Sensitivity analysis and faults diagnosis using artificial neural networks in natural gas TEG-dehydration plants

Naif A. Darwish, Nidal Hilal

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

In this work, a typical process for natural gas dehydration using triethylene glycol (TEG) as a desiccant is simulated using a steady state flowsheet simulator (Aspen Plus). The flowsheet includes all major units in a typical dehydration facility, that is: absorption column, flash unit, heat exchangers, regenerator, stripper, and reboiler. The base case operating conditions are taken to resemble field data from one of the existing TEG-dehydration units operating in United Arab Emirates (UAE). Using Aspen Plus, the flowsheet is then used to study the effects of different input parameters and operating conditions of the absorption column, the stripper and the overall plant, on BTEX emission, volatile organic components (VOCs) emission, TEG losses and water content (dew point) of the dehumidified natural gas. Contactor performance has been found to be most sensitive to disturbances in operating pressure and wet gas flow rate, whereas flow rate of stripping gas and temperature of inlet solvent have the major impact on the stripper performance. The potential of artificial neural network (ANN) to detect and diagnose process faults in the dehydration plant has also been explored. ANN successfully detects the disturbance severity levels in the input variables considered for the contactor. In particular, abnormal levels of BTEX concentrations in the rich solvent (exiting the contactor) are shown to precisely indicate the severity levels in the input variables. Faults in the stripper-regenerator unit have been perfectly predicted by the ANN for two symptoms (TEG emissions and BTEX emissions in vents) and to a lesser extent for faults in VOCs emissions. The best ANN prediction is obtained for the overall plant where the ANN simulates the imposed disturbances for three severity levels of imposed malfunctions for all symptoms considered.

Original languageEnglish
Pages (from-to)189-197
Number of pages9
JournalChemical Engineering Journal
Volume137
Issue number2
DOIs
Publication statusPublished - 1 Apr 2008
Externally publishedYes

Fingerprint

Glycols
Dehydration
dehydration
artificial neural network
Sensitivity analysis
Failure analysis
sensitivity analysis
natural gas
Natural gas
Flowcharting
BTEX
Neural networks
Regenerators
disturbance
Hygroscopic Agents
Flow rate
Reboilers
dew point
Vents
gas flow

Keywords

  • BTEX
  • Dehydration
  • Emission
  • Equations of state
  • Mixing rules
  • Natural gas
  • Simulation

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Environmental Engineering

Cite this

Sensitivity analysis and faults diagnosis using artificial neural networks in natural gas TEG-dehydration plants. / Darwish, Naif A.; Hilal, Nidal.

In: Chemical Engineering Journal, Vol. 137, No. 2, 01.04.2008, p. 189-197.

Research output: Contribution to journalArticle

@article{4c3c6916711b44eaa63699f8c43671a0,
title = "Sensitivity analysis and faults diagnosis using artificial neural networks in natural gas TEG-dehydration plants",
abstract = "In this work, a typical process for natural gas dehydration using triethylene glycol (TEG) as a desiccant is simulated using a steady state flowsheet simulator (Aspen Plus). The flowsheet includes all major units in a typical dehydration facility, that is: absorption column, flash unit, heat exchangers, regenerator, stripper, and reboiler. The base case operating conditions are taken to resemble field data from one of the existing TEG-dehydration units operating in United Arab Emirates (UAE). Using Aspen Plus, the flowsheet is then used to study the effects of different input parameters and operating conditions of the absorption column, the stripper and the overall plant, on BTEX emission, volatile organic components (VOCs) emission, TEG losses and water content (dew point) of the dehumidified natural gas. Contactor performance has been found to be most sensitive to disturbances in operating pressure and wet gas flow rate, whereas flow rate of stripping gas and temperature of inlet solvent have the major impact on the stripper performance. The potential of artificial neural network (ANN) to detect and diagnose process faults in the dehydration plant has also been explored. ANN successfully detects the disturbance severity levels in the input variables considered for the contactor. In particular, abnormal levels of BTEX concentrations in the rich solvent (exiting the contactor) are shown to precisely indicate the severity levels in the input variables. Faults in the stripper-regenerator unit have been perfectly predicted by the ANN for two symptoms (TEG emissions and BTEX emissions in vents) and to a lesser extent for faults in VOCs emissions. The best ANN prediction is obtained for the overall plant where the ANN simulates the imposed disturbances for three severity levels of imposed malfunctions for all symptoms considered.",
keywords = "BTEX, Dehydration, Emission, Equations of state, Mixing rules, Natural gas, Simulation",
author = "Darwish, {Naif A.} and Nidal Hilal",
year = "2008",
month = "4",
day = "1",
doi = "10.1016/j.cej.2007.04.008",
language = "English",
volume = "137",
pages = "189--197",
journal = "Chemical Engineering Journal",
issn = "1385-8947",
publisher = "Elsevier",
number = "2",

}

TY - JOUR

T1 - Sensitivity analysis and faults diagnosis using artificial neural networks in natural gas TEG-dehydration plants

AU - Darwish, Naif A.

