Open Access
Open access
Energies, volume 16, issue 18, pages 6524

Biomass Gasification and Applied Intelligent Retrieval in Modeling

Manish Meena 1, 2
Hrishikesh Kumar 2, 3
Nitin Dutt Chaturvedi 1
Andrey A Kovalev 4
Andrey I. Kovalev 4
Aakash Chawade 6
Manish Singh Rajput 7
Vivekanand Vivekanand 2
Vladimir Panchenko 8
Show full list: 11 authors
Publication typeJournal Article
Publication date2023-09-10
Journal: Energies
scimago Q1
SJR0.651
CiteScore6.2
Impact factor3
ISSN19961073
Electrical and Electronic Engineering
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Building and Construction
Control and Optimization
Engineering (miscellaneous)
Energy (miscellaneous)
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.

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