Artificial Intelligence Predictions of Biomass Power of an Installed Waste Water Treatment Plant

Yıl 2024, , 359 - 374, 11.07.2024

Akın İlhan

https://doi.org/10.21605/cukurovaumfd.1514358

Öz

In the current study, the power generations obtained from gas turbines of an installed waste water treatment plant were predicted, utilizing artificial intelligence method consisting of artificial neural network (ANN). In this regards, a cumulative of 445 data, found in the power generation data cluster and found in the physical and chemical data clusters has been used in the predictions based on the artificial intelligence association method. Each instant data of these total 445 data corresponds to daily average power generation (P) obtained from gas turbines of the facility and corresponds to physical and chemical parameters including the temperature (T), degree of acidity (pH), conductivity (σ), as well as the daily total volumetric flow of the waste gas to be burned at the gas generator (Q). Accordingly, the best prediction obtained by ANN approach was concluded to generate the statistical accuracy results corresponding to 6.1279% mean absolute percentage error (MAPE), 2.1540 MWh/day root mean square error (RMSE), and 0.9730 correlation coefficient (R) for power generation parameter.

Anahtar Kelimeler

Artificial intelligence, Artificial neural network (ANN), Waste water treatment

Biokütle Tipi Kurulu Atık Su Arıtma Tesisinin Enerji Geri Kazanım Analizi

Öz

Bu çalışmada, kurulu bir atık su arıtma tesisinin gaz türbinlerinden elde edilen enerji üretimleri, yapay sinir ağlarından (YSA) yararlanılarak yapay zekâ yöntemi kullanılarak tahmin edilmiştir. Bu doğrultuda, yapay zekâ ilişkilendirme yöntemine dayalı tahminlerde, elektrik üretimi veri kümesinde bulunan ve fiziksel ve kimyasal veri kümelerinde bulunan 445 verinin tamamı kullanılmıştır. Bu toplam 445 verinin her bir anlık verisi, tesisin gaz türbinlerinden elde edilen günlük ortalama elektrik üretimine (P) karşılık gelmekte olup; ayrıca, sıcaklık (T), asitlik derecesi (pH), iletkenlik (σ), ve ilaveten gaz jeneratöründe yakılacak atık gazın günlük toplam hacimsel akışını (Q) da içermektedir. Buna göre, güç üretim parametresi kapsamında, YSA yaklaşımıyla elde edilen en iyi tahminin; %6.1279’luk ortalama mutlak yüzde hata (MAPE) değerine, 2,1540 MWh/gün ortalama karekök hata (RMSE) ve 0,9730 korelasyon katsayısına (R) karşılık gelen istatistiksel doğruluk sonuçlarını ürettiği sonucuna varılmıştır.

Anahtar Kelimeler

Yapay zekâ, Yapay sinir ağları (YSA), Atık su arıtma

Kaynakça

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