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

Mahadevan Subramanya Seshasayee1, Phillip E Savage1

  • 1Chemical Engineering Department, Pennsylvania State University, 121D CBE Building, University Park, PA 16802, USA.

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|July 15, 2022
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Summary

This study details hydrothermal liquefaction (HTL) experiments and component additivity models for predicting oil yields from biomass and plastic mixtures. These models can forecast outcomes for various thermochemical processes and feedstocks.

Keywords:
ChemistryEnergyEnvironmental sciences

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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.