Document Detail

Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers.
MedLine Citation:
PMID:  18280647     Owner:  NLM     Status:  MEDLINE    
In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.
Mahboobeh Taheri; Ali Mohebbi
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2008-01-09
Journal Detail:
Title:  Journal of hazardous materials     Volume:  157     ISSN:  0304-3894     ISO Abbreviation:  J. Hazard. Mater.     Publication Date:  2008 Aug 
Date Detail:
Created Date:  2008-06-23     Completed Date:  2008-10-06     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9422688     Medline TA:  J Hazard Mater     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  122-9     Citation Subset:  IM    
Department of Chemical Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
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MeSH Terms
Air Movements
Air Pollutants / isolation & purification*
Air Pollution / prevention & control*
Gases / chemistry*
Neural Networks (Computer)*
Particle Size
Reg. No./Substance:
0/Air Pollutants; 0/Gases

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

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