Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India.
Department of Chemistry, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India.
J Colloid Interface Sci. 2023 Oct;647:174-187. doi: 10.1016/j.jcis.2023.05.052. Epub 2023 May 18.
Adsorption of CO on porous carbons has been identified as one of the promising methods for carbon capture, which is essential for meeting the sustainable developmental goal (SDG) with respect to climate action, i.e., SDG 13. This research implemented six supervised machine learning (ML) models (gradient boosting decision tree (GBDT), extreme gradient boosting (XGB), light boost gradient machine (LBGM), random forest (RF), categorical boosting (Catboost), and adaptive boosting (Adaboost)) to understand and predict the CO adsorption mechanism and adsorption uptake, respectively. The results recommended that the GBDT outperformed the remaining five ML models for CO adsorption. However, XGB, LBGM, RF, and Catboost also represented the prediction in the acceptable range. The GBDT model indicated the accurate prediction of CO uptake onto the porous carbons considering adsorbent properties and adsorption conditions as model input parameters. Next, two-factor partial dependence plots revealed a lucid explanation of how the combinations of two input features affect the model prediction. Furthermore, SHapley Additive exPlainations (SHAP), a novel explication approach based on ML models, were employed to understand and elucidate the CO adsorption and model prediction. The SHAP explanations, implemented on the GBDT model, revealed the rigorous relationships among the input features and output variables based on the GBDT prediction. Additionally, SHAP provided clear-cut feature importance analysis and individual feature impact on the prediction. SHAP also explained two instances of CO adsorption. Along with the data-driven insightful explanation of CO adsorption onto porous carbons, this study also provides a promising method to predict the clear-cut performance of porous carbons for CO adsorption without performing any experiments and open new avenues for researchers to implement this study in the field of adsorption because a lot of data is being generated.
多孔碳对 CO 的吸附已被确定为碳捕获的一种很有前途的方法，这对于实现与气候行动相关的可持续发展目标（SDG）至关重要，即 SDG13。本研究实施了六个有监督的机器学习（ML）模型（梯度提升决策树（GBDT）、极端梯度提升（XGB）、轻梯度提升机（LBGM）、随机森林（RF）、分类梯度提升（Catboost）和自适应提升（Adaboost）），以分别了解和预测 CO 的吸附机制和吸附量。结果表明，GBDT 模型在 CO 吸附方面优于其余五个 ML 模型。然而，XGB、LBGM、RF 和 Catboost 也代表了可接受范围内的预测。GBDT 模型表明，考虑到吸附剂特性和吸附条件作为模型输入参数，它可以准确地预测 CO 在多孔碳上的吸附量。接下来，双因素偏依赖图揭示了两个输入特征的组合如何影响模型预测的清晰解释。此外，基于机器学习模型的新解释方法——SHapley Additive exPlainations（SHAP），被用于理解和阐明 CO 的吸附和模型预测。在 GBDT 模型上实施的 SHAP 解释，根据 GBDT 预测，揭示了输入特征和输出变量之间的严格关系。此外，SHAP 提供了明确的特征重要性分析和对预测的单个特征影响。SHAP 还解释了 CO 吸附的两个实例。除了对多孔碳上 CO 吸附的数据分析解释外，本研究还为预测多孔碳对 CO 吸附的明确性能提供了一种有前途的方法，而无需进行任何实验，并为研究人员在吸附领域实施这项研究开辟了新的途径，因为产生了大量的数据。
