Modeling and Control of Batch Processes
Description
Modeling and Control of Batch Processes presents state-of-the-art techniques ranging from mechanistic to data-driven models. These methods are specifically tailored to handle issues pertinent to batch processes, such as nonlinear dynamics and lack of online quality measurements. In particular, the book proposes:
- a novel batch control design with well characterized feasibility properties;
- a modeling approach that unites multi-model and partial least squares techniques;
- a generalization of the subspace identification approach for batch processes; and applications to several detailed case studies, ranging from a complex simulation test bed to industrial data.
The book's proposed methodology employs statistical tools, such as partial least squares and subspace identification, and couples them with notions from state-space-based models to provide solutions to the quality control problem for batch processes. Practical implementation issues are discussed to help readers understand the application of the methods in greater depth. The book includes numerous comments and remarks providing insight and fundamental understanding into the modeling and control of batch processes.
Modeling and Control of Batch Processes includes many detailed examples of industrial relevance that can be tailored by process control engineers or researchers to a specific application. The book is also of interest to graduate students studying control systems, as it contains new research topics and references to significant recent work.
Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
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Persons
Abhinav Garg received the B. Tech degree in Electronics and Instrumentation Engineering from Uttar Pradesh Technical University in June 2011, the Masters of Science by Research degree in Chemical Engineering from Indian Institute of Technology Madras in December, 2013 and Ph.D in Chemical Engineering from McMaster University in August 2018. His research interests include system identification, time-frequency analysis, causality analysis and process monitoring, control and optimization. His research has resulted in several peer-reviewed journal and conference articles.
Brandon Corbett received the B. Eng degree in Chemical Engineering from McMaster University in June 2011 and the Ph.D. degree in Chemical Engineering from McMaster University in September 2016. In 2017, he completed an industrial postdoctoral fellowship with Professor John F. MacGregor. Dr. Corbett's research interests focus on data-driven modeling. He has publications in data-driven dynamic modeling, particularly for batch processes, and data-driven modeling of product development data.
