有色冶金炉窑仿真与优化 英文PDF|Epub|txt|kindle电子书版本下载

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1 Introduction1

1.1 Classification of the Furnaces and Kilns for Nonferrous Metallurgical Engineering(FKNME)1

1.2 The Thermophysical Processes and Thermal Systems of the FKNME2

1.3 A Review of the Methodologies for Designs and Investigations of FKNME4

1.3.1 Methodologies for design and investigation of FKNME4

1.3.2 The characteristics of the MHSO method5

1.4 Models and Modeling for the FKNME7

1.4.1 Models for the modern FKNME7

1.4.2 The modeling process7

References9

2 Modeling of the Thermophysical Processes in FKNME11

2.1 Modeling of the Fluid Flow in the FKNME11

2.1.1 Introduction11

2.1.2 The Reynolds-averaging and the Favre-averaging methods13

2.1.3 Turbulence models15

2.1.4 Low Reynolds number k-ε models21

2.1.5 Re-Normalization Group(RNG)k-εmodels25

2.1.6 Reynolds stresses model(RSM)26

2.2 The Modeling of the Heat Transfer in FKNME27

2.2.1 Characteristics of heat transfer inside furnaces27

2.2.2 Zone method29

2.2.3 Monte Carlo method33

2.2.4 Discrete transfer radiation model35

2.3 The Simulation of Combustion and Concentration Field38

2.3.1 Basic equations of fluid dynamics including chemical reactions38

2.3.2 Gaseous combustion models42

2.3.3 Droplet and particle combustion models48

2.3.4 NOx models54

2.4 Simulation of Magnetic Field60

2.4.1 Physical models60

2.4.2 Mathematical model of current field61

2.4.3 Mathematical models of magnetic field in conductive elements62

2.4.4 Magnetic field models of ferromagnetic elements66

2.4.5 Three-dimensional mathematical model of magnetic field69

2.5 Simulation on Melt Flow and Velocity Distribution in Smelting Furnaces69

2.5.1 Mathematical model for the melt flow in smelting furnace70

2.5.2 Electromagnetic flow71

2.5.3 The melt motion resulting from jet-flow75

References80

3 Hologram Simulation of the FKNME87

3 1 Concept and Characteristics of Hologram Simulation87

3.2 Mathematical Models of Hologram Simulation89

3.3 Applying Hologram Simulation to Multi-field Coupling92

3.3.1 Classification of multi-field coupling92

3.3.2 An example of intra-phase three-field coupling93

3.3.3 An example of four-field coupling94

3.4 Solutions of Hologram Simulation Models97

References98

4 Thermal Engineering Processes Simulation Based on Artificial Intelligence101

4.1 Characteristics of Thermal Engineering Processes in Nonferrous Metallurgical Furnaces101

4.2 Introduction to Artificial Intelligence Methods102

4.2.1 Expert system103

4.2.2 Fuzzy simulation104

4.2.3 Artificial neural network106

4.3 Modeling Based on Intelligent Fuzzy Analysis107

4.3.1 Intelligent fuzzy self-adaptive modeling of multi-variable system108

4.3.2 Example:fuzzy adaptive decision-making model for nickel matte smelting process in submerged arc furnace111

4.4 Modeling Based on Fuzzy Neural Network Analysis116

4.4.1 Fuzzy neural network adaptive modeling methods of multi-variable system117

4.4.2 Example:fuzzy neural network adaptive decision-making model for production process in slag cleaning furnace120

References123

5 Hologram Simulation of Aluminum Reduction Cells127

5.1 Introduction127

5.2 Computation and Analysis of the Electric Field and Magnetic Field131

5.2.1 Computation model of electric current in the bus bar132

5.2.2 Computational model of electric current in the anode133

5.2.3 Computation and analysis of electric field in the melt134

5.2.4 Computation and analysis of electric field in the cathode138

5.2.5 Computation and analysis of the magnetic field140

5.3 Computation and Analysis of the Melt Flow Field146

5.3.1 Electromagnetic force in the melt147

5.3.2 Analysis of the molten aluminum movement148

5.3.3 Analysis of the electrolyte movement149

5.3.4 Computation of the melt velocity field150

5.4 Analysis of Thermal Field in Aluminum Reduction Cells152

5.4.1 Control equations and boundary conditions153

5.4.2 Calculation methods156

5.5 Dynamic Simulation for Aluminum Reduction Cells158

5.5.1 Factors influencing operation conditions and principle of the dynamic simulation159

5.5.2 Models and algorithm160

5.5.3 Technical scheme of the dynamic simulation and function of the software system161

5.6 Model of Current Efficiency of Aluminum Reduction Cells163

