Preface	6
	Contents	7
	About the Authors	11
	List of Figures	12
	List of Tables	24
	1 Introduction	26
	Abstract	26
	1.1 Background	26
	1.2 Coal Combustion	27
	1.2.1 General Process of Coal Combustion	27
	1.2.2 The Duration of Coal Combustion	27
	1.2.3 The Characteristic of Coal Combustion	28
	1.3 Carbon Burnout	29
	1.4 Coal Combustion Optimization	30
	1.5 Outline of the Book	30
	References	31
	2 The Influence of Combustion Parameters on NOx Emissions and Carbon Burnout	32
	Abstract	32
	2.1 Introduction	32
	2.2 Influence of Combustion Parameters on NOx Emissions	33
	2.3 Influence of Combustion Parameters on Carbon Burnout	38
	References	44
	3 Modeling Methods for Combustion Characteristics	45
	Abstract	45
	3.1 Introduction	45
	3.2 Experimental Method	46
	3.2.1 Experimental Methods of Coal Combustion Characteristics Study	46
	3.2.1.1 Coal Combustion Characteristics	46
	3.2.1.2 Experimental Methods	46
	3.2.1.3 Test System of Coal Combustion	53
	3.2.2 Flame Temperature Measurement	57
	3.2.3 Flue Gas Analysis	58
	3.2.4 Application Examples	62
	3.3 CFD Method	89
	3.3.1 Turbulence Model	90
	3.3.2 Combustion Model	93
	3.3.3 Radiative Heat Transfer Model	94
	3.3.4 Discrete Phase Model	94
	3.3.5 Reaction Models of Particles	95
	3.3.6 Pollutant Formation Model	96
	3.3.7 Application Examples	96
	3.4 Computational Intelligence Method	156
	3.5 Summary	166
	References	166
	4 Neural Network Modeling of Combustion Characteristics	170
	Abstract	170
	4.1 Introduction	170
	4.1.1 Structural Model of Neuron	170
	4.1.2 MP Model	171
	4.2 Back Propagation Neural Network Method	172
	4.2.1 BPNN Algorithm	172
	4.2.2 Learning Methods	173
	4.3 General Regression Neural Network Method	174
	4.3.1 GRNN Algorithm	175
	4.3.2 GRNN Structure	175
	4.4 Comparison of BPNN Method and GRNN Method	176
	4.4.1 GRNN Advantages	176
	4.4.2 Comparison on Example	176
	4.5 Summary	177
	References	177
	5 Classification of the Combustion Characteristics based on Support Vector Machine Modeling	178
	Abstract	178
	5.1 The Introduction of Support Vector Machine	178
	5.2 The Principle of Support Vector Machine	180
	5.2.1 Support Vector Classification	180
	5.2.2 Support Vector Regression	181
	5.2.3 Kernel Function	181
	5.3 The Application of Support Vector Machine	182
	5.3.1 Coal Identification	182
	5.3.2 The Prediction of Ash Fusion Temperature	184
	5.3.3 The Prediction of Unburned Carbon in Fly Ash	186
	5.3.4 The Prediction of NOx Emission	188
	5.4 Summary	192
	References	192
	6 Combining Neural Network or Support Vector Machine with Optimization Algorithms to Optimize the Combustion	194
	Abstract	194
	6.1 Introduction of Optimization Algorithms	194
	6.1.1 Genetic Algorithms	194
	6.1.1.1 Introduction to GA	194
	6.1.1.2 The Description of GA	195
	6.1.1.3 The Process of GA Approach	195
	6.1.2 Ant Colony Algorithms	196
	6.1.2.1 Introduction to ACO	196
	6.1.2.2 The Description of ACO	196
	6.1.2.3 Another Algorithm of ACO	199
	6.1.3 Particle Swarm Algorithms	201
	6.2 Combining Neural Network and GA to Optimize the Combustion	203
	6.2.1 Experiments	203
	6.2.2 Result and Discussions	205
	6.2.3 Conclusions	210
	6.3 Combining SVM and Optimization Algorithms to Optimize the Combustion	210
