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

Area of Science:

• Biomass Conversion
• Chemical Engineering
• Renewable Energy

Background:

• Hydrothermal liquefaction (HTL) offers energy benefits over pyrolysis by eliminating biomass drying. Simultaneous prediction of HTL product yields and characteristics remains a challenge.
• Accurate modeling is crucial for optimizing HTL processes and understanding biomass conversion pathways.

Purpose of the Study:

• To develop a novel element-based kinetic model for predicting hydrothermal liquefaction product yields and fuel characteristics.
• To validate the model using experimental data from corn stover HTL.
• To investigate the influence of operating parameters on product distribution and composition.

Main Methods:

• Developed an element-based kinetic model incorporating temperature, residence time, solid loading, and elemental composition (C, H, N, O).
• Conducted HTL experiments on corn stover across temperatures (250–350 °C) and residence times (5–60 min).
• Analyzed solid and liquid products for elemental composition and ash content; utilized MATLAB for model prediction and data analysis.

Main Results:

• The model accurately predicted yields, higher heating values, and fuel characteristics of solid residue and heavy bio-oil.
• Predicted fuel characteristics for solid residues aligned with observed data on the van Krevelen diagram.
• The H/C atomic ratio of predicted heavy bio-oil matched experimental data; power function relationships identified nonlinear behavior with varying solid loading.

Conclusions:

• The element-based kinetic model provides a robust theoretical framework for predicting HTL product outcomes from biomass.
• The model's ability to predict yields and characteristics aids in optimizing HTL for biofuel production.
• Understanding the nonlinear relationship between solid loading and product fractions enhances process control and efficiency.