doi 10.1016 j.envsoft.2003.10.008

www.elsevier.com/locate/envsoft

Computer simulation of BTEX emission in natural gas dehydration using PR and RKS equations of state with different predictive

mixing rules

Naif A. Darwish

^a,

, Reyadh A. Al-Mehaideb

^a

, Ahmad M. Braek

^b

, R. Hughes

^c

aDepartment of Chemical and Petroleum Engineering, College of Engineering, United Arab Emirates University, PO Box 17555, Al-Ain, United Arab Emirates

bADCO, Abu Dhabi, United Arab Emirates

cChemical Engineering Unit, University of Salford, Salford, UK

Received 15 May 2002; received in revised form 23 July 2003; accepted 22 October 2003

Abstract

A typical natural gas dehydration plant, which employs triethylene glycol (TEG) as the dehydrating agent, is simulated using a steady state flowsheeting simulator (Aspen Plus). All major units were included in the flowsheet, 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 dehydration units operating in the United Arab Emirates (UAE). To explore effects of the thermodyn- amic model employed in the simulator on the reliability of the whole simulation process, different predictive mixing rules applied to two cubic equations of state (EOS), as programmed by the simulator, have been investigated. The EOS used in the simulation is the Redlich–Kwong–Soave (RKS) and the Peng–Robinson (PR), both with Boston–Mathias (BM) alpha function. In addition to the classical empirical mixing rules, the following ones are investigated: Predictive Soave–Redlich–Kwong–Gmehling (PRKS), Wong–Sandler (WS), and Modified-Huron–Vidal (MHV2) mixing rules. These mixing rules are all predictive in nature. The plant performance criteria that have been studied for their response to changes in the solvent circulation rate include: BTEX (benzene, toluene, ethyl benzene, and xylenes) emissions rate, desiccant losses (makeup), water content in the dehumidified natural gas, purity of the regenerated TEG, and reboiler heat duty. Comparison with the field data is done. Very diverse results have been obtained from the different models and mixing rules. No one single model gives the best results for all criteria.

#2003 Published by Elsevier Ltd.

Keywords:Natural gas; Dehydration; Emission; BTEX; Simulation; Mixing rules; Equations of state

1. Introduction and background

Gas dehydration is the process of removing water vapor (moisture) from natural gas streams to meet sales specifications and to prevent hydrate formation and corrosion in transmission pipelines (Campbell, 1992;

Manning and Thompson, 1991; Pearce and Sivalls, 1984; Grizzle, 1993). The flowsheet for the natural gas dehydration facility that has been simulated here is shown in Fig. 1. This represents a typical glycol dehy- dration unit and resembles the existing dehydration

units operating in the United Arab Emirates (UAE).

Details of the operating conditions in this facility has been presented elsewhere (Break et al., 2001).

Glycol dehydration involves the absorption of water vapor using a liquid desiccant (e.g., glycol) in an absor- ber (also called contactor) and the regeneration of this rich (water-laden) desiccant in a still column (stripper) using steam or stripping gas at high temperatures and preferably low pressures (Pearce and Sivalls, 1984). The rich desiccant leaving the bottom of the absorber is throttled into low pressure in a flash tank before being sent to the stripper/regenerator unit, where the absorbed species are stripped off the solvent. The major sources of air and water pollution in dehydration units

Corresponding author. Tel.: +962-2-7201000 Ext 22361; fax:

+962-2-7095018.

E-mail address:naif@just.edu.jo (N.A. Darwish).

1364-8152/$ - see front matter#2003 Published by Elsevier Ltd.

doi:10.1016/j.envsoft.2003.10.008

are associated with the vent gases of this stripper (Grizzle, 1993; Break et al., 2001).

Significant amounts of aromatic and volatile organic compounds (VOC) may be present in the processed wet natural gas (Robinson et al., 1991; Fitz and Hubbard, 1987). Appreciable amounts of these hazardous species may be absorbed and ultimately rejected to the atmos- phere through the vents of the stripper and the flash tank. The water recovered from the rich solvent may also contain significant amounts of these objectionable materials (Gallup et al., 1996; Rueter et al., 1996). Due to increasing concern for environmental protection all over the globe, attention has been focused on emissions associated with glycol dehydration units (Clean Air Act Amendments; Coerr, 1995; Rueter and Evans, 1995).

