Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents

Iman Salahshoori; Alireza Baghban; Amirhosein Yazdanbakhsh

doi:10.1039/d3ra05360a

Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents

RSC Adv. 2023 Oct 13;13(43):30071-30085. doi: 10.1039/d3ra05360a. eCollection 2023 Oct 11.

Authors

Iman Salahshoori^{1

2

3}, Alireza Baghban⁴, Amirhosein Yazdanbakhsh⁵

Affiliations

¹ Discipline of Chemical Engineering, School of Engineering, University of KwaZulu-Natal, Howard College Campus King George V Avenue Durban 4041 South Africa.
² Department of Polymer Processing, Iran Polymer and Petrochemical Institute P.O. Box 14965-115 Tehran Iran.
³ Department of Chemical Engineering, Science and Research Branch, Islamic Azad University Tehran Iran.
⁴ Department of Process Engineering, NISOC Company Ahvaz Iran Alireza_baghban@ut.ac.ir.
⁵ Research and Development Manager -Teb Plastic Company- Tehran Tehran Iran.

Abstract

In recent years, deep eutectic solvents (DESs) have garnered considerable attention for their potential in carbon capture and utilization processes. Predicting the carbon dioxide (CO₂) solubility in DES is crucial for optimizing these solvent systems and advancing their application in sustainable technologies. In this study, we presented an evolving hybrid Quantitative Structure-Property Relationship and Gaussian Process Regression (QSPR-GPR) model that enables accurate predictions of CO₂ solubility in various DESs. The QSPR-GPR model combined the strengths of both approaches, leveraging molecular descriptors and structural features of DES components to establish a robust and adaptable predictive framework. Through a systematic evolution process, we iteratively refined the model, enhancing its performance and generalization capacity. By incorporating experimental CO₂ solubility data in varied DES compositions and temperatures, we trained the model to capture the intricate solubility behaviour precisely. The analytical capability of the evolving hybrid model was validated against an extensive dataset of experimental CO₂ solubility values, demonstrating its superiority over individual QSPR and GPR models. The model achieves high accuracy, capturing the complex interactions between CO₂ and DES components under varying thermodynamic conditions. The versatility of the evolving hybrid model was highlighted by its ability to accommodate new experimental data and adapt to different DES compositions and temperatures. The proposed QSPR-GPR model presented a powerful tool for predicting CO₂ solubility in DES, providing valuable insights for designing and optimizing solvent systems in carbon capture technologies. The model's remarkable performance enhances our understanding of CO₂ solubility mechanisms and contributes to sustainable solutions for mitigating greenhouse gas emissions. As research in DESs progresses, the evolving hybrid QSPR-GPR model offers a versatile and accurate means for predicting CO₂ solubility, supporting advancements in carbon capture and utilization processes towards a greener and more sustainable future.