A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Machine Learning Optimization of Lignin Properties in Green Biorefineries
Tekijät: Löfgren Joakim, Tarasov Dmitry, Koitto Taru, Rinke Patrick, Balakshin Mikhail, Todorović Milica
Kustantaja: AMER CHEMICAL SOC
Julkaisuvuosi: 2022
Journal: ACS Sustainable Chemistry and Engineering
Tietokannassa oleva lehden nimi: ACS SUSTAINABLE CHEMISTRY & ENGINEERING
Lehden akronyymi: ACS SUSTAIN CHEM ENG
Vuosikerta: 10
Numero: 29
Aloitussivu: 9469
Lopetussivu: 9479
Sivujen määrä: 11
ISSN: 2168-0485
eISSN: 2168-0485
DOI: https://doi.org/10.1021/acssuschemeng.2c01895
Verkko-osoite: https://pubs.acs.org/doi/10.1021/acssuschemeng.2c01895#
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/176272236
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
