Toward Sustainable Paper Manufacturing: An Interpretable Data-Driven Strategy for Energy-Efficient Drying

The papermaking process is typically energy-intensive, with high carbon emissions, facing significant pressure for energy conservation and emission reduction. The paper drying process is the most energy-consuming stage in the papermaking process, and improving its energy efficiency is crucial for achieving energy-saving and emission-reduction effects in the papermaking industry. To address these challenges, four machine learning algorithms─Random Forest, Support Vector Machine, CatBoost, and Stacking─are employed to tackle the regression problem of predicting steam flow and the classification problem of multilevel energy efficiency states in the drying section. Additionally, the SHAP method is utilized to enhance the interpretability of machine learning models. The results demonstrate that machine learning models achieve an excellent predictive performance. Specifically, the CatBoost algorithm establishes robust accuracy in both steam flow prediction (R2 = 0.874) and energy efficiency classification (accuracy = 0.9646). Meanwhile, the Stacking algorithm also shows superior performance in both steam flow prediction (R2 = 0.873) and energy efficiency classification (accuracy = 0.9638). More importantly, through SHAP-based feature optimization on low energy efficiency samples, significant energy savings are achieved: every sample achieved an energy consumption reduction of at least 2.4%, with 39% of the samples showing energy consumption reductions exceeding 3%, with an average energy consumption reduction of 2.93% across all samples. SHAP analysis further identifies key operational parameters including winding car speed, end section inlet air flow, front section inlet air flow, basic weight, and steam pressure as critical factors for energy optimization. This data-driven, interpretable machine learning approach not only effectively predicts energy efficiency in paper drying processes but also provides substantial energy-saving potential, offering valuable insights for paper companies to optimize operations and advance the industry’s green transformation.