Machine Learning Optimization of Lignin Properties in Green Biorefineries

Joakim Löfgren, Dmitry Tarasov, Taru Koitto, Patrick Rinke, Mikhail Balakshin*, Milica Todorović

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
106 Downloads (Pure)


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 β-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.

Original languageEnglish
Pages (from-to)9469-9479
Number of pages11
JournalACS Sustainable Chemistry and Engineering
Issue number29
Publication statusPublished - 25 Jul 2022
MoE publication typeA1 Journal article-refereed


  • Bayesian optimization
  • Biomass
  • Biorefinery
  • Lignin
  • Machine learning
  • Valorization


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