Knowledge Discovery in Large-Scale Batch Processes through Explainable Boosted Models and Uncertainty Quantification: Application to Rubber Mixing

Rubber mixing (RM) is a vital batch process producing high-quality composites, which serve as input material for manufacturing different types of final products, such as tires. Due to its complexity, this process faces two main challenges regarding the final quality: i) lack of online measurement and ii) limited comprehension of the influence of the different factors involved in the process. While data-driven and machine learning (ML) based soft-sensing methods have been widely applied to address the first challenge, the second challenge, to the best of the author's knowledge, has not yet been addressed in the rubber industry. This work presents a data-driven method for extracting knowledge and providing explainability in the quality prediction in RM processes. The method centers on an XGBoost model while leveraging high-dimensional data collected over extended time periods from one of Michelin�s complex mixing processes. First, a recursive feature elimination-based procedure is used for selecting relevant features, which reduces the number of input features used for building the ML model by 82% while improving its predictive performance by 17%. Secondly, SHapley Additive exPlanations (SHAP) techniques are employed to explain the ML model�s predictions through global and local analyses of feature interactions. The selected quality-related variables can be leveraged to improve process control and supervision. Finally, an uncertainty quantification (UQ) module, based on Split Conformal Prediction (SCP), is combined with the ML model, providing confidence intervals with 90% coverage and empirically verified theoretical guarantees. This module ensures prediction reliability and robustness in real applications.