Dr. John F. MacGregor
John. F. MacGregor
Distinguished University Professor Emeritus
Department of Chemical Engineering
1280 Main Street West,
Hamilton, ON, L8S 4L7, Canada
Driving directions and map

Voice: (905) 304 9433
email: macgreg@mcmaster.ca

multivariate statistical methods for process monitoring and optimization, polymer reaction engineering.
  • Ph.D. University of Wisconsin (1972)

Dr. MacGregor retired from McMaster University in 2008 and is currently a Professor Emeritus. He is President of ProSensus Inc., a company spun out of MACC in 2004 (www.prosensus.ca). He is therefore not accepting new graduate students, but is still very actively involved with MACC and in collaborative research with other MACC faculty. Research Interests 1) Multivariate Statistical Methods Massive amounts of process data are collected routinely by on-line process computers and automated instrumentation. This area of research involves the development of multivariate statistical methods to enable engineers, scientists, managers and operators to easily use information extracted from these data in both off-line and real-time settings. The main application areas being investigated are: a) Real-time Monitoring, Control and Optimization of Processes: This area covers methods for the on-line monitoring of continuous and batch processes and for the advanced model predictive control over final product quality and yields in batch processes. b) Digital Imaging for On-line Monitoring and Control: This area covers the use of colour and multi-spectral digital imaging for the on-line monitoring and control of processes and product quality. This is a particularly important issue for industries making solid products or slurries where instruments for measuring product properties on-line are not readily available. c) Rapid Development of New Products and Formulations: The development of new products generally involves three degrees of freedom: (i) the selection of the best set of raw materials; (ii) the formulation ratios in which to combine/react the materials; (iii) the process conditions to use in manufacturing the product. This research involves developing greatly improved multivariate modeling and optimization approaches to enable such rapid develop novel products.