Maldistribution and dynamic liquid holdup quantification of quadrilobe catalyst in a trickle bed reactor using gamma-ray computed tomography: Pseudo-3D modelling and empirical modelling using deep neural network
| Autor: | Omar Farid, Muthanna H. Al-Dahhan, Binbin Qi, Sebastián Uribe |
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| Rok vydání: | 2020 |
| Předmět: |
Uniform distribution (continuous)
Materials science Artificial neural network General Chemical Engineering Empirical modelling Gamma ray 02 engineering and technology General Chemistry Radius Mechanics Trickle-bed reactor 021001 nanoscience & nanotechnology Volumetric flow rate Physics::Fluid Dynamics 020401 chemical engineering 0204 chemical engineering 0210 nano-technology Porosity |
| Zdroj: | Chemical Engineering Research and Design. 164:195-208 |
| ISSN: | 0263-8762 |
| DOI: | 10.1016/j.cherd.2020.09.024 |
| Popis: | The dynamic liquid distribution and holdup in a TBR packed with porous quadrilobe catalyst were studied using advanced Gamma-ray Computed Tomography. A multi-compartment module is used to quantify the maldistribution factor which shows that there is a transition region from high maldistribution to relatively uniform distribution depending on the flowrates. The 3D maldistribution maps show that there is more dynamic liquid close to the column center at high bed height and there is no high correlation between the average dynamic liquid holdup and the bed height. If the gas flowrate increases while keeping the liquid flowrate fixed, the average dynamic liquid holdup decreases; however, if the gas flowrate is fixed, there is no dominant increasing or decreasing trend showing up. A Deep Neural Network model and a pseudo-3D model are developed showing high accuracy for predicting the local dynamic liquid holdup at different bed heights, radius, and flowrates. |
| Databáze: | OpenAIRE |
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