Prediction Model of Blast Furnace Gas Flow Distribution Based on GWO-ELM

摘要

The distribution of blast furnace gas flow has an important influence on the use of chemical and thermal energy. The temperature of the cross measuring points of blast furnace can directly reflect the distribution of gas flow. In order to predict the temperature of the cross measuring points, a prediction model based on GWO-ELM is established in this paper. The bias of the hidden layer and the weights between the input layer and hidden layer of extreme learning machine (ELM) are generated randomly, which results in a decrease in the accuracy of the model. To solve this problem, Grey Wolf Optimizer (GWO) with good global optimization capability is utilized to optimize the ELM prediction model. The experiment results show that this method improves the accuracy of temperature prediction model and the operation of blast furnace might be well guided by this prediction model.