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Prediction of Gas Turbine Performance Degradation Between Soakwashes in Natural Gas Compressor Stations

[+] Author Affiliations
R. Chatterjee, K. K. Botros

NOVA Research and Technology Center, Calgary, AB, Canada

H. Golshan, D. Rogers, Z. Samoylove

TransCanada Pipelines Limited, Calgary, AB, Canada

Paper No. IPC2012-90048, pp. 441-451; 11 pages
doi:10.1115/IPC2012-90048
From:
  • 2012 9th International Pipeline Conference
  • Volume 1: Upstream Pipelines; Project Management; Design and Construction; Environment; Facilities Integrity Management; Operations and Maintenance; Pipeline Automation and Measurement
  • Calgary, Alberta, Canada, September 24–28, 2012
  • Conference Sponsors: International Petroleum Technology Institute, Pipeline Division
  • ISBN: 978-0-7918-4512-7
  • Copyright © 2012 by ASME

abstract

Gas Turbine (GT), like other prime movers, undergoes wear and tear over time which results in performance drop as far as available power and efficiency is concerned. In addition to routine wear and tear, the engine also undergoes corrosion, fouling etc. due to the impurities it breathes in. It is standard procedure to ‘wash’ the engine from time to time to revive it. However, it is important to establish a correct schedule for the wash to ensure optimal maintenance procedure. This calls for accurate prediction of the performance degradation of the engine over time. In this paper, a methodology is presented to predict the performance degradation in a GE LM2500 Gas Turbine engine used at one of TransCanada’s pipeline system, Canada. Emphasis is laid on analyzing the degradation of the air compressor side of the engine since it is most prone to fouling and degradation. Although the results presented are for a specific engine type, the general framework of the model could be used for other engines as well to quantify degradation over time of other components within the GT engine. The present model combines Gas Path Analysis (GPA) to evaluate the thermodynamic parameters over the engine cycle followed by parameter estimation to filter the data of possible noise due to instrumentation errors. The model helps quantify the degradation in the engine performance over time and also indicates the effectiveness of each engine wash. The analysis will lead to better scheduling of the engine wash thereby optimizing operational costs as well as engine overhaul time.

Copyright © 2012 by ASME

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