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

Abstract

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.