</td></tr><tr><td>Stimulation Optimization of Unconventional Resources </td><td>Jan 18, 2013 </td><td nowrap>1 PM MST<br>2 PM CST </td><td>Marisela Sanchez-Nagel </td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations. These techniques will allow the industry to gain greater insight and understanding of how natural fracture geometry and intensity, fracture hydromechanical properties, and stress field affect hydraulic fracturing performance in the presence of different operational parameters.

</td></tr><tr><td>Stimulation Optimization of Unconventional Resources </td><td><a href='http://youtu.be/_7rHP9-mPj0'>Presentation (55 min)</a> </td><td>Jan 18, 2013 </td><td>Marisela Sanchez-Nagel </td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations. These techniques will allow the industry to gain greater insight and understanding of how natural fracture geometry and intensity, fracture hydromechanical properties, and stress field affect hydraulic fracturing performance in the presence of different operational parameters.

</td><td><b>Registration</b>

</td><td><b>Presentation</b>

- See APMonitorCom YouTube Channel for recorded webinars

- See APMonitorCom YouTube Channel for recordings

### Webinar Series on Modeling and Optimization

(:cell:)

Webinars are held about every two-weeks at 9 AM Mountain Time / 10 AM Central Time (USA). These seminars consist of applications and tutorials in mathematical modeling, estimation, and optimization.

Webinars are held about every two-weeks. These seminars consist of applications and tutorials in mathematical modeling, estimation, and optimization.

- See APMonitorCom YouTube Channel for recorded webinars

- Contact support@apmonitor.com for video conferencing assistance

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(:table border=0 width=100%:) (:cell:)

(:cell:)

(:tableend:)

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- Join Current WebEx Session
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- Password:

</td><td><b>Registration</b>

</td><td><a href='https://byu.webex.com/byu/j.php?J=628046528&PW=NOTM5Y2Q5ZjY2'>Join Webinar<br><br>Password:<br> <b>apm2012</b></a>

</td><td>Webinar

</td><td><a href='https://byu.webex.com/byu/j.php?ED=216472567&UID=0&PW=NNDA0NDljNDcw&RT=MiM2'>Join Webinar<br><br>Password:<br> <b>apm2012</b></a>

</td><td><a href='https://byu.webex.com/byu/j.php?J=628046528&PW=NOTM5Y2Q5ZjY2'>Join Webinar<br><br>Password:<br> <b>apm2012</b></a>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=216472567&UID=0&PW=NNDA0NDljNDcw&RT=MiM2'>Join Webinar<br><br>Password:<br> <b>apm2012</b></a>

</td><td>Webinar

</td></tr><tr><td>Stimulation Optimization of Unconventional Resources </td><td><a href='https://byu.webex.com/byu/j.php?ED=216472567&UID=0&PW=NNDA0NDljNDcw&RT=MiM2'>Join Webinar<br><br>Password:<br> <b>apm2012</b></a> </td><td>Jan 18, 2013 </td><td nowrap>1 PM MST<br>2 PM CST </td><td>Marisela Sanchez-Nagel </td><td>Improved Numerical Modeling from First Physics Emerging numerical techniques are available that can address hydraulic fracturing in naturally fractured formations, such as shale gas, and that are based upon the proper first physics of the mechanical and flow behavior of fractured formations. These techniques will allow the industry to gain greater insight and understanding of how natural fracture geometry and intensity, fracture hydromechanical properties, and stress field affect hydraulic fracturing performance in the presence of different operational parameters.

</td></tr><tr><td>TBD

</td></tr><tr><td>Adaptive Model Reduction

</td><td>Jan 15, 2012 </td><td nowrap>9 AM MST </td><td>Jeff Kantor, Univ of Notre Dame </td><td>

</td></tr><tr><td>Adaptive Model Reduction </td><td>Webinar

</td></tr><tr><td>Gene Expression

</td></tr><tr><td>TBD

</td><td>Dec 6, 2012 </td><td nowrap>4 PM MST </td><td>Eric Haseltine, Vertex Pharmaceuticals </td><td>Vertex Pharmaceuticals creates new possibilities in medicine to cure diseases and improve lives. Eric will discuss genome expression for therapeutic design.

</td></tr><tr><td>TBD </td><td>Webinar

</td><td><a href='http://youtu.be/zMu1bSOlOAA'>Presentation (43 min)</a>

</td><td><a href='http://youtu.be/bDa5isKRS-4'>Presentation (43 min)</a>

</td><td>Applications of adaptive model reduction in control of distributed processes (processes modeled by PDEs). We will explore the concept of adaptive model reduction, the applications, and advantages.

</td><td>The seminar will cover the concepts and applications of adaptive model reduction in control of distributed processes (processes modeled by PDEs).

</td><td>Sivakumar Pitchaiah, MEMC Electronic Materials </td><td>Applications of adaptive model reduction in control of distributed processes (processes modeled by PDEs). We will explore the concept of adaptive model reduction, the applications, and advantages.<br>MEMC is a global leader in semiconductor and solar technology. MEMC has been a pioneer in the design and development of silicon wafer technologies for over 50 years.

</td><td>Sivakumar Pitchaiah </td><td>Applications of adaptive model reduction in control of distributed processes (processes modeled by PDEs). We will explore the concept of adaptive model reduction, the applications, and advantages.

</td><td>Dec 11, 2012

</td><td>Jan 15, 2012

</td><td>Jeff Kantor, Univ of Notre Dame </td><td>

</td></tr><tr><td>Adaptive Model Reduction </td><td>Webinar </td><td>Jan 29, 2013 </td><td nowrap>9 AM MST

</td><td>MEMC is a global leader in semiconductor and solar technology. MEMC has been a pioneer in the design and development of silicon wafer technologies for over 50 years.

</td></tr><tr><td>TBD </td><td>Webinar </td><td>Jan 15, 2012 </td><td nowrap>9 AM MST </td><td>Jeff Kantor, Univ of Notre Dame </td><td>

</td><td>Applications of adaptive model reduction in control of distributed processes (processes modeled by PDEs). We will explore the concept of adaptive model reduction, the applications, and advantages.<br>MEMC is a global leader in semiconductor and solar technology. MEMC has been a pioneer in the design and development of silicon wafer technologies for over 50 years.

</td></tr><tr><td>Efficient Surrogate Model Generation

</td></tr><tr><td>Gene Expression

</td><td>Nov 13, 2012 </td><td nowrap>9AM MST </td><td>John Eason and Selen Cremaschi<br>Univ of Tulsa </td><td>Surrogate models, by constructing simpler functional approximations of complex and computationally expensive models, allow the use of traditional optimization algorithms for otherwise intractable problems. This talk discusses three possible algorithms for efficient surrogate model building.

</td></tr><tr><td>Gene Expression </td><td>Webinar

</td></tr><tr><td>Efficient Surrogate Model Generation </td><td><a href='http://youtu.be/zMu1bSOlOAA'>Presentation (43 min)</a> </td><td>Nov 13, 2012 </td><td>John Eason and Selen Cremaschi<br>Univ of Tulsa </td><td>Surrogate models, by constructing simpler functional approximations of complex and computationally expensive models, allow the use of traditional optimization algorithms for otherwise intractable problems. This talk discusses three possible algorithms for efficient surrogate model building.

</td><td>Surrogate models, by constructing simpler functional approximations of complex and computationally expensive models, allow the use of traditional optimization algorithms for optimization. This talk discusses three possible algorithms for efficient surrogate model building.

</td><td>Surrogate models, by constructing simpler functional approximations of complex and computationally expensive models, allow the use of traditional optimization algorithms for otherwise intractable problems. This talk discusses three possible algorithms for efficient surrogate model building.

To participate in an upcoming presentation, click register and provide your name and e-mail address. Registration is used to communicate log-on information and a reminder notice just prior to the meeting. There is no charge to register for a webinar. Users can participate via audio or video conference through a telephone or internet connection.

To participate in an upcoming presentation, click the link to join the WebEx session. There is no charge to join the webinar and Windows, Linux, iOS, and mobile platforms are supported. Users can participate via audio or video conference through a telephone or internet connection. Presentations are recorded and posted to YouTube as is the case with many of the past sessions shown below.

</td><td>Surrogate models, by constructing simpler functional approximations of complex and computationally expensive models, allows the use of traditional optimization algorithms for optimization of these systems. This talk discusses three possible algorithms for efficient surrogate model building.

</td><td>Surrogate models, by constructing simpler functional approximations of complex and computationally expensive models, allow the use of traditional optimization algorithms for optimization. This talk discusses three possible algorithms for efficient surrogate model building.

</td></tr><tr><td>Fast NMPC and MHE tools </td><td><a href='https://byu.webex.com/byu/j.php?ED=214937912&UID=0&PW=NNDA0NDljNDcw&RT=MiM2'>Join Webinar<br>Password:<br> <b>apm2012</b></a> </td><td>Nov. 6, 2012 </td><td nowrap>9AM MST<br>5PM CET </td><td>Milan Vukov and Moritz Diehl, KU Leuven </td><td>Fast estimation and control techniques are critical for real-time applications. Researchers at KU Leuven will share cutting-edge methods to apply Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) for real-time applications.

</td></tr><tr><td>Fast NMPC and MHE tools </td><td><a href='http://youtu.be/zMu1bSOlOAA'>Presentation (57 min)</a> </td><td>Nov. 6, 2012 </td><td>Milan Vukov and Moritz Diehl, KU Leuven </td><td>Fast estimation and control techniques are critical for real-time applications. Researchers at KU Leuven will share cutting-edge methods to apply Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) for real-time applications.

</td><td>Webinar

</td><td><a href='https://byu.webex.com/byu/j.php?ED=216472567&UID=0&PW=NNDA0NDljNDcw&RT=MiM2'>Join Webinar<br><br>Password:<br> <b>apm2012</b></a>

</td><td nowrap>9AM MDT<br>5PM CET

</td><td nowrap>9AM MST<br>5PM CET

</td><td nowrap>9AM MDT

</td><td nowrap>9AM MST

</td><td nowrap>4 PM MDT

</td><td nowrap>4 PM MST

</td><td nowrap>9 AM MDT

</td><td nowrap>9 AM MST

</td><td nowrap>9 AM MDT

</td><td nowrap>9 AM MST

</td></tr><tr><td>TBD

</td></tr><tr><td>Efficient Surrogate Model Generation

</td><td>Selen Cremaschi<br>Univ of Tulsa </td><td>

</td><td>John Eason and Selen Cremaschi<br>Univ of Tulsa </td><td>Surrogate models, by constructing simpler functional approximations of complex and computationally expensive models, allows the use of traditional optimization algorithms for optimization of these systems. This talk discusses three possible algorithms for efficient surrogate model building.

