Beytullah Eren

Development of artificial neural network for prediction of salt recovery by nanofiltration from textile industry wastewaters

This paper presents the use of artificial neural network (ANN) to develop a model for predicting rejection rate (R o) of single salt (NaCl) by nanofiltration based on experimental data-sets. The re...

Investigation of Lake Sapanca water pollution, Adapazari, Turkey

Lake Sapanca has been the only source of drinking and recreational water for the city of Adapazari, Turkey. This paper reports a study of the variation of nutrient loading and trophic state of the lake, and also water quality parameters of Lake Sapanca compared to those of the neighbouring Lake Iznik. Through one year, samples were taken every three months from 15 different points on the streams feeding and draining off the lake. Nitrate, NO2‐N, NH3‐N, TKN, PO4‐P concentrations on the 12 streams fe and three draining off points were determined. Then, loading, discharge, and accumulation amounts of nitrogen and phosphorus causing eutrophication were calculated and the trophic state of the lake was determined. A simple model was used to analyse the response of Lake Sapanca when the phosphorus loading rate was changed. Through this model, the variation of different parameters (t, M, K, Q, V and A) with respect to phosphorus concentration (C) was studied to identify effects and results. The consequences of an eutrophic state and measures to protect the lake are also discussed.

Elektrokoagülayon Yöntemiyle Reaktif Yellow 160 Boyar Maddesinin Giderimi

Demir ve aluminyum elektrotlar ile donatilmis elektrokoagulasyon prosesinin (EP) kullanildigi calismada Reaktif Yellow 160 (RY160) boyarmaddesi giderimi uzerine baslangic pH, akim yogunlugu, iletkenlik ve elektroliz suresinin etkisi incelenmistir. EP’de elektrot materyalinden bagimsiz olarak yuksek renk giderim verimi elde edilmistir. Optimum isletme sartlari aluminyum elektrot kullanilmasi durumunda, pH = 5, akim yogunlugu 100 A/m 2 , elektroliz suresi 10 dakika, iletkenlik 1000 µS/cm iken demir elektrot kullanilmasi durumunda pH = 7, akim yogunlugu 200 A/m 2 , elektroliz suresi 5 dakika, iletkenlik 1000 µS/cm olarak belirlenmistir. Renk giderim verimi aluminyum elektrot cifti icin 2,3 kWsa/m 3 enerji sarfiyati ve 0,52

Polimer içerikli membran verimi tahmininde yapay sinir ağları öğrenme algoritmalarının değerlendirilmesi

/m 3 toplam maliyetle %96,4 iken demir elektrot cifti icin 1,7 kWsa/m 3 enerji sarfiyati ve 0,28

Application of artificial neural networks to estimate wastewater treatment plant inlet biochemical oxygen demand

/m 3 toplam maliyetle %95,8 elde edilmistir. Sonuclar, demir elektrot kullaniminin aluminyum elektrot ile karsilastirildiginda RY160 boyarmaddesinin gideriminde daha ekonomik oldugunu gostermistir.

Flood Causes, Consequences and Protection Measures in Pakistan

The aim of this study is to introduce, through an appropriate selection of the training algorithm, a better and optimum artificial neural network (ANN) that will capable to predict Polymeric Inclusion Membranes (PIMs) Cr(VI) removal efficiency from aqueous solutions. To accomplish that, three training algorithms including Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) have been assessed by training different ANN. The performances of developed models are evaluated by Coefficient of Regression (R) and Root Mean Square Error (RMSE) to find the best ANN training algorithms. This study clears that right choice of the training algorithm grants maximizing the predictive capability of the ANN models.