	6.3.1 Modeling NOx Emissions by SVM and ACO with Operating Parameters Optimizing	211
	6.3.1.1 Experimental Setup and Data Analysis	211
	6.3.1.2 Results	214
	6.3.1.3 Prediction Results of ACO–SVR	214
	6.3.1.4 Prediction Results of Grid SVR	218
	6.3.1.5 Comparison and Discussion	220
	6.3.1.6 Conclusions	222
	6.3.2 Modeling NOx Emissions by SVM and PSO with Model and Operating Parameters Optimizing	223
	6.3.2.1 Experimental Setup	223
	6.3.2.2 Optimization Results for the Boiler Load of 288.45 MW	227
	6.3.2.3 Comparison with Other Methods	228
	6.3.2.4 Conclusions	231
	6.3.3 Comparison of Optimization Algorithms for Low NOx Combustion	232
	6.3.3.1 Experimental Setup and NOx Emission Data	232
	6.3.3.2 Estimation of NOx Emissions by SVR	234
	6.3.3.3 Selection of Model Parameters	235
	6.3.3.4 NOx Emissions Prediction Results	236
	6.3.3.5 Low NOx Emissions by Combining SVR and Optimization Methods	237
	6.3.3.6 Parameter Settings for Various Algorithms	239
	6.3.3.7 Performance Comparisons	239
	6.3.3.8 Convergence Rate	244
	6.3.3.9 Conclusions	245
	6.4 Multi-objective Optimization of Coal Combustion for Utility Boilers	246
	6.4.1 Multi-objective Optimization Algorithm	246
	6.4.1.1 The Cellular Genetic Algorithm for Multi-objective Optimization (MOCell)	246
	6.4.1.2 AbYSS Algorithm	247
	6.4.1.3 OMOPSO Algorithm	247
	6.4.1.4 SPEA2 Algorithm	249
	6.4.2 Introduction and Experiment Setup	250
	6.4.3 Modeling NOx Emissions and Carbon Burnout	251
	6.4.4 Performance Metrics of Pareto Solution	253
	6.4.4.1 The Ratio of Non-dominated Individuals (RNI)	253
	6.4.4.2 Cover Rate	253
	6.4.5 Parameter Settings for Various Algorithms	254
	6.4.6 Performance Comparisons	254
	6.4.7 Conclusion	258
	6.5 Summary	258
	References	259
	7 Online Combustion Optimization System	261
	Abstract	261
	7.1 Introduction	262
	7.1.1 Data Detection Requirements	262
	7.1.2 Quickness and Accuracy Requirements	262
	7.1.3 Requirements for Different Optimization Goals	263
	7.1.4 Requirements Online Self-Learning	263
	7.1.5 Parameter Optimization Limit Requirements	263
	7.1.6 Fault Tolerance Requirements	263
	7.1.7 Alarm Requirements	264
	7.1.8 Compatibility of Off-line Data Processing and Optimizing	264
	7.2 Instruments or Sensors for Online Combustion Optimization System	264
	7.3 Online SVM Algorithm	265
	7.3.1 Algorithm Introduction	265
	7.3.2 Derivation of the Incremental Relations	268
	7.3.3 AOSVR Bookkeeping Procedure	270
	7.3.4 Efficiently Updating the R Matrix	271
	7.3.5 Initialization of the Incremental Algorithm	272
	7.3.6 Decremental Algorithm	273
	7.4 Online Combustion Optimization System	273
	7.4.1 Online Monitoring and Alarm Function	273
	7.4.2 Online Optimization and Self-Learning Function	274
	7.4.3 Off-line Modeling and Optimization Function	275
	7.5 The Application of Online Combustion Optimization System	280
	7.5.1 Train and Prediction	280
	7.5.2 Test Purpose	283
	7.5.3 Test Condition	283
	7.5.4 Test Data	283
	7.5.5 Result and Analysis	285
	7.6 Summary	285
	Reference	286
	8 Combustion Optimization Based on Computational Intelligence Applications: Future Prospect	287
	Abstract	287
	References	288
	Index	290