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Thermal Power Plants: Modeling, Control, and Efficiency Improvement [Kõva köide]

(University of Southern Queensland, Toowoomba, Australia), (University of Sharjah, UAE)
  • Formaat: Hardback, 304 pages, kõrgus x laius: 234x156 mm, kaal: 748 g, 46 Tables, black and white; 49 Illustrations, color; 154 Illustrations, black and white
  • Ilmumisaeg: 01-Jul-2016
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1498708226
  • ISBN-13: 9781498708227
Teised raamatud teemal:
Thermal Power Plants: Modeling, Control, and Efficiency ImprovementSuurem pilt
  • Formaat: Hardback, 304 pages, kõrgus x laius: 234x156 mm, kaal: 748 g, 46 Tables, black and white; 49 Illustrations, color; 154 Illustrations, black and white
  • Ilmumisaeg: 01-Jul-2016
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1498708226
  • ISBN-13: 9781498708227
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781498708227.html
Märksõnad:
Thermal Power Plants: Modeling, Control, and Efficiency Improvement explains 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 computational fluid dynamics (CFD) technology. Introducing innovative methods utilized in industrial applications, explored in scientific research, and taught at leading academic universities, this book:











Discusses thermal power plant processes and process modeling, energy conservation, performance audits, efficiency improvement modeling, and efficiency optimization supported by high-performance computing integrated with cloud computing Shows how to simulate fossil fuel power plant real-time processes, including boiler, turbine, and generator systems Provides downloadable source codes for use in CORBA C++, MATLAB®, Simulink®, VisSim, Comsol, ANSYS, and ANSYS Fluent modeling software

Although the projects in the text focus on industry automation in electrical power engineering, the methods can be applied in other industries, such as concrete and steel production for real-time process identification, control, and optimization.

Arvustused

" recommended reading for a wide audience, from engineering students to working engineers and scientists wanting a refresher and advanced innovative ideas." Dr. Adel El Shahat, Georgia Southern University, Statesboro, USA

" a comprehensive and up-to-date account of modern thermal power plants, with an emphasis on efficiency improvement, critical for reducing environmental impact." David Infield, University of Strathclyde, Glasgow, Scotland

