Unraveling the effects of sodium carbonate on hydrothermal liquefaction through individual biomass model component and machine learning-enabled prediction

Abstract

Despite sodium carbonate (Na2CO3) being commonly utilized as a catalyst in actual biomass hydrothermal liquefaction (HTL), its impact on individual biomass components hasn't been well-examined. This study thus delves into the role of Na2CO3 in HTL of biomass model components (carbohydrate, lignin, protein, and lipid) at varying conditions. Na2CO3 at 5 wt% amplified carbohydrate degradation into biocrude and aqueous-gaseous products (AG), resonating with previous work on carbohydrate-rich feedstocks. While Na2CO3 has a marginal effect on lignin HTL, it negatively influences lipid HTL. Here, 5 wt% and 13.5 wt% Na2CO3 decrease the biocrude yield from 95.6% to less than 10%, simultaneously increasing the AG yield to approximately 90%. This is presumably due to the interaction of lipid decomposition intermediates (fatty acids) with sodium cations, resulting in water-soluble soap. For protein HTL, a low Na2CO3 concentration (5 wt%) has no significant impact on product formation, but excessive Na2CO3 (27 wt%) converts a considerable portion of biocrude into AG. In addition to these explorations that provided insights for actual biomass HTL, we also developed a machine learning model to adequately predict HTL biocrude yield by taking Na2CO3 effect into consideration. The Adaboost machine learning model displayed the most satisfactory prediction performance (training and testing R2: 0.96, 0.8) among the three investigated machine learning models. The feature importance analysis reveals lipid content and Na2CO3 concentration as pivotal factors over HTL process conditions and other biochemical components. This research provides fundamental insights into the Na2CO3 role in HTL product formation pathway and offers an intelligent and precise prediction model for HTL biocrude yield, thereby advancing the development of more efficient and sustainable bioenergy production processes.