1 Sep, 2017

Effect of Methanol on Distribution of Sulfur Compounds in NGL Fractionation Train

In the February and May 2010 tips of the month (TOTM) we presented the distribution and concentration of sulfur compounds in an NGL Fractionation Train (NFT) using commercial simulation software [1-3].

Sulfur compounds that may be present in NGL include H2S, COS, CS2, and mercaptans (RSH). These are typically present in low concentrations and are often removed by adsorption on a molecular sieve, reaction with a caustic solution, or amine treating. In addition, the methanol content of the liquid hydrocarbon can be as high as 0.6 mole % (GPA-Midstream RR 149 [4]).

In this TOTM we will present the effect of methanol on the distribution and concentration of the sulfur compounds in the same NFT using ProMax [5] based on the Peng-Robinson equation of state (PR EoS) [6]. The software’s built-in binary interaction parameters were used. The tabular and graphical simulation results are presented. The feed composition, rate, and condition are shown in Table 1.

The NFT process flow diagram is shown in Figure 1 [3]. The column and product specifications are shown in Table 2. An overall tray efficiency of 90 % was used for all columns.

Figure 1. Process Flow diagram for NGL Fractionation Train (NFT) [3]

Expected Product Distribution:

Figure 2, like Figure 9 presented by Likins and Hix [7], shows a descending order log scale bar-graph of the pure compounds vapor pressure for the components of interest to this study. The vapor pressures shown in this diagram are calculated using ProMax [5].

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. CS2 should 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. Pure compound vapor pressure at 12.8°C (55°F)

 

Results of Computer Simulation:

The NFT described in the previous section was simulated using ProMax [5] based on the Peng-Robinson equation of state (PR EoS) [6]. In this study, the built-in (library) binary interaction parameters were used even though we recommend evaluating the accuracy of VLE results against experimental data and if necessary the insertion of VLE data regression into the EoS interaction parameters. This regression may be required to adequately model the systems dealing with mercaptans.

The focus of this study is on the distribution (% recovery) and concentration (PPM) of the sulfur compounds in the product streams in the absence and presence of methanol. Table 3 presents the PPM concentration of sulfur compounds in the feed and product streams. The maximum concentration of each compound in the product streams are shown in red color fonts.

Figures 2 through 6 present bar-graphs of the recovery of each sulfur compound in the “Gas” and the other product streams. Note in these figures, the “Gas” represents the sum of gas streams 9, 13, and 16. 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).  Figures 8 through 11 present the effect of methanol on the recoveries of the sulfur compounds in the product streams.

H2S and COS: Figure 3 shows the distribution and recovery of H2S and COS in the Gas, C2 and C3 product streams. As expected, much of the H2S distributes in the gas and the C2 product streams and much of the COS ends up in the C3 product stream. Note H2S in C3 ~ 0.

MeSH: Figure 4 shows the distribution and recovery of MeSH in the Gas, C2, C3, C4, and C5 product streams. Most of the MeSH distributes to the C3 and C4 stream. Note MeSH in C2 and C5 is ~ 0.

 

Figure 3. Distribution and recovery of H2S and COS in the gas, C2 and C3 product streams (H2S in C3 ~ 0)

EtSH and CS2: Figure 5 shows the distribution and recovery of EtSH and CS2 in the C3, C4, C5, and C6+ product streams. Most of the EtSH ends up in the C4 and C5 streams and most of the CS2 ends up in the C5 stream which are consistent with Figure 1 data. Note EtSH in C3 and C6+ is ~ 0 and CS2 in C6+ is ~ 0.

iC3SH and iC4SH: Figure 6 shows the distribution and recovery of iC3SH and iC4SH in the C4, C5 and C6+ product streams. As expected, iC3SH ends up in the C5 and C6+ streams and all of the iC4SH ends up in C6+ stream. Note iC3SH in the C4 is ~ 0.

 

Figure 4. Distribution and recovery of MeSH in the Gas, C2, C3, C4, and C5 product streams (MeSH in C2 and C5 is ~ 0)

 

Figure 5. Distribution and recovery of EtSH and CS2 in the Gas, C2, C3, C4, C5, and C6+ product streams (EtSH in C3 and C6+ is ~ 0 and CS2 in C6+ is ~ 0)

 

Figure 6. Distribution and recovery of iC3SH and iC4SH in the C4, C5, and C6+ product streams (iC3SH in the C4 is ~ 0)

 

Effect of Methanol:

To investigate the effect of methanol on the recoveries of sulfur compounds in product streams, the zero concentration of methanol in the feed (stream 5) was changed to 0.01, 0.1, 0.5, and 1 mole %.

