A Modified Ensemble Framework Based on Extreme Learning Machines for Gas Utilization Ratio Prediction

Abstract

Gas utilization ratio (GUR) is considered as an important indicator to evaluate the operation status and energy consumption of blast furnace (BF). This paper proposes a modified ensemble framework based on extreme learning machines (ELM), named ME-ELM, to establish a prediction model of GUR. Due to the randomly generated hidden layer parameters, ELM may have variations in different trials of simulations. In order to improve the negative impacts of randomness and obtain better prediction accuracy, we investigate an adaptive ensemble framework via ELMs, which can adjust the ensemble weights automatically according to the contribution of the individual ELM model to the final results. Meanwhile, an attenuation factor is added to highlight the role of new coming data to enhance the dynamic tracking capability. To verity the performance of the proposed ME-ELM, experimental comparisons are carried out with the classic ELM and ensemble ELM (E-ELM) to predict GUR using the actual production data. Simulation results demonstrate that ME-ELM is more stable and accurate than ELM and E-ELM.