Advertisement

Introduction to Process Control

Analysis, Mathematical Modeling, Control and Optimization

  • Victor A. Skormin

Part of the Springer Texts in Business and Economics book series (STBE)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Victor A. Skormin
    Pages 1-35
  3. Victor A. Skormin
    Pages 111-192
  4. Victor A. Skormin
    Pages 193-249
  5. Back Matter
    Pages 251-254

About this book

Introduction

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.

Keywords

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

Authors and affiliations

  • Victor A. Skormin
    • 1
  1. 1.T.J. Watson School of EngineeringBinghamton UniversityBinghamtonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-42258-9
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Business and Management
  • Print ISBN 978-3-319-42257-2
  • Online ISBN 978-3-319-42258-9
  • Series Print ISSN 2192-4333
  • Series Online ISSN 2192-4341
  • Buy this book on publisher's site
Industry Sectors
Pharma
Automotive
Biotechnology
Finance, Business & Banking
Electronics
IT & Software
Consumer Packaged Goods
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering