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Latent Variable Methods

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.

Dr. Chris L. E. Swartz
Professor and Director, MACC
Jing Wang
Ph.D. Candidate
Supply chain optimization of flare-gas-to-butanol processes in Alberta
Canadian J Chem Eng, 94 (12) 2336-2354 (2016)  -  [ Publisher Version | Open Access Version (free) ]
Flexibility analysis of process supply chain networks
Computers & Chemical Engineering, 84 409421 (2016)  -  [ Publisher Version ]
Robust decision making for hybrid process supply chain systems via model predictive control
Computers & Chemical Engineering, 62 (5) 37-55 (2014)  -  [ Publisher Version ]
Dynamic operability analysis of process supply chains for forest industry transformation
Ind Eng Chem Res, 53 9825-9840 (2014)  -  [ Publisher Version ]