Predictive Analysis of Fluid-Hammer Effect on LNG Regasification System Pipeline Network

Zalkikar, A. and Nepal, B. and Husin, H. and Yadav, O. and Banerjee, A. (2022) Predictive Analysis of Fluid-Hammer Effect on LNG Regasification System Pipeline Network. In: UNSPECIFIED.

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Abstract

This paper proposes a comparison of various machine learning models used for fluid hammer pressure surge vulnerability assessment in seawater pipeline in the LNG regasification plant. One of the most critical components of natural gas production system is the Liquefied Natural Gas (LNG) regasification system which converts the LNG back from liquid phase to gaseous phase (Natural Gas) using a complex pipeline network carrying seawater to extract cold energy from the LNG which is susceptible to fluid hammer formation. In this paper, machine learning based methodology is presented to predict fluid hammer effect in the fluid flow in the complex pipeline network of LNG regasification system. The methodology consists of two parts: first, this study includes a simulation-based model developed in Aspen HYSYS to find the design parameters affecting the water hammer effect in the LNG regasification system pipeline based on the Design of Experiments principles and second, various machine learning algorithms are proposed to predict the vulnerability of the pipeline due to water hammer effect in the pipeline. The Support Vector Machine algorithm with radial kernel was found to be the best model to predict the vulnerability of the pipeline. © 2022 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 0; Conference of 68th Annual Reliability and Maintainability Symposium, RAMS 2022 ; Conference Date: 24 January 2022 Through 27 January 2022; Conference Code:182932
Uncontrolled Keywords: Complex networks; Design of experiments; Flow of fluids; Forecasting; Learning algorithms; Liquefied natural gas; Network security; Seawater; Support vector machines, Fluid hammer; Liquefied natural gas regasification; Machine learning models; Machine-learning; Pipeline networks; Pressure surges; Re-gasification; Vulnerability; Vulnerability assessments; Water hammer effects, Pipelines
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/17510

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