ATIKSU ARITMA TESİS ÇIKIŞ SUYUNUN YAPAY SİNİR AĞLARI İLE TAHMİNİ: KOCAELİ (TÜRKİYE) İLİ ÖRNEK ÇALIŞMASI

Bu çalışmada; Kocaeli (Türkiye) ilinde bulunan iki farklı atıksu arıtma tesis çıkış suyu parametreleri üç katmanlı Yapay Sinir Ağları (YSA) ile değerlendirilerek modelleme yapılmıştır. Çıktı parametreleri olarak belirlenen kimyasal oksijen ihtiyacı (KOİ), askıda katı madde (AKM), pH ve sıcaklık değerleri beş girdi parametresi (akış hızı, KOİ, AKM, pH ve sıcaklık) ile tahmin edilmiştir. YSA modeli 400 veri seti ile geliştirilerek çıkış suyu pH, sıcaklık, KOİ ve AKM değerlerinin modellemesi yapılmıştır. YSA eğitimi için optimum algoritmayı belirlemek amacıyla birçok kıyaslama testleri gerçekleştirilmiştir. YSA modeli; gizli katmanda tanjant sigmoid transfer fonksiyonu (tansig) ve çıkış katmanında lineer transfer fonksiyonu (purelin) optimum olarak belirlenmiştir. Bu fonksiyonları kullanarak eğitim, validasyon ve test setleri için regresyon değerleri sırasıyla 0.94, 0.96 ve 0.95 olarak bulunmuştur. Gizli katmanda optimum nöron sayısı minumum ortalama kare hata değeri temel alınarak saptanmıştır. Elde edilen sonuçlara göre YSA modelinin çıkış suyu pH, sıcaklık, KOİ ve AKM değerlerinin tahmininde etkin ve doğru performans gösterdiği belirlenmiştir.

ARTIFICIAL NEURAL NETWORK APPROACH FOR THE PREDICTION OF EFFLUENTS STREAMS FROM A WASTEWATER TREATMENT PLANT: A CASE STUDY IN KOCAELI (TURKEY)

A three-layer Artificial Neural Network (ANN) model was employed to develop and estimate the effluent stream parameters of two different wastewater treatment plants (WWTP) in Kocaeli, Turkey. The chemical oxygen demand (COD), suspended solid (SS), pH and temperature as the output parameters were estimated by five input parameters such as flow rate, COD, pH, SS and temperature. The ANN model was developed with 400 data sets for prediction of effluent pH, temperature, COD and SS. The benchmark tests were employed to achieve an optimum network algorithm. The network model with optimum functions at hidden and output layers were applied for the forecasts of effluent streams of both WWTPs. The regression values of training, validation and test using this function were found as 0.94, 0.96 and 0.95, respectively. The optimum neuron numbers were determined according to the minimum mean square error values. ANN testing outputs revealed that the model exhibited well performance in forecasting the effluent pH, temperature, SS and COD values.

