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Machine Learning

Machine learning can be used for many chemical process problems that cannot be solved easily using rules-based programming. Machine learning is concerned with making computers learn through historical data, observations and interacting with the world and utilize this knowledge to generalize over new unseen settings. At the MACC, we are currently looking at machine learning as being a data-driven modelling tool for complex processes that are hard to model using first-principles models. These developed models can generate better predictions allowing more effective control for many complex chemical engineering processes. Also, we are exploring the possibility of integrating first principle models which contains process knowledge with machine learning algorithm to improve the generalization capability of the black-box models generated solely by machine learning. The capability of machine learning algorithms to find valuable underlying patterns within complex data makes it suitable for fault detection and diagnosis applications which is also part of our research here at MACC.

Dr. Prashant Mhaskar
Professor and Canada Research Chair in Nonlinear and Fault-Tolerant Control
Aswin Chandrasekar
Ph.D. Candidate
Ian Gough
Ph.D. Candidate
Hesam Hassanpour
Ph.D. Candidate
Mahir Jalanko
Ph.D. Candidate
Che Lee
M.A.Sc. Candidate
Carlos Rodriguez
Ph.D. Candidate
Samardeep Singh Sarna
M.A.Sc. Candidate
Evan Ubene
M.A.Sc. Candidate