In the November tip of the month (TOTM) we presented a single-stage compressor calculation result of a case study. We compared the rigorous method with the values from the short cut methods. The rigorous method was based on the Soave-Redlich-Kwong (SRK) for calculating the required enthalpies and entropies.

In this TOTM, we will present a case study of multistage stage compression with interstage cooling using the rigorous method. The rigorous method will be based on the Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), Lee Kesler (LK) and Benedict-Webb-Rubin-Starling (BWRS) equations of state. The K-values, enthalpies, and entropies are calculated by these EOSs to perform vapor-liquid-equilibrium (VLE) and the energy balance calculations to determine the power requirement, the discharge temperatures and the cooling load requirements. We will compare the compressor power and cooling load requirements based on the rigorous equations of state.

Power Calculations

The theoretical power requirements are independent of compressor type; the actual power requirements vary with the compressor efficiency. In general the power is calculated by:

From a calculation viewpoint alone, the power calculation is particularly sensitive to the specification of flow rate, inlet temperature and pressure, and outlet pressure. Gas composition is important but a small error here is less important providing it does not involve the erroneous exclusion of corrosive components. A compressor is going to operate under different values of the variables affecting its performance. Therefore the most difficult part of a compressor calculation is specification of a reasonable range for each variable and not the calculation itself. Reference [1] emphasizes that using a single value for each variable is not the correct way to evaluate a compression system.

Normally, the thermodynamic calculations are performed for an ideal (reversible) process. The results of a reversible process are then adapted to the real world through the use of efficiency. In the compression process there are three ideal processes that can be visualized: 1) an isothermal process, 2) an isentropic process and 3) a polytropic process. Any one of these processes can be used suitably as a basis for evaluating compression power requirements by either hand or computer calculation. The isothermal process, however, is seldom used as a basis because the normal industrial compression process is not even approximately carried out at constant temperature.

Step-by-Step Computer Solution

For known gas rate, pressure (P₁), temperature (T₁), and composition at the inlet condition and discharge pressure (P_2­) or compression ratio, computation of compressor power requirement is based on an EOS using a computer and involves two steps:

1. Determination of the ideal or isentropic (reversible and adiabatic) enthalpy change of the compression process. The ideal work requirement is obtained by multiplying mass rate by the isentropic enthalpy change.
2. Adjustment of the ideal work requirement for compressor efficiency.
   The step-by-step calculation based on an EOS is outlined below.
   1. Assume steady state, i.e.   and the feed composition remain unchanged.
   2. Assume isentropic process, i.e. adiabatic and reversible
   3. Calculate specific enthalpy h₁=f(P₁, T₁, and composition) and suction specific entropy s₁=f(P₁, T₁, and composition) at the suction condition by EOS
   4. For the isentropic process . Note the * represents ideal value.
   5. Calculate the ideal specific enthalpy  at outlet condition for known composition, P₂ and .
   6. The ideal work is 
   7. The actual work is the ideal work divided by efficiency or 
   8. The actual enthalpy at the outlet condition is calculated by 
   9. The actual outlet temperature is calculated by EOS for known h₂, P₂, and composition.

The efficiency of the compressor, and hence, the compression process obviously depends on the method used to evaluate the work requirement. The isentropic efficiency is in the range of 0.70 to 0.90.

If the manufacturer provides the compressor head curve and efficiency curve, the head is determined from the actual gas volume rate at the inlet condition. Second, from the head, the actual work, discharge pressure and finally the discharge temperature are calculated.

Case Study

The gas mixture with the composition shown in Table1 at 105 °F (40.6 °C) and 115 Psia (793 kPa) is compressed to 1015 psia (7000 kPaa) using a multistage centrifugal compressor. The total feed gas volumetric flow rate was 101 MMSCFD (2.86×10⁶ Sm³/d). This is the same feed used in the November TOTM.

Table 1. Feed gas analysis

A simplified process flow diagram is shown in Figure 1. The dry feed gas is saturated with water, passed through a scrubber (knockout drum) before entering the first stage of compressor. Each compression stage is followed by cooling and subsequent knockout drum before entering the next stage. An equal compression ratio of 3 was used for each stage. The polytropic efficiency of 86, 80, and 79 % based on the actual inlet volumetric rate (from Figure 13.23 of GPA Data book [2]) was specified for stages 1, 2, and 3 respectively. After each compression stage, the gas was cooled to the feed temperature of 105 °F (40.6 °C).

Figure 1. Three-stage compression with interstage cooling

Results and Discussions

The feed composition, suction temperature and pressure, volumetric flow rate at standard condition along with the compressor polytropic efficiency for each stage, and pressure drop for each cooler were specified. For this study, the above PFD was simulated using the Soave-Redlich-Kwong (SRK) [3], Peng-Robinson (PR) [4], Lee Kesler (LK) [5] and Benedict-Webb-Rubin-Starling (BWRS) [6] equations of states. These data were entered into the ProMax software [7] to perform the rigorous calculations based on the EOS. The program calculated discharge temperature, power for each stage, and cooling loads for each cooler. For the actual gas flow rate at the inlet condition, the polytropic efficiency was specified from the GPA data book. The calculated results for the four EOSs are presented in Table 2 (bold numbers with white background).

Table 2 (FPS units). Summary of the rigorous results for four EOSs using ProMax

The bold numbers with white background are the calculated values

 

Table 2 (SI Units). Summary of the rigorous results for four EOSs using ProMax

The bold numbers with white background are the calculated values

For the case of LK EOS, the wet feed volume flow rate at standard condition to the first stage of compressor is lower than the other cases because this EOS has not been revised to handle water content.

For the case studied, Table 2 indicates that there is 0.8 to 1.4 percent deviation in total compression power requirements among these 4 EOSs. The deviation in total heat removal using different EOSs is 1.7 to 2.2 percent. For facility type calculations and planning purposes, these differences are negligible. However, for cases with large power requirement, these small differences in terms of total HP or kW could be significant; therefore, care should be taken to choose an appropriate EOS for handling VLE calculations and accurate predictions of enthalpy and entropies for the system under consideration. The deviation range could be different for other cases depending on the flow rates, condition composition and compression ratio.