</td></tr><tr><td>TBD </td><td>Webinar </td><td>Jan 15, 2012 </td><td nowrap>9 AM MDT </td><td>Jeff Kantor, Univ of Notre Dame </td><td>

</td></tr><tr><td>Proactive Energy Management Strategies for Buildings </td><td>Presentation (32 min) </td><td>Oct. 23, 2012 </td><td nowrap>9AM MDT </td><td nowrap>Cara R. Touretzky<br>Michael Baldea<br>UT Austin </td><td>Buildings are dynamic systems defined by fluctuations in weather, occupancy and energy prices. A nonlinear model-based optimal energy management strategy demonstrates significant energy savings compared to setpoint tracking strategies.

</td></tr><tr><td>Proactive Energy Management Strategies for Buildings </td><td><a href='http://youtu.be/wQ0SsfzGo6I'>Presentation (34 min)</a> </td><td>Oct. 23, 2012 </td><td nowrap>Cara R. Touretzky<br>Michael Baldea<br>UT Austin </td><td>Buildings are dynamic systems defined by fluctuations in weather, occupancy and energy prices. A nonlinear model-based optimal energy management strategy demonstrates significant energy savings compared to setpoint tracking strategies.

</td><td><a href='https://byu.webex.com/byu/j.php?ED=214937912&UID=0&PW=NNDA0NDljNDcw&RT=MiM2'>Join Webinar<br><u>Password</u><br> <b>apm2012</b></a>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=214937912&UID=0&PW=NNDA0NDljNDcw&RT=MiM2'>Join Webinar<br>Password:<br> <b>apm2012</b></a>

</td><td nowrap>Presentation (32 min)

</td><td>Presentation (32 min)

</td><td nowrap><a href='https://byu.webex.com/byu/j.php?ED=214195087&UID=0&PW=NNDA0NDljNDcw&RT=MiM2 '>Join Webinar<br><u>Password</u><br> <b>apm2012</b></a>

</td><td nowrap>Presentation (32 min)

</td><td><a href='https://byu.webex.com/byu/j.php?ED=214937912'>Join Webinar<br><u>Password</u><br> <b>apm2012</b></a>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=214937912&UID=0&PW=NNDA0NDljNDcw&RT=MiM2'>Join Webinar<br><u>Password</u><br> <b>apm2012</b></a>

</td><td>Webinar

</td><td><a href='https://byu.webex.com/byu/j.php?ED=214937912'>Join Webinar<br><u>Password</u><br> <b>apm2012</b></a>

</td><td>Wesley Cole, UT Austin

</td><td>Wesley Cole, UT Austin <br><br>Jeremy Castagno, BYU

</td><td nowrap>9AM MST

</td><td nowrap>9AM MDT

</td><td nowrap>9AM MST<br>5PM CEST

</td><td nowrap>9AM MDT<br>5PM CET

</td><td nowrap>9AM MST

</td><td nowrap>9AM MDT

</td><td nowrap>4 PM

</td><td nowrap>4 PM MDT

</td><td nowrap>9 AM

</td><td nowrap>9 AM MDT

</td><td>

</td><td>Webinar

</td></tr><tr><td>Fast NMPC and MHE tools

</td></tr><tr><td>TBD

</td><td>Nov. 6, 2012 </td><td nowrap>9AM MST<br>5PM CEST </td><td>Milan Vukov and Moritz Diehl, KU Leuven </td><td>Fast estimation and control techniques are critical for real-time applications. Researchers at KU Leuven will share cutting-edge methods to apply Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) for real-time applications.

</td></tr><tr><td>TBD </td><td>Webinar

</td></tr><tr><td>Gene Expression </td><td>Webinar </td><td>Dec 6, 2012 </td><td nowrap>4 PM </td><td>Eric Haseltine, Vertex Pharmaceuticals </td><td>Vertex Pharmaceuticals creates new possibilities in medicine to cure diseases and improve lives. Eric will discuss genome expression for therapeutic design.

</td></tr><tr><td>Gene Expression

</td></tr><tr><td>Fast NMPC and MHE tools

</td><td>Dec 6, 2012 </td><td nowrap>4 PM </td><td>Eric Haseltine, Vertex Pharmaceuticals </td><td>Vertex Pharmaceuticals creates new possibilities in medicine to cure diseases and improve lives. Eric will discuss genome expression for therapeutic design.

</td><td>Nov. 6, 2012 </td><td nowrap>9AM MST<br>5PM CEST </td><td>Milan Vukov and Moritz Diehl, KU Leuven </td><td>Fast estimation and control techniques are critical for real-time applications. Researchers at KU Leuven will share cutting-edge methods to apply Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) for real-time applications.

</td></tr><tr><td>TBD </td><td>Webinar </td><td>Nov 13, 2012 </td><td nowrap>9AM MST </td><td>Selen Cremaschi<br>Univ of Tulsa </td><td>

</td><td>Cara R. Touretzky<br>Michael Baldea<br>UT Austin

</td><td nowrap>Cara R. Touretzky<br>Michael Baldea<br>UT Austin

</td></tr><tr><td>TBD </td><td><a href='https://byu.webex.com/byu/j.php?ED=211711962&RG=1&UID=0&RT=MiM2'>Join Webinar<br>Password <b>apm2012</b></a> </td><td>Oct. 16, 2012

</td></tr><tr><td>Proactive Energy Management Strategies for Buildings </td><td nowrap><a href='https://byu.webex.com/byu/j.php?ED=214195087&UID=0&PW=NNDA0NDljNDcw&RT=MiM2 '>Join Webinar<br><u>Password</u><br> <b>apm2012</b></a> </td><td>Oct. 23, 2012

</td><td>Michael Baldea, UT Austin </td><td>

</td></tr><tr><td>TBD </td><td> </td><td>Oct. 23, 2012 </td><td nowrap>9AM MST </td><td>Selen Cremaschi, Univ of Tulsa </td><td>

</td><td>Cara R. Touretzky<br>Michael Baldea<br>UT Austin </td><td>Buildings are dynamic systems defined by fluctuations in weather, occupancy and energy prices. A nonlinear model-based optimal energy management strategy demonstrates significant energy savings compared to setpoint tracking strategies.

</td><td><a href='http://youtu.be/dfgzaVd8gLg'>Presentation #1 (28 min)</a><br><a href='/wiki/uploads/Main/apm_minlp_tes.pdf'>Slides #1</a><br><br><a href='http://youtu.be/dfgzaVd8gLg'>Presentation #2 (14 min)</a><br><a href='/wiki/uploads/Main/apm_minlp_mpc.pdf'>Slides #1</a>

</td><td><a href='http://youtu.be/M_5OIOZcoBc'>Presentation #1 (28 min)</a><br><br><a href='/wiki/uploads/Main/apm_minlp_tes.pdf'>Slides #1</a><br><hr><br><a href='http://youtu.be/yNeTF8_XSyw'>Presentation #2 (14 min)</a><br><br><a href='/wiki/uploads/Main/apm_minlp_mpc.pdf'>Slides #2</a>

</td></tr><tr><td>Mixed Integer Nonlinear Programming

</td></tr><tr><td>TBD

</td><td>Oct. 2, 2012

</td><td>Oct. 16, 2012

</td><td>Wesley Cole, UT Austin </td><td>Many design, engineering, and scientific applications include a mixture of continuous and discrete decision variables and nonlinear relationships. Mixed Integer Nonlinear Programming (MINLP) solvers are reviewed and compared for application to a scheduling problem for UT Austin central campus cooling.

</td><td>Michael Baldea, UT Austin </td><td>

</td><td>Oct. 16, 2012

</td><td>Oct. 23, 2012

</td><td>Michael Baldea, UT Austin

</td><td>Selen Cremaschi, Univ of Tulsa

</td></tr><tr><td>TBD </td><td> </td><td>Oct. 23, 2012 </td><td nowrap>9AM MST </td><td>Selen Cremaschi, Univ of Tulsa </td><td>

</td></tr><tr><td>Mixed Integer Nonlinear Programming </td><td><a href='http://youtu.be/dfgzaVd8gLg'>Presentation #1 (28 min)</a><br><a href='/wiki/uploads/Main/apm_minlp_tes.pdf'>Slides #1</a><br><br><a href='http://youtu.be/dfgzaVd8gLg'>Presentation #2 (14 min)</a><br><a href='/wiki/uploads/Main/apm_minlp_mpc.pdf'>Slides #1</a> </td><td>Oct. 2, 2012 </td><td>Wesley Cole, UT Austin </td><td>Many design, engineering, and scientific applications include a mixture of continuous and discrete decision variables and nonlinear relationships. Mixed Integer Nonlinear Programming (MINLP) solvers are reviewed and compared for application to a scheduling problem for UT Austin central campus cooling.

</td><td><a href='http://youtu.be/dfgzaVd8gLg'>Presentation Video</a>

</td><td><a href='http://youtu.be/dfgzaVd8gLg'>Presentation (34 min)</a>

</td></tr><tr><td>Generalized Disjunctive Programming </td><td><a href='https://byu.webex.com/byu/j.php?ED=210359857&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>Sept. 18, 2012 </td><td nowrap>9AM MST </td><td>Juan Ruiz, Visual MESA </td><td>Mixed-Integer Programming (MIP) provides a framework for mathematically modeling many optimization problems that involve discrete and continuous variables. Even though there has been a significant increase in the use of these models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP), a modeling framework that aims at tackling the above question.

</td><td>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=211711962&RG=1&UID=0&RT=MiM2'>Join Webinar<br>Password <b>apm2012</b></a>

</td></tr><tr><td>Generalized Disjunctive Programming </td><td><a href='http://youtu.be/dfgzaVd8gLg'>Presentation Video</a> </td><td>Sept. 18, 2012 </td><td>Juan Ruiz, Visual MESA </td><td>Mixed-Integer Programming (MIP) provides a framework for mathematically modeling many optimization problems that involve discrete and continuous variables. Even though there has been a significant increase in the use of these models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP), a modeling framework that aims at tackling the above question.

