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Hybrid First-Principles/Data-Driven Models

Researchers at the MACC are collaborating with industrial partners (and seeking further collaboration) on the utilization of available process knowledge in synergy with various data-driven modeling approach. The hybrid modeling approaches manifest themselves in a variety of forms. One instance combines principle component analysis with neural network based dynamic modeling and control to address the possible overfitting problems with dynamic neural networks. Other applications involve combining first principles based models with subspace identification approaches for dynamic modeling and control, and yet another combines principal component analysis with first principles models for the purpose of fault diagnosis.

Dr. Prashant Mhaskar
Professor and Canada Research Chair in Nonlinear and Fault-Tolerant Control
Debanjan Ghosh
Ph.D. Candidate
Emma Hermonat
M.A.Sc. Candidate
Hybrid Modeling Approach Integrating First-Principles Models with Subspace Identification
Ghosh, D.Hermonat, E.Mhaskar, P., Spencer Snowling, Rajeev Goel
Industrial and Engineering Chemistry Research, 58 (30) 13533-13543 (2019)  -  [ Publisher Version ]
Hybrid Modeling of Petrochemical Processes
Hybrid Modeling in Process Industries, 129-165 (2018)