Investigation of the Impact of Clinker Grinding Conditions on Energy Consumption and Ball Fineness Parameters Using Statistical and Machine Learning Approaches in a Bond Ball Mill

TL;DR

This study uses machine learning to predict energy consumption and fineness in cement clinker grinding, showing that support vector regression is the most accurate method.

Contribution

The study introduces machine learning models for optimizing cement grinding parameters, with a novel focus on support vector regression's superior performance.

Findings

Support vector regression (SVR) outperformed gradient boosting and ridge regression in predicting energy consumption and Blaine fineness.

Machine learning models effectively estimated grinding parameters with high R2 and low error metrics.

Feature selection methods like mutual info regression and lasso regression identified key variables for prediction.

Abstract

This study explores the application of machine learning (ML) techniques—gradient boosting (GB), ridge regression (RR), and support vector regression (SVR)—for estimating the consumption of energy (CE) and Blaine fineness (BF) in cement clinker grinding. This study utilizes key clinker grinding parameters, such as maximum ball size, ball filling ratio, clinker mass, rotation speed, and number of revolutions, as input features. Through comprehensive preprocessing, feature selection methods (mutual info regression (MIR), lasso regression (LR), and sequential backward selection (SBS)) were employed to identify the most significant variables for predicting CE and BF. The performance of the models was optimized using a grid search for hyperparameter tuning and validated using k-fold cross-validation (k = 10). The results show that all ML methods effectively estimated the target parameters, with SVR demonstrating superior accuracy in both CE and BF predictions, as evidenced by its higher R2 and lower error metrics (MAE, MAPE, and RMSE). This research highlights the potential of ML models in optimizing cement grinding processes, offering a novel approach to parameter estimation that can reduce experimental effort and enhance production efficiency. The findings underscore the advantages of SVR, making it the most reliable method for predicting energy consumption and Blaine fineness in clinker grinding.

Figures