Prediction of carbon sequestration of biochar produced from biomass pyrolysis by artificial neural network

Biochar has demonstrated its potential in carbon sequestration to mitigate climate change. The fact that biochar can be produced from the slow pyrolysis of numerous potential biomasses has also attracted attention. However, research on predicting the effect of different pyrolysis conditions and a wide variety of biomass on carbon sequestration potential of biochar is extremely limited. In order to estimate the feasibility and performance of biomass to produce biochar with good carbon sequestering potential, it is crucial to predict the quantity and surface property of the resulting biochar based on composition of different biomass and pyrolysis conditions. In this study, the optimum feedforward artificial neural network (FANN) models were developed for 3 analytical cases based on pyrolysis conditions, proximate and ultimate analysis of biomass feedstocks to predict yield and BET surface area of the resulting biochar. These aspects can reflect the carbon sequestration potential of the resulting biochar. Data collected from literature were used to train, validate and test the ANN model for all analytical cases in this study. Levenberg-Marquardt (LM) algorithm was found to be the most suitable training algorithm for each analytical case, as it had shown satisfactory performance with low mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE). The ANN models showed good performances with correlation coefficient (R) more than 0.96, coefficient of determination (R-2) more than 0.92 and low MAPE, MAE and RMSE, thus indicating good alignment between the predicted and actual biochar yield and BET surface area. The relative importance of input was also determined using Garson's equation and connection weight approach to investigate the impact of the input variables on biochar yield and BET surface area. The proximate analysis of biomass was found to have significant impact on biochar yield whereas ultimate analysis of biomass had large influence on biochar BET surface area.