Prediction of electrical energy consumption of CONARC® furnace using machine learning techniques. (19th June 2023)

• Record Type: Journal Article
• Authors: Kota, Parul; Samiraj, Albin Rozario; Hazra, Sujoy S.
• Is Part Of: International journal of simulation and process modelling. Volume 19:Number 3/4(2023)
• Page Start: 193
• Page End: 203
• Publication Date: 2023-06-19
• Subjects: CONARC® -- steel making -- electrical energy -- statistical modelling -- machine learning; Management -- Computer simulation -- Periodicals; Mathematical models -- Periodicals; Operations research -- Periodicals; Simulation methods -- Periodicals; 003.05
• Languages: English
• ISSNs: 1740-2123

Abstract

Due to the complex nature and vast number of variables involved in the CONARC ® steelmaking process, predicting the electrical energy (EE) consumption is a non-trivial task. Although many machine learning (ML) techniques have been utilised to predict EE consumption in electric arc furnace (EAF), not much attention has been focused on predicting EE of CONARC ® furnace that uses a variety of raw material in different proportions. In the present work, EE is predicted using various ML algorithms after identifying the relevant variables through a physiochemical model. Random forest (RF) resulted in the best accuracy for the prediction with mean squared error (MSE) of 0.39 having 100 numbers of trees. RF and support vector machine (SVM) were further tuned using boosting method XGBoost (MSE = 0.006) and grid search (0.034) respectively to improve the prediction accuracy.