有色冶金炉窑仿真与优化 英文PDF电子书下载

1 Introduction 1

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

1.2 The Thermophysical Processes and Thermal Systems of the FKNME 2

1.3 A Review of the Methodologies for Designs and Investigations of FKNME 4

1.3.1 Methodologies for design and investigation of FKNME 4

1.3.2 The characteristics of the MHSO method 5

1.4 Models and Modeling for the FKNME 7

1.4.1 Models for the modern FKNME 7

1.4.2 The modeling process 7

References 9

2 Modeling of the Thermophysical Processes in FKNME 11

2.1 Modeling of the Fluid Flow in the FKNME 11

2.1.1 Introduction 11

2.1.2 The Reynolds-averaging and the Favre-averaging methods 13

2.1.3 Turbulence models 15

2.1.4 Low Reynolds number k-ε models 21

2.1.5 Re-Normalization Group(RNG)k-εmodels 25

2.1.6 Reynolds stresses model(RSM) 26

2.2 The Modeling of the Heat Transfer in FKNME 27

2.2.1 Characteristics of heat transfer inside furnaces 27

2.2.2 Zone method 29

2.2.3 Monte Carlo method 33

2.2.4 Discrete transfer radiation model 35

2.3 The Simulation of Combustion and Concentration Field 38

2.3.1 Basic equations of fluid dynamics including chemical reactions 38

2.3.2 Gaseous combustion models 42

2.3.3 Droplet and particle combustion models 48

2.3.4 NOx models 54

2.4 Simulation of Magnetic Field 60

2.4.1 Physical models 60

2.4.2 Mathematical model of current field 61

2.4.3 Mathematical models of magnetic field in conductive elements 62

2.4.4 Magnetic field models of ferromagnetic elements 66

2.4.5 Three-dimensional mathematical model of magnetic field 69

2.5 Simulation on Melt Flow and Velocity Distribution in Smelting Furnaces 69

2.5.1 Mathematical model for the melt flow in smelting furnace 70

2.5.2 Electromagnetic flow 71

2.5.3 The melt motion resulting from jet-flow 75

References 80

3 Hologram Simulation of the FKNME 87

3 1 Concept and Characteristics of Hologram Simulation 87

3.2 Mathematical Models of Hologram Simulation 89

3.3 Applying Hologram Simulation to Multi-field Coupling 92

3.3.1 Classification of multi-field coupling 92

3.3.2 An example of intra-phase three-field coupling 93

3.3.3 An example of four-field coupling 94

3.4 Solutions of Hologram Simulation Models 97

References 98

4 Thermal Engineering Processes Simulation Based on Artificial Intelligence 101

4.1 Characteristics of Thermal Engineering Processes in Nonferrous Metallurgical Furnaces 101

4.2 Introduction to Artificial Intelligence Methods 102

4.2.1 Expert system 103

4.2.2 Fuzzy simulation 104

4.2.3 Artificial neural network 106

4.3 Modeling Based on Intelligent Fuzzy Analysis 107

4.3.1 Intelligent fuzzy self-adaptive modeling of multi-variable system 108

4.3.2 Example:fuzzy adaptive decision-making model for nickel matte smelting process in submerged arc furnace 111

4.4 Modeling Based on Fuzzy Neural Network Analysis 116

4.4.1 Fuzzy neural network adaptive modeling methods of multi-variable system 117

4.4.2 Example:fuzzy neural network adaptive decision-making model for production process in slag cleaning furnace 120

References 123

5 Hologram Simulation of Aluminum Reduction Cells 127

5.1 Introduction 127

5.2 Computation and Analysis of the Electric Field and Magnetic Field 131

5.2.1 Computation model of electric current in the bus bar 132

5.2.2 Computational model of electric current in the anode 133

5.2.3 Computation and analysis of electric field in the melt 134

5.2.4 Computation and analysis of electric field in the cathode 138

5.2.5 Computation and analysis of the magnetic field 140

5.3 Computation and Analysis of the Melt Flow Field 146

5.3.1 Electromagnetic force in the melt 147

5.3.2 Analysis of the molten aluminum movement 148

5.3.3 Analysis of the electrolyte movement 149

5.3.4 Computation of the melt velocity field 150

5.4 Analysis of Thermal Field in Aluminum Reduction Cells 152

5.4.1 Control equations and boundary conditions 153

5.4.2 Calculation methods 156

5.5 Dynamic Simulation for Aluminum Reduction Cells 158

5.5.1 Factors influencing operation conditions and principle of the dynamic simulation 159

5.5.2 Models and algorithm 160

5.5.3 Technical scheme of the dynamic simulation and function of the software system 161

5.6 Model of Current Efficiency of Aluminum Reduction Cells 163

5.6.1 Factors influencing current efficiency and its measurements 164

