Pregledni rad
https://doi.org/10.15255/CABEQ.2023.2273
Systematic Review of Machine-learning Techniques to Support Development of Lignocellulose Biorefineries
A. Jurinjak Tušek
; University of Zagreb Faculty of Food Technology and Biotechnology, Pierottijeva 6, 10000 Zagreb, Croatia
A. Petrus
; Swiss Institute for Information Science, University of Applied Sciences of the Grisons, Ringstrasse 34, 7000 Chur, Switzerland
A. Weichselbraun
; Swiss Institute for Information Science, University of Applied Sciences of the Grisons, Ringstrasse 34, 7000 Chur, Switzerland
R. Mundani
; Swiss Institute for Information Science, University of Applied Sciences of the Grisons, Ringstrasse 34, 7000 Chur, Switzerland
S. Müller
; Swiss Institute for Information Science, University of Applied Sciences of the Grisons, Ringstrasse 34, 7000 Chur, Switzerland
I. Barkow
; Swiss Institute for Information Science, University of Applied Sciences of the Grisons, Ringstrasse 34, 7000 Chur, Switzerland
A. Bucić-Kojić
; Josip Juraj Strossmayer University of Osijek, Faculty of Food Technology Osijek, Franje Kuhača 18, Osijek, HR-31000, Croatia
M. Planinić
; Josip Juraj Strossmayer University of Osijek, Faculty of Food Technology Osijek, Franje Kuhača 18, Osijek, HR-31000, Croatia
M. Tišma
; Josip Juraj Strossmayer University of Osijek, Faculty of Food Technology Osijek, Franje Kuhača 18, Osijek, HR-31000, Croatia
Sažetak
Lignocellulosic biorefineries (LBRs) are platforms for the production of a variety of bio-based products such as biofuels, biomaterials, biochemicals, food, and feed using lignocellulosic biomass (LB) as feedstock. LBRs are still rare worldwide. Their commercialization depends on challenges associated with the entire feedstock supply chain, efficiency, sustainability, and scale-up of pretreatment methods, as well as isolation and purification of value-added products. Each step within LBRs requires the development of new technologies or the improvement of existing ones, considering all three sustainability dimensions, environmental, social, and economic. Machine learning (ML) methods are widely used in various industrial fields, including biotechnology. The merging of biotechnology and ML has driven scientific progress and opened new opportunities for the development of LBRs as well. In this review, ML methods and their efficiency, used in biotechnology (metabolic engineering, bioprocess development, and environmental engineering), are presented, followed by their application in various phases of LB valorization.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Ključne riječi
lignocellulosic biomass; lignocellulosic biorefinery; machine learning; sustainability
Hrčak ID:
321498
URI
Datum izdavanja:
17.10.2024.
Posjeta: 0 *