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Methods of Model Based Process Control

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Methods of Model Based Process Control Cover

 

Synopses & Reviews

Publisher Comments:

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 Based Process 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.

Book News Annotation:

Proceedings of the Nato Advanced Study Institute held in Antalya, Turkey, August 1994, to review the most recent advances in model based process control so as to foster continuing research and encourage innovative applications of the technology. The material is arranged in six parts covering the major themes presented at the Institute: process modeling, dynamic simulation and identification; robust process control; advances in model predictive control; nonlinear model predictive control; industrial applications; and fuzzy control. Among the specific topics are process identification techniques; Mp tuning and synthesis; adaptive model predictive control; and self learning model based fuzzy controller.
Annotation c. Book News, Inc., Portland, OR (booknews.com)

Table of Contents

Preface. Part I: Process Modeling, Dynamic Simulation and Identification. Towards a Process Modeling Technology; W. Marquardt. Numerical Methods for the Simulation of Differential-Algebraic Process Models; W. Marquardt. Process Identification Techniques; S. Saelid. Input/Output Modeling for Process Control; T. R. Holcomb, C. A. Rhodes, M. Morai. Part II: Robust Process Control. Frequency Domain Methods for Analysis and Design - I. H-Infinity Methods and Robust Control; S. Skogestad. Frequency Domain Methods for Analysis and Design - II. Input-Output Controllability Analysis of SISO Systems; S. Skogestad. Mp Tuning and Synthesis; M. Laiseca, C. B. Brosilow. Robust Control of Linear Time-Varying Systems with Constraints; A. Zheng, M. Morai. New Results on Robust Controller Design for Chemical Processes; A. Palazogammalu, J. A. Romagnoli. Analysis and Synthesis Methods for Robust Model Predictive Control; H. Genceli, P. Vuthandam, M. Nikolaou. Attainable Performance in LQG Control; B. Lie. Part III: Advances in Model Predictive Control. State-Space Interpretation of Model Predictive Control; J. H. Lee, M. Morari, C. E. Garcia. Topics in Model Predictive Control; E. S. Meadows, J. B. Rawlings. Nonlinear Moving Horizon State Estimation; K. R. Muske, J. B. Rawlings. Optimization in Model Predictive Control; D. Q. Mayne. Internal Model Predictive Control; E. Coulibaly, S. Maiti, C. B. Brosilow. Adaptive Model Predictive Control; M. V. Le Lann, M. Cabassud, G. Casamatta. Control of Batch Reactors: A Review; R. Berber. Real-Time Optimization and Model-Based Control of Polymer Reactors; C. Kiparissides, E. Papadopoulos, J. Morris. Part IV: Nonlinear Model Predictive Control. Control of Nonlinear Systems Using Input&endash;Output Information; Y. Arkun, E. Hernández. A Stability Analysis of Nonlinear Model Predictive Control; P. B. Sistu, B. W. Bequette. The Design of Nonlinear Predictive Controllers: Application to a Drug Infusion System; R. S. Gopinath, B. W. Bequette. Nonlinear Model Predictive Control Using Neural Net Plant Models; A. Draeger, S. Engell. Part V: Industrial Applications. An Industrial Perspective on the Evolution of Control Technology; C. R. Cutler. Modular Multivariable Control of the Shell Heavy Oil Fractionator; T. L. Chia, C. B. Brosilow. An Industrial Implementation of a Model Based Control Strategy; J. L. Figueroa, O. E. Agamennoni, G. W. Barton, J. A. Romagnoli, J. B. Lear. Model Predictive Control of a Gas&endash;Oil&endash;Water Separator Train; S. Stokke, S. Strand, D. Sjong. Integrating Information, Management and Control in Process Industries; C. Han, G. Stephanopoulos. Part VI: Fuzzy Control. Fuzzy Control &endash; An Alternative to Model-Based Control? S. Engell, T. Heckenthaler. Self-Learning Model-Based Fuzzy Controller; B. E. Postlethwaite. Fuzzy Control of Distillation Columns with and without Side Streams; C. Remberg, G. Fieg, G. Wozny, F. N. Fett. Appendices. I: Titles of Poster Presentations. II: Some Views of the Contributors about the Discussed Issues and Research Directions. III: Summary of the Control Research Presented at the Institute: A Cartoon Representation; B. W. Bequette. IV: Keeping with the Tradition: `Special Awards' of the Meeting; B. W. Bequette. V: List of Participants. Index.

Product Details

ISBN:
9780792335245
Author:
Berber, Ridvan
Publisher:
Springer
Other:
Berber, Rdvan
Editor:
Berber, R.
Author:
Berber, R.
Subject:
Engineering - Mechanical
Subject:
Chemical process control
Subject:
Congresses
Subject:
Chemical & Biochemical
Subject:
Mechanical
Subject:
Linear Programming
Subject:
Chemical process control -- Congresses.
Subject:
Mechanical engineering
Subject:
Industrial Chemistry/Chemical Engineering
Subject:
Calculus of Variations and Optimal Control; Optimization
Subject:
Electrical engineering
Subject:
Chemistry-Chemical Engineering
Copyright:
Edition Number:
1
Edition Description:
Book
Series:
NATO Science Series E: (closed)
Series Volume:
293
Publication Date:
May 1995
Binding:
TRADE PAPER
Language:
English
Illustrations:
Y
Pages:
854
Dimensions:
240 x 160 mm 1409 gr

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