Data driven methodology for model selection in flow pattern prediction. Issue 11 (November 2019)

• Record Type: Journal Article
• Authors: Hernandez, Juan Sebastian; Valencia, Carlos; Ratkovich, Nicolas; Torres, Carlos F.; Muñoz, Felipe
• Is Part Of: Heliyon. Volume 5:Issue 11(2019)
• Publication Date: 2019-11
• Subjects: Chemical engineering -- Two phase flow -- Flow pattern -- Decision tree -- Bagging -- Unified flow model; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2019.e02718
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: 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. Abstract : Chemical engineering; Two phase flow; Flow pattern; Decision tree; Bagging; Unified flow model