Sammanfattning

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies.

Nyckelord

gasification technology; machine learning; biomass gasification; energy; applications

Publicerad i

Energies
2023, Volym: 16, nummer: 18, artikelnummer: 6524
Utgivare: MDPI

SLU författare

• Chawade, Aakash

  • Institutionen för växtförädling, Sveriges lantbruksuniversitet
    

UKÄ forskningsämne

Bioenergi
Annan data- och informationsvetenskap


Publikationens identifierare

DOI: https://doi.org/10.3390/en16186524

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/126626