Biomass Gasification and Applied Intelligent Retrieval in Modeling

• Published in: Energies (Basel) Vol. 16; no. 18; p. 6524
• Main Authors: Meena, Manish, Kumar, Hrishikesh, Chaturvedi, Nitin Dutt, Kovalev, Andrey A., Bolshev, Vadim, Kovalev, Dmitriy A., Sarangi, Prakash Kumar, Chawade, Aakash, Rajput, Manish Singh, Vivekanand, Vivekanand, Panchenko, Vladimir
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2023
• Subjects: Algorithms; applications; Artificial intelligence; Biomass; Biomass energy; biomass gasification; Carbon dioxide; Carbon monoxide; Energy; Gases; gasification technology; Heat; Hydrocarbons; Machine learning; Moisture content; Neural networks; Optimization; Raw materials; Synthesis gas; Temperature; India
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16186524

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.