Simulation and Optimization of Furnaces and Kilns for Nonferrous Metallurgical Engineering

The furnaces and kilns for nonferrous metallurgical engineering (FKNME) are a big family. There were few reports on systematic research of FKNME, especially the accurate and quantitative analysis. In this chapter ideas and analysis methods are introduced for comprehensive and in-depth understanding of the FKNME system.

The fluid (including molten mass) flow, heat transfer and combustion processes in the FKNME are collectively called as the FKNME thermal processes. The modeling of these processes is the foundation to simulate the FKNME as well as the key element. In this chapter we discuss the theories and applications of the modeling of the flow filed, temperature field, species concentration field and electro-magnetic field that are usually involved in the FKNME.

In this chapter, the concept “hologram simulation” is introduced and elaborated. The multi-field coupling and equation-solving techniques are also discussed as application examples of the hologram simulation strategy.

Because of the complexity of nonferrous metallurgical processes, it is difficult to build accurate mechanistic models for them. While, artificial intelligence(AI) modeling method avoids the complex mechanism analysis and describes the object process by its historic data, therefore, it is very advantageous especially for complex industrial process in which historic process data have been accumulated plentifully. In this chapter, several important AI methods and their applications are introduced, based on these, two AI modeling methods for multi-variable systems are proposed: one is fuzzy adaptive modeling method, which has been applied to develop the fuzzy adaptive optimal decision model of the submerged arc furnace; another is fuzzy neural network adaptive modeling method, which has been applied to develop fuzzy neural network adaptive optimal decision model of the electric furnace for cleaning slag. Both of the models are self-learning and self-adaptive, and are able to avoid the disadvantage of the static decision-making model based on the calculation of material balance and thermal balance in a smelting process. They have achieved good performance in practice.

The “hologram simulation” concept is put forward to replace the traditional “Three-fields” (including magnetic field, thermal field and force field) notion in simulating aluminum reduction cells. The core of the hologram simulation is to simulate microstructures of the cells’ parameters distribution under various structural and operating conditions. In hologram simulation, the effect of various input information can no longer be treated as single or independent variables, because of the interacting they have with each other. The hologram simulation approach can be used for both static and dynamic analysis of the cells, and it as more suitable to describe the detail working of the cells. In this chapter, the models for several main physical fields including the current field, magnetic field, thermal field, and the molten metal flow field are respectively discussed; moreover, the dynamic simulation method and several calculation models of the current efficiency for aluminum reduction cells also are introduced.

In this Chapter, the simulation models, including sintering process model of self-baking electrode, flow field model and temperature field model in molten pool of electric smelting furnace are built, and the cases in practice are employed to optimize them. Eventually, the computational software is developed to solve the physical fields and production condition of the electric furnace is improved.

A tower-type zinc distillation furnace and a copper flash smelting furnace are taken as the typical examples of single-phase combustion and two-phase combustion, respectively. The applications of the coupling multi-field numerical simulation are also described.

The gas-particle two-phase flow has been widely and intensively applied and investigated in recent decades. It is very difficult to describe such a non-linear and stochastic industrial system using accurate mathematical language. In this chapter, the principles and its applications are introduced and discussed regarding the modeling of the gas-particle two-phase flow.

In this chapter the modeling of a gas-fired single-ended radiant (SER) tube is presented in full detail. The physical processes and models widely include turbulence, flow relaminarization, partially premixed combustion, radiation heat transfer, near-wall phenomena, dimension reduction modeling etc. The focus of this chapter, however, is not the SER tube modeling but the demonstration of the methodology, modeling effect assessment and problem-solving techniques that can be widely applied in numerical simulation. This chapter also demonstrates a few possible means to simplify or decompose a complex modeling problem into a number of smaller but easier modeling tasks as well as possible means to make the best use of the available computational resources for achieving optimized modeling results.

As most engineers may agree, it is not very difficult to improve the performance of an operating furnace or kiln by some means. The difficulties lie in finding the most effective strategy to optimize the system with a well-developed practical approach and bringing the ideas into practice. There fore, theoretically sound methodologies supported by necessary analyzing tools are needed. In this chapter, the optimization methodology and the objective functions are discussed for the FKNME optimization.