Journal
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
Volume 10, Issue 29, Pages 9469-9479Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acssuschemeng.2c01895
Keywords
Biomass; Valorization; Lignin; Biorefinery; Machine learning; Bayesian optimization
Categories
Funding
- Aalto University Internal Seed Fund [316601]
- Academy of Finland [341589]
- Finnish Center for Artificial Intelligence (FCAI)
- Aalto University Internal Seed Fund
- Academy of Finland
- FinnCERES BioEconomy flagship
- Finnish Center for Artificial Intelligence (FCAI)
- [52]
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This study optimized lignin in the AquaSolv omni biorefinery using Bayesian optimization and identified processing conditions that simultaneously optimize lignin yield and β-O-4 linkages.
Novel biorefineries could transform lignin, an abundant biopolymer, from side-stream waste to high-value-added byproducts at their site of production and with minimal experiments. Here, we report the optimization of the AquaSolv omni biorefinery for lignin using Bayesian optimization, a machine learning framework for sample-efficient and guided data collection. This tool allows us to relate the biorefinery conditions like hydrothermal pretreatment reaction severity and temperature with multiple experimental outputs, such as lignin structural features characterized using 2D nuclear magnetic resonance spectroscopy. By applying a Pareto front analysis to our models, we can find the processing conditions that simultaneously optimize the lignin yield and the amount of beta-O-4 linkages for the depolymerization of lignin into platform chemicals. Our study demonstrates the potential of machine learning to accelerate the development of sustainable chemical processing techniques for targeted applications and products.
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