Thermal Engineering Processes Simulation Based on Artificial Intelligence

Abstract

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.