<br><a href='http://youtu.be/wS8ru1IcVBs'>Intro_1 (2.5 min)</a> <br><a href='http://youtu.be/OG2TAYiHz6s'>Intro_2 (3 min)</a>

<br><a href='http://youtu.be/wS8ru1IcVBs'>Intro_1<br>(2.5 min)</a> <br><br><a href='http://youtu.be/OG2TAYiHz6s'>Intro_2<br>(3 min)</a>

<br><a href='http://youtu.be/wS8ru1IcVBs'>Intro 1 (2.5 min)</a> <br><a href='http://youtu.be/OG2TAYiHz6s'>Intro 2 (3 min)</a>

<br><a href='http://youtu.be/wS8ru1IcVBs'>Intro_1 (2.5 min)</a> <br><a href='http://youtu.be/OG2TAYiHz6s'>Intro_2 (3 min)</a>

<br><a href='http://youtu.be/wS8ru1IcVBs'>Intro_1</a> <br><a href='http://youtu.be/OG2TAYiHz6s'>Intro_2</a>

<br><a href='http://youtu.be/wS8ru1IcVBs'>Intro 1 (2.5 min)</a> <br><a href='http://youtu.be/OG2TAYiHz6s'>Intro 2 (3 min)</a>

</td></tr><tr><td>MPECs in Integrated Energy Storage, Forecasting, and Optimization </td><td><a href='https://byu.webex.com/byu/j.php?ED=199405892&RG=1&UID=0&RT=MiM2'>Register</a> <br><a href='http://youtu.be/wS8ru1IcVBs'>Video 1</a> <br><a href='http://youtu.be/OG2TAYiHz6s'>Video 2</a> </td><td>Sept. 4, 2012

</td></tr><tr><td>Generalized Disjunctive Programming </td><td><a href='https://byu.webex.com/byu/j.php?ED=210359857&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>Sept. 18, 2012

</td><td>Kody Powell, UT Austin </td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant requires the use of conditional arguments that change the system dynamics. New formulations of <a href='http://apmonitor.com/wiki/index.php/Apps/MpecExamples'>Mathematical Programs with Equilibrium Constraints (MPECs)</a> have improved convergence properties to enable more realistic dynamic simulations.

</td></tr><tr><td>Generalized Disjunctive Programming </td><td> </td><td>Sept. 18, 2012 </td><td nowrap>9AM MST

</td></tr><tr><td>MPECs in Integrated Energy Storage, Forecasting, and Optimization </td><td> <br><a href='http://youtu.be/wS8ru1IcVBs'>Intro_1</a> <br><a href='http://youtu.be/OG2TAYiHz6s'>Intro_2</a> <br><br><a href='http://youtu.be/5DuNvTWg62Q'>Presentation (30 min)</a> </td><td>Sept. 4, 2012 </td><td>Kody Powell, UT Austin </td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant requires the use of conditional arguments that change the system dynamics. New formulations of <a href='http://apmonitor.com/wiki/index.php/Apps/MpecExamples'>Mathematical Programs with Equilibrium Constraints (MPECs)</a> have improved convergence properties to enable more realistic dynamic simulations.

<br><br><a href='/wiki/uploads/Main/mars_rover_curiosity.pdf'>Slides</a>

</td></tr><tr><td>Integrated Energy Storage, Forecasting, and Optimization

</td></tr><tr><td>MPECs in Integrated Energy Storage, Forecasting, and Optimization

</td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant has the potential to reduce emissions equivalent to that of 800 cars annually. This green energy project seeks efficiency improvements by more tightly integrating this complex system to achieve peak efficiency.

</td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant requires the use of conditional arguments that change the system dynamics. New formulations of <a href='http://apmonitor.com/wiki/index.php/Apps/MpecExamples'>Mathematical Programs with Equilibrium Constraints (MPECs)</a> have improved convergence properties to enable more realistic dynamic simulations.

</td><td><a href='http://youtu.be/N9hXqzkH7YA'>Introduction</a> <br><br><a href='http://youtu.be/cfku_J4Ectk'>Presentation</a>

</td><td><a href='http://youtu.be/N9hXqzkH7YA'>Introduction (2.5 min)</a> <br><br><a href='http://youtu.be/cfku_J4Ectk'>Presentation (59 min)</a>

</td></tr><tr><td>MHE for UAVs </td><td><a href='https://byu.webex.com/byu/j.php?J=622274527&PW=NODM4N2FlMmZi'>Live Webinar<br>Password <b>optec2012</b></a> <br><br><a href='http://youtu.be/j2w66_yC7Jk'>Introduction (3 min)</a> <br><br><a href='http://youtu.be/toJHFdG4N2A'>Presentation (28 min)</a> </td><td>Aug. 29, 2012 </td><td nowrap>7:30AM MST </td><td>Liang Sun, BYU </td><td>Moving Horizon Estimation (MHE) is used to update a mathematical model during real-time flight tests. Results from the flight tests demonstrate that large-scale models can be applied in real-time applications for estimation and control.

</td></tr><tr><td>Mars Curiosity </td><td><a href='http://www.youtube.com/user/APMonitorCom/videos'>Live Event</a> <br><a href='http://youtu.be/N9hXqzkH7YA'>Video Intro</a> </td><td>Aug. 30, 2012 </td><td nowrap>1PM MST </td><td>Todd Barber, NASA JPL </td><td>The Curiosity robot is equipped with a nuclear-powered lab capable of vaporizing rocks and ingesting soil, measuring habitability, and potentially paving the way for human exploration. Todd will review interesting and unusual experiences in his career as a NASA engineer. He will present audio-visual highlights and recent findings of the Mars Rover Missions. (Spirit, Opportunity, and Curiosity).

</td></tr><tr><td>Mars Curiosity </td><td><a href='http://youtu.be/N9hXqzkH7YA'>Introduction</a> <br><br><a href='http://youtu.be/cfku_J4Ectk'>Presentation</a> </td><td>Aug. 30, 2012 </td><td>Todd Barber, NASA JPL </td><td>The Curiosity robot is equipped with a nuclear-powered lab capable of vaporizing rocks and ingesting soil, measuring habitability, and potentially paving the way for human exploration. Todd will review interesting and unusual experiences in his career as a NASA engineer. He will present audio-visual highlights and recent findings of the Mars Rover Missions. (Spirit, Opportunity, and Curiosity).

</td></tr><tr><td>MHE for UAVs </td><td><a href='http://youtu.be/j2w66_yC7Jk'>Introduction (3 min)</a> <br><br><a href='http://youtu.be/toJHFdG4N2A'>Presentation (28 min)</a> </td><td>Aug. 29, 2012 </td><td>Liang Sun, BYU </td><td>Moving Horizon Estimation (MHE) is used to update a mathematical model during real-time flight tests. Results from the flight tests demonstrate that large-scale models can be applied in real-time applications for estimation and control.

</td></tr><tr><td>MHE for UAVs </td><td><a href='https://byu.webex.com/byu/j.php?J=622274527&PW=NODM4N2FlMmZi'>Live Webinar<br>Password <b>optec2012</b></a> <br><br><a href='http://youtu.be/j2w66_yC7Jk'>Introduction (3 min)</a> <br><br><a href='http://youtu.be/toJHFdG4N2A'>Presentation (28 min)</a> </td><td>Aug. 29, 2012 </td><td nowrap>7:30AM MST </td><td>Liang Sun, BYU </td><td>Moving Horizon Estimation (MHE) is used to update a mathematical model during real-time flight tests. Results from the flight tests demonstrate that large-scale models can be applied in real-time applications for estimation and control.

</td></tr><tr><td>Mars Curiosity </td><td><a href='http://www.youtube.com/user/APMonitorCom/videos'>Live Event</a> <br><a href='http://youtu.be/N9hXqzkH7YA'>Video Intro</a> </td><td>Aug. 30, 2012 </td><td nowrap>1PM MST </td><td>Todd Barber, NASA JPL </td><td>The Curiosity robot is equipped with a nuclear-powered lab capable of vaporizing rocks and ingesting soil, measuring habitability, and potentially paving the way for human exploration. Todd will review interesting and unusual experiences in his career as a NASA engineer. He will present audio-visual highlights and recent findings of the Mars Rover Missions. (Spirit, Opportunity, and Curiosity).

</td></tr><tr><td>Mars Curiosity </td><td><a href='http://www.youtube.com/user/APMonitorCom/videos'>Live Event</a> <br><a href='http://youtu.be/N9hXqzkH7YA'>Video Intro</a> </td><td>Aug. 30, 2012 </td><td nowrap>1PM MST </td><td>Todd Barber, NASA JPL </td><td>The Curiosity robot is equipped with a nuclear-powered lab capable of vaporizing rocks and ingesting soil, measuring habitability, and potentially paving the way for human exploration. Todd will review interesting and unusual experiences in his career as a NASA engineer. He will present audio-visual highlights and recent findings of the Mars Rover Missions. (Spirit, Opportunity, and Curiosity).

</td></tr><tr><td>Integrated Energy Storage, Forecasting, and Optimization </td><td><a href='https://byu.webex.com/byu/j.php?ED=199405892&RG=1&UID=0&RT=MiM2'>Register</a> <br><a href='http://youtu.be/wS8ru1IcVBs'>Video 1</a> <br><a href='http://youtu.be/OG2TAYiHz6s'>Video 2</a> </td><td>Sept. 4, 2012 </td><td nowrap>9AM MST </td><td>Kody Powell, UT Austin </td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant has the potential to reduce emissions equivalent to that of 800 cars annually. This green energy project seeks efficiency improvements by more tightly integrating this complex system to achieve peak efficiency.

</td></tr><tr><td>Integrated Energy Storage, Forecasting, and Optimization </td><td><a href='https://byu.webex.com/byu/j.php?ED=199405892&RG=1&UID=0&RT=MiM2'>Register</a> <br><a href='http://youtu.be/wS8ru1IcVBs'>Video 1</a> <br><a href='http://youtu.be/OG2TAYiHz6s'>Video 2</a> </td><td>Sept. 4, 2012 </td><td nowrap>9AM MST </td><td>Kody Powell, UT Austin </td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant has the potential to reduce emissions equivalent to that of 800 cars annually. This green energy project seeks efficiency improvements by more tightly integrating this complex system to achieve peak efficiency.

</td></tr><tr><td>MHE for UAVs </td><td><a href='https://byu.webex.com/byu/j.php?J=622274527&PW=NODM4N2FlMmZi'>Live Webinar<br>Password <b>optec2012</b></a> <br><br><a href='http://youtu.be/j2w66_yC7Jk'>Introduction (3 min)</a> <br><br><a href='http://youtu.be/toJHFdG4N2A'>Presentation (28 min)</a> </td><td>Aug. 29, 2012 </td><td nowrap>7:30AM MST </td><td>Liang Sun, BYU </td><td>Moving Horizon Estimation (MHE) is used to update a mathematical model during real-time flight tests. Results from the flight tests demonstrate that large-scale models can be applied in real-time applications for estimation and control.

