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1 introduction
1.1 classification of the furnaces and kilns for nonferrous metallurgical engineering (fknme)
1.2 the thermophysical processes and thermal systems of the fknme.
1.3 a review of the methodologies for designs and investigations of fknme
1.3.1 methodologies for design and investigation of fknme
1.3.2 the characteristics of the mhso method
1.4 models and modeling for the fknme
1.4.1 models for the modem fknme
1.4.2 the modeling process
references
2 modeling of the thermophysical processes in fknme
2.1 modeling of the fluid flow in the fknme
2.1.l introduction
2.1.2 the reynolds-averaging and the favre-averaging methods
2.1.3 turbulence models
2.1.4 low reynolds number k-e models
2.1.5 re-normalization group (rng) k-e models
2.1.6 reynolds stresses model(rsm)
2.2 the modeling of the heat transfer in fknme
2.2.1 characteristics of heat transfer inside furnaces
2.2.2 zone method
2.2.3 monte carlo method
2.2.4 discrete transfer radiation model
2.3 the simulation of combustion and concentration field
2.3.1 basic equations of fluid dynamics including chemical reactions..
2.3.2 gaseous combustion models
2.3.3 droplet and particle combustion models
2.3.4 nox models
2.4 simulation of magnetic field
2.4.1 physical models
2.4.2 mathematical model of current field
2.4.3 mathematical models of magnetic field in conductive elements..
2.4.4 magnetic field models of ferromagnetic elements
2.4.5 three-dimensional mathematical model of magnetic field
2.5 simulation on melt flow and velocity distribution in smelting furnaces
2.5.1 mathematical model for the melt flow in smelting furnace
2.5.2 electromagnetic flow
2.5.3 the melt motion resulting from jet-flow
references
3 hologram simulation of the fknme
3.1 concept and characteristics of hologram simulation
3.2 mathematical models of hologram simulation
3.3 applying hologram simulation to multi-field coupling
3.3.1 classification of multi-field coupling
3.3.2 an example of intra-phase three-field coupling
3.3.3 an example of four-field coupling
3.4 solutions of hologram simulation models
references
4 thermal engineering processes simulation based on artificial intelligence
4.1 characteristics of thermal engineering processes in nonferrous metallurgical furnaces
4.2 introduction to artificial intelligence methods
4.2.1 expert system
4.2.2 fuzzy simulation
4.2.3 artificial neural network
4.3 modeling based on intelligent fuzzy analysis
4.3.1 intelligent fuzzy self-adaptive modeling of multi-variable system
4.3.2 example: fuzzy adaptive decision-making model for nickel matte smelting process in submerged arc furnace
4.4 modeling based on fuzzy neural network analysis
4.4.1 fuzzy neural network adaptive modeling methods of multi-variable system
4.4.2 example: fuzzy neural network adaptive decision-making model for production process in slag cleaning furnace
references
5 hologram simulation of aluminum reduction cells
5.1 introduction
5.2 computation and analysis of the electric field and magnetic field..
5.2.1 computation model of electric current in the bus bar
5.2.2 computational model of electric current in the anode
5.2.3 computation and analysis of electric field in the melt
5.2.4 computation and analysis of electric field in the cathode
5.2.5 computation and analysis of the magnetic field
5.3 computation and analysis of the melt flow field
5.3.1 electromagnetic force in the melt
5.3.2 analysis of the molten aluminum movement
5.3.3 analysis of the electrolyte movement
5.3.4 computation of the melt velocity field
5.4 analysis of thermal field in aluminum reduction cells
5.4.1 control equations and boundary conditions
5.4.2 calculation methods
5.5 dynamic simulation for aluminum reduction cells
5.5.1 factors influencing operation conditions and principle of the dynamic simulation
5.5.2 models and algorithm
5.5.3 technical scheme of the dynamic simulation and function of the software system
5.6 model of current efficiency of aluminum reduction cells
5.6.1 factors influencing current efficiency and its measurements
5.6.2 models of the current efficiency
references
6 simulation and optimization of electric smelting furnace
6.1 introduction
6.2 sintering process model of self-baking electrode in electric smelting furnace
6.2.1 electric and thermal analytical model of the electrode
6.2.2 simulation software
6.2.3 analysis of the computational result and the baking process
6.2.4 optimization of self-baking electrode configuration and operation regime
6.3 modeling of bath flow in electric smelting furnace
6.3.1 mathematical model for velocity field of bath
6.3.2 the forces acting on molten slag
6.3.3 solution algorithms and characters
6.4 heat transfer in the molten pool and temperature field model of the electric smelting furnace
6.4.1 mathematical model of the temperature field in the molten pool
6.4.2 simulation software
6.4.3 calculation results and verification
6.4.4 evaluation and optimization of the furnace design and operation
references
7 coupling simulation of four-fleld in flame furnace
7.1 introduction
7.2 simulation and optimization of combustion chamber of tower-type zinc distillation furnace
7.2.1 physical model
7.2.2 mathematical model
7.2.3 boundary conditions
7.2.4 simulation of the combustion chamber prior to structure optimization
7.2.5 structure simulation and optimization of combustion chamber
7.3 four-field coupling simulation and intensification of smelting in reaction shaft of flash furnace
7.3.1 mechanism of flash smelting process--particle fluctuating collision model
7.3.2 physical model
7.3.3 mathematical model----coupling computation of particle and gas phases
7.3.4 simulation results and discussion
7.3.5 enhancement of smelting intensity in flash furnace
references
8 modeling of dilute and dense phase in generalized fluidization
8.1 introduction
8.2 particle size distribution models
8.2.1 normal distribution model
8.2.2 logarithmic probability distribution model
8.2.3 weibull probability distribution function
8.2.4 r-r distribution function (rosin-rammler distribution)
8.2.5 nukiyawa-tanasawa distribution function
8.3 dilute phase models
8.3.1 non-slip model
8.3.2 small slip model
8.3.3 multi-fluid model (or two-fluid model)
8.3.4 particle group trajectory model
8.3.5 solution of the particle group trajectory model
8.4 mathematical models for dense phase
8.4.1 two-phase simple bubble model
8.4.2 bubbling bed model
8.4.3 bubble assemblage model (bam)
8.4.4 bubble assemblage model for gas-solid reactions
8.4.5 solid reaction rate model in dense phase
references
9 multiple modeling of the single-ended radiant tubes
9.1 introduction
9.1.1 the ser tubes and the investigation of ser tubes
9.1.2 the overall modeling strategy
9.2 3d cold state simulation of the ser tube
9.3 2d modeling of the ser tube
9.3.1 selecting the turbulence model
9.3.2 selecting the combustion model
9.3.3 results and analysis of the 2d simulation
9.4 one-dimensional modeling of the ser tube
references
10 multi-objective systematic optimization of fknme
10.1 introduction
10.1.1 a historic review
10.1.2 the three principles for the fknme systematic optimization
10.2 objectives of the fknme systematic optimization
10.2.1 unit output functions
10.2.2 quality control functions
10.2.3 control function of service lifetime
10.2.4 functions of energy consumption
10.2.5 control functions of air pollution emissions
10.3 the general methods of the multi-purpose synthetic optimization
10.3.1 optimization methods of artificial intelligence
10.3.2 consistent target approach
10.3.3 the main target approach
10.3.4 the coordination curve approach
10.3.5 the partition layer solving approach
10.3.6 fuzzy optimization of the multi targets
10.4 technical carriers of furnace integral optimization
10.4.1 optimum design cad
10.4.2 intelligent decision support system for furnace operation optimization
10.4.3 online optimization system
10.4.4 integrated system for monitoring, control and management
references
index