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Optimization and Control of Batch Process Systems using Data-Driven Models

Data-driven models provide a useful alternative for the control of complex systems where computation time and numerical solution robustness pose challenges to the use of detailed first-principles models. In this research, we are exploring and analyzing different types of data-driven models for the optimization and control of complex industrial processes such as electric arc furnace operation in the steel industry.

Dr. Chris L. E. Swartz
Professor and Director, MACC
Dr. Prashant Mhaskar
Professor and Canada Research Chair (Tier II)
Abhinav Garg
Ph.D. Candidate
Mudassir Rashid
Ph.D. recipient
Development of a high fidelity and subspace identification model of a hydrogen plant startup dynamics
Garg, A.Corbett, B.Mhaskar, P., Hu, G., Flores-Cerrillo, J.
American Control Conference, 2857-2862 (2017)  -  [ Publisher Version ]
Subspace identification and predictive control of batch particulate processes
American Control Conference, 505-510 (2017)  -  [ Publisher Version ]
Subspace-based model identification of a hydrogen plant startup dynamics
Garg, A.Corbett, B.Mhaskar, P., Hu, G., Flores-Cerrillo, J.
Computers & Chemical Engineering, 106 183-190 (2017)  -  [ Publisher Version ]
Subspace Identification-Based Modeling and Control of Batch Particulate Processes
Industrial & Engineering Chemistry Research, 56 (26) 7491-7502 (2017)  -  [ Publisher Version ]