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Biomass Gasification and Applied Intelligent Retrieval in Modeling

Manish Meena, Hrishikesh Kumar, Nitin Dutt Chaturvedi, А.A. Kovalev, Vadim Bolshev, Dmitriy Kovalev, Prakash Kumar Sarangi, Aakash Chawade, Manish Singh Rajput, Vivekanand Vivekanand, Владимир Панченко

2023Energies17 citationsDOIOpen Access PDF

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.

Topics & Concepts

Computer scienceField (mathematics)Artificial neural networkBoosting (machine learning)Artificial intelligenceMachine learningDecision treeRandom forestBiomass gasificationGradient boostingBiomass (ecology)OceanographyPure mathematicsMathematicsGeologyThermochemical Biomass Conversion ProcessesProcess Optimization and IntegrationIron and Steelmaking Processes
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