Thermal Power Plants Modeling Control and Efficiency Improvement
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Thermal Power Plants Modeling Control and Efficiency Improvement

Thermal Power Plants Modeling, Control, and Efficiency Improvement by Xingrang Liu and Ramesh Bansal | PDF Free Download.

Authors of Thermal Power Plants Control and Efficiency Improvement

Xingrang Liu, PhD, completed his doctoral degree focusing on fossil fuel power plant boiler combustion process optimization based on real-time simulation at the School of Information Technology and Electrical Engineering, University of Queensland (UQ) Brisbane, Australia, in October 2013.

He completed his master study of Computer Software and Theory at Xi’an Jiaotong University in Xi’an, China, in July 2003 and undergraduate study of Computer Science and Engineering at the Northeast China Institute of Electric Power Engineering in Jilin, China, in July 1992.

He has worked as a computer engineer for 10 years and worked as a senior software engineer for 5 years in the power generation industry in China.

He worked as a system developer in the Cooperative Research Centre for Integrated Engineering Asset Management, School of Engineering Systems, Queensland University of Technology (QUT), Brisbane, Australia, from 2007 to 2009.

He worked as an assistant researcher and a research software engineer at UQ from 2011 to 2013. Currently, he is working as a senior software researcher at the University of Southern Queensland (USQ), Toowoomba, Austrailia.

His research interests include cloud computing and high-performance computing supported real-time control system, control system modeling, computational fluid dynamics–supported thermal power plant process modeling, and multiobjective thermal process identification and optimization.

Ramesh Bansal has more than 25 years of teaching, research, and industrial experience. Currently, he is a professor and group head (Power) in the Department of Electrical, Electronic and Computer Engineering at the University of Pretoria, South Africa.

In previous postings, he was with the University of Queensland (UQ), Brisbane, Australia; University of the South Pacific, Suva, Fiji; Birla Institute of Technology and Science (BITS), Pilani, India; and All India Radio.

During his sabbatical leave, he worked with Powerlink (Queensland’s high voltage transmission company). Bansal has extensive experience in the development and delivery of training programs for professional engineers.

At UQ, he made significant contributions to the development and delivery of the ME Power Generation Program (a collaborative program of three of Queensland’s industries and three universities). He developed and taught Generator Technology and Plant Control Systems.

At BITS, he contributed to the development and delivery of the BS Power Engineering Program (for the National Thermal Power Corporation, NTPC, a 40000 MW Indian Power Generation Company) and two batches of more than 1000 students successfully completed the program.

Bansal has published over 230 research papers in journals and conferences. He has contributed several books/book chapters, including Handbook of Renewable Energy Technology, World Scientific Publishers, Singapore, in 2011.

He has diversified research interests in the areas of renewable energy and conventional power systems, including wind, photovoltaic (PV), distributed generation, power systems analysis (reactive power/voltage control, stability, faults, and protection), smart grid, FACTS, and power quality.

He is an editor of reputed journals including IET Renewable Power Generation, Electric Power Components and Systems, and IEEE Access. He is a fellow and chartered engineer at IET-UK, a fellow at Engineers Australia, a fellow at the Institution of Engineers (India), and a senior member at IEEE.

Thermal Power Plants Contents

Part I Thermal Power Plant Control Process

  • Performance and Energy Audits
  • Overview of Energy Conservation of Auxiliary Power in Power Plant Processes
  • Energy Conservation of In-House Auxiliary Power Equipment in Power Plant Processes
  • Energy Conservation of Common Auxiliary Power Equipment in Power Plant Processes
  • Physical Laws Applied to a Fossil Fuel Power Plant Process
  • Modeling and Simulation for Subsystems of a Fossil Fuel Power Plant

Part III Thermal Power Plant Efficiency Improvement Modeling

  • Conventional Neural Network-Based Technologies for Improving Fossil Fuel Power Plant Efficiency
  • Online Learning Integrated with CFD to Control Temperature in Combustion
  • Online Learning Integrated with CFD to Identify Slagging and Fouling Distribution
  • Integrating Multiobjective Optimization with Computational Fluid Dynamics to Optimize the BoilerCombustion Process

Part IV Thermal Power Plant Optimization Solution Supported by HighPerformance Computing and Cloud Computing

  • Internet-Supported Coal-Fired Power Plant Boiler Combustion Optimization Platform

Preface to Thermal Power Plants Control and Efficiency Improvement

The low carbon economy, environmental considerations, and fuel efficiency demands have placed strong requirements on fossil fuel-based power plants, requiring them to be operated efficiently.

