[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Accepted articles :: Archive :: Search :: Submit :: Contact ::
:: Volume 7, Issue 3 (5-2020) ::
jehe 2020, 7(3): 298-313 Back to browse issues page
Optimization and Prediction of Moving Bed Biofilm Reactor (MBBR) Using Surface Response Method (RSM) and Artificial Neural Network (ANN)
Mohammad Delnavaz , Mahsa Peidad
Department of Civil Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
Abstract:   (167 Views)
Background  In this study, the optimization and prediction of the efficiency of a moving bed biofilm reactor (MBBR) in the treatment of synthetic wastewater containing organic material including aniline was investigated using response surface methodology and artificial neural network.
Materials and Methods: Modeling results were applied to a 5-liter volume reactor filled with 30%, 50% and 70% LECA lightweight aggregates as a growth medium for microorganisms and biofilm layer formation. In order to determine the optimum conditions in the experiments results and also to predict the tests not performed, three factors were feed levels at levels of 100 to 3000 mg/L, retention time of 8 to 72 hours and filling percentage of 30 and 50 and 70% were performed using RSM. The accuracy of the presented models was evaluated by ANOVA. Prediction of system removal efficiency using radial basis ANN was also investigated.
Results: Process optimization showed that the optimum conditions for maximum removal were at feed rate of 1700 mg/l and 72 hours at 56.82% filling percentage. The results of the process prediction using radial basis ANN also showed that in the best network structure with Radbas and linear functions (Purelin) with R2 = 0.982 can predict the efficiency.
Conclusion: By comparing the radial basis ANN model and RSM and comparing the error rates of these two methods, it can be concluded that the radial base ANN method predicts the data process more accurately and with lower error.
Keywords: Moving Bed Biofilm Reactor, Aniline, Surface Response Method, Artificial Neural Network
Full-Text [PDF 956 kb]   (46 Downloads)    
Type of Study: Research | Subject: Special
Received: 2020/06/28 | Accepted: 2020/06/28 | Published: 2020/06/28
Add your comments about this article
Your username or Email:

CAPTCHA


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Delnavaz M, Peidad M. Optimization and Prediction of Moving Bed Biofilm Reactor (MBBR) Using Surface Response Method (RSM) and Artificial Neural Network (ANN). jehe. 2020; 7 (3) :298-313
URL: http://jehe.abzums.ac.ir/article-1-759-en.html


Volume 7, Issue 3 (5-2020) Back to browse issues page
مجله مهندسی بهداشت محیط Journal of Environmental Health Enginering
Persian site map - English site map - Created in 0.06 seconds with 32 queries by YEKTAWEB 4212