Estimation of Effluent TSS of Ahvaz Wastewater Treatment Plant Using Inelegant Models

Subject Areas : Environment Pullotion (water and wastewater)

Mojtaba Ghaed Rahmati ¹ , Hadi Moazed ² , Parvaneh Tishehzan ³

1 - M.Sc., Environmental Engineering, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 - Professor, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 - Assistant Professor, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran *(Corresponding Author)

Received: 2018-07-16 Accepted : 2019-07-17 Published : 2020-11-21

Keywords: ANFIS, Regression, Wastewater, Total suspended solid, ANN,

Abstract :

Introduction: The limitation of fresh water resources in the world, especially in arid and semi-arid regions such as Iran, has inevitably led to the reuse of urban wastewater. One of the most important indicators of sewage pollution and comparison with different standards for reuse or discharge to the water resources is TSS. The present study was conducted in 2016 with the aim of estimation of effluent TSS of Ahvaz wastewater treatment plant using inelegant models. Material and methods: Regard to costly and time-consuming measurement tests of TSS, the capability of multivariate linear regression model, Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) was studied to estimate (TSS) in wastewater treatment plant output by MATLAB and SPSS 21 software. Accordingly, various compounds of sewage quality parameters were evaluated during the 8-year statistical period (2008-2015) as input of models in two daily and monthly modes. Results: The results of the regression model indicated that the maximum R2 for training and verification were 0.75 and 0.67 in daily and 0.68 and 0.66 in monthly period, respectively. The root mean square error (RMSE) in this test was 0.033 and 0.025 in the daily period and 0.053 and 0.053 in the monthly period. The maximum R2 in ANN for training and verification were 0.87 and 0.79 in daily and 0.87 and 0.85 in monthly period, respectively. The RMSE in this test was 0.030 and 0.023 in the daily period and 0.034 and 0.031 in the monthly period. Meanwhile, the maximum R2 in ANFIS for training and verification were 0.91 and 0.83 in daily and 0.89 and 0.87 for monthly period, respectively. The RMSE in this test was 0.026 and 0.025 in the daily period and 0.031 and 0.028 in the monthly period. Conclusion: The results confirmed the application of three models is appropriate, but the ANFIS was considered as a more appropriate model.

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