Element-Based Predictive Modeling of Hydrothermal Liquefaction Bioproducts Derived from Corn Stover

The hydrothermal liquefaction (HTL) process offers an energetic advantage over pyrolysis because it does not require prior drying of the biomass feedstock. However, there are significant challenges in simultaneously estimating both the yields and characteristics of products from the HTL of biomass with theoretical support. This study developed a unique element-based kinetic model to predict the yields, higher heating values, and fuel characteristics of solid residue and heavy bio-oil, based on the temperature, residence time, solid loading, and elemental composition (C, H, N, and O) of corn stover. Furthermore, the model predicted the weights of dissolved carbon and nitrogen in the aqueous phase. HTL experiments were conducted using corn stover at temperatures ranging from 250 to 350 °C for residence times between 5 and 60 min. The resulting solid and liquid products were analyzed for the elemental composition and ash content. The experimental data and MATLAB program were used to predict the products. The fuel characteristics derived from predicted elemental weight data of solid residues followed the trend line of the observed data on the van Krevelen diagram. In those of heavy bio-oil, the H/C atomic ratio of the average predicted data matched the one calculated from the observed data. Additionally, power function relationships between the amounts of corn stover and obtained product fractions were identified under identical temperature and residence time conditions by varying solid loading, providing insights into the partial nonlinear behavior of the reaction system.