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[1] Beltramo, T., Klocke, M., Hitzmann, B., “Prediction of the biogas production using GA and ACO input features selection method for ANN model”, Inform. Process. Agri. 2019.
[2] Yetilmezsoy, K., Ozkaya, B., Cakmakci, M., “Artificial Intelligence-Based Prediction Models for Environmental Engineering”, Neural Network World, 3/11, 193-218, 2011.
[3] Hanbay, D., Turkoglu, I., Demir, Y., “Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks”, Expert Syst. Appl. 34 (2), 1038-1043, 2008.
[4] Gadekar, M. R., Mansoor Ahammed, M., “Modelling dye removal by adsorption onto water treatment residuals using combined response surface methodology-artificial neural network approach”, J. Environ. Manage. 231, 241–248, 2019.
[5] Haykin, S. Neural networks and learning machines. Prentice Hall, 2008.
[6] Xiong, Q. and Jutan, A. “Grey-box modelling and control of chemical processes”, Chem. Eng. Sci. 57, 1027–1039, 2002.
[7] Canete, J. D., Saz-Orozco, P. D., Baratti, R., Mulas, M., Ruano, A., Garcia-Cerezo, A., “Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network”, Expert Syst. Appl. 63, 8–19, 2016.
[8] Baklouti, I., Mansouri, M., Hamida, A. B., Nounou, H., Nounou, M., “Monitoring of wastewater treatment plants using improved univariate statistical technique”, Process Saf. Environ. 116, 287–300, 2018.
[9] Harrou, F., Dairi, A., Sun, Y., Senouci, M., “Statistical monitoring of a wastewater treatment plant: A case study”, J. Environ. Manage. 223, 807–814. 2018.
[10] Han, H.G., Zhang, L., Liu, H.-X., Qiao, J.-F., “Multiobjective design of fuzzy neural network controller for wastewater treatment process”, Appl. Soft Comput. 67, 467–478, 2018.
[11] Vijayan, A., Mohan, G.S., “Prediction of Effluent Treatment Plant Performance in a Diary Industry Using Artificial Neural Network Technique”, J. Civil Environ Eng 6, 6, 2016.
[12] Tümer, A.E., Edebali, S., “An Artificial Neural Network Model for Wastewater Treatment Plant of Konya”, IJISAE, 3(4), 131-135, 2015.
[13] Wan, J., Huang, M., Ma, Y., Guo, W., Wang, Y., Zhang, H., Li, W., Sun, X., “Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system”, Appl. Soft Comput. 11, 3238–3246, 2011.
[14] Silva, I.N. and Flauzino, R.A., “An approach based on neural networks for estimation and generalization of crossflow filtration processes”, Appl. Soft Comput. 8, 590–598, 2008.
[15] Holubar, P., Zani, L., Hager, M., Froschl, W., Radak, Z., Braun, R., “Advanced controlling of anaerobic digestion by means of hierarchical neural networks”, Water Res. 36, 2582–2588, 2002.
[16] Çinar, Ö., “New tool for evaluation of performance of wastewater treatment plant: Artificial neural network”, Process Biochem. 40, 2980–2984, 2005.
[17] Chen, J.C., Chang, N.B., Shieh, W.K., Assessing wastewater reclamation potential by neural network model”, Eng. Appl. Artif. Intell. 16, 149–157, 2003.
[18] Nasr, M. S., Moustafa, M.A.E., Seif, H.A.E., El Kobrosy, G., “Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT”, Alexandria Eng. J. 51, 37–43, 2012.
[19] Dias, A., Alves, M., Ferreira, E., Application of computational intelligence techniques for monitoring and prediction of biological wastewater treatment systems. In: In Proceedings of the Int. IWA Conf. on Automation in Water Quality Monitoring, 3:Gent, Belgium, 2007, Springer, pp. 1– 8.
[20] Raduly, B., Gernaey, K.V., Capodaglio, A.G., Mikkelsen, P.S., Henze, M., “Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study”, Environ. Modell. Softw. 22, 1208-1216, 2007.
[21] Nadiri, A. A., Shokri, S., Tsai, F.T.C., Moghaddam, A. A. “Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model”, J. Cleaner Prod. 180, 539-549, 2018.
[22] Yetilmezsoy, K., Turkdogan, F.I., Temizel, I., Gunay, A., “Development of Ann-Based Models to Predict Biogas and Methane Productions in Anaerobic Treatment of Molasses Wastewater”, Int. J. Green Energy, 10:9, 885-907, 2013.
[23] Zaghloul, M.S., Hamza, R.A., Iorhemen, O.T., Tay, J.H., “Performance prediction of an aerobic granular SBR using modular multilayer artificial neural networks”, Sci. Total Environ., 645, 449–459, 2018.
[24] Mjalli, F.S., Al-Asheh, S.A., Alfadala, H.E., “Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance”, J. Environ. Manage., 83, 329–338, 2007.
[25] Anupam, K., Dutta, S., Bhattacharjee, C., Datta, S., “Artificial neural network modelling for removal of chromium (VI) from wastewater using physisorption onto powdered activated carbon”, Desal. Water Treat, 1–10, 2014.
[26] Moral, H., Aksoy, A., Gokcay, C.F.,” Modeling of the activated sludge process by using artificial neural networks with automated architecture screening”, Comput. Chem. Eng. 32 2471–2478, 2008.