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

Yıl 2007, Cilt: 22 Sayı: 4, 1 - 10, 01.12.2007

Dünyamin Güçlü Şükrü Dursun

Öz

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Chen, J.C., Chang, N.B. and Shieh, W.K. 2003. Assessing wastewater reclamation potential by neural network model. Engineering Applications of Artificial Intelligence 16: 149-157.
• Cote, M., Grandjean, B.P.A., Lessard, P., Thibault, J. 1995. Dynamic modeling of the activated sludge process: improving prediction using neural networks. Water Research 29(4): 995–1004.
• Gokcay, C.F. and Sin, G. 2004. Modelling of a large-scale wastewater treatment plant for efficient operation. Water Science and Technology 50(7): 123-130.
• GPS-X version 5.0 user’s guide, 2006. Canada: JT Hydromantis Inc. Gujer, W., Henze, M., Mino, T.,& van Loosdrechi, M. 1999. Activated sludge model no. 3. Water Science Technology 39(1): 183-193.
• Güçlü, D., 2007. Tam Ölçekli Kentsel AtÍksu ArÍtma Tesislerinin Bilgisayar ProgramÍ kullanÍlarak Modellenmesi ve ArÍtma PerformanslarÍÍn Gncelenmesi. Doktora Tezi, S.Ü. Fen Bilimleri Enstitüsü, 2007, Konya.
• Hamed, M.M., Khalafallah, M.G. and Hassanien, E.A. 2004. Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling & Software 19: 919– 928.
• Häck, M. and Köhne, M. 1996. Estimation of wastewater process parameters using artificial neural networks. Water Science and Technology 33(1): 101-115.
• Henze, M., Grady C. P. L., Jr, Gujer, W., Marais, G. v. R., Matsuo, T. 1987. Activated sludge model no 1. IAWQ Scientific and Technical Report No 1, London, UK.
• Henze, M., Gujer, W., Mino, T., Matsuo, T., Wentzel, M. C., Marais, G. v. R., Van Loosdrecht, M. C. M. 1995. Activated sludge model no 2. IAWQ, Scientific and Technical Report No 3, London, UK.
• Ladiges G., Günner, C. und Otterpohl, R. 2001. Optimierung des Hamburger Klärwerksverbundes Köhlbrandhöft/Dradenau mithilfe der dynamischen Simulation. KA-Wasserwirtschaft, Abwasser, Abfall 48(4): 490-498.
• Lee, D. S., Vanrolleghem, P.A., Park, J.M. 2005. Parallel hybrid modeling methods for a full-scale cokes wastewater treatment plant. Journal of Biotechnology 115: 317-328.
• Morgenroth, E., Arvin, E., Vanrolleghem, P. 2002. The use of mathematical Models in teaching Wastewater treatment engineering. Water Science and Technology 45(6): 229-233.
• Nuhoglu, A., Keskinler, B., Yildiz, E. 2005. Mathematical modelling of the activated sludge processthe Erzincan case. Process Biochemistry 40: 2467-2473.
• Onkal-Engin, G., Demir, I. and Engin, S.N. 2005. Determination of the relationship between sewage odour and BOD by neural networks. Environmental Modelling & Software 20: 843–850.
• Winkler, U. und Voigtländer, G. 1995. Anwendung neuronaler Netze für die Simulation von Prozessabläufen auf vorhandenen Kläranlagen. Korrespondenz Abwasser 10: 1784–1792.
• Zhao, H., Hao, O.J. and McAvoy, T.J. 1999. Approaches to modeling nutrient dynamics: ASM2, simplified model and neural nets. Water Science and Technology 39(1): 227-234.