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Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming

Author

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  • Safiyullah, F.
  • Sulaiman, S.A.
  • Naz, M.Y.
  • Jasmani, M.S.
  • Ghazali, S.M.A.

Abstract

In oil and gas industry, the performance prediction of gas compressors is approaching criticality. Usually, maintenance engineers rely on recommendations set by the original equipment manufacturer (OEM) for maintenance activities. Since compressors are operated in offshore conditions, OEM recommendations may over predict or under predict the maintenance schedule. An improper verdict on compressor maintenance interventions may increase the equipment downtime because of unavailability of the resources and poor readiness of the spare parts. The aim of the presented research was to develop a diagnostic model for gas compressors by using the genetic programming (GP). The OEM isentropic and actual isentropic heads were compared, and the maintenance activity of a gas compressor was predicted by calculating the performance degradation. The computational codes were developed separately for OEM isentropic and actual isentropic heads through GP. Hereinafter, the empirical equations were derived from the developed computational codes to predict the optimum time for the routine maintenance. For rotational speed between the tested regions, GP predicted 92% accurate interpolation between the curves. It reveals that using the developed GP model, the operators can accurately predict the compressor's health and plan ahead the equipment maintenance at any time.

Suggested Citation

  • Safiyullah, F. & Sulaiman, S.A. & Naz, M.Y. & Jasmani, M.S. & Ghazali, S.M.A., 2018. "Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming," Energy, Elsevier, vol. 158(C), pages 485-494.
    • Handle: RePEc:eee:energy:v:158:y:2018:i:c:p:485-494
    DOI: 10.1016/j.energy.2018.06.051
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    References listed on IDEAS

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    Cited by:

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    3. Syed Ahmed Kabir, Ijaz Fazil & Safiyullah, Ferozkhan & Ng, E.Y.K. & Tam, Vivian W.Y., 2020. "New analytical wake models based on artificial intelligence and rivalling the benchmark full-rotor CFD predictions under both uniform and ABL inflows," Energy, Elsevier, vol. 193(C).
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    7. Chen, Yu-Zhi & Zhao, Xu-Dong & Xiang, Heng-Chao & Tsoutsanis, Elias, 2021. "A sequential model-based approach for gas turbine performance diagnostics," Energy, Elsevier, vol. 220(C).

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