Optimisation of industrial processes at supervisory level : application to control of thermal power plants
Author(s)
Bibliographic Information
Optimisation of industrial processes at supervisory level : application to control of thermal power plants
(Advances in industrial control)
Springer, c2002
Available at 4 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references and index
Description and Table of Contents
Description
This monograph presents new methodologies to improve power plants' efficiency, by using automatic control algorithms. This will lead to an improvement in companies' profit and also in the quality of their final product. A trans-Atlantic combination of authors ensures an unusually wide range of perspectives.
Table of Contents
Content.- 1. Introduction.- 2. Non-linear Dynamic Modelling for Control Design.- 2.1 Introduction.- 2.2 Fundamentals of Fuzzy Logic.- 2.2.1 Basic Definitions.- 2.2.2 Basic Operations for Fuzzy Sets.- 2.3 Dynamic Models Based on Fuzzy Logic.- 2.3.1 Linguistic Fuzzy Models.- 2.3.2 Takagi-and-Sugeno Models.- 2.3.3 Position Models and Models of Gradient Position.- 2.3.4 Fuzzy Relational Models.- 2.3.5 Radial Basis Function Network - a Fuzzy Approach.- 2.4 Parameters Estimation.- 2.5 Structure Identification.- 2.6 Discussion.- 2.7 A New Structure Identification Method for Fuzzy Models.- 2.7.1 Identification Procedure.- 2.7.2 Sensitivity Analysis.- 2.7.3 Application Examples.- 2.7.4 Application to Thermal Power Plant "Nueva Renca".- 2.7.5 Analysis of Results.- 3. Non-linear Predictive Control.- 3.1 Fundamentals of Predictive Control.- 3.2 Literature Review.- 3.3 Prediction from Linear Models.- 3.4 Linear Predictive Control Algorithms.- 3.4.1 Generalised Predictive Control.- 3.4.2 Dynamic Matrix Control.- 3.5 Prediction for Non-linear Models.- 3.6 Non-linear Predictive Control.- 3.6.1 MBPC Based on Fuzzy Relational Models.- 3.6.2 Fuzzy Predictive Control Algorithms Based on Takagi-and-Sugeno Models.- 3.7 Discussion.- 4. Supervisory Optimal Control for a Pre-specified Regulatory Level.- 4.1 Problem Statement.- 4.1.1 Process Modelling.- 4.1.2 Modelling of the Regulatory Level.- 4.1.3 General Objective Function and Constraints.- 4.2 Alternative Solutions.- 4.2.1 Direct Method.- 4.2.2 Indirect Method.- 4.3 Supervisory Controller Design Based on Linear Models.- 4.3.1 Problem Statement.- 4.3.2 Supervisory Controller Without Constraints.- 4.3.3 Supervisory Controller with Constraints.- 4.4 Supervisory Controller Design Based on Non-linear Models.- 4.4.1 Problem Statement.- 4.4.2 Non-linear Supervisory Controller Without Constraints.- 4.4.3 Non-linear Supervisory Controller with Constraints.- 4.5 Application to a Boiler System.- 4.5.1 Boiler System Simulator.- 4.5.2 Problem Statement.- 4.5.3 Supervisory Controller.- 4.5.4 Comparative Analysis.- 4.6 Discussion.- 5. Application to the Control of Thermal Power Plants.- 5.1 Modelling and Simulation of a Combined Cycle Power Plant.- 5.1.1 Process Description.- 5.1.2 Analysis of the Different Models.- 5.1.3 Formulation of Combined Cycle Power Plant Model.- 5.1.4 Simulator for MATLAB (R)-SMMULINK (R) Environment.- 5.1.5 Simulator Tests.- 5.2 Control of Thermal Power Plant Boiler.- 5.2.1 Analysis of Different Control Strategies.- 5.2.2 Statement of the Supervisory Optimal Control Problem.- 5.2.3 Supervisory Control Based on a Linear Model of the Boiler.- 5.2.4 Supervisory Control Based on a Non-linear Model of the Boiler.- 5.2.5 Analysis of Results.- 6. Discussion and Conclusions.- Appendix A. Sensitivity Analysis Program.- Appendix B. Prediction of Controlled Variables and Manipulated Variables.- B.1 Prediction of Controlled Variables.- B.2 Prediction of Manipulated Variables.- Appendix C. Special Cases of Polynomial Cancellations.- C.1 A Quadratic Objective Function of the Manipulated Variables at the Supervisory Level.- C.2 A GPC Objective Function at the Supervisory Level.- Appendix D. Supervisory Controller Programs.- D.1 Direct Method.- D.2 Indirect Method.- D.3 One-step Fuzzy Predictor.- D.4 Multi-step Fuzzy Predictor.- Appendix E. Main Variables of a Combined Cycle Thermal Power Plant.- E.1 Boiler.- E.1.1 Furnace.- E.1.2 Risers.- E.1.3 Drum.- E.1.4 Superheater.- E.1.5 Reheater.- E.1.6 Economiser.- E.2 Steam Turbine.- E.2.1 The High Pressure Turbine.- E.2.2 The Intermediate Pressure Turbine.- E.2.3 The Low Pressure Turbine.- E.2.4 Steam Turbine.- E.3 Gas Turbine.- E.3.1 Compressor.- E.3.2 Combustion Chamber.- E.3.3 Turbine.- Appendix F. Simulator Programs in MATLAB (R)-SIMULINK (R).- F.1 Boiler.- F.1.1 Furnace.- F.1.2 Risers.- F.1.3 Drum.- F.1.4 Superheater.- F.1.5 Reheater.- F.1.6 Economiser.- F.2 Steam Turbine.- F.2.1 The High Pressure Turbine.- F.2.2 The Intermediate Pressure Turbine.- F.2.3 The Low Pressure Turbine.- F.3 Gas Turbine.- F.3.1 Compressor.- F.3.2 Combustion Chamber.- F.3.3 Turbine.- References.
by "Nielsen BookData"