TY - GEN
T1 - Black powder source identification in a gas pipeline network based on a One-D model
AU - Shi, Jing
AU - Al-Durra, Ahmed
AU - Matraji, Imad
AU - Al-Wahedi, Khaled
AU - Abou-Khousa, Mohamed
N1 - Funding Information:
ACKNOWLEDGMENT The authors acknowledge the Gas Processing & Materials Science Research Centre (GRC) at Khalifa University of Science and Technology - The Petroleum Institute, Abu-Dhabi, in addition to Abu Dhabi Gas Industries Limited (GASCO) for funding and supporting this project.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Black Powder (BP) is a phenomenon experienced worldwide by transmission gas pipeline operators with internally uncoated lines. It can cause serious problems in pipeline operation and instruments, and contaminate customer supply. It is regenerative and forms inside natural gas pipelines due to corrosion of the internal walls of the pipeline and chemical reactions. The aim of the present study is to develop a novel algorithm for BP source identification within gas pipelines network based on a 1-D static model. The proposed identification method is based on the well-known Particle Swarm Optimization (PSO) algorithm, which is able to identify and quantify BP source at different junctions simultaneously. Extensive simulation results are given to illustrate the effectiveness of the proposed optimization algorithm for BP identification.
AB - Black Powder (BP) is a phenomenon experienced worldwide by transmission gas pipeline operators with internally uncoated lines. It can cause serious problems in pipeline operation and instruments, and contaminate customer supply. It is regenerative and forms inside natural gas pipelines due to corrosion of the internal walls of the pipeline and chemical reactions. The aim of the present study is to develop a novel algorithm for BP source identification within gas pipelines network based on a 1-D static model. The proposed identification method is based on the well-known Particle Swarm Optimization (PSO) algorithm, which is able to identify and quantify BP source at different junctions simultaneously. Extensive simulation results are given to illustrate the effectiveness of the proposed optimization algorithm for BP identification.
KW - Black Powder
KW - Gas Pipeline Network
KW - Particle Swarm Optimization
KW - Source Identification
UR - http://www.scopus.com/inward/record.url?scp=85050649938&partnerID=8YFLogxK
U2 - 10.1109/SPC.2017.8313014
DO - 10.1109/SPC.2017.8313014
M3 - Conference contribution
AN - SCOPUS:85050649938
T3 - Proceedings - 2017 IEEE Conference on Systems, Process and Control, ICSPC 2017
SP - 12
EP - 17
BT - Proceedings - 2017 IEEE Conference on Systems, Process and Control, ICSPC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Conference on Systems, Process and Control, ICSPC 2017
Y2 - 15 December 2017 through 16 December 2017
ER -