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 ME 44 (Fundamentals of Pump and compressors).

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

References:

1. Maddox, R. N. and L. L. Lilly, “Gas conditioning and processing, Volume 3: Advanced Techniques and Applications,” John M. Campbell and Company, 2^nd Ed., Norman, Oklahoma, USA, 1990.
2. Gas Processors Association Data Book, 12^th Edition, GPA, Tulsa, Oklahoma.
3. Soave, G., Chem. Eng. Sci., Vol. 27, pp. 1197-1203, 1972.
4. Peng, D. Y., and Robinson, D. B., Ind. Eng. Chem. Fundam., Vol. 15, p. 59, 1976.
5.  Lee B.I., Kesler M.G., “A Generalized Thermodynamic Correlation Based on Three-Parameter Corresponding States”, AIChE J., 21(3), 510-527, 1975
6. Starling, K. E., Fluid Thermodynamic Properties for Light Petroleum Systems,  Gulf Publishing Co., Houston, 1973.
7. ProMax 3.2, Bryan Research and Engineering, Inc, Bryan, Texas, 2011.

In this tip of the month (TOTM) we will present the compressor calculations of a case study. We will compare the rigorous method results with the values from the short cut methods. The rigorous method is based on an equation of state like the Soave-Redlich-Kwong (SRK) for calculating the required enthalpies and entropies. The enthalpies and entropies are used to determine the power requirement and the discharge temperatures.  The results indicate that the accuracy of the shortcut method is sensitive to the value of heat capacity ratio, k.

Power Calculations

The theoretical power requirements are independent of compressor type; the actual power requirements vary with the compressor efficiency. In general the power is calculated by:

where  mass flow rate and h is specific enthalpy.

From a calculation viewpoint alone, the power calculation is particularly sensitive to the specification of flow rate, inlet temperature and pressure, and outlet pressure. Gas composition is important but a small error here is less important providing it does not involve the erroneous exclusion of corrosive components. A compressor is going to operate under varying values of the variables affecting its performance. Thus the most difficult part of a compressor calculation is specification of a reasonable range for each variable and not the calculation itself. Reference [1] emphasizes that using a single value for each variable is not the correct way to evaluate a compression system.

Normally, the thermodynamic calculations are performed for an ideal (reversible process). The results of a reversible process are then adapted to the real world through the use of an efficiency. In the compression process there are three ideal processes that can be visualized: 1) an isothermal process, 2) an isentropic process and 3) a polytropic process. Any one of these processes can be used suitably as a basis for evaluating compression power requirements by either hand or computer calculation. The isothermal process, however, is seldom used as a basis because the normal industrial compression process is not even approximately carried out at constant temperature.

For an isentropic (reversible and adiabatic) process, equation 1 can be written as:

and based on the polytropic process:

The isentropic head is calculated by equation 3A:

Similarly, the polytropic head is calculated by equation 3B:

The actual discharge temperature based on the isentropic path is calculated by equation 4A.

The actual discharge temperature based on the polytropic is calculated by equation 4B.

where η and η_P are the isentropic (or adiabatic) and polytropic efficiency, respectively, P₁ suction pressure, P₂ discharge pressure, T₁ and T₂ arethe suction and discharge temperatures, respectively, q is gas volume flow rate at standard condition of P_S and T_S, Z_a average gas compressibility factor, k heat capacity ratio, R the gas constant, and n is the polytropic path exponent. Equations 1 and 2 are equally correct theoretically. The practical choice depends on the available data, although it is somewhat arbitrary. The power calculation should be made per stage of compression and then summed for all stages connected to a single driver. For general planning purposes the graphical solutions shown in reference [2] produce results comparable to these equations.

Equation of State (EOS)

The heart of any commercial process flow simulation software is an equation of state. Due to their simplicity and relative accuracy, normally a cubic EOS such as Soave Redlich-Kwong (SRK) [3] or Peng-Robinson [4] is used. These equations are used to calculate phase behavior, enthalpy, and entropy. With proper binary interaction coefficients, the process simulation results of these two equations are practically the same. Therefore, only the SRK was used in this work.

Step-by-Step Computer Solution

For known gas rate, pressure (P₁), temperature (T₁), and composition at the inlet condition and discharge pressure (P_2­), computation of compressor power requirement is based on an EOS using a computer and involves two steps:

1. Determination of the ideal or isentropic (reversible and adiabatic) enthalpy change of the compression process. The ideal work requirement is obtained by multiplying mass rate by the isentropic enthalpy change.
2. Adjustment of the ideal work requirement for compressor efficiency.
   The step-by-step calculation based an EOS is outlined below.
   1. Assume steady state, i.e.   and the feed composition remain unchanged.
   2. Assume isentropic process, i.e. adiabatic and reversible
   3. Calculate enthalpy h₁=f(P₁, T₁, and composition) and suction entropy s₁=f(P₁, T₁, and composition) at the suction condition by EOS
   4. For the isentropic process . Note the * represents ideal value.
   5. Calculate the ideal enthalpy () at outlet condition for known composition, P₂ and .
   6. The ideal work is 
   7. The actual work is the ideal work divided by efficiency or 
   8. The actual enthalpy at the outlet condition is calculated by 
   9. The actual outlet temperature is calculated by EOS for known h₂, P₂, and composition.

The efficiency of the compressor, and hence, the compression process obviously depends on the method used to evaluate the work requirement. The isentropic efficiency is in the range of 0.70 to 0.90.

If the compressor head curve and efficiency curve are provided by the manufacturer,  the head is determined from the actual gas volume rate at the inlet condition. Second, from the head, the actual work, discharge pressure and finally the discharge temperature are calculated.

