Prediction model of hot metal temperature for blast furnace based on improved multi-layer extreme learning machine

Abstract

In the blast furnace production site, the disposable thermocouple is used to measure the hot metal temperature. However, this method is not only inconvenient for continuous data acquisition but also costly for the use of one-time thermocouple. Hence, this paper establishes a prediction model to predict the hot metal temperature. Before the prediction model is established, the corresponding factors of influencing the hot metal temperature are selected, and the noises of production data are removed. In this paper, multi-layer extreme learning machine (ML-ELM) is used as the prediction algorithm of the prediction model. However, the input weights, hidden layer weights and hidden biases of ML-ELM are randomly selected, and the solution of the output weights is based on them, which makes ML-ELM inevitably have a set of non-optimal or unnecessary weights and biases. In addition, ML-ELM may suffer from over-fitting problem. Hence, this paper uses the adaptive particle swarm optimization (APSO) and the ensemble model to improve ML-ELM, and the improved algorithm is named as EAPSO-ML-ELM. APSO can optimize the selections of the input weights, hidden layer weights and hidden biases, the ensemble model can alleviate the over-fitting problem, i.e., this paper combines several of the optimized ML-ELMs which have different input weights, hidden layer weights and hidden biases. Finally, this paper also uses other algorithms to establish the prediction model, and simulation results demonstrate that the prediction model based on EAPSO-ML-ELM has better prediction accuracy and generalization performance.