1 Introduction 1.1 Complex Metallurgical Processes 1.2 Modeling, Control, and Optimization of Complex Metallurgical Processes 1.2.1 Modeling 1.2.2 Control 1.2.3 Optimization 1.3 Intelligent Control and Optimization Methods 1.3.1 Neural Network Modeling 1.3.2 Fuzzy Control 1.3.3 Expert Control 1.3.4 Decoupling Control 1.3.5 Hierarchical Intelligent Control 1.3.6 Intelligent Optimization Algorithms 1.4 Outline of This Book References 2 Intelligent Optimization and Control of Raw Material Proportioning Processes 2.1 Process Description and System Configuration 2.1.1 Process Description and Characteristic Analysis 2.1.2 Control Architecture 2.2 Intelligent Optimization and Control of Coal Blending Process 2.2.1 Quality-Prediction Models for Coal Blend 2.2.2 Quality-Prediction Models for Coke 2.2.3 Rule Models 2.2.4 Determination of Target Percentages Based on Rule Models 2.2.5 Determination of Target Percentages Based on Simulated Annealing Algorithm 2.2.6 Tracking Control of Target Percentages 2.3 System Implementation for Coal Blending Process 2.3.1 System Configuration and Implementation 2.3.2 Results of Actual Runs of Coal Blending Process 2.4 Intelligent Integrated Optimization System for Proportioning of Iron Ore in Sintering Process 2.4.1 Cascade Integrated Quality-Prediction Model for Sinter 2.4.2 Verification of Quality-Prediction Model 2.4.3 Optimization Model of Proportioning 2.4.4 Optimization Method 2.4.5 Verification of Optimization Algorithms 2.5 System Implementation for Proportioning of Iron Ore in Sintering Process 2.5.1 System Configuration and Implementation 2.5.2 Results of Actual Runs in Sintering Process 2.6 Conclusion References 3 Intelligent Optimization and Control of Coking Process 3.1 Characteristic Analysis and System Configuration 3.1.1 Process Description 3.1.2 Analysis of Characteristics 3.1.3 Control Requirements 3.1.4 System Configuration 3.2 Integrated Soft Sensing of Coke-Oven Temperature 3.2.1 Choice of Auxiliary Variables and Measurement Points 3.2.2 Structure of Soft-Sensing Model for Coke-Oven Temperature 3.2.3 Integrated Linear Regression Model
3.2.4 Supervised Distributed Neural Network Model 3.2.5 Model Adaptation 3.3 Intelligent Optimization and Control of Coke-Oven Combustion Process 3.3.1 Configuration of Hybnd Hierarchical Control System 3.3.2 Determination of Operating State 3.3.3 Design of Coke-Oven Temperature Controller 3.3.4 Design of Controller for Gas Flow Rate 3.3.5 Design of Air Suction Power Controller 3.4 Operation Planning and Optimal Scheduling of Coking 3.4.1 Analysis of Operations Planning and Optimal Scheduling of Coking 3.4.2 Configuration of Optimal Scheduling 3.4.3 Optimal Scheduling of Operating States 3.5 System Implementation and Results of Actual Runs 3.5.1 System Implementation 3.5.2 Results of Actual Runs for Integrated Soft Sensing of Coke-Oven Temperature 3.5.3 Results of Actual Runs for Intelligent Optimization and Control of Coke-Oven Combustion Process 3.5.4 Results of Actual Runs for Coke-Oven Operation Planning and Optimal Scheduling 3.6 Conclusion References 4 Intelligent Control of Thermal State Parameters in Sintering Process 4.1 Process Description and Characteristics Analysis 4.1.1 Description of Sintering Process 4.1.2 Characteristic Analysis of Thermal State Parameters in Sintering Process 4.1.3 Control Requirements 4.2 Intelligent Control of Sintering Ignition Process 4.2.1 Control System Architecture 4.2.2 Intelligent Optimization and Control Algorithm 4.2.3 Subspace Modeling of Sintering Ignition Process 4.2.4 Periodic Disturbance Rejection Using Equivalent-Input-Disturbance Estimation 4.2.5 Experimental Simulation 4.3 Intelligent Control System for Bum-Through Point 4.3.1 Control System Architecture 4.3.2 Soft Sensing and Prediction of Bum-Through Point 4.3.3 Hybrid Fuzzy-Predictive Controller 4.3.4 Bunker-Level Expert Controller 4.3.5 Coordinating Control Algorithm 4.4 Industrial Implementation and Results of Actual Runs 4.4.1 Industrial Implementation 4.4.2 Results of Actual Runs 4.5 Conclusion References 5 Intelligent Decoupling Control of Gas Collection and Mixing-and-Pressurization Processes 5.1 Process Description and Characteristic Analysis 5.1.1 Description and Analysis of Gas Collection Process 5.1.2 Description and Analysis of Gas Mixing-and-Pressurization Process 5.2 Intelligent Decoupling Control of Gas Collection Process 5.2.1 Intelligent Decoupling Control Based on Coupling Degree Analysis 5.2.2 Configuration of Intelligent Decoupling Contro 5.3 System Implementation and Results of Actual Runs for Gas Collection Process 5.3.1 System Implementation 5.3.2 Results of Actual Runs 5.4 Intelligent Decoupling Control of Gas Mixing-and-Pressurization Process 5.4.1 Configuration of Gas Mixing-and-Pressurization Control System 5.4.2 Design of Calorific-Value and Pressure Decoupling Control Subsystem 5.4.3 Design of Pressurization Control Subsystem 5.5 System Implementation and Results of Actual Runs for Gas Mixing-and-Pressurization Process 5.5.1 System Framework 5.5.2 System Implementation 5.5.3 Results of Actual Runs 5.6 Conclusion References 6 Intelligent Optimization and Control for Reheating Furnaces 6.1 Process Description and Control Requirements 6.1.1 Combustion Process and Control Requirements for the Regenerative Pusher-Type Reheating Furnace 6.1.2 Combustion Process of and Control Requirements for Compact Strip Production Soaking Furnace 6.2 Temperature Prediction Models 6.2.1 Recurrent-Neural-Network Model 6.2.2 Estimation of Zone Temperature 6.2.3 Estimation of Billet Temperature 6.2.4 Integrated Model of Billet Temperature Prediction 6.3 Optimization and Control for Regenerative Pusher-Type Reheating Furnace 6.3.1 Configuration of Optimization and Control System 6.3.2 Decoupling Control Based on Fuzzy Neural Network 6.3.3 Optimization for Temperature 6.3.4 Verification and Discussion 6.3.5 Implementation and Results of Actual Runs 6.4 Intelligent Control System for Soaking Furnace of Compact Strip Production 6.4.1 Configuration of Intelligent Control System 6.4.2 Intelligent Control 6.4.3 Implementation and Results of Actual Runs 6.5 Conclusion References Index
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