</td><td><a href='https://byu.webex.com/byu/j.php?J=622274527&PW=NODM4N2FlMmZi'>Live Webinar - Password <b>optec2012</b></a> <br><a href='http://youtu.be/j2w66_yC7Jk'>Introduction (3 min)</a> <br><a href='http://youtu.be/toJHFdG4N2A'>Presentation (28 min)</a>

</td><td><a href='https://byu.webex.com/byu/j.php?J=622274527&PW=NODM4N2FlMmZi'>Live Webinar<br>Password <b>optec2012</b></a> <br><br><a href='http://youtu.be/j2w66_yC7Jk'>Introduction (3 min)</a> <br><br><a href='http://youtu.be/toJHFdG4N2A'>Presentation (28 min)</a>

</td></tr><tr><td>MHE for UAVs </td><td><a href='https://byu.webex.com/byu/j.php?J=622274527&PW=NODM4N2FlMmZi'>Live Webinar - Password <b>optec2012</b></a> <br><a href='http://youtu.be/j2w66_yC7Jk'>Introduction (3 min)</a> <br><a href='http://youtu.be/toJHFdG4N2A'>Presentation (28 min)</a> </td><td>Aug. 29, 2012 </td><td nowrap>7:30AM MST </td><td>Liang Sun, BYU </td><td>Moving Horizon Estimation (MHE) is used to update a mathematical model during real-time flight tests. Results from the flight tests demonstrate that large-scale models can be applied in real-time applications for estimation and control.

</td><td>9AM MST<br>5PM CEST

</td><td nowrap>9AM MST<br>5PM CEST

</td><td>9AM MST / 5 PM CEST

</td><td>9AM MST<br>5PM CEST

</td></tr><tr><td>Fast NMPC and MHE tools </td><td> </td><td>Nov. 6, 2012 </td><td>9AM MST / 5 PM CEST </td><td>Milan Vukov and Moritz Diehl, KU Leuven </td><td>Fast estimation and control techniques are critical for real-time applications. Researchers at KU Leuven will share cutting-edge methods to apply Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) for real-time applications.

</td><td nowrap>4 PM MDT

</td><td nowrap>4 PM

</td></tr><tr><td>TBD </td><td>Webinar </td><td>Dec 11, 2012 </td><td nowrap>9 AM </td><td>Sivakumar Pitchaiah, MEMC Electronic Materials </td><td>MEMC is a global leader in semiconductor and solar technology. MEMC has been a pioneer in the design and development of silicon wafer technologies for over 50 years.

</td></tr><tr><td>Gene Expression </td><td>Webinar </td><td>Dec 6, 2012 </td><td nowrap>4 PM MDT </td><td>Eric Haseltine, Vertex Pharmaceuticals </td><td>Vertex Pharmaceuticals creates new possibilities in medicine to cure diseases and improve lives. Eric will discuss genome expression for therapeutic design.

</td></tr><tr><td>TBD </td><td>

</td></tr><tr><td>Generalized Disjunctive Programming </td><td>

</td><td>

</td><td>Mixed-Integer Programming (MIP) provides a framework for mathematically modeling many optimization problems that involve discrete and continuous variables. Even though there has been a significant increase in the use of these models, a question of how to develop the best model that will lead to the most efficient solution method remains to be answered. In this presentation we review the latest developments in Generalized Disjunctive Programming (GDP), a modeling framework that aims at tackling the above question.

(:title Symposium on Advanced Modeling and Optimization:)

(:title Symposium on Modeling and Optimization:)

### Webinars on Modeling and Optimization

### Webinar Series on Modeling and Optimization

(:title Symposium on Advanced Modeling and Optimization - Webinar Series:)

(:title Symposium on Advanced Modeling and Optimization:)

(:title Modeling and Optimization Webinars:)

(:title Symposium on Advanced Modeling and Optimization - Webinar Series:)

<br><a href='http://youtu.be/wS8ru1IcVBs'>Video 1</a> <br><a href='http://youtu.be/OG2TAYiHz6s'>Video 2</a>

</td></tr><tr><td>TBD

</td></tr><tr><td>Mixed Integer Nonlinear Programming

</td><td>Selen Cremaschi, Univ of Tulsa </td><td>

</td><td>Wesley Cole, UT Austin </td><td>Many design, engineering, and scientific applications include a mixture of continuous and discrete decision variables and nonlinear relationships. Mixed Integer Nonlinear Programming (MINLP) solvers are reviewed and compared for application to a scheduling problem for UT Austin central campus cooling.

</td></tr><tr><td>Mixed Integer Nonlinear Programming

</td></tr><tr><td>TBD

</td><td>Wesley Cole, UT Austin </td><td>Many design, engineering, and scientific applications include a mixture of continuous and discrete decision variables and nonlinear relationships. Mixed Integer Nonlinear Programming (MINLP) solvers are reviewed and compared for application to a scheduling problem for UT Austin central campus cooling.

</td><td>Selen Cremaschi, Univ of Tulsa </td><td>

</td><td>Kody Powell

</td><td>Kody Powell, UT Austin

</td><td>Wesley Cole

</td><td>Wesley Cole, UT Austin

</td><td>Oct. 30, 2012

</td><td>Oct. 23, 2012

</td><td>July 31, 2012

</td><td>Sept. 4, 2012

</td></tr><tr><td>Mixed Integer Nonlinear Programming

</td></tr><tr><td>TBD

</td><td>Sept. 4, 2012

</td><td>Sept. 18, 2012

</td><td>Wesley Cole </td><td>Many design, engineering, and scientific applications include a mixture of continuous and discrete decision variables and nonlinear relationships. Mixed Integer Nonlinear Programming (MINLP) solvers are reviewed and compared for application to a scheduling problem for UT Austin central campus cooling.

</td><td>Juan Ruiz, Visual MESA </td><td>

</td><td>Sept. 18, 2012

</td><td>Oct. 2, 2012

</td><td>Juan Ruiz, Visual MESA </td><td>

</td><td>Selen Cremaschi, Univ of Tulsa </td><td>

</td><td>Oct. 2, 2012

</td><td>Oct. 16, 2012

</td><td>Selen Cremaschi, Univ of Tulsa

</td><td>Michael Baldea, UT Austin

</td></tr><tr><td>TBD

</td></tr><tr><td>Mixed Integer Nonlinear Programming

</td><td>Oct. 16, 2012

</td><td>Oct. 30, 2012

</td><td>Michael Baldea, UT Austin </td><td>

</td><td>Wesley Cole </td><td>Many design, engineering, and scientific applications include a mixture of continuous and discrete decision variables and nonlinear relationships. Mixed Integer Nonlinear Programming (MINLP) solvers are reviewed and compared for application to a scheduling problem for UT Austin central campus cooling.

Webinars are held about every two-weeks at 9 AM Mountain Time / 10 AM Central Time (USA). These seminars consist of applications and tutorials in mathematical modeling, estimation, and optimization applications.

Webinars are held about every two-weeks at 9 AM Mountain Time / 10 AM Central Time (USA). These seminars consist of applications and tutorials in mathematical modeling, estimation, and optimization.

</td><td>Juan Ruiz, VisualMesa

</td><td>Juan Ruiz, Visual MESA

(:title Optimization and Advanced Control Seminars:)

(:title Modeling and Optimization Webinars:)

### Webinars on Optimization Applications

APM User's Group Webinars are held about every two-weeks at 9 AM Mountain Time / 10 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

### Webinars on Modeling and Optimization

Webinars are held about every two-weeks at 9 AM Mountain Time / 10 AM Central Time (USA). These seminars consist of applications and tutorials in mathematical modeling, estimation, and optimization applications.

</td></tr><tr><td>TBD </td><td> </td><td>Sept. 18, 2012 </td><td nowrap>9AM MST </td><td>Juan Ruiz, VisualMesa </td><td>

</td></tr><tr><td>TBD </td><td> </td><td>Oct. 2, 2012 </td><td nowrap>9AM MST </td><td>Selen Cremaschi, Univ of Tulsa </td><td>

</td></tr><tr><td>TBD </td><td> </td><td>Oct. 16, 2012 </td><td nowrap>9AM MST </td><td>Michael Baldea, UT Austin </td><td>

</td><td><a href='/wiki/uploads/Main/apm_reactive_distillation_17July12.pdf'>Presentation</a>

</td><td><a href='/wiki/uploads/Main/apm_reactive_distillation_17July12.pdf'>Presentation</a><br><a href='http://youtu.be/1hnBFU-rFBY'>Webinar Video</a>

</td><td><a href='href='/wiki/uploads/Main/apm_reactive_distillation_17July12.pdf'''>Presentation</a>

</td><td><a href='/wiki/uploads/Main/apm_reactive_distillation_17July12.pdf'>Presentation</a>

</td><td nowrap>9AM MST

</td></tr><tr><td>DAE Solution for Separation Processes </td><td><a href='https://byu.webex.com/byu/j.php?ED=205046667&RG=1&UID=0&RT=MiM2'>Join Meeting</a><br> Password: <b>apm2012</b> </td><td>July 17, 2012

</td></tr><tr><td>Integrated Energy Storage, Forecasting, and Optimization </td><td><a href='https://byu.webex.com/byu/j.php?ED=199405892&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>July 31, 2012

</td><td>David Harney </td><td>The equations that simulate separation processes naturally involve higher index Differential and Algebraic Equations (DAEs). Other popular techniques for DAE simulation can often only handle up to index-1 DAEs. New techniques for large-scale DAEs allow efficient solution for bifurcation analysis, simulation of complex systems, and stability analysis.

</td></tr><tr><td>Integrated Energy Storage, Forecasting, and Optimization </td><td><a href='https://byu.webex.com/byu/j.php?ED=199405892&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>July 31, 2012 </td><td nowrap>9AM MST

</td></tr><tr><td>DAE Solution for Separation Processes </td><td><a href='href='/wiki/uploads/Main/apm_reactive_distillation_17July12.pdf'''>Presentation</a> </td><td>July 17, 2012 </td><td nowrap>9AM MST </td><td>David Harney </td><td>The equations that simulate separation processes naturally involve higher index Differential and Algebraic Equations (DAEs). Other popular techniques for DAE simulation can often only handle up to index-1 DAEs. New techniques for large-scale DAEs allow efficient solution for bifurcation analysis, simulation of complex systems, and stability analysis.

