Optimisation of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants PDF
Preview Optimisation of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants
Advances in Industrial Control Springer-Verlag London Ltd. Other titles published in this Series: Compressor Surge and Rotating Stall: Modeling and Control Jan Tommy Gravdahl and Olav Egeland Radiotherapy Treatment Planning: New System Approaches Olivier Haas Feedback Control Theory for Dynamic Traffic Assignment Pushkin Kachroo and Kaan Ozbay Autotuning ofPID Controllers Cheng-Ching Yu Robust Aeroservoelastic Stability Analysis Rick Lind and Marty Brenner Performance Assessment of Control Loops: Theory and Applications Biao Huang and Sirish 1. Shah Data Mining and Knowledge Discovery for Process Monitoring and Control Xue Z. Wang Advances in PID Control Tan Kok Kiong, Wang Quing-Guo and Hang Chang Chieh with Tore J. Hagglund Advanced Control with Recurrent High-order Neural Networks: Theory and Indus trial Applications George A. Rovithakis and Manolis A. Christodoulou Structure and Synthesis ofP ID Controllers Aniruddha Datta, Ming-Tzu Ho and Shankar P. Bhattacharyya Data-driven Techniques for Fault Detection and Diagnosis in Chemical Processes Evan 1. Russell, Leo H. Chiang and Richard D. Braatz Bounded Dynamic Stochastic Systems: Modelling and Control Hong Wang Non-linear Model-based Process Control Rashid M. Ansari and Moses O. Tade Identification and Control of Sheet and Film Processes Andrew P. Featherstone, Jeremy G. VanAntwerp and Richard D. Braatz Precision Motion Control: Design and Implementation Tan Kok Kiong, Lee Tong Heng, Dou Huifang and Huang Sunan Nonlinear Identification and Control: A Neural Network Approach GuopingLiu Digital Controller Implementation and Fragility: A Modern Perspective Robert S.H. Istepanian and James F. Whidborne Doris Saez, AIda Cipriano and Andrzej W. Ordys Optimisation of Industrial Processes at Supervisory Level Application to Control of Thermal Power Plants With 60 Figures i Springer Doris Saez, MSc, PhD Aldo Cipriano, PhD Electrical Engineering Department, Catholic University of Chile, Vicufia Mackenna 4860, Santiago, Chile Andrzej W. Ordys, PhD Industrial Control Centre, Department of Electronic and Electrical Engineering, University of Strathclyde, Graham Hills Building, 50 George Street, Glasgow GIIQE, UK British Library Cataloguing in Publication Data Saez, Doris Optimisation of industrial processes at supervisory level : application to control of thermal power plants. -(Advances in industrial control) 1. Power-plants 2.ThermoeIectric generators -Automatic control I.TitIe II.Cipriano, Aldo III.Ordys, A.W. (Andrzej W.), 1956- 621.3'1243 ISBN 978-1-4471-1081-1 Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of Iicences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. ISBN 978-1-4471-1081-1 ISBN 978-1-4471-0113-0 (eBook) DOI 10.1007/978-1-4471-0113-0 http://www.springer.co.uk © Springer-Verlag London 2002 Originally published by Springer-Verlag London Berlin Heidelberg in 2002 Softcover reprint of the hardcover 1st edition 2002 MATLABiII and SIMULINKiII are the registered trademarks ofThe MathWorks Inc., 3 Apple Hill Drive Natick, MA 01760-2098, U.S.A. http://www.mathworks.com The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or Iiability for any errors or omissions that may be made. Typesetting: Electronic text files prepared byauthor 69/3830-543210 Printed on acid-free paper SPIN 10783171 Advances in Industrial Control Series Editors Professor Michael J. Grimble, Professor ofIndustrial Systems and Director Professor Michael A. Johnson, Professor of Control Systems and Deputy Director Industrial Control Centre Department of Electronic and Electrical Engineering University of Strathclyde Graham Hills Building 50 George Street GlasgowGllQE United Kingdom Series Advisory Board Professor Dr-Ing J. Ackermann DLR Institut fur Robotik und Systemdynamik Postfach 1116 D82230 WeBling Germany Professor I.D. Landau Laboratoire d' Automatique de Grenoble ENSIEG, BP 46 38402 Saint Martin d'Heres France Dr D.C. McFarlane Department of Engineering University of Cambridge Cambridge CB2 lQJ United Kingdom Professor B. Wittenmark Department of Automatic Control Lund Institute of Technology PO Box 118 S-221 00 Lund Sweden Professor D.W. Clarke Department of Engineering Science University of Oxford Parks Road Oxford OXI 3PJ United Kingdom Professor Dr-Ing M. Thoma Institut ftir Rege1ungstechnik Universitat Hannover Appelstr. 11 30167 Hannover Germany Professor H. Kimura Department of Mathematical Engineering and Information Physics Faculty of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Ku Tokyo 113 Japan Professor A.J. Laub College of Engineering - Dean's Office University of California One Shields Avenue Davis California 95616-5294 United States of America Professor J.B. Moore Department of Systems Engineering The Australian National University Research School of Physical Sciences GPO Box4 Canberra ACT 2601 Australia Dr M.K. Masten Texas Instruments 2309 N orthcrest Plano TX 75075 United States of America Professor Ton Backx AspenTech Europe B.V. De Waal32 NL-5684 PH Best The Netherlands ::/)orij Saez Jo my wile marta ..Angihca ..Afdo Cipriano Jo ..Anna, Szymon and Bartek and to my mother and Jatlur ..Andl'ZlJj OrdYj SERIES EDITORS' FOREWORD The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The optimisation of energy-generation processes is a topic of keen interest to many international utilities. The work reported in this monograph was motivated by the construction of the first combined-cycle plant in Chile to generate electricity. At one time, burning natural gas to produce electricity would have been