ArticleScientific reports2025
GA BP prediction model for energy consumption of steel rolling reheating furnace.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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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.
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