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An explainable data-driven framework for energy-efficiency optimization in paper drying
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DOI：10.1016/j.rineng.2026.112396.png)
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Abstract
En 中文
• A hybrid T-PLS–MLP framework optimizes paper drying energy efficiency. • SHAP diagnostics identify root causes of suboptimal energy states. • Prediction RMSE is reduced by 70.27% compared to the T-PLS baseline. • Parameter tuning reduced average steam use per ton by 1.37% in plant.
Keywords:
Papermaking drying
Energy-efficiency optimization
Suboptimal-condition root-cause analysis
Industrial data mining
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