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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.
List of contents
Content.- 1. Introduction.- 2. Non-linear Dynamic Modelling for Control Design.- 2.1 Introduction.- 2.2 Fundamentals of Fuzzy Logic.- 2.3 Dynamic Models Based on Fuzzy Logic.- 2.4 Parameters Estimation.- 2.5 Structure Identification.- 2.6 Discussion.- 2.7 A New Structure Identification Method for Fuzzy Models.- 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.- 3.5 Prediction for Non-linear Models.- 3.6 Non-linear Predictive Control.- 3.7 Discussion.- 4. Supervisory Optimal Control for a Pre-specified Regulatory Level.- 4.1 Problem Statement.- 4.2 Alternative Solutions.- 4.3 Supervisory Controller Design Based on Linear Models.- 4.4 Supervisory Controller Design Based on Non-linear Models.- 4.5 Application to a Boiler System.- 4.6 Discussion.- 5. Application to the Control of Thermal Power Plants.- 5.1 Modelling and Simulation of a Combined Cycle Power Plant.- 5.2 Control of Thermal Power Plant Boiler.- 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.
Summary
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.
