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    Modelling and simulation of MSF desalination process using gPROMS and neural network based physical property correlation

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    Publication date
    2006
    Author
    Sowgath, Md Tanvir
    Mujtaba, Iqbal M.
    Keyword
    Desalination; MSF; modelling; gPROMS; NN based correlation
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    Multi 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.
    URI
    http://hdl.handle.net/10454/10979
    Version
    No full-text in the repository
    Citation
    Sowgath 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.
    Link to publisher’s version
    http://dx.doi.org/10.1016/S1570-7946(06)80065-9
    Type
    Article
    Collections
    Engineering and Digital Technology Publications

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