Data driven methodology for model selection in flow pattern prediction

Juan Sebastian Hernandez

doi:10.1016/j.heliyon.2019.e02718

Return

Data driven methodology for model selection in flow pattern prediction

2019-11-01

Juan Sebastian Hernandez

DOI：10.1016/j.heliyon.2019.e02718

Original

Save

View PDF

Abstract

En 中文

The determination of multiphase flow parameters such as flow pattern, pressure drop and liquid holdup, is a very challenging and valuable problem in chemical, oil and gas industries, especially during transportation. There are two main approaches to solve this problem in literature: data based algorithms and mechanistic models. Although data based methods may achieve better prediction accuracy, they fail to explain the two-phase characteristics (i.e. pressure gradient, holdup, gas and liquid local velocities, etc.). Recently, many approaches have been made for establishing a unified mechanistic model for steady-state two-phase flow to predict accurately the mentioned properties. This paper proposes a novel data-driven methodology for selecting closure relationships from the models included in the unified model. A decision tree based model is built based on a data driven methodology developed from a 27670 points data set and later tested for flow pattern prediction in a set made of 9224 observations. The closure relationship selection model achieved high accuracy in classifying flow regimes for a wide range of two-phase flow conditions. Intermittent flow registering the highest accuracy (86.32%) and annular flow the lowest (49.11%). The results show that less than 10% of global accuracy is lost compared to direct data based algorithms, which is explained by the worse performance presented for atypical values and zones close to boundaries between flow patterns.

Keywords:

Chemical engineering

Two phase flow

Flow pattern

Decision tree

Bagging

Unified flow model

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.