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Biomass Gasification and Applied Intelligent Retrieval in Modeling

  • Manish Meena
  • , Hrishikesh Kumar
  • , Nitin Dutt Chaturvedi
  • , Andrey A. Kovalev
  • , Vadim Bolshev
  • , Dmitriy A. Kovalev
  • , Prakash Kumar Sarangi
  • , Aakash Chawade
  • , Manish Singh Rajput
  • , Vivekanand Vivekanand
  • , Vladimir Panchenko

Publication: Contribution to journalJournal articlepeer-review

Abstract

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.
Original languageEnglish
Article number6524
Number of pages21
JournalEnergies
Volume16
Issue number18
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • applications
  • biomass gasification
  • energy
  • gasification technology
  • machine learning

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