Optimization Control Of The Thermal Parameters In Pellet Production Process

With the incessant development of iron and steel industry, pellet has become an indispensable material for blast furnace and the grate-kiln production system has been widely used. It is not easy to grade the quality of pellet balling in the process of pellet manufacturing by the mathematical model, because the heating processes of pellet balling are complicated, serious couplings among the parameters of the processes and operations exist and many uncertainties are unavoidable. Therefore, how to find out the relations between the quality of pellet balling and the parameters of operation in order to grade its quality properly has become a challenging topic. Presently, most of pellet balling plants still manipulate the processes manually instead of automatic manipulation, which would induce many uncertainties and lead to poor quality of pellet balling. When the temperature changes, it is difficult to guarantee the quality of the pellet. It is obvious that the temperature control plays a key role in the palletizing production processes. In the aspects of the quality of pellet balling, there exists a strikingly difference at present between our country and foreign countries.This paper analyse the technology process of pellet production deeply firstly, and emphasis the dry、pre-heating、sintering、freezing system. Through process analysis to find the impact of various factors on pellet quality, and a detailed analysis of the thermal parameters in the pellet production process. Through BP neural network to establish the mathematical model of the thermal parameters. Based on the mathematical model of the thermal parameters, with thermal parameters for the decision variables, We establish a pellet thermal parameters optimization model to optimize the target of the pellet compressive strength. Based on the optimization model, we designed Genetic algorithm and get the optimal thermal parameters with MATLAB. As the thermal parameters are controled directly by the controlling the quantity of coal, based on CBR, this paper researched the method of case retrieve, case reuse, case revise, case review, case retain, and designed of the case-based reasoning algorithm, including the extraction and description of the new problems’feature, similar case retrieval, case reuse, case evaluation, new case retain and so on.Finally, through series of testing, the system is proven effective for functions of the pellet quality control, and valuable as reference for guide to the optimization of production process.