TY  - JOUR
Y1  - 2024///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187186348&doi=10.1016%2fj.enganabound.2024.03.006&partnerID=40&md5=509d02099f4eb0e04107c3db4bf78697
JF  - Engineering Analysis with Boundary Elements
A1  - Khan, U.
A1  - Pao, W.
A1  - Pilario, K.E.
A1  - Sallih, N.
VL  - 163
EP  - 174
AV  - none
N1  - cited By 1
N2  - 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
KW  - Classifiers; Discrete wavelet transforms; Discriminant analysis; E-learning; Feature extraction; Learning systems; Nearest neighbor search; Neural networks; Signal reconstruction; Support vector machines
KW  -  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
KW  -  Two phase flow
TI  - Flow regime classification using various dimensionality reduction methods and AutoML
ID  - scholars19652
SP  - 161
ER  -