工程科学与技术2025，Vol.57Issue(5)：79-90,12.DOI:10.12454/j.jsuese.202400007
机器学习解析直接空气捕集用固体胺吸附剂的构效关系
Machine Learning Analysis of the Structure‒Property Relationship of Amine-based Solid Adsorbents for Direct Air Capture
摘要
Abstract
Objective Direct air capture(DAC)emerges as a promising negative emission technology to mitigate global warming.The performance of direct air capture adsorbents,particularly amine-based solid porous adsorbents,plays a critical role in the industrial deployment of DAC.Extensive experimental studies are conducted worldwide to investigate the CO2 capture capabilities of these adsorbents,generating a substantial volume of data.However,the CO2 capture performance is influenced by multiple parameters,requiring a systematic and comprehensive analysis to clarify the relationship between the structural and conditional parameters of amine-based adsorbents and their CO2 capture capabilities. Methods This study systematically compiled experimental data on amine-based solid DAC adsorbents from peer-reviewed scientific articles.Four different types of machine learning algorithms(random forest,artificial neural network,support vector machine,and ridge regression)were employed to construct a predictive model that correlated the features of amine-based solid adsorbents with their CO2 adsorption capacity values.In addition,the study utilized shapley additive explanations(SHAP)analysis to deconstruct the machine learning model's predictive process,quantitatively revealing key parameters that determined the CO2 adsorption capacity of the adsorbents. Results and Discussions This study collected 629 valid data entries from 32 scientific publications,covering a wide range of CO2 capture capacities from 0 mmol/g to 5.0 mmol/g,to guide the design of new DAC adsorbents with enhanced CO2 capture performance.Each data entry was characterized by 24 descriptors,which encompassed information on the porous substrate components,textural properties,amine properties,and experimental conditions.An individual-variable analysis using the Pearson method revealed little linear correlation between the descriptors and CO2 capture capability,except for the amine loading in the adsorbents,with a Pearson correlation coefficient R=0.543.Machine learning models were employed to uncover potential nonlinear and multivariable relationships.Four algorithms with good fitting capabilities and robustness against information noise were selected to build predictive models for the CO2 capacity of amine-based adsorbents,namely artificial neural network(ANN),support vector machine(SVM),ridge regression(Ridge),and random forest(RF).The dataset was split into training and test sets in a 4:1 ratio,and hyperparameters were optimized using grid search and validated with 5-fold cross-validation.After the optimization of hyperparameters,the RF model showed superior performance compared to the other selected models.The optimal RF model demonstrated the best performance in predicting CO2 adsorption capacity,with R2=0.823,MAE=0.270,and RMSE=0.372 in the test set.The performance of the RF model indicated that the 24 descriptors effectively covered the key factors that determined the CO2 capacity of amine-based adsorbents within the current experimental design space.A quantitative structure-property relationship(QSPR)analysis was conducted using the SHAP analysis method based on the mentioned reliable RF model.In this case,the SHAP method quantified the contribution of each descriptor of the DAC data entry to the output(predicted CO2 adsorption capacity)of the RF model,providing interpretability and insights into the model's decision-making process.The SHAP analysis results identified the most important descriptor influencing CO2 capacity,the amine loading,which exhibited a strong positive correlation.Other significant descriptors included the molecular weight of the incorporated amines(negative correlation),CO2 concentration(positive),porosity of the adsorbent(positive),and elemental contents in the substrate material,such as O(negative),C(positive),and F(positive).Accordingly,three experimental design strategies were proposed for further exploration of amine-based DAC adsorbents:1)utilize substrates with high porosity,2)avoid using amine-based polymers with excessively high molecular weight(>10 000),and 3)select substrates containing C or F elements.These strategies contributed to the further enhancement of the CO2 adsorption performance of amine-based solid porous adsorbents and accelerated the development and deployment of DAC as a key negative emission technology.At the same time,the current limitations of chemical diversity and experimental space highlighted several areas and directions that required further research.For example,additional experimental studies were needed to investigate the CO2 capacity of amine-based adsorbents in humid and low-temperature environments,as well as the impacts of gas flow rate and gas mixture composition on the adsorbents'CO2 capacities.In addition,an ideal DAC adsorbent should have met multiple criteria beyond CO2 capacity,such as efficient mass transfer and high stability in adsorption-desorption cycles.Currently available experimental data are still insufficient to address these aspects using machine learning strategies. Conclusions This research highlights the potential of machine learning in analyzing large-scale datasets to identify factors that influence the CO2 capture performance of DAC adsorbents.Critical factors were identified,including amine loading and adsorbent porosity.The findings provide insights that can guide the design of more effective DAC adsorbents and highlight areas requiring additional experimental research,particularly regarding the effects of environmental conditions and gas composition on adsorbent performance.This study contributes to the progress of DAC technologies as a feasible solution to achieving China's carbon dioxide peaking and carbon neutrality objectives by advancing the understanding of amine-based adsorbents.关键词
二氧化碳/直接空气捕集/固体胺吸附剂/机器学习/碳捕集Key words
CO2/direct air capture/amine-based solid adsorbent/machine learning/carbon capture分类
化学化工引用本文复制引用
周志斌,周利,刘冲,代忠德,吉旭,张智渊,邱雨晴,董越,赵国江,曾垌皓,吴晓宇,郭本帅,戴一阳..机器学习解析直接空气捕集用固体胺吸附剂的构效关系[J].工程科学与技术,2025,57(5):79-90,12.基金项目
中国石油化工股份有限公司技术开发项目(323048) （323048）
