Residence time distribution (RTD) revisited

Rodrigues, Alírio E.

doi:10.1016/j.ces.2020.116188

Cited by 100 publications

(46 citation statements)

References 86 publications

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“…For this initial presentation of the design methodology, the residence time distribution (RTD) function was chosen since it provides very valuable information of the reactor that has been designed. [29][30][31][32] However, the customization of reactors for a given application requires detailed knowledge about the reaction kinetics, the flow regime and in some cases, details on the heat transfer. The RTD can be obtained by running CFD simulations over the internal shape of the reactor from a CAD (computer aided design) file, which is the starting point of the process.…”

Section: Cfd Resultsmentioning

confidence: 99%

Compact reactor architectures designed with fractals

Grande

2021

React. Chem. Eng.

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Section: Cfd Resultsmentioning

confidence: 99%

Compact reactor architectures designed with fractals

Grande

2021

React. Chem. Eng.

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“…Experimental techniques can be used to assess the flow regime and determine whether it can be a candidate to be modeled as an ideal CSTR or PFR, by determining the residence time distribution [289] or degree of mixing [290]. The challenge associated with the experimental approaches is that for every configuration, particle type, and flow condition, the experiment should be repeated.…”

Section: Complex Vs Ideal Modelsmentioning

confidence: 99%

Multi-Scale Modeling of Plastic Waste Gasification: Opportunities and Challenges

et al. 2022

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Among the different thermo-chemical recycling routes for plastic waste valorization, gasification is one of the most promising, converting plastic waste into syngas (H2+CO) and energy in the presence of an oxygen-rich gas. Plastic waste gasification is associated with many different complexities due to the multi-scale nature of the process, the feedstock complexity (mixed polyolefins with different contaminations), intricate reaction mechanisms, plastic properties (melting behavior and molecular weight distribution), and complex transport phenomena in a multi-phase flow system. Hence, creating a reliable model calls for an extensive understanding of the phenomena at all scales, and more advanced modeling approaches than those applied today are required. Indeed, modeling of plastic waste gasification (PWG) is still in its infancy today. Our review paper shows that the thermophysical properties are rarely properly defined. Challenges in this regard together with possible methodologies to decently define these properties have been elaborated. The complexities regarding the kinetic modeling of gasification are numerous, compared to, e.g., plastic waste pyrolysis, or coal and biomass gasification, which are elaborated in this work along with the possible solutions to overcome them. Moreover, transport limitations and phase transformations, which affect the apparent kinetics of the process, are not usually considered, while it is demonstrated in this review that they are crucial in the robust prediction of the outcome. Hence, possible approaches in implementing available models to consider these limitations are suggested. Finally, the reactor-scale phenomena of PWG, which are more intricate than the similar processes—due to the presence of molten plastic—are usually simplified to the gas-solid systems, which can result in unreliable modeling frameworks. In this regard, an opportunity lies in the increased computational power that helps improve the model’s precision and allows us to include those complexities within the multi-scale PWG modeling. Using the more accurate modeling methodologies in combination with multi-scale modeling approaches will, in a decade, allow us to perform a rigorous optimization of the PWG process, improve existing and develop new gasifiers, and avoid fouling issues caused by tar.

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“…A residence time distribution (RTD) experiment was performed. To characterize the gas flow pattern in the photoreactor, and in order to simulate the pulse function (Dirac delta function) a tracer (carbon dioxide) was injected for a very short time interval ( Gao et al, 2012;Bérard et al, 2021;Rodrigues, 2021). The output CO 2 concentration is measured using an infrared sensor connected to an Arduino board allowing direct reading as a function of time on a PC.…”

Section: Modeling Of Degradation Kinetics and Influence Of Mass Trans...mentioning

confidence: 99%