Experimental and Data-driven approach of investigating the effect of parameters on the fluid flow characteristic of nanosilica enhanced two phase flow in pipeline

This study investigates the effect of various parameters on the fluid flow characteristic of nanosilica enhanced two phase (oil–water) flow in the pipeline using experimental and data driven approach. Levenberg-Marquardt (LM) algorithm and Scaled Conjugate gradient (SC) were used for training 20 Art...

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Autores principales:	Zainab Y. Shnain, Asawer A. Alwaiti, Musaab K. Rashed, Zaidon Mohsin Shakor
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Nanosilica Crude oil Kinematic viscosity Dynamic viscosity Pressure drop Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/31fa693463a649aa92a03649fc7efde7
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spelling	oai:doaj.org-article:31fa693463a649aa92a03649fc7efde72021-11-18T04:45:14ZExperimental and Data-driven approach of investigating the effect of parameters on the fluid flow characteristic of nanosilica enhanced two phase flow in pipeline1110-016810.1016/j.aej.2021.06.017https://doaj.org/article/31fa693463a649aa92a03649fc7efde72022-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821003835https://doaj.org/toc/1110-0168This study investigates the effect of various parameters on the fluid flow characteristic of nanosilica enhanced two phase (oil–water) flow in the pipeline using experimental and data driven approach. Levenberg-Marquardt (LM) algorithm and Scaled Conjugate gradient (SC) were used for training 20 Artificial Neural Network (ANN) model configurations. The ANN model configurations were optimized using 1 to 20 hidden neurons. Optimized ANN architecture of 3–6-3 and 3–17-3 was obtained for the LM and SC trained ANN. The performance of both the LM and SC trained ANN models were adjudged using mean square error (MSE) and the coefficient of determinant (R). Both the optimized LM and SC trained ANN architecture accurately modeled the prediction of kinematic viscosity, dynamic viscosity, and the pressure of the nanosilica enhanced crude oil. A very low MSE of 9.35x10-7 and 5.62x10-2 were obtained for the optimized LM and SC trained ANN architecture, respectively with R values of 0.999. This is an indication of the robustness of the ANN technique used in modeling the effect of the various predictors on the fluid flow characteristic of nanosilica enhanced mixture in pipelines. The sensitivity analysis revealed that the nanosilica concentration has the most significant influence on the various outputs from the ANN model.Zainab Y. ShnainAsawer A. AlwaitiMusaab K. RashedZaidon Mohsin ShakorElsevierarticleNanosilicaCrude oilKinematic viscosityDynamic viscosityPressure dropEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 2, Pp 1159-1170 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Nanosilica Crude oil Kinematic viscosity Dynamic viscosity Pressure drop Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Nanosilica Crude oil Kinematic viscosity Dynamic viscosity Pressure drop Engineering (General). Civil engineering (General) TA1-2040 Zainab Y. Shnain Asawer A. Alwaiti Musaab K. Rashed Zaidon Mohsin Shakor Experimental and Data-driven approach of investigating the effect of parameters on the fluid flow characteristic of nanosilica enhanced two phase flow in pipeline
description	This study investigates the effect of various parameters on the fluid flow characteristic of nanosilica enhanced two phase (oil–water) flow in the pipeline using experimental and data driven approach. Levenberg-Marquardt (LM) algorithm and Scaled Conjugate gradient (SC) were used for training 20 Artificial Neural Network (ANN) model configurations. The ANN model configurations were optimized using 1 to 20 hidden neurons. Optimized ANN architecture of 3–6-3 and 3–17-3 was obtained for the LM and SC trained ANN. The performance of both the LM and SC trained ANN models were adjudged using mean square error (MSE) and the coefficient of determinant (R). Both the optimized LM and SC trained ANN architecture accurately modeled the prediction of kinematic viscosity, dynamic viscosity, and the pressure of the nanosilica enhanced crude oil. A very low MSE of 9.35x10-7 and 5.62x10-2 were obtained for the optimized LM and SC trained ANN architecture, respectively with R values of 0.999. This is an indication of the robustness of the ANN technique used in modeling the effect of the various predictors on the fluid flow characteristic of nanosilica enhanced mixture in pipelines. The sensitivity analysis revealed that the nanosilica concentration has the most significant influence on the various outputs from the ANN model.
format	article
author	Zainab Y. Shnain Asawer A. Alwaiti Musaab K. Rashed Zaidon Mohsin Shakor
author_facet	Zainab Y. Shnain Asawer A. Alwaiti Musaab K. Rashed Zaidon Mohsin Shakor
author_sort	Zainab Y. Shnain
title	Experimental and Data-driven approach of investigating the effect of parameters on the fluid flow characteristic of nanosilica enhanced two phase flow in pipeline
title_short	Experimental and Data-driven approach of investigating the effect of parameters on the fluid flow characteristic of nanosilica enhanced two phase flow in pipeline
title_full	Experimental and Data-driven approach of investigating the effect of parameters on the fluid flow characteristic of nanosilica enhanced two phase flow in pipeline
title_fullStr	Experimental and Data-driven approach of investigating the effect of parameters on the fluid flow characteristic of nanosilica enhanced two phase flow in pipeline
title_full_unstemmed	Experimental and Data-driven approach of investigating the effect of parameters on the fluid flow characteristic of nanosilica enhanced two phase flow in pipeline
title_sort	experimental and data-driven approach of investigating the effect of parameters on the fluid flow characteristic of nanosilica enhanced two phase flow in pipeline
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/31fa693463a649aa92a03649fc7efde7
work_keys_str_mv	AT zainabyshnain experimentalanddatadrivenapproachofinvestigatingtheeffectofparametersonthefluidflowcharacteristicofnanosilicaenhancedtwophaseflowinpipeline AT asaweraalwaiti experimentalanddatadrivenapproachofinvestigatingtheeffectofparametersonthefluidflowcharacteristicofnanosilicaenhancedtwophaseflowinpipeline AT musaabkrashed experimentalanddatadrivenapproachofinvestigatingtheeffectofparametersonthefluidflowcharacteristicofnanosilicaenhancedtwophaseflowinpipeline AT zaidonmohsinshakor experimentalanddatadrivenapproachofinvestigatingtheeffectofparametersonthefluidflowcharacteristicofnanosilicaenhancedtwophaseflowinpipeline
_version_	1718425031100334080

Experimental and Data-driven approach of investigating the effect of parameters on the fluid flow characteristic of nanosilica enhanced two phase flow in pipeline

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