An intelligent solvent selection approach in carbon capturing process: A comparative study of machine learning multi-class classification models

Abstract

Carbon capture is crucial for mitigating climate change and achieving global emissions reduction targets. Among various technologies, absorption-based methods using aqueous solvents are among the most common and efficient, offering high capture rates and retrofit capabilities for existing industrial facilities. However, selecting the optimal solvent is challenging due to its direct impact on the performance, efficiency, and cost-effectiveness of the carbon capture process. This research introduces a novel multi-class classification methodology for solvent selection in carbon capture. Leveraging a dataset of 656 data points, including features such as temperature, pressure, molar ratio, and CO 2 solubility, the study employs nine machine learning algorithms—traditional models (Naïve Bayes, k-Nearest Neighbors, Decision Tree) and ensemble methods (Gradient Boosted Tree, Random Forest, Bagging, AdaBoost, Stacking, Voting)—to develop robust classification models. The results highlight the superior performance of the Stacking ensemble classifier, achieving 99.24 % accuracy, 0.76 % error rate, 99.04 % weighted mean recall, 98.08 % weighted mean precision, a 98.56 % F1 score, and a 0.9911 Cohen's Kappa coefficient on the test set. The effectiveness of ensemble techniques over traditional models underscores their ability to capture the complex relationships inherent in solvent selection. This approach streamlines the decision-making process for designers and engineers, enabling rapid identification of the most suitable solvent class based on operational conditions. Integrating this methodology into chemical engineering software could offer practical benefits, facilitating real-time solvent selection and optimization in industrial carbon capture, ultimately contributing to more sustainable and efficient processes. • Innovative methodology for solvent selection in carbon capture using ML techniques. • Stacking model achieved high accuracy of 99.24 % and low error rate of 0.76 %. • Application of ROC curves and AUC metric for comprehensive classifier performance evaluation. • Introduction of hyper-parameter tuning for optimized ML model performance. • Enables expedited and accurate solvent selection for improved operational efficiency.

Topics & Concepts