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The adverse effects of improper disposal of collected and treated wastewater have become inevitable. To achieve the desired environmental standards, in addition to the construction of wastewater treatment plants, there is also a need to evaluate the continuous performance of treatment systems. In Iran, treated wastewater is mostly used in agriculture. Therefore, the use of wastewater with poor quality characteristics can endanger health. In this study, the neural network model's efficiency was investigated to predict the performance of the Perkandabad wastewater treatment plant in Mashhad in Iran. To achieve this, first, the factors affecting the TBOD parameter were identified as one of the quality indicators of the effluent. In the next step, using a genetic algorithm and network input factors, the performance of the treatment plant was predicted and evaluated. The highest correlation coefficient for the TBOD parameter was 0.89%. The results show that among the input parameters in the model, the amount of organic matter pollution load has the greatest effect on this prediction.

Today, phytoremediation is used as an effective and low-cost procedure to treat wastewater. Such is a contribution to mitigating the negative impact of climate change. In order to choose phytoremediation as a method of water purification, one of the most important parameters is the economic one. Alongside financial aspect, important criteria while selecting most appropriate method is the effectiveness of each method in wastewater treatment. There is a need for procedure which will take into the account possible variants of the phytoremediation process at the wastewater treatment plant. The present study was performed on the first module of the Parkandabad wastewater treatment plant in Iran. The plant generally receives municipal wastewater. In this study, the changes of biological oxygen demand (BOD5), chemical oxygen demand (COD), total dissolved solids (TDS) and total Kjeldahl nitrogen (TKN) were investigated after using umbrella papyrus (Cyperusalternifolius) and Vetiver grass (Vetiveriazizanioides L.). Macbeth multicriteria method was applied to rank and select the obtained experimental variants with the aim of selecting the best plant species for phytoremediation and optimizing the efficiency in terms of removing BOD5, COD, TDS and TKN.