As shown in Figure 7, simulation results indicated that almost all of the methanol was distributed only between C4 and C5 product streams. Based on Figure 2, all the of methanol should with go with C6 and C7 but it is not from the same family. Figure 7 also indicates that the level of recoveries in these two product streams is a linear function of the methanol concentration in the feed stream.

 

Figure 7. Recovery of methanol in product streams vs its concentration in the feed

The simulation results indicated that the recoveries of H2S, COS, and iC4SH are independent of methanol concentration in the feed. The effect of methanol concentration in the feed on the recoveries of the other sulfur compounds are presented in Figures 8 through 11.

Figure 8 indicates that the presence of methanol in the feed has negligible effect on the recoveries of MeSH in the product streams.

Figures 9 and 10 indicate that the presence of methanol in the feed has appreciable effect on the recoveries of EtSH and CS2 in the C4 and C5 product streams but negligible effect on the C6+ product stream, respectively.

Figure 11 indicates that the presence of methanol in the feed has considerable effect on the recoveries of iC3SH in the C5 and C6+ product streams but negligible effect on the C4 product stream.

 

Figure 8. MeSH recovery in the Gas, C3, and C4 product streams

 

Figure 9. EtSH recovery in the C4, and C5, and C6+ product streams

 

Figure 10. CS2 recovery in the C4, and C5, and C6+ product streams

 

Figure 11. iC3SH recovery in the C4, and C5, and C6+ product streams

 

Conclusions:

The calculation results presented and discussed here are specific to the NGL fractionation train considered in this work, but there are some general conclusions that can be drawn from this study. The results can be summarized as follows:

-The highest concentration of MeSH occurs in the C4 product (stream 20).

-The highest concentration of EtSH occurs in the C5 product (stream 23).

-The highest concentration of CS2 occurs in C5 product (stream 23).

-The highest concentration of iC3SH occurs in C5 Product (stream 23).

-The highest concentration of iC4SH occurs in C6+ Product (stream 24).

-Any methanol in the feed stream will be distributed between C4 and C5 product streams.

-The amount of methanol recoveries in the C4 and C5 product streams is a linear function of the methanol concentration in the feed stream.

-Presence of methanol in the feed has no effect on the recoveries of H2S, COS (the most volatile) and iC4SH (the least volatile) in the product streams.

-Presence of methanol in the feed has little effect on the recoveries of MeSH in the product streams.

-Presence of methanol in the feed has considerable effect on the recoveries of EtSH, CS2 and iC3SH in the product streams. In the absence of methanol, most of these three compounds were distributed in C4 and C5 product streams; therefore, their concentration in these two product streams are affected by presence of methanol.

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 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 work, the simulator library values of the binary interaction parameters were used.

The results also indicate that some of these compounds were not distributed among the hydrocarbon products in the same way one would expect from their volatilities and concentrations. This may be explained by the conclusion reported by Harryman and Smith [8, 9] who wrote “iC3SH is formed during fractionation within the depropanizer and the deethanizer.” It should be also noted that the VLE behavior of components in the mixture is not the same as each individual component behavior. Therefore, further evaluation should be conducted to arrive at a concrete decision. This should be a good reason to perform laboratory tests and detailed thermodynamic 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 our G4 (Gas Conditioning and Processing), G5 (Practical Computer Simulation Applications in Gas Processing), and G6 (Gas Treating and Sulfur Recovery) courses.

PetroSkills offers consulting expertise on this subject and many others. For more information about these services, visit our website at http://petroskills.com/consulting, or email us at consulting@PetroSkills.com.

By: Dr. Mahmood Moshfeghian

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Reference:

  1. Moshfeghian, M. “Distribution of Sulfur-Containing Compounds in NGL Products”, TOTM, Feb 2010.
  2. Moshfeghian, M. “Distribution of Sulfur-Containing Compounds in NGL Products by Three Simulators,” TOTM, May 2010.
  3. 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.
  4. Gas Processors Association, "GPA RR-149: Vapor-Liquid and Vapor-Liquid-Liquid Equilibrium for H2S, CO2, Selected Light Hydrocarbons and a Gas Condensate in Aqueous Methanol or Ethylene Glycol Solutions," 1995.
  5. ProMax 4.0, Bryan Research and Engineering, Inc, Bryan, Texas, 2017.
  6. Peng, D.Y. and D. B. Robinson, Ind. Eng. Chem. Fundam. 15, 59-64, 1976.
  7. 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.
  8. 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.   
  9. 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.