Case Study

The gas mixture with the composition shown in Table1 at 105 °F (40.6 °C) and 115 Psia (793 kPa) is compressed using a single-stage centrifugal compressor with the polytropic head and efficiency curves shown in Figures 1 and 2 at a speed of 7992 rpm. The total feed gas volumetric flow rate was 101 MMSCFD (2.86×10⁶ Sm³/d).

Table 1. Feed gas analysis

Figure 1. Compressor polytropic head and best efficiency point

Figure 2. Compressor polytropic efficiency

Results and Discussions

SRK (Rigorous Method): The feed composition, temperature, pressure, volumetric flow rate at standard condition along with the compressor polytropic head and efficiency curves data were entered into the ProMax  software [5] to perform the rigorous calculations based on the SRK EOS. The program calculated polytropic and isentropic efficiencies, heads, compression ratio (discharge pressure), discharge temperature and power. For the actual gas flow rate at the inlet condition, the polytropic efficiency is close to the compressor best efficiency point (BEP). The program also calculated the gas relative density, heat capacity ratio (k), and polytropic exponent (n). These calculated results are presented in the SRK columns of Table 2 (bold numbers with white background).

Table 2. Summary of the rigorous and shortcut calculated results

The bold numbers with white background are the calculated values

Short-1 (Shortcut Method): In this method, we used equations 2 through 4 to calculate the polytropic and isentropic heads, the discharge temperature and power. We used the ProMax calculated polytropic and isentropic efficiencies, compression ratio (P₂/P₁), heat capacity ratio (k) and polytropic exponent (n) to calculate head, power, and the discharge temperature. The results are presented in the short-1 columns of Table 2. Note the short-1 results (discharge temperature, adiabatic and polytropic heads and power) are very close to the SRK values. The calculated actual discharge temperature by equation 4A (isentropic path: 265.3˚F=129.6˚C) was slightly lower than by equation 4B (polytropic path: 265.9 ˚F=129.9 ˚C).

Short-2 (Shortcut Method): Similar to short-1 method, we used equations 2 through 4 to calculate the polytropic and isentropic heads, the actual discharge temperature and power. We used only the ProMax calculated values of polytropic efficiency (n_P), compression ratio (P₂/P₁), and relative density (y). The heat capacity ratio (k) was estimated by equation 5:

The polytropic exponent (n) was estimated by equation 6.

The isentropic (adiabatic) efficiency () was estimated by equation 7.

The results for this method are presented in the short-2 columns of Table 2. The calculated discharge temperature by equation 4A (isentropic path) was exactly the same as by equation 4B (polytropic). Note the short-2 results (discharge temperature, adiabatic and polytropic heads and power) are deviated from the SRK values.

The results in Table 2 indicate that an increase of 2.2% in k (from 1.224 to 1.251) results in power increase of 1.42%. The polytropic exponent (n) increased by 3% and isentropic efficiency () decreased by 0.5 %. The difference in the actual discharge temperatures of the SRK and short-2 values is 17.5 ˚F (9.7˚C).

With the exception of actual discharge temperature, these differences between the SRK and short-2 methods results for facilities calculations and planning purposes are negligible. Note that the accuracy of the shortcut method is dependent on the values of k and n. In Short-1 method in which we used the k and n values from the SRK method the results were identical to those of SRK method.

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), and G5 (Gas Conditioning and Processing-Special).

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. L. Lilly, “Gas conditioning and processing, Volume 3: Advanced Techniques and Applications,” John M. Campbell and Company, 2^nd Ed., Norman, Oklahoma, USA, 1990.
2. Campbell, J. M., “Gas Conditioning and Processing, Vol. 2, the Equipment Modules, 8^th Ed., Campbell Petroleum Series, Norman, Oklahoma, 2001
3. Soave, G., Chem. Eng. Sci., Vol. 27, pp. 1197-1203, 1972.
4. Peng, D. Y., and Robinson, D. B., Ind. Eng. Chem. Fundam., Vol. 15, p. 59, 1976.
5. ProMax 3.2, Bryan Research and Engineering, Inc, Bryan, Texas, 2011.

In the March 2011 tip of the month (TOTM) we studied a constant volume translation of liquid density method presented by Peneloux et al. [3] and demonstrated its application for pure components. Considerable improvements were obtained, specifically for the low temperature range (reduced temperature < 0.8), of saturated specific volume (or liquid density) predicted by Soave-Redlich-Kwong (SRK) [1] and Peng-Robinson (PR) [2], but, the constant volume shift fails near the critical temperature. In the July 2011 TOTM, we reviewed several temperature dependent volume correction methods [3-11]. They improved the accuracy of liquid density prediction considerably near the critical point region.

As the extension of the March and July 2011 Tips of the Month in this TOTM we will demonstrate application and accuracy of some of these methods to predict liquid density of light hydrocarbon mixtures encountered in gas processing. We will compare their accuracy against both experimental data and a few correlations.

Equations of state (EoS) are used in commercial simulation software for predicting phase behavior and thermodynamic properties. The cubic equations of state give relatively accurate results for predicting vapor-liquid equilibria, especially for non-polar or slightly polar systems. These equations can be used to accurately predict vapor densities, enthalpy and entropy. These advantages encourage the researchers to augment EoS ability more than before, especially liquid density, although their accuracy for liquid density prediction is generally poor. The popular EoSs such as SRK [1] and PR [2] predict liquid density with an average absolute error of about 8%, much higher than several good density correlations. This large magnitude of error is not acceptable by industry; therefore, they are not used for this purpose. In order to overcome this deficiency, volume translated methods have been developed.

The correlations and volume translated methods [3-11] used in this study are the same as those presented in the July 2011 TOTM. Only those methods, which gave the best results for the cases studied in this TOTM, are described briefly here.  More details about these methods can be found in the corresponding references.

