Prediction of Effluent Quality Parameters of Wastewater Treatment Plant Using Data Mining Methods (Case Study: Borujen Wastewater Treatment Plant)

Articles in Press

Document Type : Original Article

Authors

1 Master student of Shahre_kord University, Shahre_kord, Iran.

2 Water engineering department at shahrekord university.

3 Department of Water Engineering, Shahrekord University, Shahrekord, Iran

10.22125/iwe.2024.417805.1752

Abstract

Prediction of effluent quality parameters of wastewater treatment plant is essential in managing water resources, limitation of fresh water resources in the world, furthermore the ever-increasing population growth and the development of urbanization, have made the approach of urban wastewater reuse inevitable. In such a situation, the use of recycled water can be considered as one of the ways to overcome water shortage and prevent wastage of water resources. This research aimed to investigate the performance of Prediction of effluent quality parameters of wastewater treatment plant in using recycled water. On the other hand, due to the health hazards caused by the discharge of wastewater from wastewater treatment plants to water sources, achieving a precise design and correct management of wastewater treatment plants (WWTP: Wastewater Treatment Plant) is one of the important challenges of sustainable water resources management. 4 effective parameters including (BOD), (COD), (TSS) and (pH) of wastewater, were selected as input to the model, during a statistical period of 3 years (1397 to 1399). Also, 70% and 30% sizes were determined as the best sizes for training and testing stages in order to model BODeff and CODeff parameters. In this study, multi-Layer perceptron models (MLP), basic radial neural network models (RABF), as well as the integration of these models with several other algorithms, such as genetic algorithm (GA), particle swarm optimization algorithm (PSO) and sine cosine algorithm (SCA), were used in order to predict the quality parameters of wastewater treatment plant effluent.

Keywords

Main Subjects

Irrigation and Water Engineering

Articles in Press, Accepted Manuscript
Available Online from 10 March 2024

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