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    Forecasting Parameter of Kailashtilla Gas Processing Plant Using Neural Network

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    Publication date
    2012-12-22
    Author
    Kundu, S.
    Hasan, A.
    Sowgath, Md Tanvir
    Keyword
    Artificial neural networks; Correlation; Heating; Neurons; Feeds; Training; Biological neural networks; Kailashtilla
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    Neural Network (NN) is widely used in all aspects of process engineering activities, such as modeling, design, optimization and control. In this paper work, in absence of real plant data, simulated data (such as sales gas flow rate, pressure, raw gases flow rates and input heat flow associated with a heater used after dehydration) from a detailed model of Kailashtilla gas processing plant (KGP) within HYSYS is used to develop NN based model. Thereafter NN based model is trained and validated from HYSYS simulator generated data and that framework can predict the output data (sales gas flow rate and pressure) very closely with the simulated HYSYS plant data. The preliminary results show that the NN based correlation is adequately able to model and generate workable profiles for the process.
    URI
    http://hdl.handle.net/10454/10980
    Version
    No full-text in the repository
    Citation
    Kundu S, Hasan A and Sowgath MT (2016) Forecasting parameter of Kailashtilla gas processing plant using Neural Network View Document. In: Proceedings of the 7th International Conference on Electrical and Computer Engineering (ICECE) 20-22 Dec 2012, Hotel Pan Pacific Sonargaon, Dhaka, Bangladesh.
    Link to publisher’s version
    http://dx.doi.org/10.1109/ICECE.2012.6471593
    Type
    Conference paper
    Collections
    Engineering and Informatics Publications

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