Carbon dioxide adsorption performance of KOH-activated potato straw hydrochar: RSM and ML optimization, and mechanism insights

KOH-activated hydrochar (HC) prepared using biomass straw serves as a promising carbon dioxide (CO ₂ ) adsorbent. However, numerous factors influence its preparation process, leading to significant variability in the adsorption performance of the resulting products. However, thoroughly understanding and optimizing this process remains challenging. In this study, a response surface method (RSM) and machine learning model were applied to predict the CO ₂ adsorption capacity of potato straw active HC and analyze the influence of multiple factors on the CO ₂ adsorption capacity. The results showed that the random forest (RF) model had a good predictive effect on the CO ₂ adsorption effect of active HC, compared with the RSM model, based on the central composite design experimental dataset and additional experimental data. In addition, the hydrothermal carbonization temperature had the most significant influence on the CO ₂ adsorption performance among various preparation factors, based on analysis of the RSM and RF models. Through experimental characterization and analysis, we found that with an increase in hydrothermal carbonization temperature, more biomass components would be decomposed, with the degree of carbonization increasing accordingly, forming a stable aromatic structure. Subsequently, more micropores would be generated following KOH activation, thus increasing the CO ₂ adsorption performance of active HC.