Improving the fossil fuel boiler combustion process is highly significant because more than 40% of the world’s electricity is produced by fossil fuel, and fossil fuel power plants still play a dominant role in most countries.

Even though advanced supercritical fossil fuel power generation units with carbon dioxide capture and storage (CCS) technology are utilized, some combustion-related problems like slagging and fouling often occur, decreasing boiler efficiency and increasing potential unplanned outages, and creating more concerns on regulated emissions because of the highly complex conditions changing inside the boiler.

Fossil fuel power plant boiler combustion is one of the most important processes in power generation engineering, which involves thermal dynamics, turbulent fluid flow, chemical reactions, and other complicated physical and chemical processes.

Boiler combustion is a highly complex multi-input, a multi-output process that is nonlinear with strong inertia.

Therefore, it is difficult to establish an accurate mathematical model of boiler combustion. Artificial intelligence (AI) technologies such as neural networks and genetic algorithms (GA) have been widely applied in the power generation industry to optimize control system processes and improve fossil fuel power plant boiler efficiency.

For example, AI technology-based intelligent soot blowers are applied in coal-fired power plants to help effectively reduce slag buildup and increase the heat transfer rate of the boilers, and GA-based methods are applied to optimize fossil fuel power plant boiler combustion.

However, for combustion-related problems, such as slagging and fouling, the technologies that are only dependent on AI do not work successfully because not many parameters of the boiler combustion process are measured to train the neural network-based models and acquire approximate functions for such complex processes.

For example, the data regarding slagging properties are not quantified and fields of flue gas properties are not completely measured. So AI-based boiler optimization methods are limited.

A novel method of integrating online learning, GA, and multiobjective and identification optimization with computational fluid dynamics (CFD)– based real-time simulation is proposed and developed in this research to control the fields of flue gas properties, such as temperature and density fields, identify coal-fired power plant boiler slagging distribution,

And optimize the combustion process by tuning existing proportional–integral– derivative (PID) control to improve fossil fuel power plant boiler efficiency.

As compared with conventional AI-based fossil fuel boiler combustion optimization methods, the developed method in this research can obtain complete flue gas data inside the boiler through CFD-based combustion process simulation.

Moreover, the developed method in this research can not only identify the slagging distribution and help soot blowers to intelligently remove the slagging but also decrease or even avoid slagging by predictively optimizing the combustion process.

This book introduces innovative methods utilized in industrial applications, discussed in scientific research, and taught at universities.

Compared with previous books published in the area of control of the power generation industry, this book focuses on how to solve highly complex industry problems regarding identification, control,

And optimization through integrating conventional technologies, such as modern control technology, computational intelligence–based multiobjective identification and optimization, distributed computing, and cloud computing with CFD technology.

Although the projects involved in the book just cover industry automation in electrical power engineering, the methods proposed and developed in the book can be applied in other industries such as concrete and steel production for real-time process identification, control, and optimization.

This book is divided into four parts. Part I discusses thermal power plant processes, energy conservation, and performance audits.

Part II covers thermal power plant process modeling. Part III contains thermal power plant efficiency improvement modeling.

Part IV discusses a thermal power plant efficiency optimization solution supported by high-performance computing integrated with cloud computing. Part I is composed of Chapters 1, 2, 3 and 4.

Chapter 1 introduces the equipment in a fossil fuel power plant. It also introduces combustion-related slagging and fouling, which are some of the existing difficult problems of the power generation industry, and simply analyzes how to solve the problems so as to improve fossil fuel power plant efficiency.

Chapter 2 generally introduces thermal power plant processes and energy conservation, focusing on auxiliary power in power plant processes.

Chapter 3 introduces energy conservation and performance audits of inhouse auxiliary power equipment in a thermal power plant.

Chapter 4 introduces energy conservation and performance audits of common auxiliary power equipment in a thermal power plant.

Part II contains chapters 5 and 6. Chapter 5 discusses the processes in a fossil fuel power plant generally.

The processes include energy and mass flow such as heat conduction, convection, radiation, fuel and gas flow, and water and steam flow. Deeply understanding power plant processes is significant for modeling, controlling, and improving these processes in a thermal power plant.

The chapter also clarifies the main physical laws applied in power plant boiler combustion processes. Coal-fired power plant boiler combustion processes are highly complex, and heat and mass transfer are involved in these processes.

Correctly choosing the exact heat and mass balance equations is important to successfully model, controlling, and improving these processes.

