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Multiple machine learning models for prediction of CO2 solubility in potassium and sodium based amino acid salt solutions

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Version 2 2024-06-06, 01:05
Version 1 2022-03-17, 21:13
journal contribution
posted on 2024-06-06, 01:05 authored by G Yin, FJI Alazzawi, D Bokov, HA Marhoon, AS El-Shafay, ML Rahman, CH Su, YZ Lu, Hoang Chinh NguyenHoang Chinh Nguyen
In this work, we developed artificial intelligence-based models for prediction and correlation of CO2 solubility in amino acid solutions for the purpose of CO2 capture. The models were used to correlate the process parameters to the CO2 loading in the solvent. Indeed, CO2 loading/solubility in the solvent was considered as the sole model’s output. The studied solvent in this work were potassium and sodium-based amino acid salt solutions. For the predictions, we tried three potential models, including Multi-layer Perceptron (MLP), Decision Tree (DT), and AdaBoost-DT. In order to discover the ideal hyperparameters for each model, we ran the method multiple times to find out the best model. R2 scores for all three models exceeded 0.9 after optimization confirming the great prediction capabilities for all models. AdaBoost-DT indicated the highest R2 Score of 0.998. With an R2 of 0.98, Decision Tree was the second most accurate one, followed by MLP with an R2 of 0.9.

History

Journal

Arabian Journal of Chemistry

Volume

15

Article number

ARTN 103608

Pagination

1-14

Location

Amsterdam, The Netherlands

ISSN

1878-5352

eISSN

1878-5379

Language

English

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

3

Publisher

Elsevier