1. Correlations
   The following correlations were used in this study.
   1. COSTALD, 1979: The COSTALD correlation by Hankinson and Thomson [12] requires two parameters: w_SRK, the optimized value of the acentric factor based on the SRK equation of state (EoS) and; V*, the pure component characteristic volume.
   2. RSD, 1972: Spencer and Danner [13] improved the liquid density correlation of Rackett [14]. The improved correlation for saturated liquid density calculation requires only Z_RA, the improved compressibility factor.
   3. NM, 1998: Nasrifar and Moshfeghian [15, 16] presented an equation and a set of mixing rules for predicting the liquid density of pure refrigerants and liquefied natural gas.
2. Volume Translated EoS Methods
   Equation 1 is the basic volume translated (shift) method proposed by Peneloux et al. [3] and used in this study. Equations 2 and 3 utilize the Kay’s rule to estimate mixture molecular weight (MW) and volume shift correction parameter (c).

In the above equations, is the corrected liquid specific volume, is the liquid specific volume calculated by the EoS, MW is the molecular weight, ρ^L is the liquid density, and the correction term or volume shift factor “c” is determined from experimentally measured liquid density. The volume shift factor is normally regressed against several experimental data points. The following methods were used to determine c for the mixtures.

1. Temperature Independent, PRF, 1982: Peneloux et al. [3] proposed the following expression to estimate the constant temperature volume shift correction for each component “i” in the mixture. In the absence of experimentally regressed value, it can be estimated as follows:
   where Z_RA, is the Rackett [15] parameter, R is the gas constant, and T_C and P_C are the critical temperature and pressure, respectively.
2. Temperature Dependent, AG, 2001: Ahlers and Gmehling [6] temperature dependent correction factor, c, is calculated as follows:(5)(6)

In the above equations, Tr is the reduced temperature, ω is acentric factor, T_C, P_C and Z_C are component i critical temperature, pressure and compressibility factor, respectively. The correction term, c, from the above methods is substituted into equation 1 to calculate the corrected density.

Results and Discussion:

We applied the preceding methods to several natural gas mixtures shown in Table 1. The experimentally measured temperature, pressure, composition and relative liquid density for each mixture are shown in this table [17]. These mixtures and corresponding conditions represent those encountered in the cryogenic processes. As an example, the condition and phase envelope for mixture number 1 of Table 1 are presented in Figure 1.

Table 1. Experimentally measured composition, temperature, pressure and relative density for the mixtures studied [17]

Table 2 presents the summary of the error analysis for the liquid density prediction by different methods for the natural gas mixtures shown in Table 1. As can be seen in Table 2, both SRK and PR EoSs give poor results; however, considerable improvements are observed by applying temperature-independent volume translated SRK (SRK-PRF) and temperature-dependent volume translated PR (PR-AG). The volume translated results for these mixtures are much closer to the results obtained by the three correlations of RSD, NM, and COSTALD.

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), and G5 (Gas Conditioning and Processing-Special).

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

Figure 1. Phase envelope and liquid density condition for mixture 1

Table 2: Comparison of acuracy of EoS, volume translated EoS, and correlations for predicting liquid density of mixtures prsented in Table 1

Reference:

1. Soave, G., Chem. Eng. Sci., Vol. 27, pp. 1197-1203, 1972.
2. Peng, D. Y., and Robinson, D. B., Ind. Eng. Chem. Fundam., Vol. 15, p. 59, 1976.
3. Peneloux, A. E., Rauzy, E., and Freze, R., Fluid Phase Equilib., Vol. 8, pp. 7-23, 1982.
4. Tsai, J. and Y.P. Chen, J. of Fluid Phase Equilibria, Vol. 145, pp. 193-215, 1998.
5. Ahlers, J. and J. Gmehling, J. of Fluid Phase Equilibria, Vol. 191, pp. 177-188, 2001.
6. Lin, H. and Y.Y. Duan, J. of Fluid Phase Equilibria, Vol. 233, pp. 194-203, 2005.
7. Ji, W.R. and D.A. Lempe, J. of Fluid Phase Equilibria, Vol. 130, pp. 49-63, 1997.
8. Pfohl, O., J. of Fluid Phase Equilibria, Vol. 163, pp. 157-159, 1999.
9. Magoulas, K. and D. Tassios, J. of Fluid Phase Equilibria, Vol. 56, pp. 119-140-445, 1990.
10. Frey, F., Augustine, C., Ciccolini, R.P., Paap, S., Modell, M., and J. Tester, , J. of Fluid Phase Equilibria, Vol. 260, pp. 316-325, 2007.
11. Frey, F., Modell, M., and J. Tester, J. of Fluid Phase Equilibria, Vol. 279, pp. 56-63, 2009.
12. Hankinson, R. W., Thomson, G. H., AIChE J., Vol. 25, no. 4, pp. 653-663, 1979.
13. Spancer, C. F., and Danner, R. P., J. Chem. Eng. Data, vol. 17, no. 2, pp. 236-241, 1972.
14. Rackett, H. G., J. Chem. Eng. Data, vol. 15, No. 4, pp. 514-517, 1970.
15. Nasrifar, Kh. and Moshfeghian, M., Fluid Phase equilibria Vol. 153, 231-242, 1998.
16. Nasrifar, Kh. and M. Moshfeghian, J. of Fluid Phase Equilibria, Vol. 158-160, pp. 437-445, 1998.
17. Haynes, W.M.J., J. Chem. Thermodyn., Vol 14, pp. 603–612, 1982.

In the April, 2011, Tip of the Month (TOTM), we looked at a simple, graphical representation of process safety competency.  This TOTM will follow up on that by asking a simple question:

“When examining catastrophic incidents, what are the typical mistakes that engineers make?”

This question was asked of me at a lunch and learn session I conducted for a client where I had described the competency pyramid introduced in the April TOTM.  At the time, I thought, what a good question.  I replied, I have to think about that.  After reflecting for a bit, I went to the pyramid.

Looking at the pyramid, it seems to me that a lot of an engineer’s duties revolve around the “Equipment” and “Mitigation” levels.  It is here that separators are sized; pumps are chosen; inherently safer design is incorporated into a process; safety instrumented systems (SIS) are designed; pressure safety valve (PSV) sizing is calculated.  Refer to the April TOTM again.  “To become a well rounded professional in the oil and gas industry, no matter what specialty position a person works in, requires varying degrees of competency in many different areas of expertise.  Obtaining higher level competencies is a continuous process of training and performing tasks, sometimes under the direction of a mentor or coach.”

