土木与环境工程学报（中英文）2025，Vol.47Issue(3)：242-250,9.DOI:10.11835/j.issn.2096-6717.2023.060
基于机器学习的多孔生物炭吸附CO2性能预测
Prediction of CO2 adsorption performance in porous biochar based on machine learning
摘要
Abstract
CO2 capture and sequestration(CCS)is an emission reduction measure with great potential.Porous biochar contains rich multi-scale pore structure,which makes it have excellent CO2 adsorption performance.To address the shortcomings of traditional CO2 adsorption prediction models built with experimental data,such as low accuracy and complicated calculation,this paper adopts machine learning methods such as gradient boosting decision tree(GBDT),extreme gradient enhancement algorithm(XGB)and light gradient booster algorithm(LGBM)to make model predictions of CO2 adsorption by biochar,and conducts comparative analysis of the prediction results.The results showed that the three most important factors affecting CO2 adsorption were the specific surface area,C content,and O content of biochar in order.All three algorithms could effectively predict the CO2 adsorption performance of biochar.In comparison,LGBM has the highest prediction accuracy of 94%;GBDT has a significant advantage in processing anomalous sample data;and XGB has more stable prediction results for different test set variations.When designing the adsorption performance of biochar,excessive surface area should not be blindly pursued.It is recommended that the selection of biochar C content should preferably be between 83%and 88%,and O content should preferably be between 15%and 18%.关键词
生物炭/机器学习/二氧化碳吸附/特征重要性/部分依赖图Key words
biochar/machine learning/CO2 adsorption/feature importance/partial dependency map分类
建筑与水利引用本文复制引用
陈一飞,张晓晴,谭康豪,汪俊松,覃英宏..基于机器学习的多孔生物炭吸附CO2性能预测[J].土木与环境工程学报（中英文）,2025,47(3):242-250,9.基金项目
广东省省级科技计划项目国际合作专项(2021A0505030008) Guangdong Provincial Science and Technology Program International Cooperation Special Project(2021A0505030008) （2021A0505030008）
