ARTIFICIAL NEURAL NETWORK SIMULATION OF ADVANCED BIOLOGICAL WASTEWATER TREATMENT PLANT PERFORMANCE

Authors : Selami DEMİR

Pages : 1713-1728

View : 36 | Download : 11

Publication Date : 2021-10-05

Article Type : Research Paper

Abstract :Artificial neural network insert ignore into journalissuearticles values(ANN); simulation of chemical oxygen demand insert ignore into journalissuearticles values(COD);, total nitrogen insert ignore into journalissuearticles values(TN);, and total phosphorus insert ignore into journalissuearticles values(TP); removal efficiencies of an advanced biological wastewater treatment process is presented in this study. Seven input parameters insert ignore into journalissuearticles values(predictors); were used: influent COD, TN, and TP concentrations, internal recycle insert ignore into journalissuearticles values(IR); and return activated sludge insert ignore into journalissuearticles values(RAS); ratios, wastewater temperature, and total hydraulic retention time insert ignore into journalissuearticles values(HRT); of process reactors. Results showed that open-source ANN tools can easily be employed for quick and reliable simulation results. ANN with the logistic, the sinc, and the Elliot functions can be confidently employed for predicting COD, TN, and TP removal efficiencies. Mean square errors were 5.54*10-7, 2.06*10-4, and 2.26*10-3, respectively, for COD, TN, and TP removal efficiencies. Besides, wastewater temperature was found to be the major factor that determines the performance of a wastewater treatment system while RAS ratio, HRT, and influent wastewater characteristics are also effective on the performance.
Keywords : Wastewater treatment, biological nutrient removal, treatment performance, artificial neural networks

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