It could be that the typical mistakes made by engineers are a result of competency with equipment and mitigation measures.  Consider some information from JMC’s Process Safety Engineering course, PS 4.  This table lists the area of responsibility for incidents [1].

Notes:

1. The item “inspection during operation” includes some items that are not always the responsibility of the inspection department:
   1. Vibration monitoring for rotating equipment.
   2. Corrosion probes
2. The item “inspection of process fluids” includes:
   1. Flammable-gas detection in shutdown, and in tanks during operation
   2. Inspection of purchased and process fluids to determine whether they are the ones specified.
3. The average incident has 1.56 responsibilities

How many of the items on the list are engineering functions and which of them could be related to competency?  I suppose it could be argued that almost all are related to engineering functions if it is accepted that engineers provide the design, recommend inspections and maintenance tasks, provide significant information needed for development of operating procedures and have historically been assigned management responsibilities in the oil and gas industry.  Which are related to competency?  That is much more difficult to answer and could only be answered by individual organizations based on performance reviews and competency mapping.

The April, 2010, TOTM discussed the need to perform a good job task analysis to identify personal and process safety hazards.  While the checklist presented there can be modified to allow engineering personnel to analyze engineering tasks, there is a simpler way to insure that engineers reduce the likelihood of mistakes in their work.  Ask the following six questions about any project or job that is being done.  If each question is answered fully, the job should be performed mistake free.

• What are we doing? — A very simple description of what is required to perform the task.
• What is the most dangerous part? — To find the most dangerous part, all hazards will have to be identified.
• What will we do to protect ourselves? — Plan for the worst.
• How will we know that we are changing what we are doing?—Insures that scope creep doesn’t happen.
• What will we do about it? — Contingency planning prior to problems being encountered.
• How will we know we are done? — Should be able to identify everything that needs to be in place when finished.

It is difficult to determine the typical mistakes, related to technical competency, made by engineers that cause or could reasonably cause a catastrophic incident.  Root cause analysis can usually discover breakdowns in an organization’s process safety management system.  Using the six questions to analyze work prior to starting and periodically throughout the life of a project may help to keep personnel focused on the consequences of failure and reduce the likelihood of failure.  Thus, reducing risk.

Several JMC courses develop competencies associated with the equipment and mitigation levels of the competency pyramid.  To learn more about process safety for engineers, consider attending a  session of our PS 4, Process Safety Engineering course.

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:  Clyde Young

Reference:

1. Ian Duguid, “Take This Safety Database to Heart”, Chemical Engineering Magazine, July 2001.

As discussed in the May 2011 Tip Of The Month (TOTM), for hydrate inhibition, the most commonly used equilibrium inhibitors used in the upstream and midstream sectors of the oil and gas business are:  monoethylene or diethylene glycol (MEG or DEG) and methanol.  In general, glycols are more commonly used in systems requiring continuous inhibition. The glycol is typically recovered, regenerated and recirculated.  Methanol is more commonly used in systems that do not require continuous inhibition, i.e. systems only requiring inhibition during cold weather or upset conditions. Methanol is not commonly recovered and reused because of the difficulty of separation of the methanol from water. There are obviously exceptions to this. For example, methanol is used as a continuous inhibitor in a few offshore installations and in a handful of gas processing facilities. Another significant disadvantage of methanol relative to glycol is the high methanol losses to both the liquid hydrocarbon and vapor phase.

In the May TOTM, we reviewed experimental VLE data for methanol-hydrocarbon systems. We also revisited Figure 6.20 of reference [1] for methanol loss to the vapor phase using the experimental vapor liquid equilibrium data reported in the Gas Processors Association Research Report 117 (GPA RR 117) [2].

In this Tip of the Month, we will investigate further the methanol loss to the vapor phase and present a simple correlation to estimate methanol K-values. The K-value is defined as the mole fraction of methanol in vapor phase/mole fraction of methanol in aqueous liquid phase. Since the effect of gas composition is small, the K-values will be expressed only in terms of pressure and temperature. The estimated K-value can be used to calculate the rate of methanol loss to the vapor phase.  The objective of this TOTM is to develop a simple and generalized model for estimation of methanol losses in terms of K-values and present a generalized chart which is less dependent on the weight percent of methanol in the liquid aqueous phase.  A step-by-step calculation procedure using K-values will be presented as well.

Figure 1 (FPS). Effect of methanol concentration on methanol loss at -10 °F.

Figure 1 (SI). Effect of methanol concentration on methanol loss at -23.3 °C.

Methanol Loss in Terms of K-Value:

The polar version of Peng-Robinson [3] equation of state (PR EOS) in ProMax [4] was used to generate the required data in the following sections.

Figure 1 indicates that presenting charts for ratio of vapor to liquid composition in terms lb_m of methanol per MMSCF/(weight % methanol in aqueous phase) or kg of methanol per 10⁶ Sm³/(weight % methanol in aqueous phase) is very sensitive to the methanol concentration in liquid phase. Similar methanol concentration dependencies, as shown in Figure 1, were also observed for other isotherms. An alternative is to use the K-Values for the y-axis. However, we have found that the range of ratio of K-Values at 15 weight % to 25 weight % MeOH is from 0.95 to 1.0. Similarly, the range of ratio of K-Values at 50 weight% to 25 weight % MeOH is from 1.0 to 1.03. These ranges are much smaller than the corresponding ratios of methanol losses. Therefore, in the subsequent charts as well as for modeling we will use K-values as the dependent variables.

The K-Values over 25 weight % methanol solution are presented in Figure 2 as a function of pressure and temperature. It should be noted that Figure 2 can be used for methanol concentration up to 70 weight % in aqueous phase.

