Today, computer tools, especially mathematical modeling, are often used to manage environmental issues. Dynamic mathematical models are usually necessary to describe time-varying phenomena. In urban wastewater treatment processes, there is a need to consider dynamic behavior due to the large time changes that occur in the concentration composition and flow rate of wastewater more than in industrial processes. In addition, because modeling is a useful tool for predicting the performance of processes, it helps a lot in the design and optimal operation of the system. One of the most common wastewater treatment methods is the activated sludge system (SBR) system, which has been used in many treatment plants. There are various methods for modeling this system. System dynamics modeling is usually used to describe time-varying phenomena and is a useful tool for predicting the performance of transient processes. In this study, the removal of organic pollutants in the SBR treatment system has been modeled using the system dynamics method and using STELLA software. To carry out this research, the data and information available in the SBR system located in the wastewater treatment plant of Yazd city have been used as a case study for the simulation of organic pollution. For the modeling of organic compounds, the degradable part of chemical oxygen demand (bCOD) is considered as a qualitative parameter of organic matter. The considered process in bCOD removal includes microbial decomposition and MLVSS production processes, including biomass growth, cellular waste production, as well as non-biodegradable organic solids (nbvss) and considered processes. Also, in the production of MLSS, the process of introducing inactive inorganic solids (XITSS) is also added. In the case study for calibration and validation of the models made from a total of 17 monthly data, 12 data were used for the calibration period and 5 data were used for validation. Further, by calculating the Nash-Sutcliffe and RMSE coefficients, it was found that for the bCOD, MLSS and MLVSS models, the results obtained from the model are in good agreement with the real data and the model has a good ability to predict the concentrations of bCOD, MLSS and MLVSS in the studing SBR system. To eva‎luate the performance of the model and predict the behavior of the reactor under different conditions, four scenarios with different concentrations of bCOD and also different input flow rates were considered. The percentage of changes in the average output bCOD concentration compared to the initial state in the scenarios with a decrease in flow rate of 0.5 and 0.75 times 30% and 54%, respectively, and in the scenario with a flow rate increase of 1.5 times and 2 times, 207% and 300%, respectively, was achieved. While for the scenario with decreasing the input substrate concentration by 0.5 and 0.75 times, the percentage of substrate concentration changes was 45% and 70%, respectively, and in the scenario with increasing the concentration by 1.5 and 2 times, it was 160% and 209%, respectively. In fact, the obtained results showed that in order to predict and simulate the output bCOD concentrations, four scenarios with different input flow rates had a far greater impact on the output bCOD concentrations than four scenarios with different input bCOD concentrations. Therefore, the decrease or increase of the input flow rates compared to the input bCOD concentrations, resulted in more changes in the output bCOD concentrations.

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