For example, the limits placed by the current ‘‘clean air amendments’’ on benzene, toluene, ethyl benzene, and xylenes (BTEX) emissions are 25 tons per year (tpy) of total BTEX and no more than 10 tpy of any individual compound (Collie et al., 1998). Control of BTEX and other VOCs emissions from gas and oil facilities is becoming, therefore, one of the largest environmental challenges facing the natural gas industry today (Collie et al., 1998). The two most common emission control technologies in current use are combustion (sometimes called flaring or incineration) and condensation (GRI Document).

Glycol dehydration systems can be optimized to reduce emissions by adjusting process unit parameters such as the glycol circulation rate and the flash tank temperature and pressure. For some units, pollution prevention achieved through system optimization may

eliminate the need for control equipment (Break et al., 2001; Colley et al., 1992). However, some dehydrators, even under optimum operating conditions, may gener- ate air emissions above regulatory limits.

Central to the control and optimization of natural gas facilities is the availability of a reliable thermodyn- amic model for thermo-physical property prediction of the different fluids involved, such as the gas and the liquid desiccant (Satyro and Sim, 1993). Scores of such models are emerging in the open literature every year (Orbey and Sandler, 1998). Among these, cubic equations of state (EOS) remain the most popular because of their relative ease, convenience and their amenability to com- puter programming. The cubic Redlich–Kwong–Soave (RKS) (Redlich and Kwong, 1949; Soave, 1972) and the Peng–Robinson (PR) (Peng and Robinson, 1976) EOS are among the most famous models in current use. To give good prediction for fluid mixture proper- ties, these EOS require, in addition to sets of binary interaction parameters, reliable mixing rules. It is a well-known reality that the prediction capability of such EOS is very susceptible to these mixing rules and interaction parameters (Orbey and Sandler, 1998; Patel et al., 1998). Many mixing rules have been employed in EOS computations (Orbey and Sandler, 1998). In this work, different ‘‘predictive’’ mixing rules, as pro- grammed by Aspen Plus² (Aspen Plus, 2000), will be utilized in these two EOS. The mixing rules used are:

the Predictive Soave–Redlich–Kwong–Gmehling (PRKS) (Orbey and Sandler, 1998; Holderbaum and Gmehling, 1991; Aspen Plus, 2000), the Wong–Sandler (WS) (Orbey and Sandler, 1998; Wong and Sandler, 1992;

Fig. 1. Process flowsheet for the natural gas dehydration plant simulated.

Wong et al., 1992; Orbey et al., 1993; Aspen Plus, 2000), Aspen (Aspen Plus, 2000), and the Modified- Huron–Vidal (MHV2) mixing rules (Orbey and Sand- ler, 1998; Aspen Plus, 2000; Dahl and Michelsen, 1990). The two EOS, together with these predictive mixing rules, are summarized in section 2 below. No attempts have been made to interfere with the binary interaction parameters data bank in the simulator. This is so because the objective here is to expose the different models to the same input data so that we can cross- compare the performance of the different models in handling the same simulation case.

2. EOS and mixing rules considered in this work

2.1. Peng–Robinson–Boston–Mathias (PR–BM) EOS (Peng and Robinson, 1976; Aspen Plus, 2000; Boston and Mathias, 1980)

This is the standard PR EOS with BM alpha func- tion and the conventional mixing rules foraandb:

