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

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 complete combustion reduction of toxic byproducts. Characterizing complex processes inside such system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but are tedious involve assumptions. this study, we propose machine-learning models that be used tandem with CFD, accelerate high-fidelity simulation, improve turbulence modeling, enhance reduced-order models. A two-dimensional microwave-driven reactor was developed ANSYS (Ansys, Canonsburg, PA, USA) Fluent (a CFD tool), create 644 (geometry temperature) datasets for training six (ML) When fed just geometry datasets, these ML were able predict proportion area temperature K. This level considered benchmark prevent formation undesirable The model achieved highest prediction accuracy feed forward neural network; mean absolute error 0.011. novel enable future optimization experimental microwave systems application waste-to-energy.