Emerald Fulltext Archive Database 1994-2005
Artificial neural networks (ANN) are used as an alternativefunction approximation tool for predicting the performance of tricklingfilter treatment process in a municipal wastewater treatment plant,Solon, Ohio, USA, which uses a trickling filter followed by an activatedsludge process. The treatment plant had an average monthly inflow flowrate of 2.92 mgd (million gallons per day). The average raw, settled,and final BOD (biochemical oxygen demand) was 449, 235 and 4.8 mg/l,respectively, while the corresponding value for TSS (total suspendedsolids) was 296, 131, and 6.1 mg/l. The overall removal efficiency forBOD and TSS was 98.93 per cent and 97.95 per cent respectively. The bestANN model for predicting the trickling filter effluent BOD and TSS has aprediction error of 31.45 per cent and 32.54 per cent respectively. Thenumber of input variables, as well as number of nodes in hidden layerseemed not to have a definite effect on the prediction error for the ANNmodel. The prediction errors obtained with ANN models were lower thanthose obtained by multiple regression analysis.
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