P¼ RT

Vmb a

VmðVmþbÞ þbðVmbÞ ð1Þ where

b¼X

i

x_ib_i ð2Þ

a¼X

i

X

j

x_ix_jðaia_jÞ^0:5ð1k_ijÞ ð3Þ

b_i¼fðTci;P_ciÞ ¼0:07780RT_ci pci

ð4Þ

a_i¼f Tð ;T_ci;P_ci;x_iÞ ¼0:45724a_iR²T_ci² pci

ð5Þ

k_ij¼k_ji ð6Þ

In BM modification of PR EOS,a_i is given by a_i¼expc_i1T_ri^di2

ð7Þ with

di¼ ð1þmi=2Þ ð8Þ

ci¼11=di ð9Þ

mi¼0:3764þ1:54226xi0:26992x² ð10Þ For species with subcritical temperatures the original expression foraiis used, i.e.,

a_iðTÞ ¼h1þm_i1T_ri¹⁼²i2

ð11Þ

2.2. RKS and RKS–BM (Soave, 1972; Aspen Plus, 2000; Boston and Mathias, 1980)

The standard RKS EOS is:

P¼ RT

Vmb a

VmðVmþbÞ ð12Þ

where b¼X

i

x_ib_i¼0:08664RT_ci=p_ci ð13Þ a¼X

i

X

j

x_ix_jðaia_jÞ^0:5ð1k_ijÞ

¼0:42747a_iR²T_ci²=p_ci ð14Þ kij¼kji

mi¼0:48þ1:57xi0:176x²_i ð15Þ

In the RKS EOSa_iðTÞis given by aiðTÞ ¼h1þmi1T_ri¹⁼²i2

ð16Þ But in RKS–BM EOS it is given by Eqs. (8) and (9) withmi given by Eq. (15).

2.3. RKS with Aspen mixing rules (RKS–Aspen) (Aspen Plus, 2000)

Here, an interaction parameter is introduced in the mixing rule for b and an additional polar parameter is used ina_i:

a¼X

i

X

j

xixjðaiajÞ^0:5ð1ka;ijÞ ð17Þ

b¼X

i

X

j

xixj

bibj

2 ð1kb;ijÞ ð18Þ

The interaction parameters, ka;ij and kb;ij, are tem- perature dependent:

ka;ij¼k⁰_a;ijþk¹_a;ijT=1000 ð19Þ kb;ij¼k⁰_b;ijþk¹_b;ijT=1000 ð20Þ

2.4. Predictive mixing rules based on Huron–Vidal model (HV) (Vidal, 1978; Huron and Vidal, 1979)

Vidal (1978) and later Huron and Vidal (1979) developed mixing rules that have proved successful for polar mixtures containing light gases at high pressures.

These mixing rules were derived by combining an EOS with an activity coefficient model. The premise is that the mixture EOS at liquid densities should behave like an activity coefficient model.

At any temperature and pressure, the excess Gibbs energy,G_m^E, is related to the fugacity coefficients by the

relation:

G^E_m¼RTln/X

xiRTln/_i ð21Þ

At infinity pressure, the mixture, according to this model, becomes a liquid-like. Here the model assumes V¼b, andV^E¼0. Therefore,

b X

xibi ð22Þ

At infinity pressure, Eq. (21), then, becomes a

b¼X xi

ai

b_i 1

KG^E_mðp¼ 1Þ ð23Þ where

K¼ 1 k1k2

ln1þk1

1þk2

ð24Þ With k₁¼1 for RKS and 1 ffiffiffi

p2

for PR and k₂¼0 for RKS and 1þ ffiffiffi

p2

for PR EOS.

Eqs. (22) and (23) constitute the original HV mixing rule. A basicshortcoming of this mixing rule is its sen- sitivity to pressure (Orbey and Sandler, 1998). To make these mixing rules (Eqs. (22) and (23)) more predictive, several modifications have been done. Among these are the following three methods, which are utilized in Aspen Plus²:

. The MHV mixing rule, second order approximation (MHV2).

. The WS modified HV mixing rule.

. The predictive SRK method (PSRK).

