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Prediction of CO2 mole fraction via CO2CPU process using different machine learning algorithms

  • Somayeh Mansouri , Farhad Shahraki EMAIL logo , Jafar Sadeghi , Esmaeil Koohestanian and Mohammad Reza Sardashti Birjandi
Published/Copyright: August 14, 2025
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Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling

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 CO2’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 CO2 was examined. Important features were detected by SHAP. Results showed that the RF algorithm enjoyed a great CO2 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 CO2 mole fraction.

Keywords: SHAP; SVM; CPU; LIME; CO2
Corresponding author: Farhad Shahraki, Department of Chemical Engineering, University of Sistan and Baluchestan, P.O. Box 98164-161, Zahedan, Iran, E-mail:

Nomenclature

Chemical compounds

CO2

Carbon dioxide

Abbreviations

AB

AdaBoost algorithm

CCS

CO2 capture and store

CPU

Compression and purification unit

DT

Decision trees

GB

Gradient Boosting

KNN

K-Nearest Neighbor

LIME

Local interpretable model-agnostic explanations

NG

Natural gas

SHAP

Shapley additive explanations

RF

Random Forest

SVM

Support Vector Machines

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-03-04
Accepted: 2025-07-19
Published Online: 2025-08-14

© 2025 Walter de Gruyter GmbH, Berlin/Boston

https://doi.org/10.1515/cppm-2025-0049
Keywords for this article
SHAP; SVM; CPU; LIME; CO2
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