Endpoint prediction model for basic oxygen furnace steel-making based on membrane algorithm evolving extreme learning machine

Abstract

The endpoint parameters of molten steel, such as the steel temperature and the carbon content, directly affect the quality of the production steel. Moreover, these endpoint results cannot be the online continuous measurement in time. To solve the above-mentioned problems, an anti-jamming endpoint prediction model is proposed to predict the endpoint parameters of molten steel. More specifically, the model is constructed on the parameters of extreme learning machine (ELM) adaptively adjusted by the evolutionary membrane algorithm with the global optimization ability. In other words, the evolutionary membrane algorithm may find the suitable parameters of an ELM model which reduces the incidence of the overfitting of ELM affected by the noise in the actual data. Finally, the proposed model is applied to predict the endpoint parameters of molten steel in steel-making. In the simulation experiments, two test problems, including ‘SinC’ function with the Gaussian noise and the actual production data of basic oxygen furnace (BOF) steel-making, are employed to evaluate the performance of the proposed model. The results indicate that the proposed model has good prediction accuracy and robustness in the data with noise. Therefore, the proposed model has good application prospects in the industrial field.