Prediction of CO2 adsorption of biochar under KOH activation via machine learning

Autor: Junjie Zhang, Xiong Zhang, Xiaoqiang Li, Zhantao Song, Jingai Shao, Shihong Zhang, Haiping Yang, Hanping Chen
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Carbon Capture Science & Technology, Vol 13, Iss , Pp 100309- (2024)
Druh dokumentu: article
ISSN: 2772-6568
DOI: 10.1016/j.ccst.2024.100309
Popis: To effectively increase the CO2 adsorption capacity of biochar, the activation process is often an indispensable link. However, the introduction of an activation process poses challenges in clarifying the relationship between the characterization parameters of biochar, adsorption parameters, and CO2 adsorption capacity. Herein, a comprehensive dataset encompassing the CO2 adsorption data of KOH-activated biochar using a “two-sep method” was compiled. Subsequently, ridge regression, multi-layer perceptron, and random forest models were employed to predict its CO2 adsorption performance. To comprehensively explore the effects of activation conditions, physicochemical properties and adsorption parameters on CO2 adsorption capacities, partial dependence via Shapley additive explanation (SHAP) values analysis was conducted. The results demonstrate that the multilayer perceptron model exhibits the highest prediction accuracy with a test R2 value of 0.961. Additionally, it was found that the CO2 adsorption capacity of activated biochar is primarily determined by micropores and nitrogen-containing groups rather than total pore volume at low adsorption pressure (< 0.3 bar). Moreover, it increases significantly with decreasing average pore size, increasing pore volume, and increasing nitrogen content at low adsorption temperatures (< 20 °C). When the ratio of KOH to biochar is in the range of 1–2 and the activation temperature is ∼ 700 °C, activated biochar with high CO2 adsorption performance can be obtained. This study may provide valuable insights for the application of activated biochar in CO2 adsorption.
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