Optimisation of industrial processes at supervisory level : application to control of thermal power plants
著者
書誌事項
Optimisation of industrial processes at supervisory level : application to control of thermal power plants
(Advances in industrial control)
Springer, c2002
大学図書館所蔵 全4件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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.
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