</td><td><a href='https://byu.webex.com/byu/j.php?ED=205046667&RG=1&UID=0&RT=MiM2'>Join Meeting</a>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=205046667&RG=1&UID=0&RT=MiM2'>Join Meeting</a><br> Password: <b>apm2012</b>

</td><td>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=205046667&RG=1&UID=0&RT=MiM2'>Join Meeting</a>

</td></tr><tr><td>Predicting Protein Structures </td><td><a href='/wiki/uploads/Main/apm_protein_folding_22May12.pdf'>Presentation</a> </td><td>May 22, 2012

</td></tr><tr><td>DAE Solution for Separation Processes </td><td> </td><td>July 17, 2012

</td><td nowrap>Jose Mojica<br>Reza Asgharzadeh<br>Thomas Knotts<br>Josh Price </td><td>Prediction of protein structures is a rapidly advancing area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems. Methods to align experimental results with simulation are discussed.

</td></tr><tr><td>Application of Model Predictive Control </td><td> </td><td>June 12, 2012 </td><td nowrap>6AM MST </td><td>Isak Nielsen </td><td>Empirical models obtained through subspace identification are a popular form for model predictive control (MPC) applications. This presentation relates a practical application of linear MPC with models obtained by regression techniques. Methods for sparse discrete or continuous Linear Time Invariant (LTI) forms in APMonitor are also reviewed.

</td></tr><tr><td>DAE Solution for Separation Processes </td><td> </td><td>July 17, 2012 </td><td nowrap>9AM MST

</td></tr><tr><td>Predicting Protein Structures </td><td><a href='/wiki/uploads/Main/apm_protein_folding_22May12.pdf'>Presentation</a> </td><td>May 22, 2012 </td><td nowrap>Jose Mojica<br>Reza Asgharzadeh<br>Thomas Knotts<br>Josh Price </td><td>Prediction of protein structures is a rapidly advancing area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems. Methods to align experimental results with simulation are discussed.

</td><td><a href='https://byu.webex.com/byu/j.php?ED=199603747&RG=1&UID=0&RT=MiM2'>Register</a>

</td><td><a href='/wiki/uploads/Main/apm_protein_folding_22May12.pdf'>Presentation</a>

</td><td nowrap>Josh Price<br>Jose Mojica

</td><td nowrap>Jose Mojica<br>Reza Asgharzadeh<br>Thomas Knotts<br>Josh Price

</td></tr><tr><td>Optimization of Design Under Uncertainty </td><td><a href='/wiki/uploads/Main/apm_genetic_algorithms_8May12.pdf'>Presentation</a> </td><td>May 8, 2012

</td></tr><tr><td>Predicting Protein Structures </td><td><a href='https://byu.webex.com/byu/j.php?ED=199603747&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>May 22, 2012

</td><td>Casey Abbott </td><td>Process design has a number of unknown or uncertain parameters that influence the optimal design, desired operational conditions, and project profitability. Optimization of design maximizes the expected NPV (Net Present Value) considering uncertain design parameters. <a href='/wiki/uploads/Main/apm_uncertain_params.zip'>Download example application</a>

</td></tr><tr><td>Predicting Protein Structures </td><td><a href='https://byu.webex.com/byu/j.php?ED=199603747&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>May 22, 2012 </td><td nowrap>9AM MST

</td></tr><tr><td>Optimization of Design Under Uncertainty </td><td><a href='/wiki/uploads/Main/apm_genetic_algorithms_8May12.pdf'>Presentation</a> </td><td>May 8, 2012 </td><td>Casey Abbott </td><td>Process design has a number of unknown or uncertain parameters that influence the optimal design, desired operational conditions, and project profitability. Optimization of design maximizes the expected NPV (Net Present Value) considering uncertain design parameters. <a href='/wiki/uploads/Main/apm_uncertain_params.zip'>Download example application</a>

</td><td>Predicting protein structures is a critical area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems.

</td><td>Prediction of protein structures is a rapidly advancing area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems. Methods to align experimental results with simulation are discussed.

</td><td>Josh Price<br>Jose Mojica

</td><td nowrap>Josh Price<br>Jose Mojica

</td><td>Josh Price<br>Reza Asgharzadeh<br>Jose Mojica

</td><td>Josh Price<br>Jose Mojica

</td></tr><tr><td>Predicting Protein Structures </td><td><a href='https://byu.webex.com/byu/j.php?ED=199603747&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>May 22, 2012 </td><td nowrap>9AM MST </td><td>Josh Price<br>Reza Asgharzadeh<br>Jose Mojica </td><td>Predicting protein structures is a critical area of research that relies on parallel computation, advanced optimization techniques, and experimental validation. A genetic algorithm approach is detailed as one of the optimization techniques to solve the mixed-integer nonlinear programming problems.

</td></tr><tr><td>Integrated Energy Storage, Forecasting, and Optimization </td><td><a href='https://byu.webex.com/byu/j.php?ED=199405892&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>May 22, 2012 </td><td nowrap>9AM MST </td><td>Kody Powell </td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant has the potential to reduce emissions equivalent to that of 800 cars annually. This green energy project seeks efficiency improvements by more tightly integrating this complex system to achieve peak efficiency.

</td></tr><tr><td>Integrated Energy Storage, Forecasting, and Optimization </td><td><a href='https://byu.webex.com/byu/j.php?ED=199405892&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>July 31, 2012 </td><td nowrap>9AM MST </td><td>Kody Powell </td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant has the potential to reduce emissions equivalent to that of 800 cars annually. This green energy project seeks efficiency improvements by more tightly integrating this complex system to achieve peak efficiency.

</td><td><a href='https://byu.webex.com/byu/j.php?ED=192896422&RG=1&UID=0&RT=MiM2'>Register</a>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=199405892&RG=1&UID=0&RT=MiM2'>Register</a>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=192896422&RG=1&UID=0&RT=MiM2'>Register</a>

</td><td><a href='/wiki/uploads/Main/apm_genetic_algorithms_8May12.pdf'>Presentation</a>

</td><td> </td><td>May 15, 2012

</td><td><a href='https://byu.webex.com/byu/j.php?ED=192896422&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>May 22, 2012

APM User's Group Webinars are held about every two-weeks at 10 AM Mountain Time / 11 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

APM User's Group Webinars are held about every two-weeks at 9 AM Mountain Time / 10 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

</td><td nowrap>10AM MST

</td><td nowrap>9AM MST

</td><td nowrap>10AM MST

</td><td nowrap>9AM MST

</td><td nowrap>10AM MST

</td><td nowrap>9AM MST

</td><td nowrap>10AM MST

</td><td nowrap>9AM MST

</td><td>Process design has a number of unknown or uncertain parameters that influence the optimal design, desired operational conditions, and project profitability. Optimization of design maximizes the expected NPV (Net Present Value) considering uncertain design parameters.

</td><td>Process design has a number of unknown or uncertain parameters that influence the optimal design, desired operational conditions, and project profitability. Optimization of design maximizes the expected NPV (Net Present Value) considering uncertain design parameters. <a href='/wiki/uploads/Main/apm_uncertain_params.zip'>Download example application</a>

</td></tr><tr><td>SBML Models in APM </td><td><a href='/wiki/uploads/Main/apm_sbml_23Apr12.pdf'>Presentation</a><br><a href='/wiki/uploads/Main/apm_sbml_notes_23Apr12.pdf'>Slide Notes</a> </td><td>April 23, 2012

</td></tr><tr><td>Optimization of Design Under Uncertainty </td><td><a href='https://byu.webex.com/byu/j.php?ED=192896422&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>May 8, 2012

</td><td>David Grigsby </td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/wiki/index.php/Main/SBML'>APM conversion utility</a> and solving SBML example problems.

</td></tr><tr><td>Optimization of Design Under Uncertainty </td><td><a href='https://byu.webex.com/byu/j.php?ED=192896422&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>May 8, 2012 </td><td nowrap>10AM MST

</td></tr><tr><td>SBML Models in APM </td><td><a href='/wiki/uploads/Main/apm_sbml_23Apr12.pdf'>Presentation</a><br><a href='/wiki/uploads/Main/apm_sbml_notes_23Apr12.pdf'>Slide Notes</a> </td><td>April 23, 2012 </td><td>David Grigsby </td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/wiki/index.php/Main/SBML'>APM conversion utility</a> and solving SBML example problems.

</td><td><a href='/wiki/uploads/Main/apm_sbml_23Apr12.pdf'>Presentation</a><br><a href='/wiki/uploads/Main/apm_sbml_notes_23Apr12.pdf'>Slides with Notes</a>

</td><td><a href='/wiki/uploads/Main/apm_sbml_23Apr12.pdf'>Presentation</a><br><a href='/wiki/uploads/Main/apm_sbml_notes_23Apr12.pdf'>Slide Notes</a>

</td><td><a href='/wiki/uploads/Main/apm_sbml_23Apr12.pdf'>Presentation</a><br><a href='/wiki/uploads/Main/apm_sbml_notes_23Apr12.pdf'>Notes</a>

</td><td><a href='/wiki/uploads/Main/apm_sbml_23Apr12.pdf'>Presentation</a><br><a href='/wiki/uploads/Main/apm_sbml_notes_23Apr12.pdf'>Slides with Notes</a>

</td><td><a href='/wiki/uploads/Main/apm_sbml_23Apr12.pdf'>Presentation</a>

</td><td><a href='/wiki/uploads/Main/apm_sbml_23Apr12.pdf'>Presentation</a><br><a href='/wiki/uploads/Main/apm_sbml_notes_23Apr12.pdf'>Notes</a>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=196607642&RG=1&UID=0&RT=MiM2'>Register</a>

</td><td><a href='/wiki/uploads/Main/apm_sbml_23Apr12.pdf'>Presentation</a>

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/wiki/index.php/Main/SBML'>conversion utility</a> and solving SBML example problems.

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/wiki/index.php/Main/SBML'>APM conversion utility</a> and solving SBML example problems.

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/index.php/Main/SBML'>conversion utility</a> and solving SBML example problems.

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/wiki/index.php/Main/SBML'>conversion utility</a> and solving SBML example problems.

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the conversion utility and solving SBML example problems.

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the <a href='/index.php/Main/SBML'>conversion utility</a> and solving SBML example problems.

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization.

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization. The tutorial will include instruction on using the conversion utility and solving SBML example problems.

