Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Biomass to energy: a machine learning model for optimum gasification pathways
 
Loading...
Thumbnail Image
research article

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

Gil, Maria Victoria
•
Jablonka, Kevin Maik  
•
Garcia, Susana
Show more
August 8, 2023
Digital Discovery

Biomass is a highly versatile renewable resource for decarbonizing energy systems. Gasification is a promising conversion technology that can transform biomass into multiple energy carriers to produce heat, electricity, biofuels, or chemicals. At present, identifying the best gasification route for a given biomass relies on trial and error, which involves time-consuming experimentation that, given the wide range of biomass feedstocks available, slows down the deployment of the technology. Here, we use a supervised non-parametric machine-learning method, Gaussian process regression (GPR), that provides robust predictions when working with small datasets, to develop a model to find the optimal application of a particular biomass in gasification processes. Leave-one-out cross-validation (LOOCV) is used to validate the model's predictive performance. Our model can select the suitable gasification pathway from the characteristics of the biomass, and also identify the optimal operating conditions for a selected application of the produced gas. In addition, with this model, we can obtain insights into the relationships between biomass properties and gasification results, leading to a better understanding of the process. A relevant aspect of this work is that these results rely on a relatively small dataset, representative of those typically collected by research groups using different types of gasifiers worldwide. This study opens the path for future integration of such data, which would allow addressing the complexity of biomass and conversion process simultaneously. With this work, we aim to increase the flexibility of biomass gasification processes and promote the development of bioenergy technologies, considered crucial in the energy transition context.|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.

  • Details
  • Metrics
Type
research article
DOI
10.1039/d3dd00079f
Web of Science ID

WOS:001121105600001

Author(s)
Gil, Maria Victoria
•
Jablonka, Kevin Maik  
•
Garcia, Susana
•
Pevida, Covadonga
•
Smit, Berend  
Date Issued

2023-08-08

Publisher

Royal Soc Chemistry

Published in
Digital Discovery
Volume

2

Issue

4

Start page

929

End page

940

Subjects

Physical Sciences

•

Technology

•

Rich Gas-Production

•

Fluidized-Bed

•

Steam Gasification

•

Combustion Characteristics

•

Fischer-Tropsch

•

Grindability

•

Torrefaction

•

Performance

•

Technology

•

Methane

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LSMO  
FunderGrant Number

Spanish Agencia Estatal de Investigacion (AEI) - MCIN/AEI

TED2021-131693B-I00

European Union NextGenerationEU/PRTR

Spanish National Research Council (CSIC)

LINKA20412

Show more
Available on Infoscience
February 20, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/204568
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés