eprintid: 19652
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/96/52
datestamp: 2024-06-04 14:19:23
lastmod: 2024-06-04 14:19:23
status_changed: 2024-06-04 14:15:32
type: article
metadata_visibility: show
creators_name: Khan, U.
creators_name: Pao, W.
creators_name: Pilario, K.E.
creators_name: Sallih, N.
title: Flow regime classification using various dimensionality reduction methods and AutoML
ispublished: pub
keywords: Classifiers; Discrete wavelet transforms; Discriminant analysis; E-learning; Feature extraction; Learning systems; Nearest neighbor search; Neural networks; Signal reconstruction; Support vector machines, Automated machine learning; Automated machines; Dimensionality reduction; Discreate wavelet transform; Dynamic pressures; Flow regime map; Machine-learning; Two phases flow; Virtual flow regime map; Wavelets transform, Two phase flow
note: cited By 1
abstract: Accurate identification of flow regimes is paramount in several industries, especially in chemical and hydrocarbon sectors. This paper describes a comprehensive data-driven workflow for flow regime identification. The workflow encompasses: i) the collection of dynamic pressure signals using an experimentally verified numerical two-phase flow model for three different flow regimes: stratified, slug and annular flow, ii) feature extraction from pressure signals using Discrete Wavelet Transformation (DWT), iii) Evaluation and testing of 12 different Dimensionality Reduction (DR) techniques, iv) the application of an AutoML framework for automated Machine Learning classifier selection among K-Nearest Neighbors, Artificial Neural Networks, Support Vector Machines, Gradient Boosting, Random Forest, and Logistic Regression, with hyper-parameter tuning. Kernel Fisher Discriminant Analysis (KFDA) is the best DR technique, exhibiting superior goodness of clustering, while KNN proved to be the top classifier with an accuracy of 92.5  and excellent repeatability. The combination of DWT, KFDA and KNN was used to produce a virtual flow regime map. The proposed workflow represents a significant step forward in automating flow regime identification and enhancing the interpretability of ML classifiers, allowing its application to opaque pipes fitted with pressure sensors for achieving flow assurance and automatic monitoring of two-phase flow in various process industries. © 2024 Elsevier Ltd
date: 2024
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187186348&doi=10.1016%2fj.enganabound.2024.03.006&partnerID=40&md5=509d02099f4eb0e04107c3db4bf78697
id_number: 10.1016/j.enganabound.2024.03.006
full_text_status: none
publication: Engineering Analysis with Boundary Elements
volume: 163
pagerange: 161-174
refereed: TRUE
citation:   Khan, U. and Pao, W. and Pilario, K.E. and Sallih, N.  (2024) Flow regime classification using various dimensionality reduction methods and AutoML.  Engineering Analysis with Boundary Elements, 163.  pp. 161-174.