Development Of A Kiln Petcoke Mill Predictive Model Based On A Multi-Regression Xgboost Algorithm

Abstract This paper presents an investigation into the optimization of Petroleum Coke Mill or Petcoke mill processes, with the goal of improving efficiency and reducing waste in the heavy industry within the cement plant where our study is conducted. Our mission was to create a robust algorithm that can properly anticipate the mill’s performance and improve its operations. To accomplish this, we started by performing a comprehensive data analysis. Next, we built numerous regression models, then assessed the effectiveness of each model using four crucial metrics. The suggested model is a multi-regression XGBoost (eXtreme Gradient Boosting) model, performing with a 90% score. Finally, the model will then be used to build an algorithm that can optimize the input values to accomplish the intended results.