Estimation of Returned Sludge Using Artificial Neural Network and Fuzzy Inference System (Case Study: Shahrake-Gharb Waste Water Treatment Plant, Tehran)

The amount of returned sludge is considered as one of the important and controllable parameters in the operation of wastewater treatment plants and play a vital role in process. There are different approaches to measure the rate of the returned sludge from secondary sedimentation to aeriation tank but all of them rely on the results of tests that are done after the process. Therefore, determining dynamic estimation methods is very important. In the present study, artificial neural network (ANN) models and adaptive fuzzy-neural inference system (ANFIS) were used to achieve this goal. First, different compositions according to the quality parameters of wastewater such as sewage inlet flow, BOD5, temperature, TDS, TS and returned sludge flow with time delay were considered as input, and the amount of returned sludge as network output. Then, by training the network and determining the desired structure based on the type, number of membership functions and related laws, and using MATLAB software, the most appropriate model were obtained based on statistical data, the mean squared error and the efficiency of the coefficient of determination model. As a result, the inputs were introduced as the most suitable model by combining a one-dimensional Sugeno inference system with relevant membership functions. The results of different methods were compared and finally, a Genfis2 model, which is moderation of ANFIS systems, with a training coefficient above 93% (MSE = 0.0081 and RMSE = 0.0898) and a validation coefficient above 91% (MSE = 0.0027 and RMSE = 0.0518) was selected and presented for accurate estimation of the amount of returned sludge up to the next 24 h. This study was done with comprehensiveness and practicality for the first time in Iran and could lead to prevention of polluting the receiving waters.