Forecasting parameter of Kailashtilla gas processing plant using Neural Network

Subrata Kundu; Asif Hasan; Md Tanvir Sowgath

doi:10.1109/ICECE.2012.6471593

Forecasting parameter of Kailashtilla gas processing plant using Neural Network

Subrata Kundu
, Asif Hasan
, Md Tanvir Sowgath

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012
Pages	486-489
Number of pages	4
DOIs	https://doi.org/10.1109/ICECE.2012.6471593
State	Published - 2012
Externally published	Yes
Event	2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012 - Dhaka, Bangladesh Duration: Dec 20 2012 → Dec 22 2012

Publication series

Name	2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012

Conference

Conference	2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012
Country/Territory	Bangladesh
City	Dhaka
Period	12/20/12 → 12/22/12

Keywords

HYSYS
Kailashtilla Gas Processing plant
Neural Network (NN)

Access to Document

10.1109/ICECE.2012.6471593

Cite this

Kundu, S., Hasan, A., & Sowgath, M. T. (2012). Forecasting parameter of Kailashtilla gas processing plant using Neural Network. In 2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012 (pp. 486-489). Article 6471593 (2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012). https://doi.org/10.1109/ICECE.2012.6471593

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title = "Forecasting parameter of Kailashtilla gas processing plant using Neural Network",

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.",

keywords = "HYSYS, Kailashtilla Gas Processing plant, Neural Network (NN)",

author = "Subrata Kundu and Asif Hasan and Sowgath, \{Md Tanvir\}",

year = "2012",

doi = "10.1109/ICECE.2012.6471593",

language = "English",

isbn = "9781467314367",

series = "2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012",

pages = "486--489",

booktitle = "2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012",

note = "2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012 ; Conference date: 20-12-2012 Through 22-12-2012",

}

Kundu, S, Hasan, A & Sowgath, MT 2012, Forecasting parameter of Kailashtilla gas processing plant using Neural Network. in 2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012., 6471593, 2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012, pp. 486-489, 2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012, Dhaka, Bangladesh, 12/20/12. https://doi.org/10.1109/ICECE.2012.6471593

Forecasting parameter of Kailashtilla gas processing plant using Neural Network. / Kundu, Subrata; Hasan, Asif; Sowgath, Md Tanvir.
2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012. 2012. p. 486-489 6471593 (2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Forecasting parameter of Kailashtilla gas processing plant using Neural Network

AU - Kundu, Subrata

AU - Hasan, Asif

AU - Sowgath, Md Tanvir

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - HYSYS

KW - Kailashtilla Gas Processing plant

KW - Neural Network (NN)

UR - https://www.scopus.com/pages/publications/84875498117

U2 - 10.1109/ICECE.2012.6471593

DO - 10.1109/ICECE.2012.6471593

M3 - Conference contribution

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SN - 9781467314367

T3 - 2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012

SP - 486

EP - 489

BT - 2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012

T2 - 2012 7th International Conference on Electrical and Computer Engineering, ICECE 2012

Y2 - 20 December 2012 through 22 December 2012

ER -

Forecasting parameter of Kailashtilla gas processing plant using Neural Network

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