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<p>This study examines cement production optimisation through Machine Learning models (ML). In this analysis, input variables are considered as the manipulating variables, such as feed and sepax power, while folaphone and elevator power are considered as the controlled variables. SHapley Additive exPlanations (SHAP) analysis and Local Interpretable Model-agnostic Explanations (LIME) for linear and tree-based models confirmed folaphone as the more significant outcome than elevator power. Models such as Regression Trees (RT), Ensemble Bagged Trees (EBT), Random Forest (RF), Coarse Tree (CT), Boosted Trees (BT), and Support Vector Regression (SVR) are trained on pre-processed data. Their performances are compared in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The BT and RT models yielded the highest accuracy for predicting folaphone complete data, with R² values of 0.9972 and 0.9963, respectively. Additionally, the RT model achieved the best accuracy in predicting elevator power with full data, yielding an R² value of 0.9877. This study identifies that folaphone is employed as a main controlled variable since fineness determination in the online context is challenging. The outcomes are a robust, data-driven optimisation framework that can be augmented with hybrid models for extension.</p>

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Optimising cement grinding with explainable AI and interpretable ML models

  • 期刊论文

  • 发表日期：

    2025年8月12日

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  • B. Madhavan,
  • B. Raghavan,
  • S. Venkatesh,
  • Guruprasath Muralidharan,
  • Rengarajan Amirtharajan

摘要

This study examines cement production optimisation through Machine Learning models (ML). In this analysis, input variables are considered as the manipulating variables, such as feed and sepax power, while folaphone and elevator power are considered as the controlled variables. SHapley Additive exPlanations (SHAP) analysis and Local Interpretable Model-agnostic Explanations (LIME) for linear and tree-based models confirmed folaphone as the more significant outcome than elevator power. Models such as Regression Trees (RT), Ensemble Bagged Trees (EBT), Random Forest (RF), Coarse Tree (CT), Boosted Trees (BT), and Support Vector Regression (SVR) are trained on pre-processed data. Their performances are compared in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The BT and RT models yielded the highest accuracy for predicting folaphone complete data, with R² values of 0.9972 and 0.9963, respectively. Additionally, the RT model achieved the best accuracy in predicting elevator power with full data, yielding an R² value of 0.9877. This study identifies that folaphone is employed as a main controlled variable since fineness determination in the online context is challenging. The outcomes are a robust, data-driven optimisation framework that can be augmented with hybrid models for extension.