Restrictions and obstructions detection in pipe networks using incomplete and noisy flow and pressure steady‐state measurements. Issue 1 (15th October 2021)

• Record Type: Journal Article
• Authors: Mazzotti, Matteo; Khazaali, Mohanad; Bocchini, Paolo; Di Lullo, Alberto; Marzani, Alessandro
• Is Part Of: Structural control and health monitoring. Volume 29:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-15
• Subjects: genetic algorithms -- inverse problems -- obstructions -- pipelines -- pipe networks -- restrictions -- system identification; Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.2854
• Languages: English
• ISSNs: 1545-2255

Abstract

Summary: Pipe networks for both water and oil distribution are prone to the formation of restrictions and, if not managed, possible obstructions. These reduce the efficiency of pipe systems and, in turn, cause negative economic impacts, temporary losses of service, and environmental risks. The present work focuses on a noninvasive methodology for the detection of restrictions in pipe networks. Restrictions are identified by minimizing, via genetic algorithms, a function that represents the discrepancy between on‐field measured data and those simulated numerically. Measured data consist of a limited set of steady‐state pressure heads and flow rates, which are the most commonly available information for pipe networks. The outcome of the technique is the "equivalent residual diameter" of each pipe in the network. This parameter allows the company managing the pipe network to identify the pipe segments where restrictions are most likely to be present and require further investigations. The approach is numerically validated for 15 different scenarios, considering five different sets of available measures and three different restriction conditions, in a mixed branched‐looped network with complex topology for crude‐oil transportation. The results show that the presence of restrictions is clearly identified and their magnitude is generally assessed with an accuracy of 5 % .