Interpretable Data-Driven Prediction, Optimization, and Decision-Making for Coking Coal Flotation

Coking coal flotation is a typical nonlinear, multi-variable, and multi-objective process in which concentrate quality and combustible matter recovery must be balanced under fluctuating feed and operating conditions. To improve both predictive reliability and decision support, this study proposes an integrated data-driven framework that combines particle swarm optimization-back propagation (PSO-BP) prediction, SHapley Additive exPlanations (SHAP) based interpretation, Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization, and entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (Entropy-TOPSIS) decision-making. After three-sigma outlier screening, 2000 valid distributed control system (DCS) samples were retained for model development and temporal holdout evaluation, and an additional 200 later-period industrial samples were used for independent validation. The data were partitioned chronologically, with months 1–4, month 5, and month 6 used for training, validation, and temporal holdout testing, respectively, while the months 7–8 dataset was reserved for later-period validation. The results show that PSO-BP consistently outperformed conventional BP under both temporal holdout and later-period validation. SHAP analysis identified raw coal ash and collector dosage as the dominant factors for product-quality prediction, while collector dosage and frother dosage contributed most strongly to tailing heat of combustion. NSGA-II further revealed the trade-off among clean coal ash, clean coal sulfur, and tailing heat of combustion, and Entropy-TOPSIS converted the Pareto-optimal candidate set into a practically balanced operating recommendation. Sensitivity and robustness analyses indicated acceptable stability of both the optimization process and the final decision result. Overall, the proposed framework provides an interpretable prediction–optimization–decision workflow for coking coal flotation and offers a practical basis for future DCS-assisted intelligent regulation.