IDENTIFICATION OF POTENTIAL CAUSES OF CLOGGING AND DEVELOPMENT OF A MACHINE LEARNING-BASED PREDICTION MODEL

The purpose of this article is to provide a solution for cement mill clogging. Our research is grounded in a methodical approach, whereby we have developed a deeper understanding of the cement grinding process to produce reliable theories. Then, by evaluating factors such clinker feed fluctuation and grindability problems, we conducted a number of comprehensive studies to support or contradict these theories. By creating a thorough RCA model, we were able to determine the underlying reasons of clogs thanks to the outcomes of these experiments. Simultaneously, using the data gathered throughout our investigation, we investigated the application of machine learning to forecast the phenomena of tampering.