Machine Learning Prediction of the Yield and BET Area of Activated Carbon Quantitatively Relating to Biomass Compositions and Operating Conditions

Although activated carbon's yield (quantity index) and BET area (quality index) are crucial to its application, the two indexes must be accurately predicted. Herein, biomass compositions (ultimate analysis, proximate analysis, and chemical analysis), operating conditions (mass ratio, carbonization time, carbonization temperature, activation time, and activation temperature) under physical activation (CO2 and steam), and chemical activation (H3PO4, KOH, and ZnCl2) conditions as input parameters were used to predict the two indexes of activated carbon simultaneously through the random forest (RF) method for the first time. In total, the samples (>1500 data) identified from experiments in the literature were used to train, validate, and test the RF models. The results show that the model built on ultimate analysis is more suitable for predicting the BET area and yield of activated carbon prepared by both physical and chemical activation. Therein, the R2 values of activated carbon's yield and BET area under the H3PO4 activation condition were the highest, which were 0.98 and 0.97, respectively. In addition, the influence of various factors and interactions on the target variables was analyzed. The results show that the hydrogen content has a large impact on the yield under physical activation conditions, and the mass ratio has the most contribution to the BET area under chemical activation conditions. This study affords achievable hints to the quantitative prediction of porous materials affected by multiple compositions of raw materials and different operating conditions.