Glycol Loss Minimization for a Natural Gas Dehydration Plant under Upset Conditions

被引:8
|
作者
Haque, Md Emdadul [1 ]
Xu, Qiang [1 ]
Palanki, Srinivas [1 ]
机构
[1] Lamar Univ, Dan F Smith Dept Chem Engn, Beaumont, TX 77710 USA
关键词
Dehydration process - Fluctuating temperatures - Monoethylene glycol - Natural gas dehydration - Natural gas processing - Normal operating conditions - Root cause analysis - Steady-state simulations;
D O I
10.1021/acs.iecr.8b04675
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Low-temperature separation with a monoethylene glycol (MEG) injection process is a common dehydration technique for natural gas processing. However, the MEG-based dehydration system frequently suffers significant glycol loss during plant upset conditions, causing double penalties of economic loss and air emissions. Thus, it is very important to minimize MEG loss in the dehydration process. In this paper, an efficient and effective methodology to reduce MEG loss under upset conditions of a natural gas dehydration plant has been developed. First, a plant-wide steady-state simulation model is developed and validated at normal operating conditions. Next, the root cause analysis for MEG loss is performed by introducing various process upsets to the simulation model, which indicates that most MEG loss is due to the fluctuating temperature of the stripper column overhead. After that, a plant-wide dynamic simulation model is developed to help generate a new control strategy to regulate the stripper column operation and cope with other plant upset conditions, so as to minimize the MEG loss and improve natural gas product quality and plant operability. Simulation results indicate that plant MEG loss can be reduced by 37%.
引用
收藏
页码:1994 / 2008
页数:15
相关论文
共 50 条
  • [21] Investigation of natural gas dehydration process using triethylene glycol (TEG) based on statistical approach
    Moghaddam, Amin Hedayati
    CHEMICAL PAPERS, 2023, 77 (03) : 1433 - 1443
  • [22] Temperature change from isenthalpic expansion of aqueous triethylene glycol mixtures for natural gas dehydration
    Satyro, Marco A.
    Schoeggl, Florian
    Yarranton, Harvey W.
    FLUID PHASE EQUILIBRIA, 2011, 305 (01) : 62 - 67
  • [23] Analysis of the system of dehydration of natural gas with triethylene glicol of a plant of extraction of liquids
    Moncada, Fidelina
    Molina, David
    Raven, Hernan
    Salazar, Iliana
    REVISTA TECNICA DE LA FACULTAD DE INGENIERIA UNIVERSIDAD DEL ZULIA, 2007, 30 : 464 - 471
  • [24] SENSITIVITY ANALYSIS USING DEHYDRATION PROCESS SIMULATION OF A CONDITIONING PLANT FOR NATURAL GAS
    Erdmann, Eleonora
    Ale Ruiz, Liliana
    Benitez, Leonel
    Tarifa, Enrique
    AVANCES EN CIENCIAS E INGENIERIA, 2012, 3 (03): : 119 - 130
  • [25] LOSS OF ADDITIVES FROM POLYETHYLENE UNDER NATURAL CONDITIONS
    MARIN, AP
    SHLYAPNIKOV, YA
    POLYMER DEGRADATION AND STABILITY, 1991, 31 (02) : 181 - 188
  • [26] Circadian Regulation of the Plant Transcriptome Under Natural Conditions
    Panter, Paige E.
    Muranaka, Tomoaki
    Cuitun-Coronado, David
    Graham, Calum A.
    Yochikawa, Aline
    Kudoh, Hiroshi
    Dodd, Antony N.
    FRONTIERS IN GENETICS, 2019, 10
  • [27] Technical and economic evaluation of triethylene glycol regeneration process using flash gas as stripping gas in a domestic natural gas dehydration unit
    Affandy, Sony A.
    Kurniawan, Adhi
    Handogo, Renanto
    Sutikno, Juwari P.
    Chien, I-Lung
    ENGINEERING REPORTS, 2020, 2 (05)
  • [28] Thermodynamic Modeling and Simulation of Natural Gas Dehydration Using Triethylene Glycol with the UMR-PRU Model
    Petropoulou, Eirini G.
    Voutsas, Epaminondas C.
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2018, 57 (25) : 8584 - 8604
  • [29] Determination of Triethylene Glycol Concentration in Natural Gas Dehydration Systems Using Support Vector Machine Algorithm
    Kamari, A.
    Mohammadi, A. H.
    Bahadori, A.
    PETROLEUM SCIENCE AND TECHNOLOGY, 2015, 33 (06) : 649 - 656
  • [30] Estimation of triethylene glycol (TEG) purity in natural gas dehydration units using fuzzy neural network
    Ghiasi, Mohammad M.
    Bahadori, Alireza
    Zendehboudi, Sohrab
    JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING, 2014, 17 : 26 - 32