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Modeling of activated sludge process by using artificial neural networks

Moral, Hakan
Current activated sludge models are deterministic in character and are constructed by basing on the fundamental biokinetics. However, calibrating these models are extremely time consuming and laborious. An easy-to-calibrate and user friendly computer model, one of the artificial intelligence techniques, Artificial Neural Networks (ANNs) were used in this study. These models can be used not only directly as a substitute for deterministic models but also can be plugged into the system as error predictors. Three systems were modeled by using ANN models. Initially, a hypothetical wastewater treatment plant constructed in Simulation of Single-Sludge Processes for Carbon Oxidation, Nitrification & Denitrification (SSSP) program, which is an implementation of Activated Sludge Model No 1 (ASM1), was used as the source of input and output data. The other systems were actual treatment plants, Ankara Central Wastewater Treatment Plant, ACWTP and Iskenderun Wastewater Treatment Plant (IskWTP). A sensitivity analysis was applied for the hypothetical plant for both of the model simulation results obtained by the SSSP program and the developed ANN model. Sensitivity tests carried out by comparing the responses of the two models indicated parallel sensitivities. In hypothetical WWTP modeling, the highest correlation coefficient obtained with ANN model versus SSSP was about 0.980. By using actual data from IskWTP the best fit obtained by the ANN model yielded R value of 0.795 can be considered very high with such a noisy data. Similarly, ACWTP the R value obtained was 0.688, where accuracy of fit is debatable.