Grant: Strategic Projects - Group
University of Alberta
$134,650.00
Optimization of manufacturing process automation in the presence of time-varying operating conditions
EF 2010-11
Résumé
Automatic control systems are critical to manufacturing today because they allow for steady process operations, consistent product quality, less waste, and control of emissions. However, most current systems are not designed to optimally handle time-varying operating conditions introduced by time-varying factors such as feedstock changes, product grade changes, demand changes, multi-modes due to process nonlinearities and even operator shift changes. The inability to finely control processes can lead to lower product quality, additional waste, and increased emissions of environmentally undesirable substances. As a result, it is difficult to meet both increased product quality specifications and more stringent environmental regulations - facts of life for industry today - with conventional automation control. We propose to develop a set of tools for improved process control of systems with time-varying characteristics. The approach we will take is based on system identification and optimization of a set of control laws including time invariant as well as time variant control laws for a class of time-varying processes. Our objective is to develop strategies to ensure product quality specifications while minimizing control input energy. Time-varying system identification and process control, particularly for the hybrid or switching systems, are areas of active research. Our solution will be designed to leverage automation system infrastructure from the existing to a foreseeable 10-year horizon so that it can be implemented in the industrial distributed control systems (DCS) without additional capital cost. This will make it possible to quickly apply the technology to industry. Improved control reduces variability, allowing processes to be operated closer to their setpoints without violating constraints. This will lead to better product quality and a reduced environmental footprint. Optimizing the control of time-varying operations will make industry more efficient and more competitive in the global marketplace.
Programme
Strategic Projects - Group
Bénéficiaire
Emplacement
Alberta, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
380880-2009
Name nom
Huang, Biao
Department département
Chemical and Materials Engineering
Organization id
9
Institution établissement
University of Alberta
Province en
Alberta
Province fr
Alberta
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2010
Competition year année de concours
2009
Award amount
134650
Program id
STPGP
Program name en
Strategic Projects - Group
Program name fr
Projets stratégiques - De groupe
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1043
Committee name en
Strategic Projects Selection Panel - Manufacturing
Committee name fr
Comité de sélection des projets stratégiques - Fabrication
Area of application code
500
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Manufacturing processes and products
Area of application fr
Procédés et produits industriels
Research subject code
1702
Research subject group en
Design and manufacturing
Research subject group fr
Conception et fabrication
Research subject en
Advanced manufacturing
Research subject fr
Méthodes de fabrication avancées
Partie
2
Nb partie
3
Application title
Optimization of manufacturing process automation in the presence of time-varying operating conditions
Keyword
NSERC
Application summary
Automatic control systems are critical to manufacturing today because they allow for steady process operations, consistent product quality, less waste, and control of emissions. However, most current systems are not designed to optimally handle time-varying operating conditions introduced by time-varying factors such as feedstock changes, product grade changes, demand changes, multi-modes due to process nonlinearities and even operator shift changes. The inability to finely control processes can lead to lower product quality, additional waste, and increased emissions of environmentally undesirable substances. As a result, it is difficult to meet both increased product quality specifications and more stringent environmental regulations - facts of life for industry today - with conventional automation control. We propose to develop a set of tools for improved process control of systems with time-varying characteristics. The approach we will take is based on system identification and optimization of a set of control laws including time invariant as well as time variant control laws for a class of time-varying processes. Our objective is to develop strategies to ensure product quality specifications while minimizing control input energy. Time-varying system identification and process control, particularly for the hybrid or switching systems, are areas of active research. Our solution will be designed to leverage automation system infrastructure from the existing to a foreseeable 10-year horizon so that it can be implemented in the industrial distributed control systems (DCS) without additional capital cost. This will make it possible to quickly apply the technology to industry. Improved control reduces variability, allowing processes to be operated closer to their setpoints without violating constraints. This will lead to better product quality and a reduced environmental footprint. Optimizing the control of time-varying operations will make industry more efficient and more competitive in the global marketplace.
