Abstract
Artificial neutral networks (ANN) are used in analyzing the factors affecting the removal of organic pollutants and suspended solids in the municipal wastewater treatment plant, Painesville, Ohio, using the activated sludge process. The average raw, settled, secondary, and final BOD concentration were 117, 68, 6, and 2 mg/L, respectively. The corresponding TSS values were 114, 54, 12 and 2.2 mg/L, respectively. The overall BOD and TSS removal efficiencies were 98.29, and 98.07%, respectively. The ANN model with the maximum number of input variables produced the lowest training and testing error for both effluent BOD and TSS. The best ANN structure does not necessarily mean the most number of nodes in the hidden layer. The lowest testing errors for effluent BOD and TSS were 24.91% and 33.84%, respectively. Neural networks as a modeling tool provided an alternative methodology for predicting the performance of wastewater treatment plants.
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