How to approach a real CFD problem—A decision-making process for gasification

Silva, Valter; Cardoso, João B.

doi:10.1016/b978-0-12-817540-8.00002-9

Cited by 2 publications

(2 citation statements)

References 68 publications

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“…Typical models within the reviewed articles employ the principles of Navier-Stokes equations for mass, momentum, and energy balance, as well as a Reynolds-averaged Navier-Stokes simulation (RANS) model equation for the turbulence in the reactor. In order to simplify the fluid motion within the reactor, the Eulerian-Eulerian approach is commonly used where both the gas and solid phases are combined into a single continuum [55]. This approach is common for plasma gasification systems because the high heat and turbulence within the reactor, as well as the small solid feedstock size and carrier fluid, the gas and solid phase flows to behave similarly to each other.…”

Section: Governing Equations Within An Emipg Reactormentioning

confidence: 99%

“…The RANS approach typically employs the standard k-epsilon model for turbulence. This model is widely accepted for plasma reactor modeling due to its low computational cost and accuracy [55]. All models for mass, momentum, energy conservation, and turbulence are defined in Tables 7 and 8 and were solved using the finite volume method.…”

Section: Governing Equations Within An Emipg Reactormentioning

confidence: 99%

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CFD Modeling of a Lab-Scale Microwave Plasma Reactor for Waste-to-Energy Applications: A Review

Sedej

Mbonimpa

2021

Gases

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Rapidly increasing solid waste generation and energy demand are two critical issues of the current century. Plasma gasification, a type of waste-to-energy (WtE) technology, has the potential to produce clean energy from waste and safely destroy hazardous waste. Among plasma gasification technologies, microwave (MW)-driven plasma offers numerous potential advantages to be scaled as a leading WtE technology if its processes are well understood and optimized. This paper reviews studies on modeling experimental microwave-induced plasma gasification systems. The system characterization requires developing mathematical models to describe the multiphysics phenomena within the reactor. The injection of plasma-forming gases and carrier gases, the rate of the waste stream, and the operational power heavily influence the initiation of various chemical reactions that produce syngas. The type and kinetics of the chemical reactions taking place are primarily influenced by either the turbulence or temperature. Navier–Stokes equations are used to describe the mass, momentum, and energy transfer, and the k-epsilon model is often used to describe the turbulence within the reactor. Computational fluid dynamics software offers the ability to solve these multiphysics mathematical models efficiently and accurately.

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Section: Governing Equations Within An Emipg Reactormentioning

confidence: 99%

Section: Governing Equations Within An Emipg Reactormentioning

confidence: 99%

CFD Modeling of a Lab-Scale Microwave Plasma Reactor for Waste-to-Energy Applications: A Review

Sedej

Mbonimpa

2021

Gases

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

et al. 2022

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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 processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A two-dimensional microwave-driven plasma gasification reactor was developed in ANSYS (Ansys, Canonsburg, PA, USA) Fluent (a CFD tool), to create 644 (geometry and temperature) datasets for training six machine-learning (ML) models. When fed with just geometry datasets, these ML models were able to predict the proportion of the reactor area with temperature above 2000 K. This temperature level is considered a benchmark to prevent formation of undesirable byproducts. The ML model that achieved highest prediction accuracy was the feed forward neural network; the mean absolute error was 0.011. This novel machine-learning model can enable future optimization of experimental microwave plasma gasification systems for application in waste-to-energy.

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How to approach a real CFD problem—A decision-making process for gasification

Cited by 2 publications

References 68 publications

CFD Modeling of a Lab-Scale Microwave Plasma Reactor for Waste-to-Energy Applications: A Review

CFD Modeling of a Lab-Scale Microwave Plasma Reactor for Waste-to-Energy Applications: A Review

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

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