AU - Hilal, Nidal

PY - 2008/4/1

Y1 - 2008/4/1

N2 - In this work, a typical process for natural gas dehydration using triethylene glycol (TEG) as a desiccant is simulated using a steady state flowsheet simulator (Aspen Plus). The flowsheet includes all major units in a typical dehydration facility, that is: absorption column, flash unit, heat exchangers, regenerator, stripper, and reboiler. The base case operating conditions are taken to resemble field data from one of the existing TEG-dehydration units operating in United Arab Emirates (UAE). Using Aspen Plus, the flowsheet is then used to study the effects of different input parameters and operating conditions of the absorption column, the stripper and the overall plant, on BTEX emission, volatile organic components (VOCs) emission, TEG losses and water content (dew point) of the dehumidified natural gas. Contactor performance has been found to be most sensitive to disturbances in operating pressure and wet gas flow rate, whereas flow rate of stripping gas and temperature of inlet solvent have the major impact on the stripper performance. The potential of artificial neural network (ANN) to detect and diagnose process faults in the dehydration plant has also been explored. ANN successfully detects the disturbance severity levels in the input variables considered for the contactor. In particular, abnormal levels of BTEX concentrations in the rich solvent (exiting the contactor) are shown to precisely indicate the severity levels in the input variables. Faults in the stripper-regenerator unit have been perfectly predicted by the ANN for two symptoms (TEG emissions and BTEX emissions in vents) and to a lesser extent for faults in VOCs emissions. The best ANN prediction is obtained for the overall plant where the ANN simulates the imposed disturbances for three severity levels of imposed malfunctions for all symptoms considered.

AB - In this work, a typical process for natural gas dehydration using triethylene glycol (TEG) as a desiccant is simulated using a steady state flowsheet simulator (Aspen Plus). The flowsheet includes all major units in a typical dehydration facility, that is: absorption column, flash unit, heat exchangers, regenerator, stripper, and reboiler. The base case operating conditions are taken to resemble field data from one of the existing TEG-dehydration units operating in United Arab Emirates (UAE). Using Aspen Plus, the flowsheet is then used to study the effects of different input parameters and operating conditions of the absorption column, the stripper and the overall plant, on BTEX emission, volatile organic components (VOCs) emission, TEG losses and water content (dew point) of the dehumidified natural gas. Contactor performance has been found to be most sensitive to disturbances in operating pressure and wet gas flow rate, whereas flow rate of stripping gas and temperature of inlet solvent have the major impact on the stripper performance. The potential of artificial neural network (ANN) to detect and diagnose process faults in the dehydration plant has also been explored. ANN successfully detects the disturbance severity levels in the input variables considered for the contactor. In particular, abnormal levels of BTEX concentrations in the rich solvent (exiting the contactor) are shown to precisely indicate the severity levels in the input variables. Faults in the stripper-regenerator unit have been perfectly predicted by the ANN for two symptoms (TEG emissions and BTEX emissions in vents) and to a lesser extent for faults in VOCs emissions. The best ANN prediction is obtained for the overall plant where the ANN simulates the imposed disturbances for three severity levels of imposed malfunctions for all symptoms considered.

KW - BTEX

KW - Dehydration

KW - Emission

KW - Equations of state

KW - Mixing rules

KW - Natural gas

KW - Simulation

UR - http://www.scopus.com/inward/record.url?scp=38949112454&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38949112454&partnerID=8YFLogxK

U2 - 10.1016/j.cej.2007.04.008

DO - 10.1016/j.cej.2007.04.008

M3 - Article

AN - SCOPUS:38949112454

VL - 137

SP - 189

EP - 197

JO - Chemical Engineering Journal

JF - Chemical Engineering Journal

SN - 1385-8947

IS - 2

ER -