2.5. MHV2 mixing rules (Aspen Plus, 2000; Dahl and Michelsen, 1990)

Here, an expression similar to Eq. (21) is derived using a thermodynamicrelationship between excess Gibbs energy and the fugacity computed by EOS. The resulting expression is:

G^E_m=RT¼lnðf=RTÞ X

xilnðfi=RTÞ ð25Þ The advantage here is that the expressions for mix- ture and pure species fugacities containV/banda/bRT but not the pressure:

lnf_i=RT

þlnbi¼Q V _i=bi;ai

ð26Þ where

ai¼ai=ðbiRTÞ ð27Þ

and

lnðf=RTÞ þlnb¼QðVm=b;aÞ ð28Þ

with

a¼a=ðbRTÞ ð29Þ

Now, instead of using infinity pressure to simplify Eq. (25), the condition of zero pressure is used. Eq. (25) becomes:

G_m^Eðp¼0Þ=RTþX

x_ilnðbi=bÞ ¼qðaÞ X x_iqðaiÞ

ð30Þ Only for values of a>5:8,qðaÞexplicitly. Therefore, Dahl and Michelsen (1990)used a second order poly- nomial fitted to the analytical solution for 10<a<13 that can be extrapolated to low values of alpha:

qðaÞ ¼q₁aþq₂a² ð31Þ

Eqs. (30) and (31) form the MHV2 mixing rules. To computeb, Eq. (22) is used.

2.6. PRKS mixing rule (Holderbaum and Gmehling, 1991; Aspen Plus, 2000)

Here, the pressure-explicit expression for the EOS is substituted in the relation:

P¼ ð@A=@VÞ_T ð32Þ

and Helmoltz energy is calculated by integration and the following relation for excess Helmoltz energy is obtained:

A^E_m¼AmX

xiA_i RTX

xilnxi ð33Þ The resulting mixing rule (with V_i^;l=bi¼V_m^l=b, bgiven by Eq. (22), and hence

V_m^Eðp¼ 1Þ ¼0a=b¼X

x_iðai=b_iÞ A^E_mðpÞ=K ð34Þ whereKis slightly different fromKin Eq. (24):

K¼ 1

k₁k₂lnVm=bþk1

V_m=bþk₂ ð35Þ

At infinity pressure, the packing factor, V_m^l=b, goes to 1 and the excess Helmoltz energy (A^E_m) is equal to the excess Gibbs energy (G^E_m). Thus, HV mixing rule is recovered. The special feature in the PSRK mixing rule is the ability to use binary interaction parameters for activity coefficients models at any pressure. UNIFAC is chosen for its predictive character. But since UNIFAC parameters have been derived at low pressure,A^E_m and G_m^E are not equal. Also the packing factor is not equal to 1. This deficiency has been corrected later, where an average value of 1.1 was used for the packing factor and consequently the difference between A^E_m and G^E_m was computed (Aspen Plus, 2000).

2.7. WS mixing rules (Wong and Sandler, 1992; Wong et al., 1992; Orbey et al., 1993; Aspen Plus, 2000)

Like HV mixing rules, the limiting case of infinity press- ure is used, but in terms of Helmoltz energy (Eqs. (32)

and (33)). The resulting mixing rule is:

a=b¼X

xiðai=biÞ A^E_mðp¼ 1Þ=K ð36Þ The linear mixing rule for b is abandoned here and an alternative rule is used:

b¼

P

i

P

ixixjBij

1A^E_mðp¼ 1Þ=KRT P

ixiBii

ð37Þ Here, B_ij is the second cross Virial coefficient.

Eq. (37) was found to satisfy the quadraticdependence of the mixture second Virial coefficient on mole frac- tions.

3. Results and discussions

The base case operating conditions in the different units, in addition to the composition of the inlet wet gas, which are used in the current simulation are pre- sented in Tables 1 and 2. These conditions resemble one of the onshore oil and gas-processing facilities in the UAE, which is operated by Abu Dhabi Company for Onshore Oil Operations (ADCO) (Break et al., 2001). These conditions are based on new sampling tests and are slightly different from those presented before (Break et al., 2001). The flowsheet representing this process is shown inFig. 1. As shown in this figure, all major units are taken into account, i.e., the absorp- tion column, flash unit, heat exchangers, stripper, regenerator, and reboiler.

One of the most important input parameters in natu- ral gas dehydration facilities is the desiccant circulation rate, i.e., the triethylene glycol (TEG) gallon per

minute (gpm) that is circulating in the plant. The per- formance of the different EOS and mixing rules will be investigated through studying the effect of TEG circu- lation rate on the following performance measures:

. Mass percentage of BTEX in the wet gas absorbed in the rich solvent.

