本文總結(jié)作者多年來的研究工作和實(shí)踐經(jīng)驗(yàn)，綜合大量的國(guó)內(nèi)外相關(guān)文獻(xiàn)資料，分別針對(duì)復(fù)雜冶金過程中的原料配備過程、煉焦過程、燒結(jié)過程、集氣和煤氣混合加壓過程、加熱爐燃燒過程控制問題，分析其生產(chǎn)過程和控制目標(biāo)，提出一系列的建模、優(yōu)化、控制方法和技術(shù)，建立智能優(yōu)化控制系統(tǒng)，討論系統(tǒng)在實(shí)際工業(yè)的應(yīng)用效果。
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Contents
1 Introduction 1
1.1 Complex Metallurgical Processes 1
1.2 Modeling, Control, and Optimization of Complex Metallurgical Processes 3
1.2.1 Modeling 3
1.2.2 Control 4
1.2.3 Optimization 6
1.3 Intelligent Control and Optimization Methods 6
1.3.1 Neural Network Modeling 6
1.3.2 Fuzzy Control 11
1.3.3 Expert Control 12
1.3.4 Decoupling Control 16
1.3.5 Hierarchical Intelligent Control 19
1.3.6 Intelligent Optimization Algorithms 21
1.4 Outline of This Book 29
References 30
2 Intelligent Optimization and Control of Raw Material Proportioning Processes 33
2.1 Process Description and System Configuration 36
2.1.1 Process Description and Characteristic Analysis 36
2.1.2 Control Architecture 40
2.2 Intelligent Optimization and Control of Coal Blending Process 41
2.2.1 Quality-Prediction Models for Coal Blend 41
2.2.2 Quality-Prediction Models for Coke 43
2.2.3 Rule Models 45
2.2.4 Determination of Target Percentages Based on Rule Models 46
2.2.5 Determination of Target Percentages Based on Simulated Annealing Algorithm 49
2.2.6 Tracking Control of Target Percentages 51
2.3 System Implementation for Coal Blending Process 52
2.3.1 System Configuration and Implementation 52
2.3.2 Results of Actual Runs of Coal Blending Process 53
2.4 Intelligent Integrated Optimization System for Proportioning of Iron Ore in Sintering Process 54
2.4.1 Cascade Integrated Quality-Prediction Model for Sinter 56
2.4.2 Verification of Quality-Prediction Model 63
2.4.3 Optimization Model of Proportioning 65
2.4.4 Optimization Method 68
2.4.5 Verification of Optimization Algorithms 73
2.5 System Implementation for Proportioning of Iron Ore in Sintering Process 77
2.5.1 System Configuration and Implementation 77
2.5.2 Results of Actual Runs in Sintering Process 79
2.6 Conclusion 80
References 81
3 Intelligent Optimization and Control of Coking Process 83
3.1 Characteristic Analysis and System Configuration 85
3.1.1 Process Description 86
3.1.2 Analysis of Characteristics 88
3.1.3 Control Requirements 90
3.1.4 System Configuration 91
3.2 Integrated Soft Sensing of Coke-Oven Temperature 93
3.2.1 Choice of Auxiliary Variables and Measurement Points 93
3.2.2 Structure of Soft-Sensing Model for Coke-Oven Temperature 93
3.2.3 Integrated Linear Regression Model 95
3.2.4 Supervised Distributed Neural Network Model 97
3.2.5 Model Adaptation 100
3.3 Intelligent Optimization and Control of Coke-Oven Combustion Process 101
3.3.1 Configuration of Hybnd Hierarchical Control System 101
3.3.2 Determination of Operating State 103
3.3.3 Design of Coke-Oven Temperature Controller 105
3.3.4 Design of Controller for Gas Flow Rate 110
3.3.5 Design of Air Suction Power Controller 111
3.4 Operation Planning and Optimal Scheduling of Coking 112
3.4.1 Analysis of Operations Planning and Optimal Scheduling of Coking 112
3.4.2 Configuration of Optimal Scheduling 114
3.4.3 Optimal Scheduling of Operating States 115
3.5 System Implementation and Results of Actual Runs 122
3.5.1 System Implementation 123
3.5.2 Results of Actual Runs for Integrated Soft Sensing of Coke-Oven Temperature 124
3.5.3 Results of Actual Runs for Intelligent Optimization and Control of Coke-Oven Combustion Process 124
3.5.4 Results of Actual Runs for Coke-Oven Operation Planning and Optimal Scheduling 129
