Biomass to energy: a machine learning model for optimum gasification pathways-Reference-Cited by-同舟云学术

Biomass to energy: a machine learning model for optimum gasification pathways

Published:2023 Issue:4 Volume:2 Page:929-940
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Gil María Victoria¹^ORCID,Jablonka Kevin Maik²^ORCID,Garcia Susana³,Pevida Covadonga¹,Smit Berend²^ORCID

Affiliation:

1. Instituto de Ciencia y Tecnología del Carbono (INCAR), CSIC, Francisco Pintado Fe 26, 33011 Oviedo, Spain

2. Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL), Valais, Rue de l’Industrie 17, Sion CH-1951, Switzerland

3. The Research Center for Carbon Solutions (RCCS), School of Engineering and Physical Sciences, Heriot-Watt University, EH14 4AS Edinburgh, UK

Abstract

Machine learning model to identify the optimal gasification-based biomass conversion route from biomass properties. It allows us to connect the wide diversity of biomass feedstocks with the most suitable application.

Funder

Consejo Superior de Investigaciones Científicas

Agencia Estatal de Investigación

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00079F

Reference79 articles.

1. IEA , Net Zero by 2050: A Roadmap for the Global Energy Sector , International Energy Agency , 2021

2. IRENA , Bioenergy for the energy transition: Ensuring sustainability and overcoming barriers , International Renewable Energy Agency , Abu Dhabi , 2022

3. Environmental co-benefits and adverse side-effects of alternative power sector decarbonization strategies

4. Thermochemical processing of woody biomass: A review focused on energy-driven applications and catalytic upgrading

5. Valorisation and emerging perspective of biomass based waste-to-energy technologies and their socio-environmental impact: A review