As shown in Figure 2, at lower temperatures considerable curvature is observed, which makes modeling rather difficult. However, if the same chart is replotted in terms of pressure times K-value, (P)(K) on the y-axis, as shown in Figure 3, much less curvature is observed.

In order to use Figure 2 to calculate the rate of methanol loss to vapor phase, the following steps should be followed:

1. At specified pressure (P) and temperature (T), read methanol K-Value from Figure 2.
2. Convert weight % of methanol (wt%) in aqueous phase to mole fraction (x) by: (1A)
3. Calculate methanol mole fraction in the gas phase (y) by y = (K)(x) (1B)
4. Calculate mass of methanol in vapor phase

FPS: (MeOH lb_m/MMSCF) = [(y lbmole MeOH)/(Total lbmole of gas)](32 lb_m/lbmole

MeOH) (lbmole of gas/379.5SCF) (10⁶ SCF/MMSCF) = 84 321.5 y ≈ 84 322 y    (1C)

SI: (MeOH kg/10⁶ Sm³) = [(y kmole MeOH)/(Total kmole of gas)](32 kg/kmole MeOH) (kmole of gas/23.64 Sm³) (10⁶) = 1 353 638 y ≈ 1 353 640 y     (1D)

A worked example is shown in Appendix A.

Figure 2 (FPS). Variation of methanol K-Values as a function of pressure and temperature.

Figure 2 (SI). Variation of methanol K-Values as a function of pressure and temperature.

Development of Mathematical Model

An alternative to Figure 2 is a simple and generalized correlation which can estimate the K-values easily. This will be handy when one intends to use spreadsheet calculations to estimate methanol vapor losses. A simple model presented by Wilson [5] for light hydrocarbon mixtures is shown in Equation 2.

(2)

where:

Tc_i critical temperature, °R or K

Pc_i critical pressure, psi, kPa or bar

ω_i acentric factor

P system pressure, psia, kPa or bar

T system temperature, °R or K

This correlation is applicable to low and moderate pressure, up to 500 Psia (3.5 MPa), and the K-values are assumed to be independent of composition.

We propose to use a similar equation in the form of equation 3 to estimate methanol K-value at specified pressure and temperature.

(3)

In the above equation, P^*, T^* and ω^* are the normalized pressure, temperature, and acentric factor; respectively. The same data that were used to generate Figure 2 were also used regress the above equation parameters using a non-linear program and the following expressions were developed.

P^* =  P/35  with P in Psia  (4)

T^* = T/615 with T in °R (5)

ω^* = 2.95-(0.02607)P^*+(8.92828×10^-5)(P^*)²-0.851257/ T^* (6)

We will refer to the above model as the “K-Value Express”. Spreadsheet estimation of methanol vapor losses can be developed by using equation 3 to determine methanol K values, and then applying the calculation method as discussed for the application of Figure 2.

Overall, 156 data points covering temperature range of -10 to 100 °F, and pressure range of 100 to 5000 psia for  25 weight % methanol solution were used. The overall Average Absolute Percent Error (AAPE) for the K-Values was 3.6% with a Maximum Absolute Percent Error (MAPE) for K was 19.7%. The MAPE occurred at -10 °F and 2000 psia where ProMax K was 0.001 and K-Value Express K was 0.0008.

Figure 3 presents the comparison between the methanol K-Values calculated by ProMax (solid lines in Figure 3) and those estimated by K-Value Express (identified by dashed line in Figure 3).

Results and Discussion:

The K-Value Express model with the parameters shown in Equations 4 to 6 were used without any further fitting to predict K-values over 15 and 50 weight % methanol for wide ranges of pressures and temperature. For the case of 15 weight % methanol with 149 data points, the AAPE for K was 5.2% with a MAPE of 14.7%. For the case 50 weight % methanol with 155 data points, the AAPE for K was 3.6% with a MAPE for K was 22.1%.

The maximum average absolute % error occurred at -10 °F and 2000 psia where ProMax K was 0.00098 and K-Value Express K was 0.00077 for the case of 50 weight % methanol.

Figure 4 presents the K-Value Express K-Values vs ProMax K-Values for more than 500 data points over 15, 25, and 50 weight % methanol solution. This figure indicates relatively good agreement between the two methods.

Figure 5 is a revised and extended version of Figure 6.20 in reference [1]. Notice y-axis and x-axis variables are switched.  In this way the dependent variable is on the y-axis and independent variable is on x-axis.

Conclusion:

ProMax was used to reproduce Figure 6.20 in reference 1 and presented here in this work as Figure 5.  This figure covers wider ranges of pressure, temperature, and methanol weight percent (up to 70 weight %). However, we suggest using Figure 2 as a better chart since it is less sensitive to methanol weight % in aqueous phase.  In addition, we developed a simple and generalized K-Value Express model that can be used to estimate methanol K-values for wide ranges of pressure, temperature, and methanol weight %. As shown in Figures 3 and 4, the proposed model is in good agreement with the results obtained from ProMax. The sample calculations in Appendix indicate good agreement between the methanol losses to vapor phase obtained from Figures 2, 3, 5, and the K-Values Express model.

Figure 3 (FPS). Comparison of predicted methanol K-Values by ProMax and the proposed Express K-Value model.

Figure 3 (SI). Comparison of predicted methanol K-Values by ProMax and the proposed Express K-Value model.

Figure 4. Accuracy of the proposed Express K-Value model against ProMax

Figure 5. Variation of methanol loss to vapor phase with pressure and temperature for methanol concentration of 25 weight % in the aqueous phase

Figure 5 (SI). Variation of methanol loss to vapor phase with pressure and temperature for methanol concentration of 25 weight % in the aqueous phase

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) and G5 (Gas Conditioning and Processing-Special).

