Prediction of specific energy consumption in electric arc furnaces: A data-centric multi-stage collaborative filtering framework

Abstract

In the context of electric arc furnace (EAF) steelmaking, a typical process industry scenario, the conventional model-centric artificial intelligence (MCAI) paradigm faces a critical bottleneck caused by low-quality raw industrial data. Specifically, the low quality of raw industrial data results in prediction models for specific energy consumption (SEC, i.e. energy consumption per ton of steel) exhibiting both low accuracy and high volatility (i.e. poor robustness). To address this challenge, this study embraces the data-centric artificial intelligence (DCAI) paradigm and proposes an innovative four-stage multi-stage collaborative filtering (MCF) framework. This framework serves as a practical implementation of the DCAI philosophy, systematically refining the EAF SEC dataset to construct a ‘golden dataset’ through four progressive and synergistic stages: preliminary feature pruning, global instance denoising, collaborative feature selection, and boundary instance condensation (removal of ambiguous samples near decision boundaries). A core design principle of the framework is its adherence to a collaborative logic: stabilising the feature space prior to refining the instance distribution. Rigorous evaluation based on the resultant ‘golden dataset’ demonstrates that the MCF framework not only significantly enhances model prediction accuracy, as evidenced by metrics such as the hit rate under critical error tolerances and root mean square error (RMSE), but also substantially reduces the volatility of this accuracy, measured by the standard deviation (std.) of these metrics. Consequently, industrial-grade robustness is achieved. A decisive finding of this study is that a fundamental Lasso model, when applied to the ‘golden dataset’, comprehensively surpasses the overall performance (encompassing both accuracy and robustness) of a complex, hyperparameter-tuned XGBoost model trained on the original, unrefined data. This research demonstrates that the MCF framework, by concurrently improving prediction accuracy and ensuring prediction stability, offers an effective technical solution for implementing the DCAI paradigm in process industries such as EAF steelmaking. Furthermore, it strategically validates that investing in data quality is a higher-leverage, high-value pathway than complex model tuning.