Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization

In the present investigation, bed porosity and solid holdup in viscous three-phase inverse fluidized bed (TPIFB) are determined for aqueous solutions of carboxy methyl cellulose (CMC) system using polyethylene and polypropylene as a particles with low-density and diameter (5 mm) in a (9.2 cm) inne...

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Autor principal:	Amer A. Abdulrahman
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2017
Materias:	bed porosity solid holdup three phase inverse fluidization ANNs Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/5dad9cbab9dd456598c302281b9cc04e
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spelling	oai:doaj.org-article:5dad9cbab9dd456598c302281b9cc04e2021-12-02T04:46:21ZExperimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization1818-11712312-0789https://doaj.org/article/5dad9cbab9dd456598c302281b9cc04e2017-12-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/305https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 In the present investigation, bed porosity and solid holdup in viscous three-phase inverse fluidized bed (TPIFB) are determined for aqueous solutions of carboxy methyl cellulose (CMC) system using polyethylene and polypropylene as a particles with low-density and diameter (5 mm) in a (9.2 cm) inner diameter with height (200 cm) of vertical perspex column. The effectiveness of gas velocity Ug , liquid velocity UL, liquid viscosity μL, and particle density ρs on bed porosity BP and solid holdups εg were determined. The bed porosity increases with "increasing gas velocity", "liquid velocity", and "liquid viscosity". Solid holdup decreases with increasing gas, liquid velocities and liquid viscosity. Solid holdup with "low density particles" shows a higher numerical quantity "than that in the beds" with "high density". Levenberg-Marquardt back propagation of "artificial neural network (ANNs)" was utilized to predict the bed porosity and solid holdup. The expected values are in an excellent relationship with the experimental values, where the advanced model is high-fidelity and own a large capacity to predict bed porosity and solid holdup. Amer A. AbdulrahmanAl-Khwarizmi College of Engineering – University of Baghdadarticlebed porositysolid holdupthree phaseinverse fluidizationANNsChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 12, Iss 3 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	bed porosity solid holdup three phase inverse fluidization ANNs Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	bed porosity solid holdup three phase inverse fluidization ANNs Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Amer A. Abdulrahman Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization
description	In the present investigation, bed porosity and solid holdup in viscous three-phase inverse fluidized bed (TPIFB) are determined for aqueous solutions of carboxy methyl cellulose (CMC) system using polyethylene and polypropylene as a particles with low-density and diameter (5 mm) in a (9.2 cm) inner diameter with height (200 cm) of vertical perspex column. The effectiveness of gas velocity Ug , liquid velocity UL, liquid viscosity μL, and particle density ρs on bed porosity BP and solid holdups εg were determined. The bed porosity increases with "increasing gas velocity", "liquid velocity", and "liquid viscosity". Solid holdup decreases with increasing gas, liquid velocities and liquid viscosity. Solid holdup with "low density particles" shows a higher numerical quantity "than that in the beds" with "high density". Levenberg-Marquardt back propagation of "artificial neural network (ANNs)" was utilized to predict the bed porosity and solid holdup. The expected values are in an excellent relationship with the experimental values, where the advanced model is high-fidelity and own a large capacity to predict bed porosity and solid holdup.
format	article
author	Amer A. Abdulrahman
author_facet	Amer A. Abdulrahman
author_sort	Amer A. Abdulrahman
title	Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization
title_short	Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization
title_full	Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization
title_fullStr	Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization
title_full_unstemmed	Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization
title_sort	experimental and prediction using artificial neural network of bed porosity and solid holdup in viscous 3-phase inverse fluidization
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2017
url	https://doaj.org/article/5dad9cbab9dd456598c302281b9cc04e
work_keys_str_mv	AT ameraabdulrahman experimentalandpredictionusingartificialneuralnetworkofbedporosityandsolidholdupinviscous3phaseinversefluidization
_version_	1718401057435942912

Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization

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