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dc.contributor.authorSowgath, Md Tanvir*
dc.contributor.authorMujtaba, Iqbal M.*
dc.date.accessioned2016-12-15T14:58:19Z
dc.date.available2016-12-15T14:58:19Z
dc.date.issued2006
dc.identifier.citationSowgath MT and Mujtaba IM (2006) Modelling and simulation of MSF desalination process using gPROMS and neural network based physical property correlation. Computer Aided Chemical Engineering. 21: 315-320.en_US
dc.identifier.urihttp://hdl.handle.net/10454/10979
dc.descriptionNoen_US
dc.description.abstractMulti Stage Flash (MSF) desalination plants are a sustainable source of fresh water in arid regions. Modelling plays an important role in simulation, optimisation and control of MSF processes. In this work an MSF process model is developed, using gPROMS modelling tool. Accurate estimation of Temperature Elevation (TE) due to salinity is important in developing reliable process model. Here, instead of using empirical correlations from literature, a Neural Network based correlation is used to determine the TE. This correlation is embedded in the gPROMS based process model. We obtained a good agreement between the results reported by Rosso et. al. (1996) and those predicted by our model. Effects of seawater temperature (Tseawater) and steam temperature (Tsteam) on the performance of the MSF process are also studied and reported.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://dx.doi.org/10.1016/S1570-7946(06)80065-9en_US
dc.subjectDesalination; MSF; modelling; gPROMS; NN based correlationen_US
dc.titleModelling and simulation of MSF desalination process using gPROMS and neural network based physical property correlationen_US
dc.status.refereedYesen_US
dc.typeArticleen_US
dc.type.versionNo full-text in the repositoryen_US


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