considered heretical but optimised plant design has pushed efficiencies into the 50-60% region. Advanced control has a role to play in achieving better plant performance. This monograph continues the research direction reported in an earlier Advances in Industrial Control volume, Modelling and Simulation of Power Generation Plants, by Ordys et al. (l994, ISBN 3-540-19907-1). In the present monograph Doris Saez, Aldo Cipriano and Andrzej Ordys develop an original approach to the supervisory control of the thermal units in a power generation plant. Process non linearity is accommodated using a fuzzy modelling approach. Control is pursued using the model-based predictive control paradigm. The novelty comes from putting these two approaches together and moving the control design activity into the supervisory level. The monograph has a full presentation of the theory for one approach to model-based predictive supervisory control: this is found in Chapter 4. In the following chapter is a fully worked out application study for a combined-cycle plant and a boiler unit. This should be of considerable interest to industrial engineers and applications-oriented control academics and postgraduate students. The monograph chapters are also supported by some appendices covering some of the derivations, and the MATLAB®-SIMULINK® programs developed by the authors. Altogether, the monograph is a very welcome and interesting addition to the Advances in Industrial Control series. M.J. Grimble and M.A. Johnson Industrial Control Centre Glasgow, Scotland, UK ACKNOWLEDGEMENTS Several persons have helped me a lot during the preparation of the manuscript. Specially, I would like to mention Jose Pedro Prina for his comments and points of view, and Betty Andonaegui for her constant support. A very special thought to my husband Andres for always being on my side. I would also like to thank the National Fund for Scientific and Technological Development FONDECYT for the support given for the projects 4000026 "Stability for Optimal Supervisory Control Systems with a Pre-specified Regulatory Lever', 2980029 "Design of Predictive Control Strategies Based on Non-Linear Models and their Application to the Control of Thermal Power Plants" and 1990101 "Non Linear Predictive Control with Fuzzy Constraints and Fuzzy Objective Functions". Finally, I would like to thank Electrica Santiago power company for supplying relevant data. Doris Saez H. TABLE OF CONTENTS 1. Introduction ......................................................................................................... 1 2. Non-linear Dynamic Modelling for Control Design ......................................... 5 2.1 Introduction .................................................................................................... 5 2.2 Fundamentals of Fuzzy Logic ........................................................................ 6 2.2.1 Basic Definitions .................................................................................. 6 2.2.2 Basic Operations for Fuzzy Sets .......................................................... 7 2.3 Dynamic Models Based on Fuzzy Logic ........................................................ 8 2.3.1 Linguistic Fuzzy Models ...................................................................... 8 2.3.2 Takagi-and-Sugeno Models ............................................................... 10 2.3.3 Position Models and Models of Gradient Position ............................. 11 2.3.4 Fuzzy Relational Models .................................................................... 12 2.3.5 Radial Basis Function Network -a Fuzzy Approach ......................... 13 2.4 Parameters Estimation .................................................................................. 14 2.5 Structure Identification ................................................................................. 19 2.6 Discussion .................................................................................................... 19 2.7 A New Structure Identification Method for Fuzzy Models .......................... 20 2.7.1 Identification Procedure ..................................................................... 20 2.7.2 Sensitivity Analysis ............................................................................ 21 2.7.3 Application Examples ........................................................................ 24 2.7.4 Application to Thermal Power Plant ''Nueva Renca" ........................ 28 2.7.5 Analysis of Results ............................................................................. 31 3. Non-linear Predictive Control. ......................................................................... 33 3.1 Fundamentals of Predictive Control... .......................................................... 33 3.2 Literature Review ......................................................................................... 35 3.3 Prediction from Linear Models .................................................................... 36 3.4 Linear Predictive Control Algorithms .......................................................... 37 3.4.1 Generalised Predictive Control .......................................................... 38 3.4.2 Dynamic Matrix Control .................................................................... 40 3.5 Prediction for Non-linear Models ................................................................. 42 3.6 Non-linear Predictive Control ...................................................................... 43 3.6.1 MBPC Based on Fuzzy Relational Models ........................................ 43
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