Journal article
SELECTION OF OPTIMUM OPERATIONAL CONDITIONS FOR THE TREATMENT PERFORMANCE OF GEOTEXTILE BIOFILTERS USING ARTIFICIAL NEURAL NETWORKS
Fresenius environmental bulletin, Vol.19(11), pp.2587-2596
01 Jan 2010
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The premise of this study is to develop an artificial neural networks (ANNs) based method to model and simulate the effluent concentrations of NH(3), NO(3)(-), BOD(5) and other parameters for a geotextile biofilter developed for wastewater treatment. The model selects the best backpropagation algorithm and optimizes the structure of selected algorithm for any type of input and output parameters. Using the obtained model, the effluent concentrations of a specially designed geotextile biofilter are predicted under different operational conditions and the results are compared with the measured data. It is concluded that neural networks based models are appropriate for modeling nonlinear dependence of the treatment performance of geotextile biofilters. Then, this model is used to simulate the effects of input variables on the treatment performance of the geotextile biofilter. Finally, the model is used as a tool to define the optimum range of operational parameters of the geotextile biofilter.
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Details
- Title
- SELECTION OF OPTIMUM OPERATIONAL CONDITIONS FOR THE TREATMENT PERFORMANCE OF GEOTEXTILE BIOFILTERS USING ARTIFICIAL NEURAL NETWORKS
- Creators
- Cevat Yaman - Istanbul Metropolitan Municipal, EU Relat, Directorate EU Relat, Istanbul, TurkeyFerhat Karaca - Fatih Univ, Dept Environm Engn, TR-34500 Istanbul, TurkeyEyup N. Korkut - Istanbul Metropolitan Municipal, Directorate Waste Management, Istanbul, TurkeyJoseph Paul Martin - Drexel UniversityOzer Cinar - Kahramanmaras Sutcu Imam Univ, Dept Environm Engnl, Kahramanmaras, Turkey
- Publication Details
- Fresenius environmental bulletin, Vol.19(11), pp.2587-2596
- Publisher
- Parlar Scientific Publications (P S P)
- Number of pages
- 10
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Identifiers
- 991019170330404721
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- Domestic collaboration
- International collaboration
- Web of Science research areas
- Environmental Sciences