Natural gas liquids (NGLs) consist of the hydrocarbon components in a produced gas stream that can be extracted and sold. Common NGL products are ethane (C2H6), propane (C3H8), butanes (iC4H10 and nC4H10) and natural gasoline (C5+).  Ethane is the lightest NGL and its recovery can be justified in those areas where a ready petrochemical market and a viable transportation network exist. Ethane is mainly used as a petrochemical feedstock. Propane is used for petrochemical feedstock, and also finds wide application as a domestic and industrial fuel. Propane is frequently sold as a mixture of propane and butane called LPG (Liquefied Petroleum Gas).

The market for butanes is primarily as a petrochemical feedstock, fuel and/or for gasoline blending when vapor pressure requirements allow it. Isobutane (iC4) is the most valuable of the NGLs. Its primary use is as refinery feedstock for manufacture of high octane blending components for motor gasoline. Normal butane can be used as a feedstock to olefin plants where it is converted to mono-olefins (ethylene and propylene) and the diolefin, butadiene as well as other by-products. The largest use for isobutane is as a gasoline blending component for octane number and vapor pressure control. Natural gasoline refers to the pentanes and heavier components in a gas stream and they are also commonly referred to as condensate or naphtha; it usually consists primarily of straight and branched chain paraffins. Natural gasoline is most commonly used as refinery feedstock, although it can also be used as a petrochemical feedstock. The details of the processes required, and the principles of their operation are discussed in Maddox and Lilly [1], and Maddox and Morgan [2]. A summary about the distribution of sulfur-containing compounds is presented on pages of 287-291 [2].  Specifically, page 290 presents the conclusions from the papers presented by Harryman and Smith [3, 4] which highlight the complexity of sulfur-containing distribution in the NGL product streams.

Raw NGL feed to an NGL fractionation (NF) plant may contain sulfur-containing compounds such as carbonyl sulfide (COS), methyl mercaptan (MeSH), ethyl mercaptan (EtSH), carbon disulfide (CS2), isopropyl mercaptan (iC3SH), isobutyl mercaptan (iC4SH), etc. For the purpose of meeting NGL products specification, it is important to accurately determine the distribution and concentration of the various mercaptans during NF process.

Likins and Hix [5] evaluated the accuracy of four commercial simulation programs by comparing their predicted K-values with the experimentally measured values. They concluded that “In this limited evaluation against laboratory VLE data, no one program can be claimed to be an outstanding winner. Although simulator D does an excellent job with one system, it poorly predicts behavior in the second system and is surpassed by simulator B. Simulator C behaves erratically in that its predictions range from excellent to horrible (dimethyl sulfide) depending on the component.” They also simulated two different NF plants using commercial simulation programs and compared the distribution and concentration of mercaptans in different product streams with field data.  Again, they concluded that none of the simulators do a good job modeling the sulfur distribution overall.

In order to improve the accuracy of commercial simulators, Alsayegh et al. [6] presented a procedure to determine the binary interaction parameters between mercaptans and hydrocarbons using experimentally measured vapor-liquid equilibria (VLE).

In this tip of the month (TOTM), we will determine the distribution and concentration of different mercaptans in an NGL fractionation plant using HYSYS [7] Peng-Robinson [8] equation of state. The built-in HYSYS binary interaction parameters were used in this study. The NF plant is the same as the one described by Alsayegh et al. [6]. The feed composition, rate, and condition are shown in Table 1 [6] and the plant process flow diagram is shown in Figure 1 [6].

Table 1

Figure 1

The column specifications are shown in Table 2 [6].  An overall tray efficiency of 90 percent was used for all columns. In the last column of Table 2, DV and D represent the vapor and the total rate of the overhead stream, respectively. Therefore, the DV/D is the vapor fraction in the overhead product stream. In addition, reflux ratio (L/D) is defined as the reflux rate (L) divided by the total overhead stream rate.

Table 2

Expected Product Distribution: Figure 2, reproduced from Figure 9 of Likins and Hix paper [5], shows a descending order log scale bar-graph of the pure compounds vapor pressure for the components of interest to this study. This figure shows that COS should distribute to both the ethane and the propane streams. MeSH, with a vapor pressure close to n-butane should distribute primarily with the butanes with a small amount distributing to the pentane stream. EtSH, having a vapor pressure between butane and pentane, should distribute primarily with butane and pentane. CS2should distribute primarily to the pentane and the C6+ streams with only minor distribution to the butane stream. The heavier sulfur compounds should end up almost entirely in the C6+ stream.

Figure 2

Results of Computer Simulation:

The NF plant described in the previous section was simulated using HYSYS [7] based on the Peng-Robinson equation of state (EOS) [8]. In this study, the HYSYS built-in binary interaction parameters were used even though we recommend insertion of VLE data regression into the EOS interaction parameters. This regression is required to adequately model the systems dealing with mercaptans. Table 3 presents the mole percent recovery of each component in the product and gas streams predicted by HYSYS. The mole percent recovery is defined as the number of moles of a component in the product stream divided by the moles of the same component in the feed stream (Stream 5). Table 3 also presents the vapor fraction, temperature, pressure, and flow rate of each stream. The focus of this study is on the distribution (% recovery) and concentration (PPM) of the sulfur-containing compounds in the product streams. Table 4 presents the PPM concentration of sulfur-containing compounds in the product streams.

