Prediction of hot metal temperature in a blast furnace iron making process using multivariate data analysis and machine learning methodology

Abstract

The feed-forward back propagation neural (FFBPN) network method and multivariate data analysis are used to present a new approach for predicting the health of a blast furnace in the form of hot metal temperature (HMT), which is a crucial parameter to control the stable flow of hot metal production while avoiding major danger incidents during the ironmaking process. The health status also appears to predict the performance level of BF at a premature time, allowing the operator to take necessary steps to avoid BF deterioration. The BF’s health status designates the stability or instability of the BF, which may arise during the manufacturing process of hot molten iron, and is used to find the fault. In this paper, the health status of BF was determined with the help of a FFBPN and correlation matrix. This was done with Matlab (Version 2018Rb) software that uses data pre-processing, variable reduction, and a selective attribute of a data set. The FFBPN model has been trained, tested, and validated, and it has got 96% correlation coefficient of HMT prediction of combination of all data sets. The predicted HMT using several actual process data sets has been helpful in identifying the process irregularity in BF.