Pregled bibliografske jedinice broj: 1201321
Qualitative Evaluation of Wastewater Treatment Plant Performance by Neural Network Model Optimized by Genetic Algorithm
Qualitative Evaluation of Wastewater Treatment Plant Performance by Neural Network Model Optimized by Genetic Algorithm // E-Zbornik, elektronički zbornik radova Građevinskog fakulteta, 12 (2022), 23; 12-19 doi:10.47960/2232-9080.2022.23.12.12 (međunarodna recenzija, članak, znanstveni)
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Naslov
Qualitative Evaluation of Wastewater Treatment
Plant Performance by Neural Network Model
Optimized by Genetic Algorithm
(Qualitative Evaluation of Wastewater Treatment
Plant
Performance by Neural Network Model Optimized by
Genetic
Algorithm)
Autori
Dadar, Sara ; Pezeshki, Atena ; Đurin, Bojan ; Dogančić, Dragana
Izvornik
E-Zbornik, elektronički zbornik radova Građevinskog fakulteta (2232-9080) 12
(2022), 23;
12-19
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
wastewater ; neural network ; treatment plant ; genetic algorithm ; TBOD
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Građevinarstvo, Kemijsko inženjerstvo, Interdisciplinarne tehničke znanosti
POVEZANOST RADA
Ustanove:
Geotehnički fakultet, Varaždin,
Sveučilište Sjever, Koprivnica