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Research Papers: Multiphase Flows

An Optimized Artificial Neural Network Unifying Model for Steady-State Liquid Holdup Estimation in Two-Phase Gas–Liquid Flow

[+] Author and Article Information
Majdi Chaari

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

Abdennour C. Seibi

Mem. ASME
Department of Petroleum Engineering,
University of Louisiana at Lafayette,
P.O. Box 44690,
Lafayette, LA 70504
e-mail: acs9955@louisiana.edu

Jalel Ben Hmida

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

Afef Fekih

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

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received October 5, 2017; final manuscript received March 7, 2018; published online May 2, 2018. Assoc. Editor: Riccardo Mereu.

J. Fluids Eng 140(10), 101301 (May 02, 2018) (11 pages) Paper No: FE-17-1640; doi: 10.1115/1.4039710 History: Received October 05, 2017; Revised March 07, 2018

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 Π 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.

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Figures

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Fig. 1

Architecture of an m-input single-output three-layered perceptron

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Fig. 2

Flowchart of the ANN input selection approach

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Fig. 3

m-input selector encoding solution

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Fig. 4

Two-point crossover of five-one-valued-bit individuals: (a) successful crossover and (b) unsuccessful crossover

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Fig. 5

Mutation of a five-one-valued-bit individual

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Fig. 6

Observed flow patterns

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Fig. 7

Evolution of the best fitness for different initial pc and pm: (a) m = 5, (b) m = 6, and (c) m = 7

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Fig. 8

(a) Evolution of the best fitness for various numbers of inputs m and (b) evolution of pc and pm for m = 6

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Fig. 9

Experimental and predicted liquid holdup parity charts: (a) elite ANN model, (b) Beggs and Brill's model, and (c) Petalas and Aziz's model (solid line represents parity line)

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Fig. 10

Absolute relative error of the elite ANN model

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