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is a repository of hundreds of dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization.

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is standard format for expressing dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization.

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is a repository of hundreds of dynamic models in computational biology. This presentation will provide a tutorial on using SBML models for dynamic simulation, parameter estimation, and optimization.

</td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is a repository of hundreds of dynamic models in computational biology. This presentation will provide a tutorial on using large-scale SBML models for dynamic simulation, parameter estimation, and optimization.

</td><td>April 24, 2012

</td><td>April 23, 2012

</td><td>David Grigsby and Casey Abbott

</td><td>David Grigsby

</td><td>May 1, 2012

</td><td>May 8, 2012

</td></tr><tr><td>Fast Model Predictive Control </td><td><a href='/wiki/uploads/Main/apm_fast_mpc_3Apr12.pdf'>Presentation</a> </td><td>April 3, 2012

</td></tr><tr><td>SBML Models in APM </td><td><a href='https://byu.webex.com/byu/j.php?ED=196607642&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>April 24, 2012

</td><td>Trevor Slade and Reza Asgharzadeh </td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

</td></tr><tr><td>SBML Models in APM </td><td><a href='https://byu.webex.com/byu/j.php?ED=196607642&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>April 24, 2012 </td><td nowrap>10AM MST

</td></tr><tr><td>Fast Model Predictive Control </td><td><a href='/wiki/uploads/Main/apm_fast_mpc_3Apr12.pdf'>Presentation</a> </td><td>April 3, 2012 </td><td>Trevor Slade and Reza Asgharzadeh </td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

</td></tr><tr><td>Optimal Identification for Model Predictive Control </td><td><a href='/wiki/uploads/Main/apm_darby_13Mar12.pdf'>Presentation</a> </td><td>March 13, 2012 </td><td>Mark Darby </td><td>To identify models for MPC applications in chemical plants and refineries, the process is perturbed through increasingly automated step testing. Mark will give a tutorial overview of how to apply practical control engineering and design experiments for model development of dynamic systems.

</td></tr><tr><td>Thermal Oxidizer Boundary Value Management / InBound </td><td nowrap><a href='/wiki/uploads/Main/apm_bvm_6Mar12.pdf'>Presentation 1</a><br><a href='/wiki/uploads/Main/apm_pas_6Mar12.pdf'>Presentation 2</a> </td><td>March 6, 2012 </td><td>Reza Asgharzadeh, Chris Lyden, and Jim Huff </td><td>Thermal oxidizers are used in chemical plants and refineries to combust waste streams with low concentrations of reactants. The design and operation of the thermal oxidizer is of crucial importance for safety, environmental, and economic reasons.

</td></tr><tr><td>Nonlinear Programming with APM MATLAB </td><td><a href='/wiki/uploads/Main/apm_tutorial_21Feb12.pdf'>Presentation</a> </td><td>February 21, 2012 </td><td>John Hedengren </td><td>The APM interface extends MATLAB to be used for parameter estimation, nonlinear control, and optimization. Participants can <a href='/wiki/uploads/Main/apm_demo.zip'>download a few example applications</a> to run prior to the meeting that will be covered as part of the tutorial.

</td></tr><tr><td>Nonlinear Programming with APM Python </td><td><a href='/wiki/uploads/Main/apm_tutorial_7Feb12.pdf'>Presentation</a> </td><td>February 7, 2012 </td><td>John Hedengren </td><td>The APM interface extends Python to be used a variety of optimization applications. Dynamic optimization programming applications are demonstrated with Python.

</td></tr><tr><td>Solid Oxide Fuel Cell Modeling and Control </td><td><a href='/wiki/uploads/Main/apm_sofc_24Jan12.pdf'>Presentation</a> </td><td>January 24, 2012 </td><td>Lee Jacobsen </td><td>Solid Oxide Fuel Cells (SOFCs) can be damaged by load changes that shorten life through delamination and micro-cracking. Application of SOFCs in load following applications is discussed to improve lifecycle costs.

</td></tr><tr><td>Aerial Vehicle Control for Drogue Following </td><td><a href='/wiki/uploads/Main/apm_uav_17Jan12.pdf'>Presentation</a> </td><td>January 17, 2012 </td><td>Solomon Sun </td><td>Path optimization of an Unmanned Aerial Vehicle (UAV) becomes more challenging with select constraints. Application of a UAV application is discussed from the MAGICC Lab group at BYU.

</td></tr><tr><td>Dynamic Energy Storage </td><td><a href='/wiki/uploads/Main/apm_energy_storage_13Dec11.pdf'>Presentation</a> </td><td>December 13, 2011 </td><td>Kody Powell, UT Austin </td><td>Dynamic thermal energy storage with weather forecasting has the potential to improve solar energy capture by up to 64%. Results from a recent study are discussed to demonstrate the benefits of dynamic optimization.

</td></tr><tr><td>Virus and Biological Modeling </td><td><a href='/wiki/uploads/Main/apmug_bio_29Nov11.pdf'>Presentation</a> </td><td>November 29, 2011 </td><td>Casey Abbott </td><td>Large scale biological models are important to gain an understanding of complex pathways to improve treatments such as anti-viral application. Simulated clinical trial data is aligned to an available model of HIV infection.

</td></tr><tr><td>Virus and Biological Modeling </td><td><a href='/wiki/uploads/Main/apmug_bio_29Nov11.pdf'>Presentation</a> </td><td>November 29, 2011 </td><td>Casey Abbott </td><td>Large scale biological models are important to gain an understanding of complex pathways to improve treatments such as anti-viral application. Simulated clinical trial data is aligned to an available model of HIV infection.

</td></tr><tr><td>Dynamic Energy Storage </td><td><a href='/wiki/uploads/Main/apm_energy_storage_13Dec11.pdf'>Presentation</a> </td><td>December 13, 2011 </td><td>Kody Powell, UT Austin </td><td>Dynamic thermal energy storage with weather forecasting has the potential to improve solar energy capture by up to 64%. Results from a recent study are discussed to demonstrate the benefits of dynamic optimization.

</td></tr><tr><td>Aerial Vehicle Control for Drogue Following </td><td><a href='/wiki/uploads/Main/apm_uav_17Jan12.pdf'>Presentation</a> </td><td>January 17, 2012 </td><td>Solomon Sun </td><td>Path optimization of an Unmanned Aerial Vehicle (UAV) becomes more challenging with select constraints. Application of a UAV application is discussed from the MAGICC Lab group at BYU.

</td></tr><tr><td>Solid Oxide Fuel Cell Modeling and Control </td><td><a href='/wiki/uploads/Main/apm_sofc_24Jan12.pdf'>Presentation</a> </td><td>January 24, 2012 </td><td>Lee Jacobsen </td><td>Solid Oxide Fuel Cells (SOFCs) can be damaged by load changes that shorten life through delamination and micro-cracking. Application of SOFCs in load following applications is discussed to improve lifecycle costs.

</td></tr><tr><td>Nonlinear Programming with APM Python </td><td><a href='/wiki/uploads/Main/apm_tutorial_7Feb12.pdf'>Presentation</a> </td><td>February 7, 2012 </td><td>John Hedengren </td><td>The APM interface extends Python to be used a variety of optimization applications. Dynamic optimization programming applications are demonstrated with Python.

</td></tr><tr><td>Nonlinear Programming with APM MATLAB </td><td><a href='/wiki/uploads/Main/apm_tutorial_21Feb12.pdf'>Presentation</a> </td><td>February 21, 2012 </td><td>John Hedengren </td><td>The APM interface extends MATLAB to be used for parameter estimation, nonlinear control, and optimization. Participants can <a href='/wiki/uploads/Main/apm_demo.zip'>download a few example applications</a> to run prior to the meeting that will be covered as part of the tutorial.

</td></tr><tr><td>Thermal Oxidizer Boundary Value Management / InBound </td><td nowrap><a href='/wiki/uploads/Main/apm_bvm_6Mar12.pdf'>Presentation 1</a><br><a href='/wiki/uploads/Main/apm_pas_6Mar12.pdf'>Presentation 2</a> </td><td>March 6, 2012 </td><td>Reza Asgharzadeh, Chris Lyden, and Jim Huff </td><td>Thermal oxidizers are used in chemical plants and refineries to combust waste streams with low concentrations of reactants. The design and operation of the thermal oxidizer is of crucial importance for safety, environmental, and economic reasons.

</td></tr><tr><td>Optimal Identification for Model Predictive Control </td><td><a href='/wiki/uploads/Main/apm_darby_13Mar12.pdf'>Presentation</a> </td><td>March 13, 2012 </td><td>Mark Darby </td><td>To identify models for MPC applications in chemical plants and refineries, the process is perturbed through increasingly automated step testing. Mark will give a tutorial overview of how to apply practical control engineering and design experiments for model development of dynamic systems.

APM User's Group Webinars are held every two-weeks at 10 AM Mountain Time / 11 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

APM User's Group Webinars are held about every two-weeks at 10 AM Mountain Time / 11 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

</td><td> </td><td>April 17, 2012

</td><td><a href='https://byu.webex.com/byu/j.php?ED=196607642&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>April 24, 2012

</td><td>David Grigsby </td><td>

</td><td>David Grigsby and Casey Abbott </td><td>The <a href='http://sbml.org'>Systems Biology Markup Language (SBML)</a> is a repository of hundreds of dynamic models in computational biology. This presentation will provide a tutorial on using SBML models for dynamic simulation, parameter estimation, and optimization.

</td></tr><tr><td>SBML Models in APM </td><td> </td><td>April 17, 2012 </td><td nowrap>10AM MST </td><td>David Grigsby </td><td>

</td></tr><tr><td>Model Predictive Control for Friction Stir Welding

</td></tr><tr><td>Application of Model Predictive Control

</td><td><a href='/wiki/uploads/Main/apmug_fsw_15Nov11.pdf'>Presentation</a>

</td><td>Presentation

</td><td><a href='https://byu.webex.com/byu/j.php?ED=193601947&RG=1&UID=0&RT=MiM2 '>Register</a>

</td><td><a href='/wiki/uploads/Main/apm_fast_mpc_3Apr12.pdf'>Presentation</a>

</td><td>The equations that simulate separation processes naturally involve higher index Differential and Algebraic Equations (DAEs). Other popular techniques for DAE simulation can often only handle up to index-1 DAEs. New techniques for large-scale DAEs allow efficient solution for bifurcation analysis, simulation of complex systems, and optimization.

