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research-article

An Optimized ANN Unifying Model for Steady-State Liquid Holdup Estimation in Two-Phase Gas-Liquid Flow

[+] Author and Article Information
Chaari Majdi

Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, P.O. Box 43890, Lafayette, LA 70504-3890, USA
mxc0798@louisiana.edu

Seibi Abdennour C.

ASME Senior Member, Department of Petroleum Engineering, University of Louisiana at Lafayette, P.O. Box 44690, Lafayette, LA 70504, USA
acs9955@louisiana.edu

Ben Hmida Jalel

Department of Mechanical Engineering, University of Louisiana at Lafayette, P.O. Box 43678, Lafayette, LA 70504, USA
jxb9360@louisiana.edu

Fekih Afef

Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, P.O. Box 43890, Lafayette, LA 70504-3890, USA
afef.fekih@louisiana.edu

1Corresponding author.

ASME doi:10.1115/1.4039710 History: Received October 05, 2017; Revised March 07, 2018

Abstract

Simplifying assumptions and empirical closure relations are often required in existing two-phase flow modeling based on first-principle equations, hence limiting its prediction accuracy and in some instances compromising safety and productivity. State-of-the-art models used in the industry still include correlations that were developed in the sixties, whose prediction performances are at best acceptable. To better improve the prediction accuracy and encompass all pipe inclinations and flow patterns, we propose in this paper an artificial neural network (ANN)-based model for steady-state two-phase flow liquid holdup estimation in pipes. Deriving the best input combination among a large reservoir of dimensionless Pi groups with various fluid properties, pipe characteristics, and operating conditions, is a laborious trial-and-error procedure. Thus, a self-adaptive genetic algorithm (GA) is proposed in this work to both ease the computational complexity associated with finding the elite ANN model and lead to the best prediction accuracy of the liquid holdup. The proposed approach was implemented using the Stanford multiphase flow database (SMFD), chosen for being among the largest and most complete databases in the literature. The performance of the proposed approach was further compared to that of two prominent models, namely a standard empirical correlation-based model and a mechanistic model. The obtained results along with the comparison analysis confirmed the enhanced accuracy of the proposed approach in predicting liquid holdup for all pipe inclinations and fluid flow patterns.

Copyright (c) 2018 by ASME
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