Forecasting Parameter of Kailashtilla Gas Processing Plant Using Neural Network
Kundu, S. Hasan, A. Sowgath, Md Tanvir
Kundu, S.
Hasan, A.
Sowgath, Md Tanvir
Publication Date
2012-12-22
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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.
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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.
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