Lignin Biorefinery Experimental Optimization Using BOSS

Experimental data from a study optimizing lignin properties in green biorefineries. The dataset contains results from processing biomass under varying reactor temperature and P-factor conditions, with measured lignin yield and NMR-quantified structural properties. It was created by Joakim Löfgren of Aalto University for a 2022 manuscript.

Use Cases

• Training machine learning models to predict lignin yield based on reactor temperature and P-factor.
• Optimizing biorefinery processing conditions for desired lignin structural properties using Bayesian optimization.
• Analyzing the relationship between reaction severity (P-factor) and the number of ß-O-4 linkages in lignin.
• Comparing the performance of different NMR acquisition strategies (CA vs PA) for quantifying lignin composition.

Strengths

• Data is derived from controlled experimental processing with explicitly stated input conditions.
• Output includes multiple quantified lignin properties (yield, linkages, unit ratios, carbohydrate content) from 2D NMR analysis.
• Data is structured for machine learning with separate training and test files for two experimental strategies.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Aalto University

Collection Method

Experimental processing in a biorefinery, with output quantified via 2D NMR HSQC.

Time Range

Associated with a 2022 manuscript; specific experimental dates are not provided.

Freshness

Last updated date is unknown.

Geography

Geographic coverage of the source biomass or experiment location is not specified.