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Research Themes
Data Analytics and Machine Learning
Multivariate data analysis techniques have been developed and applied and applied to process plants over the span of about 3 decades, with much work in this area spearheaded by MACC emeritus faculty member Dr. John MacGregor. MACC’s strong foundation and in data analytics, mathematical modeling, advanced control and optimization have positioned it well to make contributions in the present explosion of interest in big data and intelligent systems.

Latent variable (LV) techniques such as principal components analysis (PCA) and partial least square (PLS) area at the core of many of the developments in data analytics techniques within MACC. These methods compress the data into a low dimensional space that captures the bulk of the variation in the data. Applications include process monitoring and fault detection, model predictive control, image analysis, and product design. Current work includes use of LV methods for monitoring the performance of supply chain systems, and detecting abnormal conditions such as transportation delays. [read more]

Dr. Chris L. E. Swartz
Professor and Director, MACC
Jing Wang
Ph.D. Candidate

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... [read more]

Dr. Prashant Mhaskar
Professor and Canada Research Chair in Nonlinear and Fault-Tolerant Control
Hesam Hassanpour
Ph.D. Candidate
Mahir Jalanko
Ph.D. Candidate
Carlos Rodriguez
Ph.D. Candidate

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. [read more]

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