Pregled bibliografske jedinice broj: 1098937
Qualitative evaluation of wastewater treatment plant performance by a neural network model optimized by genetic algorithm
Qualitative evaluation of wastewater treatment plant performance by a neural network model optimized by genetic algorithm // 5th International Electronic Conference on Water Sciences
Basel, Švicarska, 2020. str. 1-7 doi:10.3390/ECWS-5-08047 (ostalo, međunarodna recenzija, sažetak, znanstveni)
CROSBI ID: 1098937 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Qualitative evaluation of wastewater treatment
plant performance by a neural network model
optimized by genetic algorithm
Autori
Dadar, Sara ; Pezeshki, Atena ; Đurin, Bojan ; Dogančić, Dragana
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Skup
5th International Electronic Conference on Water Sciences
Mjesto i datum
Basel, Švicarska, 16.11.2020. - 30.11.2020
Vrsta sudjelovanja
Ostalo
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
waste water ; neural network ; treatment plant ; genetic algorithm ; TBOD
Sažetak
The adverse effects of improper disposal of collected and treated wastewater have become inevitable. In order to achieve the desired environmental standards, in addition to the construction of a wastewater treatment plant, 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 efficiency of the neural network model in order to predict the performance of the Parkandabad waste water treatment plant in Mashhad, with a semi- mechanical treatment system, was investigated. The first step in predicting the performance of the treatment plant was identification of factors affecting the Total Biochemical Oxygen Demand (TBOD) parameter which is one of the quality indicators of the effluent. In the next step, the neural network model optimized with a genetic algorithm, and effective features as network inputs were used for the predictions of the performance of the treatment plant. Based on the results obtained from the model, the parameters that affect the prediction of TBOD concentration the most were singled out. They are flow rate, organic matter load, dissolved oxygen concentration, temperature, and some active aerators. Paper will consider replacing the semi- mechanical treatment system with the activated sludge process.
Izvorni jezik
Engleski
Znanstvena područja
Građevinarstvo, Kemijsko inženjerstvo, Interdisciplinarne tehničke znanosti
POVEZANOST RADA
Ustanove:
Geotehnički fakultet, Varaždin,
Sveučilište Sjever, Koprivnica