Preface xi
Authors xvii
Part I Thermal Power Plant Control Process Performance and Energy Audits
1 Introduction to Improving Thermal Power Plant Efficiency
3(16)
1.1 Power Plant Introduction
3(1)
1.2 Specific Problems of Fossil Fuel Boiler Combustion
4(2)
1.3 Significance of the Research to Electrical Power Industry
6(3)
1.4 Fouling and Slagging Distribution-Identification Model
9(1)
1.5 Fireball Control and Optimization Model
10(3)
1.6 Slagging Distribution Identification and Combustion Optimization
13(3)
1.7 An Innovative a Method to Optimize Fossil Fuel Power Plant Combustion and Limiting or Even Removing the Tendency of Slagging
16(1)
1.8 Creating a Novel Method to Identify the Distribution of Slagging inside of a Coal-Fired Boiler
17(1)
1.9 Conclusions
17(2)
2 Overview of Energy Conservation of Auxiliary Power in Power Plant Processes
19(16)
Rajashekar P. Mandi
Udaykumar R. Yaragatti
2.1 Introduction
19(3)
2.2 Energy Conservation
22(3)
2.2.1 Energy Audit
22(1)
2.2.1.1 Preliminary Energy Audit
23(1)
2.2.1.2 Detailed Energy Audit
23(1)
2.2.1.3 Energy Audit Report
25(1)
2.3 Auxiliary Power
25(9)
2.3.1 Total AP
31(1)
2.3.2 Unit AP
32(2)
2.4 Conclusions
34(1)
3 Energy Conservation of In-House Auxiliary Power Equipment in Power Plant Processes
35(50)
Rajashekar P. Mandi
Udaykumar R. Yaragatti
3.1 In-House HT Equipment
36(42)
3.1.1 Boiler Feed Pumps
38(1)
3.1.1.1 Energy Conservation Measures
44(1)
3.1.2 Condensate Extraction Pumps
45(1)
3.1.2.1 Energy Conservation Measures
49(1)
3.1.3 ID Fans
49(1)
3.1.3.1 Energy Conservation Measures
53(6)
3.1.4 FD Fans
59(1)
3.1.4.1 Energy Conservation Measures
63(2)
3.1.5 PA Fans
65(1)
3.1.5.1 Energy Conservation Measures
68(3)
3.1.6 Coal Mills
71(1)
3.1.6.1 Energy Conservation Measures
74(4)
3.2 In-House LT AP
78(4)
3.3 Conclusions
82(3)
4 Energy Conservation of Common Auxiliary Power Equipment in Power Plant Processes
85(34)
Rajashekar P. Mandi
Udaykumar R. Yaragatti
4.1 Introduction
85(1)
4.2 Coal-Handling Plant
86(9)
4.2.1 Energy Conservation Measures
92(3)
4.3 Ash-Handling Plant
95(4)
4.3.1 Bottom Ash
96(1)
4.3.2 Fly Ash
97(1)
4.3.3 Slurry Pumps
98(1)
4.3.4 Energy Conservation
98(1)
4.4 Circulating Water Plant
99(9)
4.4.1 Circulating Water Pumps
100(1)
4.4.1.1 Energy Conservation Measures
101(1)
4.4.2 Cooling Tower
102(1)
4.4.2.1 Range
103(1)
4.4.2.2 Approach
104(1)
4.4.2.3 Effectiveness
105(1)
4.4.2.4 Specific Energy Consumption
105(1)
4.4.2.5 Fan Efficiency
106(1)
4.4.2.6 Performance Results of Replacement of GRP Fan Blades by FRP Fan Blades and Optimum Motor
106(2)
4.5 Water Treatment Plant
108(6)
4.6 Conclusions
114(5)
Part II Thermal Power Plant Control Process Modeling
5 Physical Laws Applied to a Fossil Fuel Power Plant Process
119(10)
5.1 Introduction
119(1)
5.2 Heat Conduction, Convection, and Radiation
119(4)
5.3 Heat Balance
123(2)
5.4 Mass Balance
125(1)
5.5 Turbulent Combustion Gas Flow and Steam Flow
126(1)
5.6 Conclusion
127(2)
6 Modeling and Simulation for Subsystems of a Fossil Fuel Power Plant
129(62)
6.1 Introduction
129(1)
6.2 Development of a Boiler System Model
129(16)
6.2.1 Furnace Modeling
130(4)
6.2.2 Riser Modeling
134(2)
6.2.3 Reheater Modeling
136(3)
6.2.4 Superheater and Attemperator Modeling
139(3)
6.2.5 Drum Modeling
142(3)
6.3 Development of Boiler System Model Using Simulink
145(3)
6.4 Development of Steam-Temperature Control Using VisSim
148(5)
6.4.1 The Fire Side Process Simulation
149(1)
6.4.2 The Water Side Process Simulation
150(3)
6.4.3 Combining the Fire and Water Side Models
153(1)
6.5 Simulation of Heat-Transfer Processes Using Comsol 4.3
153(24)
6.5.1 Introduction
153(2)
6.5.2 A Simple Model of a Combustion Process with Heat-Transfer Efficiency Influenced by Slagging
155(14)
6.5.3 Creating a GA Model and Validating It Using Simulink
169(3)
6.5.4 Integrating a GA with CFD to Optimize the Heat-Transfer Process in Boiler Combustion
172(5)
6.6 Modeling the Combustion Processes in a Coal-Fired Power Plant Boiler Using ANSYS 14.5 and ANSYS Fluent 14.5
177(1)
6.7 How to Integrate the Boiler, Turbine, and Generator System
178(3)
6.8 Developing Models to Integrate the Boiler, Turbine, and Generator
181(7)
6.8.1 Saturated Steam in the High-Pressure Section of a Turbine
181(1)
6.8.2 Generator Models
182(1)
6.8.3 Integration of All the Models
182(22)
6.8.3.1 Connection of Furnace Fuel and Gas Model with Drum Model
182(2)
6.8.3.2 Superheater Steam Models Combined with Drum Models
184(1)
6.8.3.3 Furnace Gas Models Combined with Superheater Steam Models
185(1)
6.8.3.4 Superheater Steam Model Combined with High-Pressure Steam Model of Turbine
185(1)
6.8.3.5 Control Model Integrated with Gas or Steam Process Models
185(3)
6.8.3.6 Control Models
188(1)
6.9 Conclusion
188(3)
Part III Thermal Power Plant Efficiency Improvement Modeling
7 Conventional Neural Network-Based Technologies for Improving Fossil Fuel Power Plant Efficiency
191(10)
7.1 Introduction
191(1)
7.2 NN-Based Power Plant Optimization Technology
191(1)
7.3 Online-Learning Applications
192(2)
