GA BP prediction model for energy consumption of steel rolling reheating furnace

Abstract

Energy consumption serves as a critical indicator of energy utilization efficiency and environmental sustainability in the steel production process. Accurately predicting the Heat energy consumption per ton (HEC, GJ/t) of steel billet in Steel Rolling Reheating Furnace (SRRF) presents a formidable challenge owing to the complex interplay of factors such as production scheduling, raw material characteristics, process parameters, and equipment condition. This study proposes a novel approach to predict HEC (GJ/t) by utilizing actual production data from SRRF. A genetic algorithm (GA) optimized back-propagation neural network (BPNN) is developed and its performance is compared to that of a standard BP model. Experimental results reveal that the optimized GA-BP model, with a neural network structure of 17-10-1, achieves a prediction accuracy of 94.7% surpassing the 90.24% accuracy of the standard BP model. The proposed GA-BP model demonstrates superior predictive capabilities and robustness, offering valuable insights for optimizing process parameters and improving energy efficiency in SRRF operations.