Applications of Soft Computing Methods in Environmental Engineering

Yetilmezsoy, Kaan

doi:10.1007/978-3-319-58538-3_149-1

Cited by 2 publications

(1 citation statement)

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“…Additionally, there are other uncountable physical models [53][54][55][56][57][58] aimed at illustrating this mechanism, but with a wide range of assumptions, simplifications and neglections. On the other hand, the fuzzy-based models provide a transparent and a systematic analysis for modelling highly complicated and dynamic systems [59]. This prognostic technique has the benefit of the relatively straightforward sets of logical connectives rather than describing complicated mathematical statements for the model components in dealing with nonlinear systems [42].…”

Section: Discussion On Importance and Advantages Of Prognostic Modellmentioning

confidence: 99%

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

Abdul‐Wahab

Omer

Yetilmezsoy

et al. 2020

Mathematical and Computer Modelling of Dynamical Systems

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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.

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