| Summary: | This study uses machine learning methods to model different stages of the calcination process in cement, with the goal of improving knowledge of the generation of CO<sub>2</sub> during cement manufacturing. Calcination is necessary to determine the clinker quality, energy needs, and CO<sub>2</sub> emissions in a cement-producing facility. Due to the intricacy of the calcination process, it has historically been challenging to precisely anticipate the CO<sub>2</sub> produced. The purpose of this study is to determine a direct association between CO<sub>2</sub> generation from the manufacture of raw materials and the process factors. In this paper, six machine learning techniques are investigated to explore two output variables: (1) the apparent degree of oxidation, and (2) the apparent degree of calcination. CO<sub>2</sub> molecular composition (dry basis) sensitivity analysis uses over 6000 historical manufacturing health data points as input variables, and the results are used to train the algorithms. The Root Mean Squared Error (RMSE) of various regression models is examined, and the models are then run to ascertain which independent variables in cement manufacturing had the largest impact on the dependent variables. To establish which independent variable has the biggest impact on CO<sub>2</sub> emissions, the significance of the other factors is also assessed.
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