5.6.1 Factors influencing current efficiency and its measurements164

5.6.2 Models of the current efficiency166

References169

6 Simulation and Optimization of Electric Smelting Furnace175

6.1 Introduction175

6.2 Sintering Process Model of Self-baking Electrode in Electric Smelting Furnace176

6.2.1 Electric and thermal analytical model of the electrode178

6.2.2 Simulation software182

6.2.3 Analysis of the computational result and the baking process183

6.2.4 Optimization of self-baking electrode configuration and operation regime190

6.3 Modeling of Bath Flow in Electric Smelting Furnace192

6.3.1 Mathematical model for velocity field of bath193

6.3.2 The forces acting on molten slag194

6.3.3 Solution algorithms and characters196

6.4 Heat Transfer in the Molten Pool and Temperature Field Model of the Electric Smelting Furnace198

6.4.1 Mathematical model of the temperature field in the molten pool199

6.4.2 Simulation software203

6.4.3 Calculation results and verification203

6.4.4 Evaluation and optimization of the furnace design and operation208

References210

7 Coupling Simulation of Four-field in Flame Furnace213

7.1 Introduction213

7.2 Simulation and Optimization of Combustion Chamber of Tower-Type Zinc Distillation Furnace215

7.2.1 Physical model216

7.2.2 Mathematical model217

7.2.3 Boundary conditions217

7.2.4 Simulation of the combustion chamber prior to structure optimization218

7.2.5 Structure simulation and optimization of combustion chamber220

7.3 Four-field Coupling Simulation and Intensification of Smelting in Reaction Shaft of Flash Furnace221

7.3.1 Mechanism of flash smelting process—particle fluctuating collision model223

7.3.2 Physical model224

7.3.3 Mathematical model—coupling computation of particle and gas phases225

7.3.4 Simulation results and discussion227

7.3.5 Enhancement of smelting intensity in flash furnace229

References232

8 Modeling of Dilute and Dense Phase in Generalized Fluidization235

8.1 Introduction235

8.2 Particle Size Distribution Models238

8.2.1 Normal distribution model239

8.2.2 Logarithmic probability distribution model240

8.2.3 Weibull probability distribution function241

8.2.4 R-R distribution function(Rosin-Rammler distribution)241

8.2.5 Nukiyawa-Tanasawa distribution function242

8.3 Dilute Phase Models244

8.3.1 Non-slip model245

8.3.2 Small slip model247

8.3.3 Multi-fluid model(or two-fluid model)248

8.3.4 Particle group trajectory model251

8.3.5 Solution of the particle group trajectory model256

8.4 Mathematical Models for Dense Phase257

8.4.1 Two-phase simple bubble model258

8.4.2 Bubbling bed model259

8.4.3 Bubble assemblage model(BAM)261

8.4.4 Bubble assemblage model for gas-solid reactions265

8.4.5 Solid reaction rate model in dense phase267

References272

9 Multiple Modeling of the Single-ended Radiant Tubes275

9.1 Introduction275

9.1.1 The SER tubes and the investigation of SER tubes276

9.1.2 The overall modeling strategy278

9.2 3D Cold State Simulation of the SER Tube279

9.3 2D Modeling of the SER Tube283

9.3.1 Selecting the turbulence model283

9.3.2 Selecting the combustion model286

9.3.3 Results and analysis of the 2D simulation289

9.4 One-dimensional Modeling of the SER Tube291

References295

10 Multi-objective Systematic Optimization of FKNME297

10.1 Introduction297

10.1.1 A historic review297

10.1.2 The three principles for the FKNME systematic optimization298

10.2 Objectives of the FKNME Systematic Optimization299

10.2.1 Unit output functions300

10.2.2 Quality control functions305

10.2.3 Control function of service lifetime306

10.2.4 Functions of energy consumption308

10.2.5 Control functions of air pollution emissions309

10.3 The General Methods of the Multi-purpose Synthetic Optimization309

10.3.1 Optimization methods of artificial intelligence309

10.3.2 Consistent target approach312

10.3.3 The main target approach314

10.3.4 The coordination curve approach315

10.3.5 The partition layer solving approach315

10.3.6 Fuzzy optimization of the multi targets316

10.4 Technical Carriers of Furnace Integral Optimization318

10.4.1 Optimum design CAD319

10.4.2 Intelligent decision support system for furnace operation optimization320

10.4.3 Online optimization system327

10.4.4 Integrated system for monitoring,control and management330

References334

Index337