436653 Environmental Impacts of a Natural Gas Dehydration Plant- Simulation and Process Optimization

Monday, November 9, 2015
Exhibit Hall 1 (Salt Palace Convention Center)
Mehdi Amouei Torkmahalleh, Chemical Engineering Program, Middle East Technical University Northern Cyprus Campus, Guzelyurt, Mersin 10, Turkey and Milad Malekipirbazari, Department of Industrial Engineering, Istanbul Sehir University, 34662, Istanbul, Turkey

Environmental impacts of a natural gas dehydration plant- Simulation and Process Optimization


1Mehdi Amouei Torkmahalleh, 2Milad Malekipirbazari

1Chemical Engineering Program, Middle East Technical University Northern Cyprus Campus, Guzelyurt, Mersin 10, Turkey

2Department of Industrial Engineering, Istanbul Sehir University, 34662, Istanbul, Turkey



The natural gas dehydration process is employed to reduce the water content of the natural gas at high pressure to prevent hydrate formation and corrosion in natural gas processing. Typically, glycol solvents such as triethylen glycol (TEG) are utilized to remove water from natural gas through a high pressure absorption column. Natural gas includes some volatile organic compounds (VOCs) as well as benzene, toluene, ethylbenzene and xylene, collectively named BTEX. The environmentally hazardous compounds (VOCs and BTEX) are absorbed by TEG in an absorption column resulting in the emission of such pollutants to the atmosphere during solvent purification in a stripper column. Therefore, it is imperative to estimate the emission rates of such pollutants to perform accurate exposure analyses for the workers of such plants and people residing nearby. This estimation is achieved by performing an accurate steady state simulation.


 In this study, steady state simulation and optimization (sensitivity analysis) of the one of the existing natural gas dehydration units operated in the United Arab Emirates (UAE) were performed to understand the emission rates of environmental hazardous, dew point of the dry gas, solvent loss and heat duty of the process. The simulation results were compared with available plant data to ensure the accuracy of the simulation package.

This study had two novel aims. Typically in the simulation of natural gas dehydration plants, one thermodynamic model is utilized throughout the process. However, the question is “would it be possible to improve the simulation by employing multiple thermodynamic models in the process?”. The first novel goal of the current study was to address this question.

Literature review reveals that so far simulation and optimization studies of natural gas dehydration plants have been focused on BTEX and VOCs emissions, while other potential environmental impacts of such plants have not been yet carefully explored. These additional impacts would be Green House Gases (GHG) and aerosol emissions. Hence, the second novel objective of this work was to estimate the GHG emissions and investigate the supersaturation of the exhaust gas streams in the plant which is a prerequisite for aerosol formation.  


Aspen Plus (V8.6) was utilized to perform the simulation in this study.  No changes were made to the available binary interaction data in the simulator. The studied natural gas dehydration plant includes an absorption column (618 psia), a flash unit (58 psia), two heat exchangers and atmospheric regenerator and stripper columns with TEG as solvent constituting two recycle loops. Six plant operating conditions were employed to validate simulation results. The standard Equation of State (EOS) and predictive EOS models employed in this study were PR, RKS and PRMHV2, RKSMHV2, PRWS, RKSWS, PRBM, RKSBM, RKS-Aspen, PSRK, SRPOLAR, respectively. NRTL-RK model together with other predictive EOS models were used collectively in part of the study.

In the first part of this study, more than 100 simulation runs representing single or multiple thermodynamic models were systematically performed to find the best combination of the thermodynamic models. The absolute difference between the simulation results and plant data (N=6) were calculated for each run. Then, all runs were ranked for each plant data resulted in six rank values for each run. Then, run with the lowest sum of the rank values was selected to find the best thermodynamic model combination. This combination was then used for the rest of the study. In the second phase of this study, sensitivity analysis (optimization) studies were performed to find the impact of the process operating conditions on the emissions of BTEX, VOCs, GHG, potential supersaturation of stripper and flash vents, dew point of the dry gas, solvent loss and heat duty of the process.


The first part of this study is completed, and the second part is under progress.

A proper combination of RKSMHV2 and PSRK models showed excellent agreement with plant data compared with other possible model combinations. Table 1 presents the plant data and the results of three simulation runs. It is shown that the combination of RKSMHV2 and PSRK models showed higher accuracy compared to the single model runs (RKSMHV2 or PSRK).

Table 1. Simulation results and plant data-








Plant Data




























In Table 1, X1 represents BTEX wt% absorbed in the rich TEG under conditions of the contactor; X2 is TEG losses in lb/h; X3 shows water content in the dried gas in lbH2O/MMSCF (million standard cubic feet); X4 represents BTEX emission rate from stripper (lb/h); X5 is mass fraction of regenerated (lean) TEG and X6 represents heat duty of reboiler (regenerator) in MMBtu/h (millions Btu/h).

Figures 1 to 4 show the effect of solvent circulation rate on the emissions of BTEX, VOCs, GHG and saturation ratio, respectively. As can be seen, increasing the solvent circulation rate increases the emissions of BTEX, VOCs and GHG while has no significant influence on the saturation ratio of the stripper and flash vents. It was found that the main source of the hazardous emissions is the stripper. The impact of other operating conditions is being studied and will be presented in Optimize 2015.  


Figure 1. BTEX emission

Figure 2. VOC emission

Figure 3. GHG emission

Figure 4. Supersaturation


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