Prediction of CO ₂ mole fraction via CO ₂ CPU process using different machine learning algorithms

Abstract This research discusses the application of decision trees, Adaboosting, random forests, machines that support vectors, and k-nearest neighbor classifiers and gradient boosting in predicting CO 2 ’s mole fraction from flue gases of oxyfuel’s combustion emitted from the power plant. First of all, a training and test dataset was developed using the different variables. Then, a total of 491 simulations were performed, and the mole fraction of CO 2 was examined. Important features were detected by SHAP. Results showed that the RF algorithm enjoyed a great CO 2 mole fraction ability to predict and displayed the very best ability for generalization and most reliable prediction precision among all four, with an accuracy of 97 %. After that, LIME was used to explain the results of the RF algorithm. Out of the various variables studied, the pressure of the multistage compressor had the highest effect on the CO 2 mole fraction.