. Total desiccant losses (makeup needed).

. Dry gas dew point (or equivalently, water content of the dry gas).

. BTEX emission rate from the stripper.

. Regenerated (lean) solvent purity.

. Reboiler duty.

Effects of solvent circulation rate on these perform- ance measures, as predicted by the RKS and PR EOS with different mixing rules, are displayed in Figs. 2–6.

The field values of these performance measures, as well as the predicted ones corresponding to the field value of the circulation rate, are summarized inTable 3.

Percentage of the total BTEX in the inlet gas that has been absorbed by TEG in the absorber is shown in Fig. 2. The higher the amount of BTEX absorbed, the more stringent and pressing the environmental problem is becoming. This is so because the amount of BTEX absorbed will be ultimately released during the regener- ation process either to the atmosphere or to the recov- ered water. In both cases a pollution problem is generated. The optimum solvent in this regard is one, which reduces the water content of the incoming

Table 1

Summary of operating conditions of the base case employed in the simulation

Stream or unit Operating conditions (1) Wet gas Temperature¼136:4 F,

pressure¼618 psia,

volume flow¼11 MMSCFD, mass flow¼31915 lb=h

(2) Lean TEG Temperature¼148 F, pressure¼618 psia, purity¼0:998, circulation rate¼9:25 gpm (3) Stripping gas 80% of the flash tank vent

(4) Absorber Number of stages¼3, pressure¼618 psia, simulator input: no reboiler (Q_N¼0), no condenser (Q₁¼0)

(5) Flash tank Temperature¼100 F, pressure¼58 psia (6) Stripper Number of stages¼5,

pressure¼14:7 psia, simulator input: no reboiler (QN¼0), no condenser (Q1¼0) (7) Regenerator Number of ideal stages¼2,

pressure¼14:7 psia, simulator input: no condenser (Q1¼0), heat duty controlled to give lean solvent temperature of 400 F. The field value is 0.6 MMBtu/h

Table 2

Temperature, pressure, flow rates, and composition of the base case wet gas processed

Species Mass flow

rate (lb/h)

Mass fraction Mole fraction

H2O 58.140 0.002 0.003

CO2 2678.476 0.084 0.050

N2 175.263 0.005 0.005

C1 12319.319 0.386 0.636

C2 4124.638 0.129 0.114

C3 5034.634 0.158 0.095

n-C4 3098.684 0.097 0.044

i-C4(isobutane) 1645.518 0.052 0.023

n-C5 1021.315 0.032 0.012

i-C5 1046.586 0.033 0.012

n-C6 193.597 0.006 0.002

c-C6(cyclo hexane) 59.973 0.002 590 PPM

i-C6 202.965 0.006 0.002

n-C7 35.098 0.001 290 PPM

i-C7 47.200 0.001 390 PPM

c-C7 69.969 0.002 590 PPM

i-C8 13.797 432 PPM 100 PPM

c-C8 25.751 807 PPM 190 PPM

C6H6(benzene) 27.360 857 PPM 290 PPM C7H8(toluene) 22.257 697 PPM 200 PPM C8H10(xylene) 12.823 402 PPM 100 PPM C8H10(ethyl benzene) 1.282 40 PPM 10 PPM

natural gas while absorbing minimal amounts of the BTEX and VOCs. As expected, all EOS predict an increasing absorption of BTEX with increasing circu- lation rate. But at the field circulation rate of 9.25 gpm (see Table 1), Fig. 2 shows that different results are obtained from different EOS and mixing rules.

Moreover, it is seen that the type of mixing rule has a predominant effect over the type of the EOS. That is, different EOS with the same mixing rules show compa- rable predictive capability. For example, it is clear from Fig. 2 that RKS and PR with MHV2 mixing rules show close predictive capability. Also, RKS–BM and PR–BM (i.e., using the BM alpha function) with

Fig. 2. Comparison between different EOS in predicting the absorp- tion of BTEX.

