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Updated: Oct 10, 2025

Extraction of Lignin with High β-O-4 Content by Mild Ethanol Extraction and Its Effect on the Depolymerization Yield

Extraction of Lignin with High β-O-4 Content by Mild Ethanol Extraction and Its Effect on the Depolymerization Yield

Published on: January 7, 2019

Machine learning based analysis of reaction phenomena in catalytic lignin depolymerization.

Abraham Castro Garcia¹, Cheng Shuo¹, Jeffrey S Cross¹

¹Department of Transdisciplinary Science and Engineering, School of Environment and Society, Tokyo Institute of Technology, 2-12-1 S6-10, Ookayama, Meguro-ku, Tokyo 152-8552, Japan.

Bioresource Technology

|December 10, 2021

Summary

This summary is machine-generated.

This study develops a predictive model for lignin solvolysis, improving the conversion of biomass into valuable aromatic chemicals. The model accurately predicts bio-oil and char yields, offering insights into reaction parameters.

Keywords:

Bio-oil yield Catalyst Lignin depolymerization Machine learning

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Fractionation of Lignocellulosic Biomass using the OrganoCat Process

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Published on: June 5, 2021

Area of Science:

Chemical Engineering
Biomass Conversion
Catalysis

Background:

Heterogeneously catalyzed lignin solvolysis is key for converting low-value biomass into high-value aromatic chemicals.
Understanding the complex interactions between reaction parameters is crucial but challenging.

Purpose of the Study:

To develop a novel predictive model for bio-oil yield, char yield, and reaction time in lignin solvolysis.
To investigate the impact of catalyst surface properties and lignin molecular weight on solvolysis outcomes.

Main Methods:

Utilized Random Forest (RF) regression, a machine learning technique.
Analyzed literature data to train and validate the predictive models.
Assessed feature importance to understand parameter influence.

Main Results:

Achieved high predictive accuracy with R-squared scores of 0.9062 (bio-oil yield), 0.9428 (char yield), and 0.8327 (reaction time).
Found that catalyst surface properties and lignin molecular weight had no significant impact on bio-oil yield prediction.
Identified average pore diameter as a minor contributor (3% feature importance) to reaction time prediction.

Conclusions:

The developed RF models provide a robust framework for predicting lignin solvolysis outcomes.
The study highlights the limited influence of catalyst surface properties and lignin molecular weight on bio-oil yield.
Further research can refine models by incorporating other influential parameters for reaction time.