Prediction of Methane Hydrate Formation Rate in Multiphase System using Artificial Neural Network

Nadzri, W.A.N.B.W.A. and Nashed, O. and Lal, B. and Foo, K.S. and Sabil, K.M. (2022) Prediction of Methane Hydrate Formation Rate in Multiphase System using Artificial Neural Network. Lecture Notes in Electrical Engineering, 758. pp. 859-865. ISSN 18761100

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Abstract

The research of the hydrate formation has advanced over the last few decades. Several models have been developed to understand the hydrates formation kinetics and conditions. In this study, computer based model is used to predict the gas hydrate formation rate. Since the hydrae formation is stochastic phenomenon and it is common to get inconsistent data, Artificial Neural Network (ANN) model has potential to outstand other conventional kinetic models. ANN used to predict the methane hydrate formation rate in multiphase system. The liquid phase composed of water + drilling oil + nonionic surfactants was used to form methane hydrates at pressure 8.80 MPa and temperature of 274.15�277.15 K. This research would essentially assess the effectiveness of ANN model for the kinetic modeling of the formation of gas hydrate from the acquired regression analysis. The result of this research revealed that ANN model with 16 number of hidden neurons had a better prediction as the highest regression value R was found to be 0.9956. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Item Type: Article
Additional Information: cited By 0; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
Uncontrolled Keywords: Forecasting; Gas hydrates; Hydration; Kinetic theory; Kinetics; Methane; Nonionic surfactants; Regression analysis; Stochastic models; Stochastic systems, Artificial neural network modeling; Computer-based modeling; Formation condition; Formation kinetics; Formation rates; Gas hydrates formation; Hydrate formation; Kinetic models; Methane hydrates; Multi phase systems, Neural networks
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 19 Dec 2023 03:23
Last Modified: 19 Dec 2023 03:23
URI: https://khub.utp.edu.my/scholars/id/eprint/17413

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