The Process Analytics, Optimization, and Control (PAOC) Group in the Department of Chemical Engineering is internationally recognized for research activities in control, optimization, and applied statistics, with research in control systems design being a central activity. Control systems research focuses on the development and application of controller design techniques that include distributed control, extremum seeking control, model predictive control, geometric control, and stochastic control for linear and nonlinear dynamical systems. Activities include the development of new sustainable design methodologies with applications to alternative energy systems, smart buildings, and smart manufacturing systems. Research in the area of optimization focuses on the formulation and solution of large-scale, non-convex optimization problems and optimization under uncertainty (see Figure 1), with emphases on supply chain management, planning and scheduling, energy transport and utilization, and deep learning. Improvements in algorithmic performance has led to the development of state-of-the art optimization methods that can be used to implement real-time decision-making platforms.

Researchers discuss the optimal design and operation of an energy network
Figure 1: Researchers discuss the optimal design and operation of an energy network.
RResearchers at the Chemical Process Mathematics Lab
Figure 2: Researchers at the Chemical Process Mathematics Lab combine data and mathematical models to simulate and improve chemical processes. These include the chemical reactors for the production of monomers, polymers, and biolubricants, and the 3D radiation dose sensors used in cancer treatments.

The PAOC group is also widely recognized for the development of a new generation of online data-driven optimization systems. These model-free methods are widely applicable to a large class of dynamical systems, and can be used to design effective online optimization systems for systems that lack detailed mathematical descriptions. This research enables new developments in a wide range of applications, including smart cities, smart buildings, cybersecurity, energy utilization, and storage technologies. Research in the area of applied statistics and modeling are central to process systems engineering (see Figure 2). 

The group also engages in industrial research, where process models generate avenues of process development, process performance improvements, and process understanding. This work has resulted in a wide range of applications in diverse sectors such as environmental technology. 

Members of the PAOC group include a fellow of the Chemical Institute of Canada (CIC), a fellow of the CAE, winners of the DG Fisher Award for outstanding contributions to the Control and Systems Division of the Canadian Society for Chemical Engineering (CSChE), and a winner of the Syncrude Innovation Award from the CSChE. Members are also contributing to the editorial activities of some of the best scientific journals in the field, including those associated with the Institute of Electrical and Electronics Engineers (IEEE). Their roles have included: deputy editor-in-chief of the Journal of Process Control; senior editor of IEEE CSS Letters; review editor of The Canadian Journal of Chemical Engineering; and associate editor of IEEE Transactions on Automatic Control, Automatica, Nonlinear Analysis and Hybrid Systems, and the Journal of Process Control. Members are also involved in the organization of international conferences for the International Federation of Automatic Control (IFAC) and the IEEE Control Systems Society (IEEE CSS). 

Researchers in this group:

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