Artificial neural network model with the parameter tuning assisted by a differential evolution technique: the study of the hold up of the slurry flow in a pipeline
This paper describes a robust hybrid artificial neural network (ANN) methodology which can offer a superior performance for the important process engineering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE) for the efficient tuning of...
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Association of the Chemical Engineers of Serbia
2009-05-01
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Series: | Chemical Industry and Chemical Engineering Quarterly |
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Online Access: | http://www.ache.org.rs/CICEQ/2009/No2/CICEQ_Vol15_%20No2_pp103-117_Apr-Jun_2009.pdf |
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doaj-493de1f4c40347eabbf2105c1ddd9a072020-11-24T22:03:57ZengAssociation of the Chemical Engineers of SerbiaChemical Industry and Chemical Engineering Quarterly1451-93722009-05-01152103117Artificial neural network model with the parameter tuning assisted by a differential evolution technique: the study of the hold up of the slurry flow in a pipelineS. K. LahiriK. C. GhantaThis paper describes a robust hybrid artificial neural network (ANN) methodology which can offer a superior performance for the important process engineering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE) for the efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of the hold up of the solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical properties, and pipe diameters. http://www.ache.org.rs/CICEQ/2009/No2/CICEQ_Vol15_%20No2_pp103-117_Apr-Jun_2009.pdfartificial neural networkdifferential evolutionslurry hold upslurry flow |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
S. K. Lahiri K. C. Ghanta |
spellingShingle |
S. K. Lahiri K. C. Ghanta Artificial neural network model with the parameter tuning assisted by a differential evolution technique: the study of the hold up of the slurry flow in a pipeline Chemical Industry and Chemical Engineering Quarterly artificial neural network differential evolution slurry hold up slurry flow |
author_facet |
S. K. Lahiri K. C. Ghanta |
author_sort |
S. K. Lahiri |
title |
Artificial neural network model with the parameter tuning assisted by a differential evolution technique: the study of the hold up of the slurry flow in a pipeline |
title_short |
Artificial neural network model with the parameter tuning assisted by a differential evolution technique: the study of the hold up of the slurry flow in a pipeline |
title_full |
Artificial neural network model with the parameter tuning assisted by a differential evolution technique: the study of the hold up of the slurry flow in a pipeline |
title_fullStr |
Artificial neural network model with the parameter tuning assisted by a differential evolution technique: the study of the hold up of the slurry flow in a pipeline |
title_full_unstemmed |
Artificial neural network model with the parameter tuning assisted by a differential evolution technique: the study of the hold up of the slurry flow in a pipeline |
title_sort |
artificial neural network model with the parameter tuning assisted by a differential evolution technique: the study of the hold up of the slurry flow in a pipeline |
publisher |
Association of the Chemical Engineers of Serbia |
series |
Chemical Industry and Chemical Engineering Quarterly |
issn |
1451-9372 |
publishDate |
2009-05-01 |
description |
This paper describes a robust hybrid artificial neural network (ANN) methodology which can offer a superior performance for the important process engineering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE) for the efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of the hold up of the solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical properties, and pipe diameters. |
topic |
artificial neural network differential evolution slurry hold up slurry flow |
url |
http://www.ache.org.rs/CICEQ/2009/No2/CICEQ_Vol15_%20No2_pp103-117_Apr-Jun_2009.pdf |
work_keys_str_mv |
AT sklahiri artificialneuralnetworkmodelwiththeparametertuningassistedbyadifferentialevolutiontechniquethestudyoftheholdupoftheslurryflowinapipeline AT kcghanta artificialneuralnetworkmodelwiththeparametertuningassistedbyadifferentialevolutiontechniquethestudyoftheholdupoftheslurryflowinapipeline |
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1725831351859412992 |