Fig. 3. Comparison between different EOS in predicting TEG losses in a typical dehydration plant (note: RKS–WS gives TEG losses of 6.7–12.2 lb/h over TEG rate of 0.5–15 gpm. Hence, excluded to clearly show differences between other EOS).

Fig. 4. Comparison between different EOS in predicting water con- tent of the dry gas.

Fig. 5. Comparison between different EOS in predicting emission of BTEX in the stripper overhead.

the classical mixing rules used with the original RKS and PR EOS show comparable prediction. More importantly, however, is the observation that none of the studied EOS came very close in predicting the actual percentage of BTEX absorption, which is 60%

at 9.25 gpm circulation rate. RKS with the predictive mixing rules and WS mixing rules (i.e., PRKS and RKS–WS) underestimate that by giving 45%, and 38%

BTEX absorption, respectively, whereas the rest of the studied EOS overestimates the percentage of BTEX absorption (Fig. 2, Table 3). PR and RKS with BM alpha function and the standard mixing rules perform as well as PRKS and RKS–WS, but overestimate the percentage absorption of BTEX in the rich solvent.

The worst results are given by RKS–Aspen, PR–BM and RKS–BM (with the classical mixing rules).

Fig. 3shows the effects of circulation rate on solvent losses (makeup needed). With the exception of PRKS EOS, which shows a shallow maximum in TEG losses, all models predict the expected trend of incurring higher losses at increasing solvent rate. However, com- pared with the 0.4 lb/h field makeup rate, MHV2 and PRKS mixing rules are giving comparable results, in particular for solvent circulation rate below 5 gpm.

RKS–BM and RKS–Aspen models give similar results for the solvent losses, that is 1.13 lb/h. RKS–WS makes the worst prediction for solvent losses, where it predicts a solvent losses of 6.7–12.2 lb/h over the a cir- culation rate of 0.5–15 gpm. Therefore, to keep clarity of the plots in Fig. 3, results for RKS–WS prediction of solvent losses has been excluded from the figure.

PR–BM with the classical mixing rules is seen to over- estimate solvent losses by giving about 1.88 lb/h of TEG makeup. A summary of the different models’ pre- diction of the solvent losses at the field circulation rate of 9.25 gpm is shown inTable 3.

The third performance measure considered in this study is the water content in the dried exit gas leaving the top of the contactor. This is a very important para- meter because it directly reflects the exit gas dew point.

The variation of water content of the dehumidified (dried) natural gas with the solvent circulation rate is presented inFig. 4. RKS–WS, PRKS and MHV2-mixing rules-based EOS predict water content in the range of 2–4 lb/MMSCF. At the field circulation rate of 9.25 gpm, PRKS and PR–MHV2 give the best prediction of water content (Table 1). The classical mixing rules, i.e., PR–BM and RKS–BM, in addition to RKS–Aspen, predict a very high water content. For example, RKS–

BM, and RKS–Aspen predict an exit water content of about 49 lb/MMSCF, whereas PR–BM predicts even a higher value of about 58 lb/MMSCF.

The major source for BTEX pollution problem is associated with the stripper top gas emission. This emission rate (in lb/h) is shown in Fig. 5. With the exception of RKS–WS and PR–MHV2 equation, which show a monotonic increase in emission rate with increasing solvent circulation rate, all other EOS show a maximum in the mission rate. More interestingly, PRKS and RKS–MHV2 show a maximum in BTEX emission to occur at a circulation rate near 10 gpm (Fig. 5), which is close to the actual field used by ADCO dehydration facility in the UAE (that is 9.25 gpm). In view of the good performance of these two EOS, it seems that the natural gas dehydration plant in the UAE is operating at a circulation rate far from optimum in regard to minimizing the emission rate.

