Regular Article
Application of Particle Swarm Optimization (PSO) algorithm for Black Powder (BP) source identification in gas pipeline network based on 1-D model
Khalifa University, Sas Al-Nakhl Campus, Abu Dhabi, United Arab Emirates
* Corresponding author: jing.shi5@student.unsw.edu.au
Received:
20
August
2018
Accepted:
18
March
2019
Black Powder (BP) is a worldwide challenge that spans all stages of the natural gas industry from the producing wells to the consuming points. It can endanger the pipeline operations, damage instruments and contaminate customer supplies. The formation of BP inside natural gas pipeline mainly results from the corrosion of internal walls of the pipeline, which is a complex chemical reaction. This work aims to develop a novel algorithm for BP source identification within gas pipelines network based on a 1-D model of BP transport and deposition. The optimization algorithm for BP source identification is developed based on the well-known Particle Swarm Optimization (PSO) algorithm, which can solve constrained optimization problems. By applying this optimization algorithm on the gas transmission pipeline network, the BP source at different junctions could be identified and quantified simultaneously. Extensive simulation studies are conducted to validate the effectivity of the optimization algorithm.
© J. Shi et al., published by IFP Energies nouvelles, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
