Predicting biochar properties and pyrolysis life-cycle inventories with compositional modeling.

Area of Science:

• Biomass pyrolysis
• Carbon sequestration technologies
• Life cycle assessment

Background:

• Biochar production via slow pyrolysis is a recognized method for atmospheric carbon sequestration.
• Existing life cycle assessment data for biomass pyrolysis processes often lack quality and detail, especially for novel applications.
• Accurate data is crucial for optimizing pyrolysis for carbon capture and energy generation.

Purpose of the Study:

• To develop a compositional, predictive model for slow pyrolysis of lignocellulosic biomass.
• To focus the model on predicting CO2 fluxes and energy products from pyrolysis.
• To provide a tool for quantifying biochar yield, energy output, and CO2 emissions across various temperatures.

Main Methods:

• A compositional model was developed based on mass-weighted pyrolysis products of cellulose, hemicellulose, and lignin.
• The model predicts biochar yields and composition, as well as bio-oil and syngas yields.
• Model performance was validated against experimental data for common feedstocks.

Main Results:

• The model accurately predicts biochar yields and composition within 5% of experimental values.
• Predictions for bio-oil and syngas showed broader distributions, typically within 20%.
• Quantified key life cycle inventory flows, such as 0.73 kg CO2/kg poplar biochar bound carbon at 500°C.

Conclusions:

• The developed predictive model enhances the quality of life cycle assessment data for biomass pyrolysis.
• This model can be adapted for various lignocellulosic biomass types to optimize pyrolysis processes.
• It supports the development of efficient carbon sequestration strategies using biochar.