However, the dew point (water content) of the dried gas is another constraint to be met, which means that some kind of optimization is in order here. It is inter- esting too to observe that the equations predicting the highest emission rates, which are the MHV2-based EOS, are not necessarily the ones predicting the highest absorption rate (refer toFigs. 2 and 5). This reflects the variability of the different mixing rules in modeling the different unit operations in the plant, in particular, the stripper and the flash unit operation. This is also clarified in Fig. 6, which presents BTEX content in the overhead gas stream of the flash unit. Here, the highest emission rate, which is 3.25 lb/h at 9.25 gpm, is asso- ciated with PR–MHV2, whereas the lowest is associa- ted with RKS–WS equation. It is clear from this figure that the contribution of the flash unit to the BTEX pollution problem is not as crucial as that of the strip- per. This is also in accordance with the actual plant performance (Break et al., 2001).

Fig. 6. Comparison between different EOS in predicting emissions of BTEX in the overhead of the flash unit.

The purity of the regenerated lean solvent (regener- ated effluent) is a very crucial factor in affecting the required dew point reduction for the dehumidified gas in the contactor. The dehydration case simulated here (ADCO-UAE) is being operated with 99.8% regener- ated TEG (based on mass) (Break et al., 2001). The purity (i.e., mass fraction) of the regenerated solvent, predicted by the different EOS studied here, is shown in Table 3. It can be seen from this figure that the best prediction is made by the MHV2-based mixing rules.

Shown also in Table 3 is the reboiler duty in millions Btu/h. The variability in the predicted reboiler duties among the different models reflects the variability in the phase behavior predictive capability of these EOS, which is in turn manifested in the energy balance around the reboiler. In the simulation, the reboiler heat duty was manipulated so as to control the regenerator effluent temperature of the lean solvent at 400 F (the field temperature). The reboiler heat duty in the actual facility is 0.98 million Btu/h (Break et al., 2001). It can be seen from Table 3 that, with the exception of the classical mixing rules used by RKS–BM and PR–BM, all models overpredict the reboiler duty. It is worth mentioning here that PR EOS with WS mixing rules, as programmed by Aspen Plus simulator, has not con- verged for the case simulated in this work.

4. Conclusions

A typical natural gas dehydration plant, which employs triethylene glycol (TEG) as the dehydrating agent, has been simulated using a steady state flow- sheet simulator (Aspen Plus). The base case operating conditions are taken to resemble field data from one of the existing dehydration units operating in the UAE.

Different predictive mixing rules applied to RKS and PR EOS, as programmed by the simulator, have been investigated. The mixing rules investigated are: PRKS, WS, and MHV2 mixing rules. The performance of

these different models have been cross-compared by studying the effect of the solvent circulation on the BTEX emissions rate, the desiccant losses, water con- tent in the dehumidified natural gas, the purity of the regenerated TEG, and the reboiler heat duty. Compari- son with the field data has also been done. Very diverse results have been obtained from the different models and mixing rules. No one single model gives the best results for all criteria. In general however, the PRKS and MHV2 predictive mixing rules gave reasonable prediction of the field situation.

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Table 3

Some important properties calculated from PR and RKS EOS with different mixing rules for the base case representing actual field operating con- ditions in ADCO dehydration plant. (Number in parenthesis refer to field data in units associated with definitions below the table, whereas aster- isked numbers identify the closest to the field value.)

EOS/mixing rule Property (see definitions below the table)

X1 (60) X2 (0.4) X3 (3) X4 (41) X5 Purity (0.998) Qreb(0.98)

PRKS 45 0.07 2 30.4 0.75 0.991 1.263

RKS–BM 95 1.13 49 32.2 2.33 0.980 0.706

RKS–MHV2 76 0.12 4 46.8 2.44 0.992 1.341

RKS–Aspen 95 1.13 49 32.2 2.33 0.980 0.706

RKS–WS 38 11.6 0.35 16.6 0.38 0.986 1.252

PR–MHV2 80 0.27 2 50.6 3.25 0.994 1.358

PR–BM 95 1.88 58 34.8 2.65 0.981 0.688

X1, BTEX wt% absorbed in the rich TEG under conditions of the contactor; X2, TEG losses in lb/h; X3, water content in the dried gas in lb H2O/MMSCF (million standard cubic feet); X4, BTEX emission rate from stripper (lb/h); X5, BTEX emission rate from flash unit (lb/h);

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