Document Detail

Prediction of anoxic sulfide biooxidation under various HRTs using artificial neural networks.
MedLine Citation:
PMID:  18188992     Owner:  NLM     Status:  MEDLINE    
OBJECTIVE: During present investigation the data of a laboratory-scale anoxic sulfide oxidizing (ASO) reactor were used in a neural network system to predict its performance. METHODS: Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a back propagated neural network. RESULTS: Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner. CONCLUSION: Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASObased denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant (WTP) can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.
Qaisar Mahmood; Ping Zheng; Dong-Lei Wu; Xu-Sheng Wang; Hayat Yousaf; Ejaz Ul-Islam; Muhammad Jaffar Hassan; Ghulam Jilani; Muhammad Rashid Azim
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Biomedical and environmental sciences : BES     Volume:  20     ISSN:  0895-3988     ISO Abbreviation:  Biomed. Environ. Sci.     Publication Date:  2007 Oct 
Date Detail:
Created Date:  2008-01-14     Completed Date:  2008-02-28     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8909524     Medline TA:  Biomed Environ Sci     Country:  United States    
Other Details:
Languages:  eng     Pagination:  398-403     Citation Subset:  IM    
Department of Environmental Engineering, College of Environment and Resource Science, Zhejiang University, Hangzhou 310029, Zhejiang, China.
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MeSH Terms
Neural Networks (Computer)*
Sulfates / chemistry
Sulfides / chemistry*
Time Factors
Waste Disposal, Fluid / methods*
Reg. No./Substance:
0/Sulfates; 0/Sulfides

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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