relation: https://khub.utp.edu.my/scholars/13283/
title: Machine learning methods for herschel-bulkley fluids in annulus: Pressure drop predictions and algorithm performance evaluation
creator: Kumar, A.
creator: Ridha, S.
creator: Ganet, T.
creator: Vasant, P.
creator: Ilyas, S.U.
description: Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel-Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2 and 2.57, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm. © 2020 by the authors.
publisher: MDPI AG
date: 2020
type: Article
type: PeerReviewed
identifier:   Kumar, A. and Ridha, S. and Ganet, T. and Vasant, P. and Ilyas, S.U.  (2020) Machine learning methods for herschel-bulkley fluids in annulus: Pressure drop predictions and algorithm performance evaluation.  Applied Sciences (Switzerland), 10 (7).   ISSN 20763417     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083329648&doi=10.3390%2fapp10072588&partnerID=40&md5=162212254b81d0045c6d4ad2b1030b25
relation: 10.3390/app10072588
identifier: 10.3390/app10072588