Some experimental heat transfer equations are also discussed to model heat transfer processes inside the furnace of a boiler. Chapter 6 focuses on how to develop industrial process models using MATLAB®, Simulink®, VisSim, Comsol, ANSYS, and ANSYS Fluent.

Detailed model development for fossil fuel power plant boiler combustion processes is provided. Effectively using these software packages can both exactly and efficiently model, control, and optimize power plant boiler combustion processes.

Chapter 6 also introduces how to develop steam turbine and generator models. It also discusses how to create a model for the integration of a boiler, turbine, and generator in a fossil fuel power plant. VisSim, MATLAB, Simulink, Comsol, ANSYS, and ANSYS Fluent are used to create models of power plant combustion processes.

Part III contains chapters 7, 8, 9, and 10. Chapter 7 reviews traditional methods such as PID-based control technology and AI technology.

The chapter also reviews the finite element method–supported CFD technology, which is used to simulate power plant boiler combustion.

In addition, this chapter analyzes the limitation of conventional methods for the existing highly complex combustion-related slagging and fouling.

Chapter 8 clarifies how to integrate computational intelligence-based online learning with CFD technology to control temperature in a heat transfer process.

The detailed method of how to integrate an online indirect adaptive controller based on the radial basis function (RBF) with CFD is given.

A PID controller is also used to control the temperature. The results show that the proposed online learning integrated with CFD can control the flue gas temperature field.

In addition, the proposed method can achieve the desired objective with higher performance compared to a PID controller.

Chapter 9 covers the details of how to integrate multiobjective identification technology with CFD technology to identify the distribution of slagging inside the furnace of a coal-fired power plant boiler. A real tangential coal-fired boiler with 44 burners is simulated in threedimensional fashion using ANSYS Fluent 14.5.

The simulation achieves encouraging results compared with the corresponding results in other research.

The distributed computing technology CORBA C++ is used to combine the online learning model with a CFD-based coal-fired boiler model to optimize the fields of flue gas properties, such as flue gas temperature and density field. In addition, digital probes are set in the model to support slagging identification.

The outputs of this research show that online learning combined with CFD can identify the slagging distribution inside a coal-fired boiler.

Chapter 10 provides the innovative method of integrating computational intelligence-based multiobjective optimization with CFD to improve coalfired power plant boiler efficiency. Two objectives are set for coal-fired boiler combustion in this research.

The first objective is maintaining the coal boiler so it runs at a higher heat transfer rate. The second objective is controlling the temperature in the vicinity of the water wall tubes of the boiler and keeping the temperature within the ash melting temperature limit.

Then 10 input parameters, including the velocity of each burner with primary air, the velocity of each burner with secondary air, primary air temperature, and secondary air temperature are adjusted to achieve the two objectives.

Compared with the conventional neural network-based boiler optimization methods, the method developed in the work can control and optimize the fields of flue gas properties, such as the temperature field inside a boiler, by adjusting the temperature and velocity of primary and secondary air in coal-fired power plant boiler control systems.

If the temperature in the vicinity of the water wall tubes of a boiler can be maintained within the ash melting temperature limit, then the incoming ash particles cannot melt and bond to the surface of the heat transfer equipment of a boiler and the trend of slagging inside the furnace is controlled.

Furthermore, optimized boiler combustion can maintain a higher heat transfer efficiency than that of nonoptimized boiler combustion.

Software is developed to realize the proposed method and obtain encouraging results by combining ANSYS 14.5, ANSYS Fluent 14.5, and CORBA C++. Part IV contains Chapter 11, which simply focuses on how to apply this research achievement in coal-fired power plants efficiently by building an Internet-supported boiler combustion optimization platform.

The chapter also analyzes the online learning and CFD-supported local boiler combustion optimization solution and Internet-based global boiler combustion optimization platform solution.

In addition, how to combine high-performance computing technology, cloud computing technology, and computational intelligence–based identification, control, and optimization with CFD to build an Internet-supported industrial process optimization platform is discussed in detail.

The chapter also presents the scale to which the technologies of modeling, control, and optimization discussed in the book can be extended.

A list of references follows Chapter 11. The authors of this book sincerely thank Dr. Gagandeep Singh, Jennifer Ahringer, and Kyra Lindholm of CRC Press/Taylor & Francis Group for all their help in the publication of this book.

The authors also thank Professor Rajashekar P. Mandi, School of Electrical and Electronics Engineering, REVA University, Bangalore, India,

And Dr. Udaykumar R. Yaragatti, Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka (NITK), Surathkal, India, for contributing Chapters 2, 3 and 4 of this book.

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