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Gas Turbines Modeling Simulation and Control Using Artificial Neural Networks 1st Edition by Hamid Asgari, XiaoQi Chen ISBN 1498726631 9781498777544

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Gas Turbines Modeling Simulation and Control Using Artificial Neural Networks 1st Edition Hamid Asgari (Author) Digital Instant Download

Author(s): Hamid Asgari (Author); XiaoQi Chen (Author)
ISBN(s): 9781498777544, 1498726631
Edition: 1
File Details: PDF, 23.30 MB
Year: 2015
Language: english
SKU: EB-12193984 Category: Tags: ,

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Product details:

ISBN 10: 1498726631 
ISBN 13: 9781498777544
Author: Hamid Asgari, XiaoQi Chen

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 consi

Gas Turbines Modeling Simulation and Control Using Artificial Neural Networks 1st Table of contents:

Chapter 1: Introduction to Modeling of Gas Turbines

  • 1.1 GT Performance

  • 1.2 GT Classification

  • 1.3 Considerations in GT Modeling

    • 1.3.1 GT Type

    • 1.3.2 GT Configuration

    • 1.3.3 GT Modeling Methods

      • 1.3.3.1 Linear and Nonlinear Models

      • 1.3.3.2 Deterministic and Stochastic (Probabilistic) Models

      • 1.3.3.3 Static and Dynamic Models

      • 1.3.3.4 Discrete and Continuous Models

    • 1.3.4 GT Control System Type and Configuration

    • 1.3.5 GT Modeling Objectives

      • 1.3.5.1 Condition Monitoring

      • 1.3.5.2 Fault Detection and Diagnosis

      • 1.3.5.3 Sensor Validation

      • 1.3.5.4 System Identification

      • 1.3.5.5 Design and Optimization of Control System

    • 1.3.6 GT Model Construction Approaches

      • 1.3.6.1 White-box Models

      • 1.3.6.2 Black-box Models

      • 1.3.6.3 Gray-box Models

  • 1.4 Problems and Limitations

  • 1.5 Objectives and Scope

  • 1.6 Summary

Chapter 2: White-box Modeling, Simulation, and Control of GTs

  • 2.1 White-box Modeling and Simulation of GTs

    • 2.1.1 White-box Models of Low-Power GTs

    • 2.1.2 White-box Models of IPGTs

    • 2.1.3 White-box Models of Aero GTs

  • 2.2 White-box Approach in Control System Design

  • 2.3 Final Statement

  • 2.4 Summary

Chapter 3: Black-box Modeling, Simulation, and Control of GTs

  • 3.1 Black-box Modeling and Simulation of GTs

    • 3.1.1 Black-box Models of Low-Power GTs

    • 3.1.2 Black-box Models of IPGTs

    • 3.1.3 Black-box Models of Aero GTs

  • 3.2 Black-box Approach in Control System Design

  • 3.3 Final Statement

  • 3.4 Summary

Chapter 4: ANN-based System Identification for Industrial Systems

  • 4.1 Artificial Neural Network (ANN)

  • 4.2 The Model of an Artificial Neuron

  • 4.3 ANN-based Model Building Procedure

    • 4.3.1 System Analysis

    • 4.3.2 Data Acquisition and Preparation

    • 4.3.3 Network Architecture

      • 4.3.3.1 Feedforward Neural Network

      • 4.3.3.2 Feedback Neural Network

    • 4.3.4 Network Training and Validation

      • 4.3.4.1 Number of Hidden Layers and Neurons

      • 4.3.4.2 Training Algorithms

      • 4.3.4.3 Transfer Functions

      • 4.3.4.4 Weight Values

      • 4.3.4.5 Error Criteria

      • 4.3.4.6 Training Stop Criteria and Overfitting

  • 4.4 ANN Applications to Industrial Systems

  • 4.5 ANN Limitations

  • 4.6 Summary

Chapter 5: Modeling and Simulation of a Single-Shaft GT

  • 5.1 GT Simulink® Model

  • 5.2 ANN-based System Identification

    • 5.2.1 Data Generation

    • 5.2.2 Training Process

    • 5.2.3 Code Generation

  • 5.3 Model Selection Process

  • 5.4 Summary

Chapter 6: Modeling and Simulation of Dynamic Behavior of an IPGT

  • 6.1 GT Specifications

  • 6.2 Data Acquisition and Preparation

  • 6.3 Physics-based Model of IPGT by Using Simulink®: MATLAB®

    • 6.3.1 Measured Parameters

    • 6.3.2 Calculated or Estimated Parameters

      • 6.3.2.1 Turbine Inlet Stagnation Pressure

      • 6.3.2.2 Turbine Outlet Stagnation Pressure

      • 6.3.2.3 Specific Heat of Air and Gas at Constant Pressure

      • 6.3.2.4 Turbine Inlet Temperature and Mass Flow Rate of Air

      • 6.3.2.5 Efficiency and Corrected Parameters of the Compressor and Turbine

    • 6.3.3 Model Architecture

    • 6.3.4 Discussion on Physics-based Modeling Approach

  • 6.4 NARX Model of IPGT

  • 6.5 Comparison of Physics-based and NARX Models

  • 6.6 Summary

Chapter 7: Modeling and Simulation of the Start-up Operation of an IPGT Using NARX Models

  • 7.1 GT Start-up

  • 7.2 Data Acquisition and Preparation

  • 7.3 GT Start-up Modeling by Using NARX Models

    • 7.3.1 NARX Model Training

    • 7.3.2 NARX Model Validation

  • 7.4 Summary

Chapter 8: Design of Neural Network-Based Controllers for GTs

  • 8.1 GT Control System

  • 8.2 Model Predictive Controller (MPC)

    • 8.2.1 Design of ANN-based MPC

      • 8.2.1.1 System Identification of ANN-based MPC

      • 8.2.1.2 Adjustment of Controller Parameters for ANN-based MPC

    • 8.2.2 Simulation of ANN-based MPC

  • 8.3 Feedback Linearization Controller (NARMA-L2)

    • 8.3.1 Design of NARMA-L2

    • 8.3.2 Simulation of NARMA-L2

  • 8.4 PID Controller

    • 8.4.1 Design of PID Controller

    • 8.4.2 Simulation of PID Controller

  • 8.5 Comparison of Controllers Performance

  • 8.6 NMP Systems

  • 8.7 Summary

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Tags: Hamid Asgari, XiaoQi Chen, Gas Turbines