Introduction and overview of using computational fluid dynamics tools

Silva, Valter; Cardoso, João

doi:10.1016/b978-0-12-817540-8.00001-7

Cited by 5 publications

(9 citation statements)

References 13 publications

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“…In order to accurately model the reaction kinetics within an EMIPG reactor given the previous scenario, a common method used is the finite rate chemistry/eddy dissipation (FRC/EDM) Model. The FRC/EDM model considers both the Arrhenius and eddy dissipation reaction rates taking place within the reactor and chooses the minimum value of the two contributions in order to establish the reaction rate [48]. The FRC/EDM model as previously described is commonly referred to as the Kobayashi model [49].…”

Section: Rate Of Feedstock Input •mentioning

confidence: 99%

“…Additionally, it is important to consider the devolatilization of organic feedstocks within an EMIPG reactor. Devolatilization drives moisture and volatile matter from the organic feedstock through the heat within the reactor, and it must be considered in order to build an accurate model of the reactor chemistry [48]. Reviewed literature typically used the FRC/EDM model while simultaneously considering devolatilization in modeling the chemical kinetics within a plasma gasification system reactor.…”

Section: Rate Of Feedstock Input •mentioning

confidence: 99%

“…The reaction kinetics discussed previously are mostly governed by both the temperature and turbulence parameters inside the reactor. Turbulence and temperature directly influence the variable velocity fields (gasifier flow phenomena) at high Reynolds numbers and thus affect momentum, energy, species concentration and transport, heat transfer, drag, vorticity distribution, and swirl flows [48]. In order to properly understand which of the two parameters is controlling the reaction kinetics at a given place and time in the reactor, a complex mathematical model must be created.…”

Section: Governing Equations Within An Emipg Reactormentioning

confidence: 99%

“…Fortunately, with the advent of technology, computational power has evolved in parallel with numerical model solver efficiency [59]. Due to the rapid advancement in both computation and calculation capacity, CFD is a tool that can be used to design, optimize, predict processes within WtE systems [48]. CFD modeling software uses mathematical models to solve complicated partial differential equations of conservation laws for mass, momentum, and energy, as well as their theoretical and empirical correlations [48].…”

Section: Governing Equations Within An Emipg Reactormentioning

confidence: 99%

“…Due to the rapid advancement in both computation and calculation capacity, CFD is a tool that can be used to design, optimize, predict processes within WtE systems [48]. CFD modeling software uses mathematical models to solve complicated partial differential equations of conservation laws for mass, momentum, and energy, as well as their theoretical and empirical correlations [48]. By synthesizing these models, CFD software has the ability to design reactor simulations incorporating structural, thermal, and chemical analysis [60].…”

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: Rate Of Feedstock Input •mentioning

confidence: 99%

Section: Rate Of Feedstock Input •mentioning

confidence: 99%

Section: Governing Equations Within An Emipg Reactormentioning

confidence: 99%

Section: Governing Equations Within An Emipg Reactormentioning

confidence: 99%

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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Thermochemical treatment of daily COVID-19 single-use facemask waste: Power generation potential and environmental impact analysis

Cudjoe

Wang

zhu

2022

Energy

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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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Introduction and overview of using computational fluid dynamics tools

Cited by 5 publications

References 13 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

Thermochemical treatment of daily COVID-19 single-use facemask waste: Power generation potential and environmental impact analysis

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

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