Application of Machine Learning to Predict the Performance of an EMIPG Reactor Using Data from Numerical Simulations

Application of Machine Learning to Predict the Performance of an EMIPG Reactor Using Data from Numerical Simulations

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Microwave‐driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex...

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Bibliographic Details
Main Authors: Mbonimpa, E. (Author), Sedej, O. (Author), Slagley, J. (Author), Sleight, T. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
ANN
CFD modeling
Computational fluid dynamics
Computational fluid dynamics modeling
Feedforward neural networks
Fluidized beds
Forecasting
Gasification
GBM
machine learning
Machine learning
Machine learning models
Machine-learning
microwave induced plasma gasification
Microwave induced plasma gasification
Microwave-driven
Microwave-induced plasmas
Microwaves
Municipal solid waste
Performance
Plasma gasification
Waste incineration
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