</td><td>The equations that simulate separation processes naturally involve higher index Differential and Algebraic Equations (DAEs). Other popular techniques for DAE simulation can often only handle up to index-1 DAEs. New techniques for large-scale DAEs allow efficient solution for bifurcation analysis, simulation of complex systems, and stability analysis.

</td><td>TBD

</td><td>July 17, 2012

</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (10 Hz) without storage and retrieval for a four tank process.

</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (1-5 Hz) without storage and retrieval for a four tank process.

</td></tr><tr><td>Model Predictive Control for Friction Stir Welding

</td></tr><tr><td>Fast Model Predictive Control

</td><td>Isak Nielsen </td><td>Empirical models obtained through subspace identification are a popular form for model predictive control (MPC) applications. This presentation relates a practical application of linear MPC with models obtained by regression techniques. Methods for sparse discrete or continuous Linear Time Invariant (LTI) forms in APMonitor are also reviewed.

</td></tr><tr><td>Fast Model Predictive Control </td><td> </td><td>April 24, 2012 </td><td nowrap>10AM MST </td><td>Trevor Slade

</td><td>Trevor Slade and Reza Asgharzadeh

</td></tr><tr><td>Model Predictive Control for Friction Stir Welding </td><td> </td><td>June 12, 2012 </td><td nowrap>6AM MST </td><td>Isak Nielsen </td><td>Empirical models obtained through subspace identification are a popular form for model predictive control (MPC) applications. This presentation relates a practical application of linear MPC with models obtained by regression techniques. Methods for sparse discrete or continuous Linear Time Invariant (LTI) forms in APMonitor are also reviewed.

</td></tr><tr><td>Linear Model Predictive Control

</td></tr><tr><td>Model Predictive Control for Friction Stir Welding

</td><td nowrap>10AM MST

</td><td nowrap>10AM MST

</td><td nowrap>10AM MST

</td></tr><tr><td>Optimal Boiler Control </td><td><a href='/wiki/uploads/Main/apm_boilers_3Mar12.pdf'>Presentation</a> </td><td>March 20, 2012

</td></tr><tr><td>Linear Model Predictive Control </td><td><a href='https://byu.webex.com/byu/j.php?ED=193601947&RG=1&UID=0&RT=MiM2 '>Register</a> </td><td>April 3, 2012

</td><td>Jose Mojica </td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers. <a href='/wiki/uploads/Main/apm_mpc_demo.zip'>Download example application</a>

</td></tr><tr><td>Linear Model Predictive Control </td><td><a href='https://byu.webex.com/byu/j.php?ED=193601947&RG=1&UID=0&RT=MiM2 '>Register</a> </td><td>April 3, 2012 </td><td nowrap>10AM MST

</td></tr><tr><td>Optimal Boiler Control </td><td><a href='/wiki/uploads/Main/apm_boilers_3Mar12.pdf'>Presentation</a> </td><td>March 20, 2012 </td><td nowrap>10AM MST </td><td>Jose Mojica </td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers. <a href='/wiki/uploads/Main/apm_mpc_demo.zip'>Download example application</a>

To participate in an upcoming presentation, click register and provide your name and e-mail address. Log-on information is used to communicate log-on information and a reminder notice just prior to the meeting. There is no charge to register for a webinar. Users can participate via audio or video conference through a telephone or internet connection.

To participate in an upcoming presentation, click register and provide your name and e-mail address. Registration is used to communicate log-on information and a reminder notice just prior to the meeting. There is no charge to register for a webinar. Users can participate via audio or video conference through a telephone or internet connection.

</td><td>Traditionally, Model Predictive Control (MPC) is executed at a rate of 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (10 Hz) without storage and retrieval for a four tank process.

</td><td>Traditionally, Model Predictive Control (MPC) is executed every 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (10 Hz) without storage and retrieval for a four tank process.

</td></tr><tr><td>Fast Model Predictive Control </td><td> </td><td>April 24, 2012 </td><td nowrap>10AM MST </td><td>Trevor Slade </td><td>Traditionally, Model Predictive Control (MPC) is executed at a rate of 15-60 seconds. Recent developments have explored storage and retrieval to increase the speed. This work is an implementation of Fast Model Predictive Control (10 Hz) without storage and retrieval for a four tank process.

</td><td><a href='https://byu.webex.com/byu/j.php?ED=190523552&RG=1&UID=0&RT=MiM2 '>Join</a> Password: apm2012

</td><td><a href='/wiki/uploads/Main/apm_boilers_3Mar12.pdf'>Presentation</a>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=190523552&RG=1&UID=0&RT=MiM2 '>Join</a>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=190523552&RG=1&UID=0&RT=MiM2 '>Join</a> Password: apm2012

</td><td><a href='https://byu.webex.com/byu/j.php?ED=190523552&RG=1&UID=0&RT=MiM2 '>Register</a>

</td><td><a href='https://byu.webex.com/byu/j.php?ED=190523552&RG=1&UID=0&RT=MiM2 '>Join</a>

</td><td>TBD

</td><td>May 15, 2012

</td><td>TBD

</td><td>Sept. 4, 2012

</td><td><b>Time</b>

</td><td nowrap>8AM MST

</td><td nowrap>8AM MST

</td><td nowrap>8AM MST

</td><td nowrap>8AM MST

</td><td nowrap><b>10AM</b> MST

</td><td nowrap>8AM MST

</td><td nowrap>8AM MST

</td><td nowrap>10AM MST

</td><td nowrap>4PM MST

APM User's Group Webinars are held every two-weeks at 8 AM Mountain Time / 9 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

APM User's Group Webinars are held every two-weeks at 10 AM Mountain Time / 11 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

</table>

</table><br>

</td><td>Isak Nielsen and John Hedengren

</td><td>Isak Nielsen

</td></tr><tr><td>Integrated Energy Storage, Forecasting, and Optimization </td><td> </td><td>TBD </td><td nowrap>10AM MST </td><td>Kody Powell </td><td>Optimizing the University of Texas at Austin utilities (heat, cooling, and power) plant has the potential to reduce emissions equivalent to that of 800 cars annually. This green energy project seeks efficiency improvements by more tightly integrating this complex system to achieve peak efficiency.

</td></tr><tr><td>DAE Solution for Separation Processes </td><td> </td><td>TBD </td><td nowrap>10AM MST </td><td>David Harney </td><td>The equations that simulate separation processes naturally involve higher index Differential and Algebraic Equations (DAEs). Other popular techniques for DAE simulation can often only handle up to index-1 DAEs. New techniques for large-scale DAEs allow efficient solution for bifurcation analysis, simulation of complex systems, and optimization.

</td></tr><tr><td>Mixed Integer Nonlinear Programming </td><td> </td><td>TBD </td><td nowrap>10AM MST </td><td>Wesley Cole </td><td>Many design, engineering, and scientific applications include a mixture of continuous and discrete decision variables and nonlinear relationships. Mixed Integer Nonlinear Programming (MINLP) solvers are reviewed and compared for application to a scheduling problem for UT Austin central campus cooling.

</td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers.

</td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers. <a href='/wiki/uploads/Main/apm_mpc_demo.zip'>Download example application</a>

### Past Presentations and Future Schedule

To participate in an upcoming presentation, click register and provide your name and e-mail address. Log-on information is used to communicate log-on information and a reminder notice just prior to the meeting. There is no charge to register for a webinar. Users can participate via audio or video conference through a telephone or internet connection.

</td></tr><tr><td>Friction Stir Welding </td><td><a href='/wiki/uploads/Main/apmug_fsw_15Nov11.pdf'>Presentation</a> </td><td>November 15, 2011 </td><td nowrap>8AM MST </td><td>Dustin Marshall </td><td>Friction Stir Welding (FSW) temperature control is important to achieve uniform and consistent weld properties. This application is using a PDAE model for estimation and control. The model validation, control, and experimental data testing are discussed.

</td></tr><tr><td>Virus and Biological Modeling </td><td><a href='/wiki/uploads/Main/apmug_bio_29Nov11.pdf'>Presentation</a> </td><td>November 29, 2011 </td><td nowrap>8AM MST

</td></tr><tr><td>Optimal Boiler Control </td><td><a href='https://byu.webex.com/byu/j.php?ED=190523552&RG=1&UID=0&RT=MiM2 '>Register</a> </td><td>March 20, 2012 </td><td nowrap>10AM MST </td><td>Jose Mojica </td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers.

</td></tr><tr><td>Linear Model Predictive Control </td><td><a href='https://byu.webex.com/byu/j.php?ED=193601947&RG=1&UID=0&RT=MiM2 '>Register</a> </td><td>April 3, 2012 </td><td nowrap>10AM MST </td><td>Isak Nielsen and John Hedengren </td><td>Empirical models obtained through subspace identification are a popular form for model predictive control (MPC) applications. This presentation relates a practical application of linear MPC with models obtained by regression techniques. Methods for sparse discrete or continuous Linear Time Invariant (LTI) forms in APMonitor are also reviewed.

</td></tr><tr><td>Optimization of Design Under Uncertainty </td><td><a href='https://byu.webex.com/byu/j.php?ED=192896422&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>May 1, 2012 </td><td nowrap>10AM MST

</td><td>Large scale biological models are important to gain an understanding of complex pathways to improve treatments such as anti-viral application. Simulated clinical trial data is aligned to an available model of HIV infection.

</td></tr><tr><td>Dynamic Energy Storage </td><td><a href='/wiki/uploads/Main/apm_energy_storage_13Dec11.pdf'>Presentation</a> </td><td>December 13, 2011

</td><td>Process design has a number of unknown or uncertain parameters that influence the optimal design, desired operational conditions, and project profitability. Optimization of design maximizes the expected NPV (Net Present Value) considering uncertain design parameters.

</td></tr>

</table> (:htmlend:)

To participate in an upcoming presentation, click register and provide your name and e-mail address. Log-on information is used to communicate log-on information and a reminder notice just prior to the meeting. There is no charge to register for a webinar. Users can participate via audio or video conference through a telephone or internet connection.

### Past Presentations

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<tr><td><b>Topic</b> </td><td><b>Registration</b> </td><td><b>Date</b> </td><td><b>Time</b> </td><td><b>Presenter</b> </td><td><b>Description</b>

</td></tr><tr><td>Friction Stir Welding </td><td><a href='/wiki/uploads/Main/apmug_fsw_15Nov11.pdf'>Presentation</a> </td><td>November 15, 2011

</td><td>Kody Powell, UT Austin </td><td>Dynamic thermal energy storage with weather forecasting has the potential to improve solar energy capture by up to 64%. Results from a recent study are discussed to demonstrate the benefits of dynamic optimization.

