Intelligent regulation of blast furnace molten iron quality integrating prediction-evaluation and optimal decision-making

To address the key challenges in blast furnace ironmaking, such as the strong coupling among multiple molten iron quality variables and significant nonlinearity, this paper constructs an intelligent whole-process control method that integrates prediction, evaluation, and optimisation decision-making. Based on actual blast furnace production data, a feature selection framework that accounts for both nonlinear correlations and causal mechanisms is established by combining Hilbert-Schmidt Independence Criterion (HSIC)-Lasso and the Peter-Clark Momentary Conditional Independence (PCMCI) algorithm, effectively eliminating spurious feature associations. Using the selected features, a TimeMixer multi-scale time-series prediction model is constructed to achieve high-precision prediction of four core quality indicators: molten iron temperature (MIT), molten iron silicon content ([Si]), molten iron phosphorus content ([P]), and molten iron sulphur content ([S]). The root mean square errors are reduced by more than 12.5% on average compared with traditional time-series models. On this basis, the prediction results are used to build an improved TOPSIS evaluation model that integrates game theory with subjective and objective combined weighting, and K-Means++ clustering is applied to achieve quantitative characterisation and four-level grading of molten iron quality. Finally, using the evaluation results as decision criteria, a Bayesian optimisation control model is further constructed to efficiently search for optimal control parameters under the dual constraints of process hard limits and operational change rates. Industrial application and validation results show that the qualification rates of the core targets MIT and [Si] are significantly improved from 71% and 75% to 85% and 89%, respectively; the comprehensive evaluation score of molten iron quality increases from 0.768 to 0.864; and the quality grade is upgraded from ‘Good’ to ‘Excellent’. This method not only greatly enhances the ability to meet key quality indicators but also provides an innovative integrated solution for intelligent control of the blast furnace ironmaking process.