This textbook is intended for an introductory graduate level on process control, taught in most engineering curricula. It focuses on the statistical techniques and methods of control and system optimization needed for the mathematical modeling, analysis, simulation, control and optimization of multivariable manufacturing processes. In four sections, it covers:

1.  Relevant mathematical methods, including random events, variables and processes, and their characteristics; estimation and confidence intervals; Bayes applications; correlation and regression analysis; statistical cluster analysis; and singular value decomposition for classification applications.
2. Mathematical description of manufacturing processes, including static and dynamic models; model validation; confidence intervals for model parameters; principal component analysis; conventional and recursive least squares procedures; nonlinear least squares; and continuous-time, discrete-time, s-domain and Z-domain models.
3. Control of manufacturing processes, including transfer function/transfer matrix models; state-variable models; methods of discrete-time classical control; state variable discrete-time control; state observers/estimators in control systems; methods of decoupling control; and methods of adaptive control.
4. Methods and applications of system optimization, including unconstrained and constrained optimization; analytical and numerical optimization procedures; use of penalty functions; methods of linear programming; gradient methods; direct search methods; genetic optimization; methods and applications of dynamic programming; and applications to estimation, design, control, and planning.

Each section of the book will include end-of-chapter exercises, and the book will be suitable for any systems, electrical, chemical, or industrial engineering program, as it focuses on the processes themselves, and not on the product being manufactured.  Students will be able to obtain a mathematical model of any manufacturing process, to design a computer-based control system for a particular continuous manufacturing process, and be able to formulate an engineering problem in terms of optimization, as well as the ability to choose and apply the appropriate optimization technique.

Process Control
System Optimization
Multivariable Manufacturing Processes
Random Events
State-Variable Control
Dynamic Programming
Bayes Applications
Statistical Cluster Analysis
Manufacturing Processes
Model-Based Predictions
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Part I: Introduction

1. Why Process Control?

2. Definitions and Terminology

Part II: Modeling for Control

3. Basic Concepts in Modeling

4. Development of Models from Fundamental Laws

5. Input–Output Models: The Transfer Function

6. Models from Process Data

Part III: Process Analysis

7. Stability

8. Dynamic Performance

9. Frequency Response

Part IV: Feedback Control

10. Basic Elements of Feedback Control

11. Stability Analysis of Closed-Loop Processes

12. Feedback Control Design

Part V: Model-Based Control

13. Model-Based Control

14. Model Uncertainty and Robustness

15. Model Predictive Control

Part VI: Multivariable Control

16. Multivariable Systems: Special Cases

17. Multivariable Systems: General Concepts

18. Design of Multivariable Controllers

Part VII: Control in Modern Manufacturing

19. Practical Control of Nonlinear Processes

20. Process Optimization and Control

21. Industrial Control Technology

22. Role of Process Control in Modern Manufacturing

23. Data Processing and Reconciliation

24. Process Monitoring

• T.J. Watson School of Engineering, Binghamton University, Binghamton, USA

  Victor A. Skormin

Book Title
Introduction to Process Control

Book Subtitle
Analysis, Mathematical Modeling, Control and Optimization

Authors
Victor A. Skormin

Series Title
Springer Texts in Business and Economics

DOI
https://doi.org/10.1007/978-3-319-42258-9

Hardcover ISBN
978-3-319-42257-2
Published: 28 October 2016

Softcover ISBN
978-3-319-82540-3
Published: 24 June 2018

eBook ISBN
978-3-319-42258-9
Published: 19 October 2016

Series ISSN
2192-4333

Series E-ISSN
2192-4341

Edition Number
1

Number of Pages
XVII, 254

Number of Illustrations
56 b/w illustrations, 50 illustrations in colour

Topics
Operations Research/Decision Theory, Business Process Management, Industrial and Production Engineering