Machine Learning Optimization of Lignin Properties in Green Biorefineries

Funding Information: The authors gratefully acknowledge support from the Aalto University Internal Seed Fund, the Academy of Finland through project nos. 316601 and 341589, the FinnCERES BioEconomy flagship, and the Finnish Center for Artificial Intelligence (FCAI). The data and code that were used in this study are freely available online. Publisher Copyright: © 2022 American Chemical Society. All rights reserved.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.Peer reviewe