Modelling the clogging of gas turbine filter houses in heavy-duty power generation systems

Abdul-Wahab S. A., Omer A. S. M., Yetilmezsoy K., Bahramian M.

MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS, cilt.26, sa.2, ss.119-143, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Sayı: 2
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1080/13873954.2020.1713821
  • Dergi Adı: MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.119-143
  • Anahtar Kelimeler: Clogging phenomenon, fuzzy logic model, heavy-duty power generation system, PRESSURE-DROP, AEROSOL-PARTICLES, FIBROUS FILTERS, AIR FILTRATION, HEPA FILTERS, PREDICTION, OPTIMIZATION, PERFORMANCE, RESISTANCE, HUMIDITY
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

A prognostic approach based on a MISO (multiple inputs and single output) fuzzy logic model was introduced to estimate the pressure difference across a gas turbine (GT) filter house in a heavy-duty power generation system. For modelling and simulation of clogging of the GT filter house, nine real-time process variables (ambient temperature, humidity, ambient pressure, GT produced load, inlet guide vane position, airflow rate, wind speed, wind direction and PM10 dust concentration) were fuzzified using a graphical user interface within the framework of an artificial intelligence-based methodology. The results revealed that the proposed fuzzy logic model produced very small deviations and showed a superior predictive performance than the conventional multiple regression methodology, with a very high determination coefficient of 0.974. A complicated dynamic process, such as clogging phenomenonin heavy-duty GT system, was successfully modelled due to high capability of the fuzzy logic-based prognostic approach in capturing the nonlinear interactions.