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 e ciency 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.