Machine learning predicting and engineering the yield, N content, and specific surface area of biochar derived from pyrolysis of biomass

Abstract Biochar produced from pyrolysis of biomass has been developed as a platform carbonaceous material that can be used in various applications. The specific surface area (SSA) and functionalities such N-containing functional groups biochar are the most significant properties determining application performance carbon areas, removal pollutants, adsorption CO 2 H , catalysis, energy storage. Producing with preferable SSA N is among frontiers to engineer materials. This study attempted build machine learning models predict optimize (SSA-char), content (N-char), yield (Yield-char) individually or simultaneously, by using elemental, proximate, biochemical compositions conditions input variables. predictions Yield-char, N-char, SSA-char were compared random forest (RF) gradient boosting regression (GBR) models. GBR outperformed RF for predictions. When parameters included elemental proximate well conditions, test R values single-target multi-target 0.90–0.95 except two-target prediction Yield-char which had 0.84 three-target model 0.81. As indicated Pearson correlation coefficient between variables feature importance these models, top influencing factors toward predicting three targets specified follows: temperature, residence time, fixed Yield-char; ash N-char; temperature SSA-char. effects on different, but trade-offs balanced during ML optimization. optimum solutions then experimentally verified, opens new way designing smart target oriented potential. Graphical