AKTGF ÇAMUR PROSESG HAVALANDIRMA HAVUZU ASKIDA KATI MADDE (AKM) KONSANTRASYONUNUN MEKANGSTGK, YAPAY SG

Dinamik simülasyon atıksu arıtma tesislerinde işletmenin iyileştirilmesinde önemli bir araçtIr. Bu çalışmada, Ankara Merkezi Atıksu Arıtma Tesisinin dinamik simülasyon modeli tasarlanmıştır. Öncelikle, evsel atIksu arıtma prosesinin mekanistik modeli Activated Sludge ModelNo. 1 bazında GPS-X bilgisayar programI kullanılarak geliştirilmiştir. Yapay Sinir aĞI modeli de geriye yayılım algoritmasını esas alan MLP sinir aĞI yardımı ile oluşturulmuştur. Daha sonra, mekanistik model yapay sinir aĞI ile birleştirilmiştir. Yapay sinir aĞI modellerinin en uygun aĞ yapılı modellerin birçok adımda eğitilmesi ve test edilmesi ile tespit edilmiştir. Her üç model,prosesin dinamik davranışlı tahmin etmek için tesisinin işletme ve laboratuar analizlerinden elde edilen aynı veriler ile oluşturulmuştur. Havalandırma tankı Askıda Katı Madde (AKM)konsantrasyonu tahmin edilmiş ve sonuçları karşılaştırılmıştır. Hibrit model yaklaşımının daha başarıl sonuçlar verdiği ve tesisin işletme koşullarının ASM1 ve YSA modellerinden daha iyi tanımlandığı gözlenmiştir.

Anahtar Kelimeler:

Modelleme, Activated Sludge Model No.1 (ASM1), Yapay sinir Ağı (YSA), Hibrit Modelleme, Evsel Atıksu Arıtma Tesisi

AKTGF ÇAMUR PROSESG HAVALANDIRMA HAVUZU ASKIDA KATI MADDE (AKM) KONSANTRASYONUNUN MEKANGSTGK, YAPAY SG

Dynamic simulation is an important tool for the improvement of wastewater treatmentplant operation. In this study, dynamic simulation model of the Ankara central wastewater treatmentplant (ACWT) were evaluated. First, a mechanistic model of the municipal wastewater treatmentprocess is developed based on Activated Sludge Model No. 1 by using a GPS-X computer program.Artificial neural network model is also developed with the help of MLP neuronal networks based onback-propagation algorithm. Then, the mechanistic model is combined with artificial neural networkin parallel configuration. The appropriate architecture of the neural network models was determinedthrough several steps of trainings and testing of the models. Both three models are performed withthe same data obtained from the plant operation and laboratory analysis to predict dynamicbehaviour of the process. Using these three models, by the purpose of evaluation of treatmentperformance, aeration tank MLSS concentrations have been predicted and the results have beencompared. It is observed that the hybrid model approach gives more successful results and describesthe operation conditions of the plant better than ASM1and ANN.

Keywords:

Modelling, Activated Sludge Model No.1 (ASM1), Artificial neural network (ANN), Hybrid Modelling, Municipal Wastewater Treatment plant,

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