Methods of Model Based Process Control

✍ Scribed by W. Marquardt (auth.), Ridvan Berber (eds.)

• Publisher: Springer Netherlands
• Year: 1995
• Tongue: English
• Leaves: 813
• Series: NATO ASI Series 293
• Edition: 1
• Category: Library

✦ Synopsis

Model based control has emerged as an important way to improve plant efficiency in the process industries, while meeting processing and operating policy constraints. The reader of Methods of Model BasedProcess Control will find state of the art reports on model based control technology presented by the world's leading scientists and experts from industry. All the important issues that a model based control system has to address are covered in depth, ranging from dynamic simulation and control-relevant identification to information integration. Specific emerging topics are also covered, such as robust control and nonlinear model predictive control. In addition to critical reviews of recent advances, the reader will find new ideas, industrial applications and views of future needs and challenges.
Audience: A reference for graduate-level courses and a comprehensive guide for researchers and industrial control engineers in their exploration of the latest trends in the area.

✦ Table of Contents

Front Matter....Pages i-xiv
Front Matter....Pages 1-1
Towards a Process Modeling Methodolgy....Pages 3-40
Numerical Methods for the Simulation of Differential-Algebraic Process Models....Pages 41-79
Process Identification Techniques....Pages 81-97
Input/Output Modeling for Process Control....Pages 99-109
Front Matter....Pages 111-111
Frequency Domain Methods for Analysis and Design....Pages 113-152
Frequency Domain Methods for Analysis and Design....Pages 153-191
M p Tuning and Synthesis....Pages 193-204
Robust Control of Linear Time-Varying Systems with Constraints....Pages 205-220
New Results on Robust Controller Design for Chemical Processes....Pages 221-234
Analysis and Synthesis Methods for Robust Model Predictive Control....Pages 235-262
Attainable Performance in LQG Control....Pages 263-295
Front Matter....Pages 297-297
State-Space Interpretation of Model Predictive Control....Pages 299-330
Topics in Model Predictive Control....Pages 331-347
Nonlinear Moving Horizon State Estimation....Pages 349-365
Optimization in Model Predictive Control....Pages 367-396
Internal Model Predictive Control (IMPC)....Pages 397-425
Adaptive Model Predictive Control....Pages 427-457
Control of Batch Reactors: A Review....Pages 459-494
Real-Time Optimization and Model-Based Control of Polymer Reactors....Pages 495-530
Front Matter....Pages 531-531
Control of Nonlinear Systems Using Input Output Information....Pages 533-576
Front Matter....Pages 531-531
A Stability Analysis of Nonlinear Model Predictive Control....Pages 577-605
The Design of Nonlinear Predictive Controllers: Application to a Drug Infusion System....Pages 607-625
Nonlinear Model Predictive Control Using Neural Net Plant Models....Pages 627-639
Front Matter....Pages 641-641
An Industrial Perspective on the Evolution of Control Technology....Pages 643-658
Modular Multivariable Control of the Shell Heavy Oil Fractionator....Pages 659-682
An Industrial Implementation of a Model Based Control Strategy....Pages 683-700
Model Predictive Control (MPC) of a Gas-Oil-Water Separator Train....Pages 701-713
Integrating Information, Management and Control in Process Industries....Pages 715-751
Front Matter....Pages 753-753
Fuzzy Control....Pages 755-773
A Self Learning Model-Based Fuzzy Controller....Pages 775-783
Fuzzy Control of Distillation Columns with and without Side Streams....Pages 785-796
Back Matter....Pages 797-826

✦ Subjects

Mechanical Engineering; Industrial Chemistry/Chemical Engineering; Calculus of Variations and Optimal Control; Optimization; Electrical Engineering