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. Campbell, J. M. “Gas conditioning and processing, Volume 1: Fundamentals,” John M. Campbell and Company, Norman, Oklahoma, USA, 2001.
2. Ng, H. J., Chen, C. J., and D. B. Robinson, D.B.; RR-117, “The Solubility of Methanol or Glycol in Water – Hydrocarbon Systems,” Gas Processors Association (Mar. 1988).
3. Peng, D. Y., and Robinson, D. B., Ind. Eng. Chem. Fundam., Vol. 15, p. 59, 1976.
4. ProMax 3.1, Bryan Research and Engineering, Inc, Bryan, Texas, 2010.
5. Wilson, G., “A modified Redlich-Kwong equation of state applicable to general physical data calculations,” Paper No15C, 65^th AIChE National meeting, May, (1968).

Appendix A – Sample Calculations

Determine mass of methanol in vapor per MMSCF (10⁶ Sm³) at 1000 psia (6 897 kPa) and 10 °F (-12.2 °C) over a rich solution containing 25 weight % methanol.

Solution: Method 1 (Figure 5)

At 1000 psia (6 897 kPa) and 10 °F (-12.2 °C) using Figure 5;

FPS:

lbm MeOH/MMSCF/Wt%=0.5  or  lb_m MeOH/MMSCF = (0.5) (25) = 12.5

SI:

kg MeOH/10⁶ Sm³/Wt% = 8.2  or  MeOH/10⁶ Sm³ = (8.2) (25) = 205

Solution: Method 2 (Figure 2 or 3)

At 1000 psia (6 897 kPa) and 10 °F (-12.2 °C) using Figure 2; K=0.00095 or

Figure 3; PK=0.95 psia which gives K=0.00095

Convert 25 wt% to mole fraction by Eq 1A:

Calculate y by Eq 1B: y = (K)(x) = (0.00095)(0.1579) = 0.00015

FPS: Using Eq 1C: (MeOH lb_m/MMSCF) = 84 322 y = 84322(0.00015)  =  12.6

SI: Using Eq 1D: (MeOH kg/10⁶ Sm³) = 1 353 640 y = 1 353 640(0.00015) = 203

Solution: Method 3 (Express K-Value Model)

At 1000 psia (6 897 kPa) and 10 °F (-12.2 °C) using Eqs 3 through 6 calculate K.

Using Eq 4: P^* =  P/35 = 1000/35 = 28.57

Using Eq 5: T^* = T/615 = (460+10)/615 = 0.762

Using Equation 6:

ω^* = 2.95-(0.02607)P^*+(8.92828×10^-5)(P^*)²-0.851257/ T^*

ω^* = 2.95-(0.02607)( 28.57)+(8.92828×10^-5)( 28.57)²-0.851257/ 0.762

ω^* = 1.161

Using Eq 3:

Calculate methanol mole fraction in gas phase ( y) by Eq 1B:

y = (K)(x) = (0.00093)(0.1579) = 0.000147

FPS: Using Eq 1C: (MeOH lb_m/MMSCF) = 84 322 y = 84322(0.000147)  =  12.4

SI: Using Eq 1D: (MeOH kg/10⁶ Sm³) = 1 353 640 y = 1 353 640(0.000147)  = 199

Equations of state (EoS) are used in commercial simulation software for predicting phase behavior and thermodynamic properties. The cubic equations of state (EoS) give relatively accurate results for predicting vapor-liquid equilibria, especially for non-polar or slightly polar systems. Furthermore, these equations can be used to accurately predict vapor densities, enthalpy and entropy. These advantages encourage the researchers to augment EoS ability more than before, especially liquid density, although their accuracy for liquid density prediction is generally poor. The popular EoSs such as Soave-Redlich-Kwong (SRK) [1] and Peng-Robinson (PR) [2] predict liquid density with an average absolute error of about 8%, much higher than several good density correlations. This large magnitude of error is not acceptable by industry; therefore, they are not used for this purpose. In order to overcome this deficiency, volume translated methods have been developed.

In the March 2011 tip of the month (TOTM) we studied a constant volume translation of liquid density Method presented by Peneloux et al. [3] and demonstrated its application for hydrocarbons such as pure methane, n-pentane, decane, pentadecane and carbon dioxide. Considerable improvements, specifically for the low temperature range (T r < 0.8), of saturated specific volume (or liquid density) predicted by PR and SRK were obtained. On the other hand, the constant volume shift fails near the critical temperature, because the slope of volume with respect to temperature greatly increases in this region.

Since Peneloux et al. presented their constant volume translation (shift) method in 1982, several temperature dependent volume correction methods [4-11] have been proposed. In this TOTM we will demonstrate application and accuracy of some of these methods to predict liquid density of common hydrocarbons and non-hydrocarbons in gas processing. We will compare their accuracy against both experimental data and a few correlations.

1. CorrelationsThe following correlations were used in this study.
   1. COSTALD, 1979: The COSTALD correlation by Hankinson and Thomson [12] requires two parameters: SRK, the optimized value of the acentric factor based on the SRK equation of state (EoS) and; V*, the pure component characteristic volume.
   2. RSD, 1972: Spencer and Danner [13] improved the liquid density correlation of Rackett [14]. The improved correlation for saturated liquid density calculation requires only ZRA, the improved compressibility factor.
   3. NM, 1998: Nasrifar and Moshfeghian [15, 16] presented an equation and a set of mixing rules for predicting the liquid density of pure refrigerants and liquefied natural gas.
2. Volume Translated EoS Methods The following volume translated (shift) methods were used in this study.
   1. PRF, 1982: Peneloux et al. [3] proposed the following constant volume shift correction. (1)In the above equation, V^L is the corrected liquid specific volume, V^EoS is the liquid specific volume calculated by the EoS, MW is the molecular weight, p^L is the liquid density, and the correction term or volume shift factor “c” is determined from experimentally measured liquid density. The volume shift factor is normally regressed against several data points. In the absence of experimentally regressed value, it can be estimated as follows: (2)where Z_RA, is the Rackett [15] parameter, R is the gas constant, and T_C and P_C are the critical temperature and pressure, respectively.We determined the optimum value of “c” for each compound by the procedure described in
      the March 2011TOTM.
   2. MT, 1990: Magoulas and Tassios [4] temperature dependent correction factor is calculated as follows: (3) (4) (5) (6)We will refer to this method as MT-VTPR.
   3. TC, 1998: Tsai and Chen [5] temperature dependent correction factor is calculated as follows: (7) (8) (9) (10)
   4. AG, 2001: Ahlers and Gmehling [6] temperature dependent correction factor, c, is calculated as follows:
      (12) (13)

      (14)

      (15)

      (16)

      (17)

   5. LD, 2005: Lin and Duan [7] presented a temperature dependent factor, c, as follows: (18) (19) (20) (21)

In the above equations, Tr is the reduced temperature, ω is acentric factor, T_C, P_C and Z_C are critical temperature, pressure and compressibility factor, respectively. The correction term, c, from the above methods is substituted in equation 1 to calculate the corrected density.

