Protocol to develop component additivity models that predict oil yield from hydrothermal liquefaction

Area of Science:

• Biomass and plastic valorization
• Thermochemical conversion processes
• Sustainable materials science

Background:

• Hydrothermal liquefaction (HTL) offers a promising route for converting biomass and plastic waste into valuable products.
• Predictive models are crucial for optimizing HTL processes and understanding feedstock interactions.
• Current models often lack the specificity to accurately predict yields from complex mixtures.

Purpose of the Study:

• To outline a detailed protocol for conducting hydrothermal liquefaction (HTL) experiments.
• To develop component additivity models for predicting oil yields from HTL of biomass and plastic mixtures.
• To establish a framework for predicting outcomes in various thermochemical valorization processes.

Main Methods:

• Detailed experimental procedures for single-component and mixture HTL.
• Development of component additivity models based on experimental data.
• Constraining models to specific plastic feedstocks and product recovery solvents.

Main Results:

• Successful execution of HTL experiments with diverse feedstocks.
• Validated component additivity models demonstrating predictive capability for oil yields.
• Demonstrated applicability of the modeling approach to mixtures of biomass and plastics.

Conclusions:

• The developed HTL protocol and modeling approach provide a robust method for yield prediction.
• Component additivity models are effective for forecasting HTL outcomes from mixed feedstocks.
• This work facilitates the optimization of thermochemical conversion processes for waste valorization.