Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks, 1st Edition (Paperback) book cover

Gas Turbines Modeling, Simulation, and Control

Using Artificial Neural Networks, 1st Edition

By Hamid Asgari, XiaoQi Chen

CRC Press

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Description

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.

Reviews

"… specifically deals with modeling gas turbine behavior under unsteady conditions. This topic is rarely addressed in detail in current technical literature. The selected approach covers both physics-based and black-box models. The approaches are compared and the respective strong/weak points are highlighted. The book is written under a user perspective and provides several examples of applications of the proposed methodologies."

—Prof. Mauro Venturini, Università degli Studi di Ferrara, Italy

"… offers a mechatronics approach for gas turbines through a complete cycle of modeling, simulation/analysis, and control designs. This book is helpful for mechatronics engineers and graduate researchers."

—Yangquan Chen, University of California, Merced, USA

"… very helpful for students in areas such as mechanics, mechatronics, control, and automation. … a very good book."

—De Xu, Institute of Automation, Chinese Academy of Sciences, Beijing

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

About the Authors

Hamid Asgari received his Ph.D in mechanical engineering from the University of Canterbury, Christchurch, New Zealand in 2014. He obtained his ME in aerospace engineering from Tarbiat Modares University, Tehran, Iran, and his BE in mechanical engineering from Iran University of Science and Technology, Tehran. He has worked more than 15 years in his professional field as a lead mechanical engineer and project coordinator in highly prestigious industrial companies. During his professional experience, he has been a key member of engineering teams in design, research and development, and maintenance planning departments. He has invaluable theoretical and hands-on experience in technical support, design, and maintenance of a variety of mechanical equipment and rotating machinery, such as gas turbines, pumps, and compressors, in large-scale projects in power plants and in the oil and gas industry.

XiaoQi Chen is a professor in the Department of Mechanical Engineering at the University of Canterbury, Christchurch, New Zealand. After obtaining his BE in 1984 from South China University of Technology, Guangzhou, he received the China-UK Technical Co-Operation Award for his MS study in the Department of Materials Technology at Brunel University, London, UK (1985–1986) and his Ph.D study in the Department of Electrical Engineering and Electronics at the University of Liverpool, UK (1986–1989). He has been a senior scientist at the Singapore Institute of Manufacturing Technology (1992–2006) and a recipient of the Singapore National Technology Award (1999). His research interests include mechatronic systems, mobile robotics, assistive devices, and manufacturing automation. He has been elected to Fellow of IPENZ and Fellow of SME.

Subject Categories

BISAC Subject Codes/Headings:
SCI024000
SCIENCE / Energy
TEC010000
TECHNOLOGY & ENGINEERING / Environmental / General
TEC016000
TECHNOLOGY & ENGINEERING / Industrial Design / General