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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
Hesam Hassanpour
Ph.D. Candidate
Mahir Jalanko
Ph.D. Candidate
Carlos Rodriguez
Ph.D. Candidate
Optimal Short-Term Scheduling for Cascaded Hydroelectric Power Systems considering Variations in Electricity Prices
Mathur, P.Swartz, C. L. E., Zyngier, D., Welt, F.
Computer Aided Chemical Engineering, 44 1345-1350 (2018)
A game theoretic framework for strategic production planning
AIChE J (2017)  -  [ Publisher Version ]
Global Optimization of Nonlinear Blend-Scheduling Problems
Castillo, P., Pedro M. Castro, Mahalec, V.
Engineering, 3 (2) 188-201 (2017)  -  [ Publisher Version ]
Global Optimization Algorithm for Large-Scale Refinery Planning Models with Bilinear Terms
Castillo, P., Pedro M. Castro, Mahalec, V.
Industrial & Engineering Chemistry Research, 56 (2) 530-548 (2017)  -  [ Publisher Version ]
Hybrid Model for Optimization of Crude Distillation Units
Fu, G., Sanchez, Yoel, Mahalec, V.
AIChE J, 62 (4) 1065-1078 (2016)  -  [ Publisher Version | Open Access Version (free) ]
Planning and Scheduling of Steel Plates Production. Part II: Scheduling of Continuous Casting
Computers & Chemical Engineering (2016)  -  [ Publisher Version | Open Access Version (free) ]
Improved continuous-time model for gasoline blend scheduling
Computers & Chemical Engineering, 84 627-646 (2016)  -  [ Publisher Version | Open Access Version (free) ]
Inventory pinch gasoline blend scheduling algorithm combining discrete- and continuous-time models
Computers & Chemical Engineering, (84) 611-626 (2016)  -  [ Publisher Version | Open Access Version (free) ]
Bayesian Inference in Hybrid Networks with Large Discrete and Continuous Domains
Expert Systems with Applications, 49 1-19 (2016)  -  [ Publisher Version | Open Access Version (free) ]
Comparison of methods for computing crude distillation product properties in planning and scheduling
Ind Eng Chem Res, 54 (45) 11371-11382 (2016)  -  [ Publisher Version ]