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Read more about Chemical Process Dynamics and Controls

Chemical Process Dynamics and Controls

(1 review)

Peter J. Woolf

Copyright Year: 2009

Publisher: openmichigan

Language: English

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CC BY

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Reviewed by Jeffrey Phillips, Assistant Professor, Hanover College on 2/18/19

This book is 1400 pages long, so it covers a lot of material. In fact, you can to go more than 500 pages into the book to find coverage on Chemical Process Dynamics and Control. The table of contents alone is 30 pages long. In reality this book... read more

Table of Contents

Part I Process Control Introduction

  • Chapter 1: Overview
  • Chapter 2: Modeling Basics
  • Chapter 3: Sensors and Actuators
  • Chapter 4: Piping and Instrumentation Diagrams
  • Chapter 5: Logical Modeling
  • Chapter 6: Modeling Case Studies

Part II Chemical Process Controls

  • Chapter 7: Mathematics for Control Systems
  • Chapter 8: Optimization
  • Chapter 9: PID Control
  • Chapter 10: Dynamical Systems Analysis
  • Chapter 11: Control Architectures
  • Chapter 12: MIMO Control

Part III Statistical Analysis for Chemical Process Control

  • Chapter 13: Statistics and Probability Background
  • Chapter 14: Design of Experiments

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About the Book

Process controls is a mixture between the statistics and engineering discipline that deals with the mechanism, architectures, and algorithms for controlling a process. Some examples of controlled processes are:

•Controlling the temperature of a water stream by controlling the amount of steam added to the shell of a heat exchanger.

•Operating a jacketed reactor isothermally by controlling the mixture of cold water and steam that flows through the jacket of a jacketed reactor.

•Maintaining a set ratio of reactants to be added to a reactor by controlling their flow rates.

•Controlling the height of fluid in a tank to ensure that it does not overflow.

About the Contributors

Author

Peter Woolf is an Assistant Professor College of Engineering Department of Chemical Engineering. The goal of the research in my group is to integrate experimental data together to create computational, systems-level models of how cancer initiates and grows.

  • B.S. Cornell University, Chemical Engineering
  • Ph.D. University of Michigan, Chemical Engineering

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