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Advanced Process Control
Process control is essential for the successful operation of any chemical process. At the MACC, we are researching and developing cutting edge control technologies to meet the challenges of today's industry. We are currently looking at process control for continuous, semicontinuous, and batch processes; advanced model predictive control techniques; strategies for detecting and responding to process faults; integrated optimization and control strategies; and strategies for integrated design and control. Advancements in these areas are applied to applications in energy, biofuels, and bulk chemicals.

Model predictive control (MPC) has become the advanced control method of choice in the chemical process industry, and has been widely adopted in other industrial sectors. It utilizes an internal dynamic model which is embedded in an optimization formulation to calculate future plant inputs to minimize a performance criterion such as the set-point tracking error over a future time horizon. Key advantages are its ability to handle input and output constraints directly, its accounting for multivariable interactions through the process model, and its flexibility in in terns of model type, performance objectives and constraints. MACC has been active in MPC applications involving high-fidelity nonlinear models and an economic objective function. Applications include economic MPC (EMPC) of electric arc furnaces that includes use of a nonlinear state estimation scheme, and EMCP of process plants under unit failure conditions. We have also explored the use of linear dynamic... [read more]

Dr. Chris L. E. Swartz
Professor and Director, MACC
Dr. Prashant Mhaskar
Professor and Canada Research Chair in Nonlinear and Fault-Tolerant Control


Traditionally, most of the research in fault-tolerant control has been concerned with preserving nominal process operation in the presence of faults. This has been addressed within the so-called reliable control approaches (which essentially treats the faults as disturbances and designs fault-tolerant controllers) and reconfiguration-based control approaches that assume the existence of a backup control configuration. Yet, there are numerous examples in the chemical process industries where the process economics do not permit deployment of redundant control configurations. In such scenarios, the only recourse is the swift recovery of the failed component. During the fault-recovery period, however, the absence of an established framework to handle such situations, and the use of ad-hoc approaches could lead to the onset of hazardous situations or the inability to resume nominal operation upon fault-recovery. Motivated by these considerations we have developed, utilizing... [read more]

Dr. Prashant Mhaskar
Professor and Canada Research Chair in Nonlinear and Fault-Tolerant Control
Maaz Mahmood
Part-time Ph.D. student


Batch processes are indispensable constituent of chemical process industries and are universally used for manufacturing of high-quality products. The preeminent reason for their popularity can be attributed to their flexibility to control different grades of products by changing the initial conditions and input trajectories. However, a batch process is characterized by absence of equilibrium conditions and a highly nonlinear and time-varying dynamics, which make the classical approaches (for continuous processes) not directly applicable. A fundamental objective in a typical batch process is to achieve the final product quality specifications. The measurements related to the terminal quality are usually not available during the batch operation and can only be accessible at the end of batch operation. Thus, it is not feasible to directly measure/control the quality of the product during the operation and can be achieved indirectly via trajectory tracking approach. An integral... [read more]

Dr. Prashant Mhaskar
Professor and Canada Research Chair in Nonlinear and Fault-Tolerant Control
Dr. Abhinav Garg
Postdoctoral Fellow
Debanjan Ghosh
Ph.D. Candidate
Emma Hermonat
M.A.Sc. Candidate
Nikesh Patel
Ph.D. Candidate


Real-time optimization (RTO) involves the adjustment of plant operating conditions to track the economic optimum which may shift with time due to external disturbances as well as changing plant parameters. Process plants operate in an increasingly dynamic environment due to variation in demand, raw material quality, and utility prices, leading to exploration of the use of dynamic models in RTO. Our key contribution in this area has been the development of a novel dynamic RTO (DRTO) formulation that accounts for the effects of an underlying constrained model predictive control (MPC) system on the predicted plant response, and its extension for coordination of multiple MPC systems in a distributed MPC architecture. The strategy has recently been extended to include production scheduling decisions ‐ this results in production scheduling decisions in which the dynamics of transitions as well as the actions of the underlying control system are taken into account. [read more]

Dr. Chris L. E. Swartz
Professor and Director, MACC
Dr. Prashant Mhaskar
Professor and Canada Research Chair in Nonlinear and Fault-Tolerant Control
Lloyd Mackinnon
Ph.D. Candidate