Data-driven prediction model for adjusting burden distribution matrix of blast furnace based on improved multilayer extreme learning machine

Abstract

Reasonable burden distribution matrix is one of important requirements that can realize low consumption, high efficiency, high quality and long campaign life of the blast furnace. This paper proposes a data-driven prediction model of adjusting the burden distribution matrix based on the improved multilayer extreme learning machine (ML-ELM) algorithm. The improved ML-ELM algorithm is based on our previously modified ML-ELM algorithm (named as PLS-ML-ELM) and the ensemble model. It is named as EPLS-ML-ELM. The PLS-ML-ELM algorithm uses the partial least square (PLS) method to improve the algebraic property of the last hidden layer output matrix for the ML-ELM algorithm. However, the PLS-ML-ELM algorithm may have different results in different trails of simulations. The ensemble model can overcome this problem. Moreover, it can improve the generalization performance. Hence, the EPLS-ML-ELM algorithm is consisted of several PLS-ML-ELMs. The real blast furnace data are used to testify the data-driven prediction model. Compared with other prediction models which are based on the SVM algorithm, the ELM algorithm, the ML-ELM algorithm and the PLS-ML-ELM algorithm, the simulation results demonstrate that the data-driven prediction model based on the EPLS-ML-ELM algorithm has better prediction accuracy and generalization performance.