Review article
https://doi.org/10.47960/2232-9080.2022.23.12.12
QUALITATIVE EVALUATION OF WASTEWATER TREATMENT PLANT PERFORMANCE BY NEURAL NETWORK MODEL OPTIMIZED BY GENETIC ALGORITHM
Sara Dadar
; Ferdowsi University of Mashhad
Atena Pezeshki
; Ferdowsi University of Mashhad
Bojan Đurin
orcid.org/0000-0002-2361-8036
; University North
Dragana Dogančić
orcid.org/0000-0002-1749-5842
; University of Zagreb
Abstract
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.
Keywords
wastewater; neural network; treatment plant; genetic algorithm; TBOD
Hrčak ID:
279428
URI
Publication date:
21.6.2022.
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