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Modeling and Control of Batch Processes

Theory and Applications

  • Prashant Mhaskar
  • Abhinav Garg
  • Brandon Corbett

Part of the Advances in Industrial Control book series (AIC)

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. Motivation

    1. Front Matter
      Pages 1-1
    2. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 3-10
    3. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 11-19
  3. First-Principles Model Based Control

    1. Front Matter
      Pages 21-21
    2. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 23-45
    3. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 47-68
  4. Integrating Multi-model Dynamics with PLS Based Approaches

    1. Front Matter
      Pages 85-85
    2. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 87-113
    3. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 115-135
    4. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 137-153
    5. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 155-169
    6. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 171-196
  5. Subspace Identification Based Modeling Approach for Batch Processes

    1. Front Matter
      Pages 197-197
    2. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 199-234
    3. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 299-319
    4. Prashant Mhaskar, Abhinav Garg, Brandon Corbett
      Pages 321-335

About this book

Introduction

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.


Keywords

Batch Process Modeling and Control Chemical Batch Processes Batch Subspace Identification Data-driven Control of Batch Processes Plant Startup and Shutdown Model-based Techniques for Batch Process

Authors and affiliations

  • Prashant Mhaskar
    • 1
  • Abhinav Garg
    • 2
  • Brandon Corbett
    • 3
  1. 1.Department of Chemical EngineeringMcMaster UniversityHamiltonCanada
  2. 2.Department of Chemical EngineeringMcMaster UniversityHamiltonCanada
  3. 3.Department of Chemical EngineeringMcMaster UniversityHamiltonCanada

Bibliographic information