Modeling of Zn Removal from Wastewater with Artificial Neural Network (ANN)

Abstract

In this study, Zn removal estimation for the Ankara Chamber of Industry 2nd and 3rd Organized Industrial Zone wastewater treatment plant was performed by Artificial Neural Network (ANN). It is very important to reduce the zinc, which is toxic and frequently encountered in wastewater within the scope of heavy metal pollution, to the limit determined after treatment. In the modeling studies, pH, Zn and Fe concentration, SS (Suspended Solids) and TSS (Total Suspended Solids) were selected as input parameters, while Zn output concentration was chosen as the output parameter. The training of the data was done with the Levenberg-Marquardt feed forward algorithm and the experimental data were divided into 75% training, 15% validation and 15% test. In the study, scenarios created by different combinations for input parameters were tried and the results obtained by determining the maximum cycle (epoch) value of the system, training, validation and R and MSE values for the whole biosorption system were compared. In order to see the effect of activation function on the results, tansig, purelin and logsis transfer functions were used. As a result of the study, when the Zn concentration values in the experimental and the model estimated output current were compared, it was seen that the system was well modeled with ANN and the model has a good prediction ability.

Keywords

• Wastewater
• Modeling
• Zinc
• Artificial Neural Network

Atıksulardan Zn Gideriminin Yapay Sinir Ağı (YSA) ile Modellenmesi

Öz

Yapılan bu çalışmada Ankara Sanayi Odası 2. ve 3. Organize Sanayi Bölgesi Atık Su arıtma tesisi için Zn giderim tahmini YSA ile gerçekleştirilmiştir. Toksik olup ağır metal kirliliği kapsamında atıksularda oldukça sık karşılaşılan çinkonun arıtım sonrası belirlenen limite indirgenmesi oldukça önemlidir. YSA ile modelleme çalışmalarında giriş parametresi olarak giriş pH, Zn ve Fe konsantrasyonu, AKM ( Askıda Katı Madde) ve TKM (Toplam Katı Madde) seçilirken çıkış parametresi olarak Zn çıkış konsantrasyonu seçilmiştir. Verilerin eğitimi Levenberg–Marquardt ileri besleme algoritması ile yapılmış olup deneysel veriler %75 eğitim, %15 validasyon ve %15 test olarak ayrılmıştır. Çalışmada giriş parametreleri için farklı kombinasyonların oluşturduğu senaryolar denenmiş ve sisteme ait maksimum devir (epoch) değeri, eğitim, validasyon ve tüm biyosorpsiyon sistemi için R ve MSE değeri belirlenerek elde edilen sonuçlar karşılaştırılmıştır. Aktivasyon fonksiyonunun sonuçlar üzerine etkisini görebilmek için tansig, pürelin ve logsis transfer fonksiyonları kullanılmıştır. Çalışma sonucunda deneysel ve model tahmini çıkış akımındaki Zn konsantrasyon değerleri karşılaştırıldığında, YSA ile sistemin iyi bir şekilde modellendiği ve modelin iyi bir tahmin yeteneğine sahip olduğu görülmüştür.

Anahtar Kelimeler

• atık su
• modelleme
• çinko
• yapay sinir ağı

Kaynakça

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