Optimisation of Industrial Processes at Supervisory Level : Application to Control of Thermal Power Plants

Saez, Doris A.; Cipriano, Aldo; Ordys, Andrzej W.

ISBN 10: 1447110811 / ISBN 13: 9781447110811

Editore: Springer, 2012

Codice articolo 19848753-n

Riassunto:

In the increasingly competitive modern world, the industrial sector faces new challenges such as improving productivity and reducing costs while taking into account the process operational constraints.
As energy demand increases in many countries, especially in big cities where the environmental concerns are very important and resources to produce energy are limited, the efficiency of operation of power plants becomes of paramount importance.
Under this scenario, this book presents new methodologies to improve power plants' efficiency, by using automatic control algorithms. This will lead to an improvement in the generation of companies' profit and also in the quality of their final product.

Contenuti: 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®-SMMULINK® 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®-SIMULINK®.- 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.

Dati bibliografici

Titolo: Optimisation of Industrial Processes at ...
Casa editrice: Springer
Data di pubblicazione: 2012
Legatura: Brossura
Condizione: New