Моделирование процесса выплавки стали в металлургическом производстве

Abstract

The analysis of features of modeling the process of steelmaking, concluded that the most appropriate approach to the construction of models depending on the hardness of steel from its chemical composition are statistical modeling techniques in combination with the methods of fuzzy logic, and control of the process of steelmaking (choice of chemical composition) may be represented as either a solution of systems of nonlinear equations, or as a model of optimal choice. The study developed a model depending on the depth distribution of the hardness of steel from its chemical composition in the form of fuzzy production rules (model Tokagi-Sugeno-Kang TSK), which will determine the predictive value of the distribution of hardness of steel as the weighted average yields of complex linear regression models and eliminate the problem of choice most appropriate regression model. This model TSK planned to further explore its value as part of the requirements for the accuracy of determining the distribution of the hardness of steel, set individual customer specifications on smelted products.