5.6.2 Models of the current efficiency 166

References 169

6 Simulation and Optimization of Electric Smelting Furnace 175

6.1 Introduction 175

6.2 Sintering Process Model of Self-baking Electrode in Electric Smelting Furnace 176

6.2.1 Electric and thermal analytical model of the electrode 178

6.2.2 Simulation software 182

6.2.3 Analysis of the computational result and the baking process 183

6.2.4 Optimization of self-baking electrode configuration and operation regime 190

6.3 Modeling of Bath Flow in Electric Smelting Furnace 192

6.3.1 Mathematical model for velocity field of bath 193

6.3.2 The forces acting on molten slag 194

6.3.3 Solution algorithms and characters 196

6.4 Heat Transfer in the Molten Pool and Temperature Field Model of the Electric Smelting Furnace 198

6.4.1 Mathematical model of the temperature field in the molten pool 199

6.4.2 Simulation software 203

6.4.3 Calculation results and verification 203

6.4.4 Evaluation and optimization of the furnace design and operation 208

References 210

7 Coupling Simulation of Four-field in Flame Furnace 213

7.1 Introduction 213

7.2 Simulation and Optimization of Combustion Chamber of Tower-Type Zinc Distillation Furnace 215

7.2.1 Physical model 216

7.2.2 Mathematical model 217

7.2.3 Boundary conditions 217

7.2.4 Simulation of the combustion chamber prior to structure optimization 218

7.2.5 Structure simulation and optimization of combustion chamber 220

7.3 Four-field Coupling Simulation and Intensification of Smelting in Reaction Shaft of Flash Furnace 221

7.3.1 Mechanism of flash smelting process—particle fluctuating collision model 223

7.3.2 Physical model 224

7.3.3 Mathematical model—coupling computation of particle and gas phases 225

7.3.4 Simulation results and discussion 227

7.3.5 Enhancement of smelting intensity in flash furnace 229

References 232

8 Modeling of Dilute and Dense Phase in Generalized Fluidization 235

8.1 Introduction 235

8.2 Particle Size Distribution Models 238

8.2.1 Normal distribution model 239

8.2.2 Logarithmic probability distribution model 240

8.2.3 Weibull probability distribution function 241

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

8.2.5 Nukiyawa-Tanasawa distribution function 242

8.3 Dilute Phase Models 244

8.3.1 Non-slip model 245

8.3.2 Small slip model 247

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

8.3.4 Particle group trajectory model 251

8.3.5 Solution of the particle group trajectory model 256

8.4 Mathematical Models for Dense Phase 257

8.4.1 Two-phase simple bubble model 258

8.4.2 Bubbling bed model 259

8.4.3 Bubble assemblage model(BAM) 261

8.4.4 Bubble assemblage model for gas-solid reactions 265

8.4.5 Solid reaction rate model in dense phase 267

References 272

9 Multiple Modeling of the Single-ended Radiant Tubes 275

9.1 Introduction 275

9.1.1 The SER tubes and the investigation of SER tubes 276

9.1.2 The overall modeling strategy 278

9.2 3D Cold State Simulation of the SER Tube 279

9.3 2D Modeling of the SER Tube 283

9.3.1 Selecting the turbulence model 283

9.3.2 Selecting the combustion model 286

9.3.3 Results and analysis of the 2D simulation 289

9.4 One-dimensional Modeling of the SER Tube 291

References 295

10 Multi-objective Systematic Optimization of FKNME 297

10.1 Introduction 297

10.1.1 A historic review 297

10.1.2 The three principles for the FKNME systematic optimization 298

10.2 Objectives of the FKNME Systematic Optimization 299

10.2.1 Unit output functions 300

10.2.2 Quality control functions 305

10.2.3 Control function of service lifetime 306

10.2.4 Functions of energy consumption 308

10.2.5 Control functions of air pollution emissions 309

10.3 The General Methods of the Multi-purpose Synthetic Optimization 309

10.3.1 Optimization methods of artificial intelligence 309

10.3.2 Consistent target approach 312

10.3.3 The main target approach 314

10.3.4 The coordination curve approach 315

10.3.5 The partition layer solving approach 315

10.3.6 Fuzzy optimization of the multi targets 316

10.4 Technical Carriers of Furnace Integral Optimization 318

10.4.1 Optimum design CAD 319

10.4.2 Intelligent decision support system for furnace operation optimization 320

10.4.3 Online optimization system 327

10.4.4 Integrated system for monitoring,control and management 330

References 334

Index 337