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Batch Process Modeling and Control

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 component of such approach is to build an accurate model for the process; particularly from past process data as high-fidelity model of such processes have confined applicability in practice. Therefore, MACC researchers are investigating and developing approaches based on PLS, subspace identification and machine learning, specifically tailored to batch processes, for identifying process and/or quality model using historical data. The predictive ability of the identified data-driven model to reasonably predict the terminal quality during the batch is then utilized within Model Predictive Control (MPC) framework for accomplishing desired terminal quality of the batch. Some of the applications being considered include pharmaceutical processes, hydrogen startup process and rotational molding process. Many of the projects in these areas are in collaboration with industries (e.g. Imperial Oil, Sartorious, Linde.Digital) and other academic research groups at McMaster (e.g. McMaster Manufacturing Research Institute).

Dr. Prashant Mhaskar
Professor and Canada Research Chair in Nonlinear and Fault-Tolerant Control
Aswin Chandrasekar
Ph.D. Candidate
Debanjan Ghosh
Ph.D. Candidate
Ian Gough
Ph.D. Candidate
Emma Hermonat
M.A.Sc. Candidate
Alexander McKay
M.A.Sc. Candidate
Nikesh Patel
Ph.D. Candidate
Samardeep Singh Sarna
M.A.Sc. Candidate
Data-Driven Control of Rotational Molding Process
Garg, A., Felipe P.C. Gomes, Mhaskar, P., Michael R. Thompson
American Control Conference, 5117-5122 (2019)  -  [ Publisher Version ]
Model predictive control of uni-axial rotational molding process
Garg, A., Felipe P.C. Gomes, Mhaskar, P., Micheal R. Thompson
Computers & Chemical Engineering, 121 306-316 (2019)  -  [ Publisher Version ]
Data-Driven Advances in Manufacturing for Batch Polymer Processing Using Multivariate Nondestructive Monitoring
Felipe P.C. Gomes, Garg, A.Mhaskar, P., Michael R. Thompson
Industrial and Engineering Chemistry Research, 23 (58) 9940-9951 (2019)  -  [ Publisher Version ]
Real-time energy management for electric arc furnace operation
Journal of Process Control, 74 50-62 (2019)  -  [ Publisher Version ]
Handling Constraints and Raw Material Variability in Rotomolding through Data-Driven Model Predictive Control
Garg, A., Hassan A. Abdulhussain, Mhaskar, P., Michael R. Thompson
Processes, 7 (9) 1-14 (2019)  -  [ Publisher Version ]
Modeling and Control of Batch Processes: Theory and Applications
Springer International Publishing (2019)  -  [ Publisher Version ]
Utilizing Big Data for Batch Process Modeling and Control
Computers and Chemical Engineering (2018)  -  [ Publisher Version ]
Handling multi-rate and missing data in variable duration economic model predictive control of batch processes
AIChE J (2017)  -  [ Publisher Version ]
Handling multi‐rate and missing data in variable duration economic model predictive control of batch processes
AIChE Journal, 63 (7) 2705-2718 (2017)  -  [ Publisher Version ]
Subspace identification and predictive control of batch particulate processes
American Control Conference, 505-510 (2017)  -  [ Publisher Version ]