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Prediction of carbon-dioxide activity coefficient for solubility in ionic liquids using multi-non-linear regression analysis.

  • Sami ullah Rather
  • , Azmi M. Shariff
  • , Aliyu Adebayo Sulaimon*
  • , Hisham S. Bamufleh
  • , Ali Qasim
  • , Muhammad Saad Khan
  • , Hesham Alhumade
  • , Usman Saeed
  • , Walid M. Alalayah
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Activity coefficient values offer insight into the intermolecular interactions between the solute and the solvent and the deviation from the ideal behavior. CO2 capture from different industrial processes is a globally pertinent issue and the search for suitable chemicals is required. To address the issue, knowledge of activity coefficient values is crucial for CO2 separation-based process. In this regard, a correlation is developed that predicts the coefficient of CO2 activity in ionic liquids by multi-nonlinear regression analysis. The correlation is developed between the pressure range of 1–50 bar and the temperature range of 298.15–33.15 K for mole fractions of 0.3, 0.5, and 0.7. Outliers’ analysis is performed using the boxplot method to determine the suitability of ranges of the selected input parameters. The preceding literature does not predict the activity coefficient in relatively lower to higher temperature and pressure ranges for CO2 solubility in ionic liquids. Initially, the activity coefficient values from COSMO-RS were obtained and compared with the correlation results. The COSMO-RS and the correlation predicted results were subsequently validated with the experimental data. The average absolute error (AAE%) of the predicted correlation values is 19.53% while the root mean square error (RMSE) value is 0.465. The correlation can be used in the future to predict the CO2 activity coefficient values in ionic liquids to facilitate qualitative analyses of their CO2 capture efficiency.

Original languageEnglish
Article number137102
JournalChemosphere
Volume311
DOIs
StatePublished - Jan 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Activity coefficient
  • Carbon dioxide
  • Ionic liquids
  • Multi non-linear regression

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • General Chemistry
  • Pollution
  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

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