DEVELOPMENT OF SUITABLE MACHINE LEARNING MODEL FOR A CEMENT PLANT CALCINER

Abstract

Cement industry is one of the largest CO2 emitters and continuously working to minimize these emissions. Use of Artificial intelligence (AI) in manufacturing helps in reducing breakdowns/ failures by avoiding frequency of startups, reducing fuel fluctuations which will ultimately reduce carbon footprint. AI offers a new mode of digital manufacturing process that can increase productivity by optimizing the use of assets at the fraction of cost. Calciner is one of the key equipment of a cement plant which dissociates the calcium carbonate into calcium oxide and carbon dioxide by taking heat input from fuel combustion. An AI model of a calciner can provide valuable information which can be implemented in real time to optimize the calciner operation resulting in fuel savings. In this study, key process parameters of continuous operation for a period of 3 months were used to train the various machine learning models and a best suitable model was selected based on metrics like RMSE and R2 value. It is found that Artificial neural network is best fitted model for the calciner. This model is able to predict the calciner outlet temperature with high degree of accuracy (+/- 2% error) when validated against real world data. This model can be used by industries to estimate the calciner outlet temperature by changing the input parameters as it is not based on the chemical and physical process taking place in the calciner but on real world historical data.