Table 3

Table 4

Figures 3 through 9 present bar-graphs of the recovery of each sulfur-containing compound in the product streams.

H2S: Figure 3 shows the distribution and recovery of H2S in the gas, C2 and C3 streams. As expected, the majority of the H2S distributes in the gas and the C2 streams.

Figure 3

COS: Figure 4 shows the distribution and recovery of COS in the gas, C2, and C3. As expected, the majority of the COS ends up in the C3 stream.

Figure 4

MeSH: Figure 5 shows the distribution and recovery of MeSH in the gas, C3, and C4 streams. Contrary to the data presented in Figure 2, the majority of the MeSH distributes to the C3 stream rather than to the C4 stream.

Figure 5

EtSH: Figure 6 shows the distribution and recovery of EtSH in the C3, C4, and C5 streams. Unexpectedly, the majority of the EtSH ends up in the C4 stream rather than C5 as would be expected in Figure 2.

Figure 6

CS2: Figure 7 shows the distribution and recovery of CS2 in the C4, and C5 streams. Contrary to the pure CS2 behavior (Figure 2), the majority of the CS2 ends up in C4 stream.

Figure 7

iC3SH: Figure 8 shows the distribution and recovery of iC3SH in the C4, C5 and C6+. As expected, iC3SH ends up in C5 and C6+ streams.

Figure 8

iC4SH: Figure 9 shows recovery of iC4SH in the C6+ stream. As expected, all of the iC4SH ends up in the C6+ stream.

Figure 9

Conclusions:

The calculation results presented and discussed here are specific to the liquid fractionation plant studied here, but there are some general conclusions that can be drawn from this study.

The results indicate that the highest concentration of ethyl mercaptan (EtSH) and carbon disulfide (CS2) are present in the C4 product (stream 20) and C5 Product (stream 23), respectively. The highest concentration of methyl mercaptan (MeSH) is present in the C3 product (stream 15).

The binary interaction parameters used in the EOS play an important role in the VLE behavior of the system under study, and affect the distribution of the sulfur-containing compounds present in the feed. Use of improper or incorrect binary interaction parameters may generate erroneous results. Care must be taken to use correct values of binary interaction parameters. In this study, the HYSYS library values of the binary interaction parameters were used.

Some of the sulfur-containing compounds (i.e. MeSH, EtSH, and CS2) were not distributed among the hydrocarbon products in the same the way one would expect from their volatilities and concentrations. This may be explained by the conclusion reported by Harryman and Smith who wrote “iC3SH is formed during fractionation within the depropanizer and the deethanizer”.  This should be a good reason to perform laboratory tests and detailed thermodynamic tray calculations to determine process flow rates and composition. Detailed process analysis should always be made to justify and prove correct decisions as to selection of process flow schemes.

To learn more about similar cases and how to minimize operational problems, we suggest attending the John M. Campbell courses; G4 (Gas Conditioning and Processing), G5 (Gas Conditioning and Processing-Special) and G6 (Gas Treating and Sulfur Recovery).

John M. Campbell Consulting (JMCC) offers consulting expertise on this subject and many others. For more information about the services JMCC provides, visit our website at www.jmcampbellconsulting.com, or email your consulting needs to consulting@jmcampbell.com.

By: Dr. Mahmood Moshfeghian

Reference:

  1. Maddox, R. N. and L. Lilly, “Gas Conditioning and Processing, Computer Applications for Production/Processing Facilities,” John M. Campbell and Company, Norman, Oklahoma, 1995.
  2. Maddox, R. N. and D. J. Morgan, “Gas Conditioning and Processing, Gas Treating and Sulfur Recovery Vol. 4,” John M. Campbell and Company, Norman, Oklahoma, 2006.
  3. Harryman, J.M. and B. Smith, “Sulfur Compounds Distribution in NGL’s; Plant Test Data – GPA Section A Committee, Plant design,“ Proceedings 73rd GPA Annual Convention, New Orleans, Louisiana, March, 1994.
  4. Harryman, J.M. and B. Smith, “Update on Sulfur Compounds Distribution in NGL’s; Plant Test Data – GPA Section A Committee, Plant design,“ Proceedings 75th GPA Annual Convention, Denver, Colorado, March, 1996.
  5. Likins, W. and M. Hix, “Sulfur Distribution Prediction with Commercial Simulators,” the 46th Annual Laurance Reid Gas Conditioning Conference Norman, OK 3 – 6 March, 1996.
  6. Al-Sayegh, A.R., Moshfeghian, M.  Abbszadeh, M.R., Johannes, A. H. and R. N. Maddox, “Computer simulation accurately  determines volatile sulfur compounds,” Oil and Gas J., Oct 21, 2002.
  7. ASPENone, Engineering Suite, HYSYS Version 7.0, Aspen Technology, Inc., Cambridge, Massachusetts U.S.A., 2009.
  8. Peng, D.,Y. and D. B. Robinson, Ind. Eng. Chem. Fundam. 15, 59-64, 1976.
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