relation: https://khub.utp.edu.my/scholars/16546/
title: Development of a Prediction Model for Gas Hydrate Formation in Multiphase Pipelines by Artificial Intelligence
creator: Sayani, J.K.S.
creator: Sivabalan, V.
creator: Foo, K.S.
creator: Pedapati, S.R.
creator: Lal, B.
description: A prediction model is developed by means of artificial neural networks (ANNs) to determine the gas hydrate formation kinetics in multiphase gas dominant pipelines with crude oil. Experiments are conducted to determine the rate of formation and reaction kinetics of hydrates formation in multiphase systems. Based on the results, an artificial intelligence model is proposed to predict the gas hydrate formation rate in multiphase transmission pipelines. Two ANN models are suggested with single-layer perceptron (SLP) and multilayer perceptron (MLP). The MLP shows more accurate prediction when compared to SLP. The models were predicted accurately with high prediction accuracy both for the pure and multiphase systems. © 2022 Wiley-VCH GmbH.
publisher: John Wiley and Sons Inc
date: 2022
type: Article
type: PeerReviewed
identifier:   Sayani, J.K.S. and Sivabalan, V. and Foo, K.S. and Pedapati, S.R. and Lal, B.  (2022) Development of a Prediction Model for Gas Hydrate Formation in Multiphase Pipelines by Artificial Intelligence.  Chemical Engineering and Technology, 45 (8).  pp. 1482-1488.  ISSN 09307516     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131558344&doi=10.1002%2fceat.202100359&partnerID=40&md5=b819ba87c634114e0372e4f1bdea8f1a
relation: 10.1002/ceat.202100359
identifier: 10.1002/ceat.202100359