3.6 Conclusion 130
References 131
4 Intelligent Control of Thermal State Parameters in Sintering Process 135
4.1 Process Description and Characteristics Analysis 135
4.1.1 Description of Sintering Process 135
4.1.2 Characteristic Analysis of Thermal State Parameters in Sintering Process 136
4.1.3 Control Requirements 139
4.2 Intelligent Control of Sintering Ignition Process 140
4.2.1 Control System Architecture 140
4.2.2 Intelligent Optimization and Control Algorithm 141
4.2.3 Subspace Modeling of Sintering Ignition Process 142
4.2.4 Periodic Disturbance Rejection Using Equivalent-Input-Disturbance Estimation 147
4.2.5 Experimental Simulation 151
4.3 Intelligent Control System for Bum-Through Point 155
4.3.1 Control System Architecture 155
4.3.2 Soft Sensing and Prediction of Bum-Through Point 157
4.3.3 Hybrid Fuzzy-Predictive Controller 161
4.3.4 Bunker-Level Expert Controller 165
4.3.5 Coordinating Control Algorithm 165
4.4 Industrial Implementation and Results of Actual Runs 168
4.4.1 Industrial Implementation 168
4.4.2 Results of Actual Runs 169
4.5 Conclusion 172
References 173
5 Intelligent Decoupling Control of Gas Collection and Mixing-and-Pressurization Processes 177
5.1 Process Description and Characteristic Analysis 180
5.1.1 Description and Analysis of Gas Collection Process 180
5.1.2 Description and Analysis of Gas Mixing-and-Pressurization Process 183
5.2 Intelligent Decoupling Control of Gas Collection Process 184
5.2.1 Intelligent Decoupling Control Based on Coupling Degree Analysis 185
5.2.2 Configuration of Intelligent Decoupling Control System 189
5.2.3 Decoupling Control Strategies 191
5.2.4 Design of Intelligent Decoupling Control System 191
5.3 System Implementation and Results of Actual Runs for Gas Collection Process 197
5.3.1 System Implementation 197
5.3.2 Results of Actual Runs 198
5.4 Intelligent Decoupling Control of Gas Mixing-and-Pressurization Process 200
5.4.1 Configuration of Gas Mixing-and-Pressurization Control System 203
5.4.2 Design of Calorific-Value and Pressure Decoupling Control Subsystem 204
5.4.3 Design of Pressurization Control Subsystem 212
5.5 System Implementation and Results of Actual Runs for Gas Mixing-and-Pressurization Process 213
5.5.1 System Framework 213
5.5.2 System Implementation 215
5.5.3 Results of Actual Runs 216
5.6 Conclusion 217
References 218
6 Intelligent Optimization and Control for Reheating Furnaces 223
6.1 Process Description and Control Requirements 224
6.1.1 Combustion Process and Control Requirements for the Regenerative Pusher-Type Reheating Furnace 224
6.1.2 Combustion Process of and Control Requirements for Compact Strip Production Soaking Furnace 226
6.2 Temperature Prediction Models 229
6.2.1 Recurrent-Neural-Network Model 229
6.2.2 Estimation of Zone Temperature 232
6.2.3 Estimation of Billet Temperature 233
6.2.4 Integrated Model of Billet Temperature Prediction 234
6.3 Optimization and Control for Regenerative Pusher-Type Reheating Furnace 237
6.3.1 Configuration of Optimization and Control System 237
6.3.2 Decoupling Control Based on Fuzzy Neural Network 239
6.3.3 Optimization for Temperature 241
6.3.4 Verification and Discussion 247
6.3.5 Implementation and Results of Actual Runs 252
6.4 Intelligent Control System for Soaking Furnace of Compact Strip Production 255
6.4.1 Configuration of Intelligent Control System 256
6.4.2 Intelligent Control 258
6.4.3 Implementation and Results of Actual Runs 264
6.5 Conclusion 267
References 269
Index 273
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