Details
Reconstruction and Intelligent Control for Power Plant
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96,29 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 21.09.2022 |
| ISBN/EAN: | 9789811955747 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<p>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.</p><p></p>
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 <i>K</i>-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.
<p>Chen Peng received the Ph.D. degree in control theory and control engineering from the Chinese University of Mining Technology, Xuzhou, China, in 2002.</p>
<p>From November 2004 to January 2005, he was Research Associate with the University of Hong Kong, Hong Kong. From July 2006 to August 2007, he was Visiting Scholar with the Queensland University of Technology, Brisbane, QLD, Australia. From July 2011 to August 2012, he was Postdoctoral Research Fellow with Central Queensland University, Rockhampton, QLD, Australia. In 2012, he was appointed as Eastern Scholar with the Municipal Commission of Education, Shanghai, China, and joined Shanghai University, Shanghai, where he is currently Director with the Center of Networked Control Systems and Distinguished Professor. In 2018, he was appointed as Outstanding Academic Leader with the Municipal Commission of Science and Technology, Shanghai. His current research interests include networked control systems, intelligent control, optimizated control, and CPS.</p>
<p>Professor Peng is Associate Editor of a number of international journals, including the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Information Sciences, and Transactions of the Institute of Measurement and Control and so on. He was named Highly Cited Researcher in 2020 and 2021 by Clarivate Analytics.</p>Chuanliang Cheng received the B.Sc. degree in automation from Shandong Technology and Business University, Yantai, China, in 2012, and the M.Sc. degree in control science and engineering from Qingdao University, Qingdao, China in 2017. He is currently pursuing the Ph.D. degree with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. His current research interest includes modeling, optimization, and nonlinear model predictive control of power plants.<p></p><p>Ling Wang received the Ph.D. degree from the East China University of Science and Technology, Shanghai, China, in 2007. From March 2012 toMarch 2013, he was Visiting Scholar with the University of Florida, Gainesville, USA. He is currently Professor with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. He has authored or co-authored more than 80 publications. His current research interests include evolutionary computation, data-driven control, intelligent control, and machine learning.</p><br><p></p>
<p>From November 2004 to January 2005, he was Research Associate with the University of Hong Kong, Hong Kong. From July 2006 to August 2007, he was Visiting Scholar with the Queensland University of Technology, Brisbane, QLD, Australia. From July 2011 to August 2012, he was Postdoctoral Research Fellow with Central Queensland University, Rockhampton, QLD, Australia. In 2012, he was appointed as Eastern Scholar with the Municipal Commission of Education, Shanghai, China, and joined Shanghai University, Shanghai, where he is currently Director with the Center of Networked Control Systems and Distinguished Professor. In 2018, he was appointed as Outstanding Academic Leader with the Municipal Commission of Science and Technology, Shanghai. His current research interests include networked control systems, intelligent control, optimizated control, and CPS.</p>
<p>Professor Peng is Associate Editor of a number of international journals, including the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Information Sciences, and Transactions of the Institute of Measurement and Control and so on. He was named Highly Cited Researcher in 2020 and 2021 by Clarivate Analytics.</p>Chuanliang Cheng received the B.Sc. degree in automation from Shandong Technology and Business University, Yantai, China, in 2012, and the M.Sc. degree in control science and engineering from Qingdao University, Qingdao, China in 2017. He is currently pursuing the Ph.D. degree with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. His current research interest includes modeling, optimization, and nonlinear model predictive control of power plants.<p></p><p>Ling Wang received the Ph.D. degree from the East China University of Science and Technology, Shanghai, China, in 2007. From March 2012 toMarch 2013, he was Visiting Scholar with the University of Florida, Gainesville, USA. He is currently Professor with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. He has authored or co-authored more than 80 publications. His current research interests include evolutionary computation, data-driven control, intelligent control, and machine learning.</p><br><p></p>
<p>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.</p>
Is the first book to introduce the cognition and reconstruction of furnace combustion field in power plant Uses intelligent methods to realize the optimal learning of the characteristics of power plant Offers originally an in-depth examination of online intelligent control
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