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dc.contributor.authorCao, W.*
dc.contributor.authorLiu, Q.*
dc.contributor.authorWang, Y.*
dc.contributor.authorMujtaba, Iqbal M.*
dc.date.accessioned2016-03-18T15:41:43Z
dc.date.available2016-03-18T15:41:43Z
dc.date.issued2016-01-04
dc.identifier.citationCao W, Liu Q, Wang Y and Mujtaba IM (2016) Modeling and simulation of VMD desalination process by ANN. Computers and Chemical Engineering. 84: 96-103.en_US
dc.identifier.urihttp://hdl.handle.net/10454/7945
dc.descriptionYesen_US
dc.description.abstractIn this work, an artificial neural network (ANN) model based on the experimental data was developed to study the performance of vacuum membrane distillation (VMD) desalination process under different operating parameters such as the feed inlet temperature, the vacuum pressure, the feed flow rate and the feed salt concentration. The proposed model was found to be capable of predicting accurately the unseen data of the VMD desalination process. The correlation coefficient of the overall agreement between the ANN predictions and experimental data was found to be more than 0.994. The calculation value of the coefficient of variation (CV) was 0.02622, and there was coincident overlap between the target and the output data from the 3D generalization diagrams. The optimal operating conditions of the VMD process can be obtained from the performance analysis of the ANN model with a maximum permeate flux and an acceptable CV value based on the experiment.en_US
dc.language.isoenen_US
dc.rights© 2016 Elsevier Ltd. Full-text reproduced in accordance with the publisher’s self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectVacuum membrane distillation; Desalination; Artificial neural network; Simulation; Modellingen_US
dc.titleModeling and simulation of VMD desalination process by ANNen_US
dc.status.refereedYesen_US
dc.date.Accepted2015-08-21
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
dc.identifier.doihttps://doi.org/10.1016/j.compchemeng.2015.08.019
refterms.dateFOA2018-07-25T14:19:35Z


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