Modeling of submerged membrane bioreactor treating cheese whey wastewater by artificial neural network.
A submerged membrane bioreactor receiving cheese whey was modeled by artificial neural network and its performance over a period of 100 days at different solids retention times was evaluated with this robust tool. A cascade-forward network was used to model the membrane bioreactor and normalization was used as a preprocessing method. The network was fed with two subsets of operational data, with two-thirds being used for training and one-third for testing the performance of the artificial neural network. The training procedure for effluent chemical oxygen demand (COD), ammonia, nitrate and total phosphate concentrations was very successful and a perfect match was obtained between the measured and the calculated concentrations. The results of the confirmation (or testing) procedure for effluent ammonia and nitrate concentrations were very successful; however, the results of the confirmation procedure for effluent COD and total phosphate concentrations were only satisfactory.
Cinar
Ozer
O
Kahramanmaraş Sütçü Imam University, Department of Environmental Engineering, Campus of Avsar, 46001 Kahramanmaraş, Turkey. ocinar@alumni.clemson.edu
Hasar
Halil
H
Kinaci
Cumali
C
D000465:Algorithms; D001421:Bacteria, Anaerobic / Q000378:metabolism*; D019149:Bioreactors / Q000382:microbiology*; D018929:Cell Culture Techniques / Q000379:methods; D002611:Cheese / Q000382:microbiology*; D003198:Computer Simulation; D007220:Industrial Waste / Q000517:prevention & control*; D008954:Models, Biological; D010100:Oxygen / Q000378:metabolism; D014874:Water Pollutants, Chemical / Q000378:metabolism*; D018508:Water Purification / Q000379:methods*
D007220:Industrial Waste; D014874:Water Pollutants, Chemical; D010100:Oxygen