7.4 Finite Element Method-Supported Computational Fluid Dynamics (CFD) Technology Applications in Power Plant Boiler Simulation
194(2)
7.5 Optimization Technologies Applied in the Power-Generation Industry
196(1)
7.6 Differential Equation-Based Heat-Transfer Process Simulation for a Coal-Fired Power Plant
197(1)
7.7 Existing Problems for Coal-Fired Power Plants
198(1)
7.8 Conclusion
199(2)
8 Online Learning Integrated with CFD to Control Temperature in Combustion
201(18)
8.1 Introduction
201(1)
8.2 Boiler-Combustion Process
201(3)
8.3 Integrating Online-Learning Technology with CFD-Based Real-Time Simulation to Control the Combustion Process
204(9)
8.3.1 Online-Learning Technology Method
205(1)
8.3.2 CFD Model Method
206(2)
8.3.3 Integrating Online Learning with CFD
208(5)
8.4 Results and Discussion
213(4)
8.5 Conclusion
217(2)
9 Online Learning Integrated with CFD to Identify Slagging and Fouling Distribution
219(34)
9.1 Introduction
219(1)
9.2 Multiobjective Online Learning
220(4)
9.2.1 The Proposed Multiobjective Learning System
220(3)
9.2.2 Validation of the Proposed Multiobjective Online Learning
223(1)
9.3 Modeling of a Power Plant Boiler-Combustion Process Based on CFD
224(8)
9.3.1 Geometry of the Furnace of Coal-Fired Power Plant
224(4)
9.3.2 Modeling the Combustion Process
228(4)
9.4 Analyzing the Results of the Boiler-Combustion Process Model
232(14)
9.4.1 The Predicted Temperature Field Analysis
232(4)
9.4.2 The Predicted Incident Radiation Analysis
236(1)
9.4.3 The Predicted Gas Particle Trajectory Analysis
236(4)
9.4.4 The Predicted Nitrogen and Carbon Oxide Analysis
240(6)
9.5 Integrate Online Learning with CFD for Identification of Slagging and Fouling Distribution
246(6)
9.5.1 Identifying Slagging and Fouling Distribution
247(1)
9.5.2 Analysis of the Results of the Proposed Methodology
248(4)
9.6 Conclusion
252(1)
10 Integrating Multiobjective Optimization with Computational Fluid Dynamics to Optimize the Boiler-Combustion Process
253(22)
10.1 Introduction
253(1)
10.2 Principle Mechanism of Combustion Process and Slagging inside a Coal-Fired Power Plant Boiler
254(2)
10.2.1 The Heat-Transfer Process inside a Boiler
254(1)
10.2.2 The Predicted Temperature Field Analysis
255(1)
10.2.3 The Mechanisms of Slagging in the Coal-Fired Boiler
256(1)
10.3 Modeling of Coal-Fired Power Plant Boiler-Combustion Process
256(1)
10.4 NSGA II-Based Multiobjective Optimization Model
257(2)
10.5 Integrating the NSGA II Multiobjective-Optimization Method with CFD to Optimize the Coal-Fired Power Plant Boiler-Combustion Process
259(13)
10.6 Conclusion
272(3)
Part IV Thermal Power Plant Optimization Solution Supported by High-Performance Computing and Cloud Computing
11 Internet-Supported Coal-Fired Power Plant Boiler Combustion Optimization Platform
275(10)
11.1 Introduction
275(1)
11.2 Building a Coal-Fired Power Plant Combustion Optimization System Supported by Online Learning Integrated with CFD in a Local Area Network
276(1)
11.3 Using High-Performance Computer Technology to Build a Coal-Fired Power Plant Combustion Optimization System Supported by Online Learning Integrated with CFD
277(2)
11.4 Using Cloud-Computing Technology to Build a Coal-Fired Power Plant Combustion Optimization System Supported by Online Learning Integrated with CFD
279(1)
11.5 Integrating Online Learning Technology with CFD to Build a Coal-Fired Power Plant Boiler Combustion Optimization Platform Supported by High-Performance, Cloud-Computing, CORBA, and Web Services Technologies
279(3)
11.6 Conclusion
282(1)
11.7 Scope for Future Works
282(3)
References 285(10)
Index 295
Xingrang Liu completed his PhD with a focus on fossil fuel power plant boiler combustion process optimization based on real-time simulation at the University of Queensland (UQ), Brisbane, Australia. He completed his masters study of computer software and theory at Xian Jiaotong University, China, and his undergraduate study of computer science and engineering at the Northeast China Institute of Electric Power Engineering, Jilin. He worked in the Chinese power generation industry as a computer engineer for 10 years and as a senior software engineer for 5 years. He also worked as a system developer in the Cooperative Research Centre for Integrated Engineering Asset Management at Queensland University of Technology, Brisbane, Australia, and as an assistant researcher and research software engineer at UQ. Currently, he is a senior software researcher at the University of Southern Queensland, Toowoomba, Australia.

Ramesh Bansal has more than 25 years of teaching, research, and industrial experience. Currently, he is a professor and group head (power) of 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, Brisbane, Australia; University of the South Pacific, Suva, Fiji; Birla Institute of Technology and Science, Pilani, India; and All India Radio. During his sabbatical leave, he worked with Powerlink (Queenslands high-voltage transmission company). Bansal is both widely published and an editor of several 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.