Reconstruction and Intelligent Control for Power Plant [electronic resource] / by Chen Peng, Chuanliang Cheng, Ling Wang.

Por: Peng, Chen [author.]Colaborador(es): Cheng, Chuanliang [author.] | Wang, Ling [author.] | SpringerLink (Online service)Tipo de material: TextoTextoEditor: Singapore : Springer Nature Singapore : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: XV, 208 p. 100 illus., 90 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789811955747Tema(s): Control engineering | Robotics | Automation | Electric power production | Computational intelligence | Control, Robotics, Automation | Electrical Power Engineering | Computational IntelligenceFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 629.8 Clasificación LoC:TJ212-225TJ210.2-211.495Recursos en línea: Libro electrónicoTexto
Contenidos:
Introduction -- Adaptive mixed edge detection of furnace flame image -- Intelligent flame image segmentation of furnace flame image -- Reconstruction of temperature field based on limited flame image information -- Furnace temperature prediction based on optimized kernel extreme learning machine -- Process modeling of power plant -- Fuzzy K-means network based generalized predictive control for power plant -- Deep-neural-network based nonlinear predictive control for power plant -- Intelligent virtual reference feedback tuning based data driven control for power plant.
En: Springer Nature eBookResumen: The authors' innovative research ideas in power plant control are presented in this book. This book focuses on 1) cognition and reconstruction of the temperature field; 2) intelligent setting and learning of power plants; 3) energy efficiency optimization and intelligent control for power plants, and so on, using historical power plant operation data and creative methods such as reconstruction of the combustion field, deep reinforcement learning, and networked collaborative control. It could help researchers, industrial engineers, and graduate students in the areas of signal detection, image processing, and control engineering.
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Introduction -- Adaptive mixed edge detection of furnace flame image -- Intelligent flame image segmentation of furnace flame image -- Reconstruction of temperature field based on limited flame image information -- Furnace temperature prediction based on optimized kernel extreme learning machine -- Process modeling of power plant -- Fuzzy K-means network based generalized predictive control for power plant -- Deep-neural-network based nonlinear predictive control for power plant -- Intelligent virtual reference feedback tuning based data driven control for power plant.

The authors' innovative research ideas in power plant control are presented in this book. This book focuses on 1) cognition and reconstruction of the temperature field; 2) intelligent setting and learning of power plants; 3) energy efficiency optimization and intelligent control for power plants, and so on, using historical power plant operation data and creative methods such as reconstruction of the combustion field, deep reinforcement learning, and networked collaborative control. It could help researchers, industrial engineers, and graduate students in the areas of signal detection, image processing, and control engineering.

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