(248g) Environmental Impacts of a Natural Gas Dehydration Plant- Simulation and Process Optimization

Authors: 
Amouei Torkmahalleh, M. - Presenter, Chemical Engineering Program, Middle East Technical University Northern Cyprus Campus

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

 

Introduction

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.

Objective

 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.  

Simulation

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.

Results

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-

X1

X2

X3

X4

X5

X6

Plant Data

60

0.4

3

41

0.998

0.98

RKSMHV2

69

0.2

17

42

0.993

0.90

PSRK

37

0.2

14

24

0.991

0.89

RKSMHV2 and PSRK

69

0.4

3

43

0.998

0.96

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