</td></tr><tr><td>Aerial Vehicle Control for Drogue Following </td><td><a href='/wiki/uploads/Main/apm_uav_17Jan12.pdf'>Presentation</a> </td><td>January 17, 2012

</td><td>Dustin Marshall </td><td>Friction Stir Welding (FSW) temperature control is important to achieve uniform and consistent weld properties. This application is using a PDAE model for estimation and control. The model validation, control, and experimental data testing are discussed.

</td></tr><tr><td>Virus and Biological Modeling </td><td><a href='/wiki/uploads/Main/apmug_bio_29Nov11.pdf'>Presentation</a> </td><td>November 29, 2011

</td><td>Casey Abbott </td><td>Large scale biological models are important to gain an understanding of complex pathways to improve treatments such as anti-viral application. Simulated clinical trial data is aligned to an available model of HIV infection.

</td></tr><tr><td>Dynamic Energy Storage </td><td><a href='/wiki/uploads/Main/apm_energy_storage_13Dec11.pdf'>Presentation</a> </td><td>December 13, 2011 </td><td nowrap>8AM MST </td><td>Kody Powell, UT Austin </td><td>Dynamic thermal energy storage with weather forecasting has the potential to improve solar energy capture by up to 64%. Results from a recent study are discussed to demonstrate the benefits of dynamic optimization.

</td></tr><tr><td>Aerial Vehicle Control for Drogue Following </td><td><a href='/wiki/uploads/Main/apm_uav_17Jan12.pdf'>Presentation</a> </td><td>January 17, 2012 </td><td nowrap>8AM MST

</td></tr><tr><td>Optimal Boiler Control </td><td><a href='https://byu.webex.com/byu/j.php?ED=190523552&RG=1&UID=0&RT=MiM2 '>Register</a> </td><td>March 20, 2012 </td><td nowrap>10AM MST </td><td>Jose Mojica </td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers.

</td></tr><tr><td>Linear Model Predictive Control </td><td><a href='https://byu.webex.com/byu/j.php?ED=193601947&RG=1&UID=0&RT=MiM2 '>Register</a> </td><td>April 3, 2012 </td><td nowrap>10AM MST </td><td>Isak Nielsen and John Hedengren </td><td>Empirical models obtained through subspace identification are a popular form for model predictive control (MPC) applications. This presentation relates a practical application of linear MPC with models obtained by regression techniques. Methods for sparse discrete or continuous Linear Time Invariant (LTI) forms in APMonitor are also reviewed.

</td></tr><tr><td>Optimization of Design Under Uncertainty </td><td><a href='https://byu.webex.com/byu/j.php?ED=192896422&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>May 1, 2012 </td><td nowrap>10AM MST </td><td>Casey Abbott </td><td>Process design has a number of unknown or uncertain parameters that influence the optimal design, desired operational conditions, and project profitability. Optimization of design maximizes the expected NPV (Net Present Value) considering uncertain design parameters.

</td><td><a href='https://byu.webex.com/byu/j.php?ED=194630077&UID=501980762&PW=NYmI5MTM4Yjlk&RT=MiM2'>Join</a>

</td><td><a href='/wiki/uploads/Main/apm_darby_13Mar12.pdf'>Presentation</a>

</td></tr><tr><td>Optimal Identification for Model Predictive Control </td><td><a href='https://byu.webex.com/byu/j.php?ED=194630077&UID=501980762&PW=NYmI5MTM4Yjlk&RT=MiM2'>Join</a> </td><td>March 13, 2012 </td><td nowrap>4PM MST </td><td>Mark Darby </td><td>To identify models for MPC applications in chemical plants and refineries, the process is perturbed through increasingly automated step testing. Mark will give a tutorial overview of how to apply practical control engineering and design experiments for model development of dynamic systems.

</td><td><a href='/wiki/uploads/Main/apm_bvm_6Mar12.pdf'>Presentation</a>

</td><td nowrap><a href='/wiki/uploads/Main/apm_bvm_6Mar12.pdf'>Presentation 1</a><br><a href='/wiki/uploads/Main/apm_pas_6Mar12.pdf'>Presentation 2</a>

(:htmlend:)

(:htmlend:)

</td><td><a href='https://byu.webex.com/byu/j.php?ED=187746022&RG=1&UID=0&RT=MiM2'>Register</a>

</td><td><a href='/wiki/uploads/Main/apm_bvm_6Mar12.pdf'>Presentation</a>

(:title Optimization and Advanced Control Seminars:) (:keywords nonlinear, model, predictive control, APMonitor, differential, algebraic, modeling language:) (:description Simulation, optimization, estimation, and control with APMonitor:)

### Webinars on Optimization Applications

APM User's Group Webinars are held every two-weeks at 8 AM Mountain Time / 9 AM Central Time (USA). Seminars consist of applications and tutorials using APM in modeling, estimation, and control applications.

### Past Presentations and Future Schedule

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<table class="sample">

<tr><td><b>Topic</b> </td><td><b>Registration</b> </td><td><b>Date</b> </td><td><b>Time</b> </td><td><b>Presenter</b> </td><td><b>Description</b>

</td></tr><tr><td>Friction Stir Welding </td><td><a href='/wiki/uploads/Main/apmug_fsw_15Nov11.pdf'>Presentation</a> </td><td>November 15, 2011 </td><td nowrap>8AM MST </td><td>Dustin Marshall </td><td>Friction Stir Welding (FSW) temperature control is important to achieve uniform and consistent weld properties. This application is using a PDAE model for estimation and control. The model validation, control, and experimental data testing are discussed.

</td></tr><tr><td>Virus and Biological Modeling </td><td><a href='/wiki/uploads/Main/apmug_bio_29Nov11.pdf'>Presentation</a> </td><td>November 29, 2011 </td><td nowrap>8AM MST </td><td>Casey Abbott </td><td>Large scale biological models are important to gain an understanding of complex pathways to improve treatments such as anti-viral application. Simulated clinical trial data is aligned to an available model of HIV infection.

</td></tr><tr><td>Dynamic Energy Storage </td><td><a href='/wiki/uploads/Main/apm_energy_storage_13Dec11.pdf'>Presentation</a> </td><td>December 13, 2011 </td><td nowrap>8AM MST </td><td>Kody Powell, UT Austin </td><td>Dynamic thermal energy storage with weather forecasting has the potential to improve solar energy capture by up to 64%. Results from a recent study are discussed to demonstrate the benefits of dynamic optimization.

</td></tr><tr><td>Aerial Vehicle Control for Drogue Following </td><td><a href='/wiki/uploads/Main/apm_uav_17Jan12.pdf'>Presentation</a> </td><td>January 17, 2012 </td><td nowrap>8AM MST </td><td>Solomon Sun </td><td>Path optimization of an Unmanned Aerial Vehicle (UAV) becomes more challenging with select constraints. Application of a UAV application is discussed from the MAGICC Lab group at BYU.

</td></tr><tr><td>Solid Oxide Fuel Cell Modeling and Control </td><td><a href='/wiki/uploads/Main/apm_sofc_24Jan12.pdf'>Presentation</a> </td><td>January 24, 2012 </td><td nowrap><b>10AM</b> MST </td><td>Lee Jacobsen </td><td>Solid Oxide Fuel Cells (SOFCs) can be damaged by load changes that shorten life through delamination and micro-cracking. Application of SOFCs in load following applications is discussed to improve lifecycle costs.

</td></tr><tr><td>Nonlinear Programming with APM Python </td><td><a href='/wiki/uploads/Main/apm_tutorial_7Feb12.pdf'>Presentation</a> </td><td>February 7, 2012 </td><td nowrap>8AM MST </td><td>John Hedengren </td><td>The APM interface extends Python to be used a variety of optimization applications. Dynamic optimization programming applications are demonstrated with Python.

</td></tr><tr><td>Nonlinear Programming with APM MATLAB </td><td><a href='/wiki/uploads/Main/apm_tutorial_21Feb12.pdf'>Presentation</a> </td><td>February 21, 2012 </td><td nowrap>8AM MST </td><td>John Hedengren </td><td>The APM interface extends MATLAB to be used for parameter estimation, nonlinear control, and optimization. Participants can <a href='/wiki/uploads/Main/apm_demo.zip'>download a few example applications</a> to run prior to the meeting that will be covered as part of the tutorial.

</td></tr><tr><td>Thermal Oxidizer Boundary Value Management / InBound </td><td><a href='https://byu.webex.com/byu/j.php?ED=187746022&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>March 6, 2012 </td><td nowrap>10AM MST </td><td>Reza Asgharzadeh, Chris Lyden, and Jim Huff </td><td>Thermal oxidizers are used in chemical plants and refineries to combust waste streams with low concentrations of reactants. The design and operation of the thermal oxidizer is of crucial importance for safety, environmental, and economic reasons.

</td></tr><tr><td>Optimal Boiler Control </td><td><a href='https://byu.webex.com/byu/j.php?ED=190523552&RG=1&UID=0&RT=MiM2 '>Register</a> </td><td>March 20, 2012 </td><td nowrap>10AM MST </td><td>Jose Mojica </td><td>Load following in power generation is a recent opportunity as time of day pricing and cogeneration are adopted in refining, chemical, and power plants. This work is to improve the load following capabilities of natural gas and coal-fired boilers.

</td></tr><tr><td>Linear Model Predictive Control </td><td><a href='https://byu.webex.com/byu/j.php?ED=193601947&RG=1&UID=0&RT=MiM2 '>Register</a> </td><td>April 3, 2012 </td><td nowrap>10AM MST </td><td>Isak Nielsen and John Hedengren </td><td>Empirical models obtained through subspace identification are a popular form for model predictive control (MPC) applications. This presentation relates a practical application of linear MPC with models obtained by regression techniques. Methods for sparse discrete or continuous Linear Time Invariant (LTI) forms in APMonitor are also reviewed.

</td></tr><tr><td>Optimization of Design Under Uncertainty </td><td><a href='https://byu.webex.com/byu/j.php?ED=192896422&RG=1&UID=0&RT=MiM2'>Register</a> </td><td>May 1, 2012 </td><td nowrap>10AM MST </td><td>Casey Abbott </td><td>Process design has a number of unknown or uncertain parameters that influence the optimal design, desired operational conditions, and project profitability. Optimization of design maximizes the expected NPV (Net Present Value) considering uncertain design parameters.

</td></tr>

</table> (:htmlend:)