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ABOUT THE BOOK

Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the modeling, simulation, and control of gas turbines (GTs) using artificial neural networks (ANNs). After delivering a brief introduction to GT performance and classification, the book:

• Outlines important criteria to consider at the beginning of the GT modeling process, such as GT types and configurations, control system types and configurations, and modeling methods and objectives
• Highlights research in the fields of white-box and black-box modeling, simulation, and control of GTs, exploring models of low-power GTs, industrial power plant gas turbines (IPGTs), and aero GTs
• Discusses the structure of ANNs and the ANN-based model-building process, including system analysis, data acquisition and preparation, network architecture, and network training and validation
• Presents a noteworthy ANN-based methodology for offline system identification of GTs, complete with validated models using both simulated and real operational data
• Covers the modeling of GT transient behavior and start-up operation, and the design of proportional-integral-derivative (PID) and neural network-based controllers

Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks not only offers a comprehensive review of the state of the art of gas turbine modeling and intelligent techniques, but also demonstrates how artificial intelligence can be used to solve complicated industrial problems, specifically in the area of GTs.

 TABLE OF CONTENTS

Introduction to Modeling of Gas Turbines
GT Performance
GT Classification
Considerations in GT Modeling
Problems and Limitations
Objectives and Scope
Summary

White-Box Modeling, Simulation, and Control of GTs
White-Box Modeling and Simulation of GTs
White-Box Approach in Control System Design
Final Statement
Summary

Black-Box Modeling, Simulation, and Control of GTs
Black-Box Modeling and Simulation of GTs
Black-Box Approach in Control System Design
Final Statement
Summary

ANN-Based System Identification for Industrial Systems
Artificial Neural Network (ANN)
The Model of an Artificial Neuron
ANN-Based Model Building Procedure
ANN Applications to Industrial Systems
ANN Limitations
Summary

Modeling and Simulation of a Single-Shaft GT
GT Simulink Model
ANN-Based System Identification
Model Selection Process
Summary

Modeling and Simulation of Dynamic Behavior of an IPGT
GT Specifications
Data Acquisition and Preparation
Physics-Based Model of IPGT by Using Simulink: MATLAB
NARX Model of IPGT
Comparison of Physics-Based and NARX Models
Summary

Modeling and Simulation of the Start-Up Operation of an IPGT by Using NARX Models
GT Start-Up
Data Acquisition and Preparation
GT Start-Up Modeling by Using NARX Models
Summary

Design of Neural Network-Based Controllers for GTs
GT Control System
Model Predictive Controller
Feedback Linearization Controller (NARMA-L2)
PID Controller
Comparison of Controllers Performance
NMP Systems
Summary