Results and Discussion:

A simple MathCad program was written to perform all of the calculations based on the above methods. We applied the preceding methods to several pure compounds shown in Table 1. The reduced temperature (Tr) and number of points (N) for each compound are also shown in Table 1. This Table presents the summary of the error analysis for different methods for the pure compounds. As can be seen in Table 1, these generalized temperature dependent volume shift methods improve the accuracy but yet not as good as the generalized correlation methods shown in the last three column of Table 1.

Table 1. Summary of error analysis for different methods studied

Figures 1 through 4 present graphical comparisons between the predicted and experimental [17] liquid density values of methane, n-pentane, decane and pentadecane; respectively. Similar trends were observed for the other compounds shown in Table. For clarity, only the results for PR EoS, PRF-VTPR (constant volume shift) and LD-VTPR (temperature dependent volume shift) are presented in these figures. A much better accuracy is obtained near the critical region by applying the temperature dependent volume shift.

Figure 1. Comparison of predicted liquid density of CH₄ by PR EoS, volume translated PRF-VTPR and LD-VTPR against experimental data [17]

Figure 2. Comparison of predicted liquid density of C₅H₁₂ by PR EoS, volume translated PRF-VTPR and LD-VTPR against experimental data [17]

Figure 3. Comparison of predicted liquid density of C₁₀H₂₂ by PR EoS, volume translated PRF-VTPR and LD-VTPR against experimental data [17]

Figure 4. Comparison of predicted liquid density of C₁₅H₃₂ by PR EoS, volume translated PRF-VTPR and LD-VTPR against experimental data [17]

In order to show the sensitivity of the LD-VTPR method and the applicability of tuning its parameters, the Z_C value for pentadecane was changed from 0.243, represented by the solid black curve in Figure 4, to 0.231 which is represented by the dashed black curve in Figure 4. The curve for Z_C=0.231 is labeled as LD-VTPR*. This sensitivity is used for practical applications to tune the volume translated model parameters (e.g. Z_C) to match the predicted liquid density with the experimentally measured data.

Table 1 indicates that considerable improvements are obtained by applying temperature dependent volume shift corrections to the liquid specific volume (or liquid density) near the critical point region. However, the accuracy of the COSTALD, RSD and NM correlations are still by far much better than the volume translation applied to these two EoSs. As shown in Figure 4, further improvement of volume shift methods are obtained by tuning the parameters of volume shift methods with experimental measurement.

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) and G5 (Gas Conditioning and Processing-Special).

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.

A free copy of the MathCad Version 14 file showing the calculations steps for methane is available upon request.

By Dr. Mahmood Moshfeghian

Reference:

1. Soave, G., Chem. Eng. Sci., Vol. 27, pp. 1197-1203, 1972.
2. Peng, D. Y., and Robinson, D. B., Ind. Eng. Chem. Fundam., Vol. 15, p. 59, 1976.
3. Peneloux, A. E., Rauzy, E., and Freze, R., Fluid Phase Equilib., Vol. 8, pp. 7-23, 1982.
4. Magoulas, K. and D. Tassios, J. of Fluid Phase Equilibria, Vol. 56, pp. 119-140-445, 1990.
5. Tsai, J. and Y.P. Chen, J. of Fluid Phase Equilibria, Vol. 145, pp. 193-215, 1998.
6. Ahlers, J. and J. Gmehling, J. of Fluid Phase Equilibria, Vol. 191, pp. 177-188, 2001.
7. Lin, H. and Y.Y. Duan, J. of Fluid Phase Equilibria, Vol. 233, pp. 194-203, 2005.
8. Ji, W.R. and D.A. Lempe, J. of Fluid Phase Equilibria, Vol. 130, pp. 49-63, 1997.
9. Pfohl, O., J. of Fluid Phase Equilibria, Vol. 163, pp. 157-159, 1999.
10. Frey, F., Augustine, C., Ciccolini, R.P., Paap, S., Modell, M., and J. Tester, , J. of Fluid Phase Equilibria, Vol. 260, pp. 316-325, 2007.
11. Frey, F., Modell, M., and J. Tester, J. of Fluid Phase Equilibria, Vol. 279, pp. 56-63, 2009.
12. Hankinson, R. W., Thomson, G. H., AIChE J., Vol. 25, no. 4, pp. 653-663, 1979.
13. Spancer, C. F., and Danner, R. P., J. Chem. Eng. Data, vol. 17, no. 2, pp. 236-241, 1972.
14. Rackett, H. G., J. Chem. Eng. Data, vol. 15, No. 4, pp. 514-517, 1970.
15. Nasrifar, Kh. and Moshfeghian, M., Fluid Phase equilibria Vol. 153, 231-242, 1998.
16. Nasrifar, Kh. and M. Moshfeghian, J. of Fluid Phase Equilibria, Vol. 158-160, pp. 437-445, 1998.
17. Vargaftik, N.B., Handbook of Physical Properties of Liquids and Gases (Pure Substances and Mixtures), 2^nd ed., English Translation, Hemisphere Publication, 1975.

BTEX stands for benzene, toluene, ethylbenzene, and xylene, a group of compounds all that also belong to the broader category of Hazardous Air Pollutants (HAPs). Benzene is a known carcinogen, and has also been shown to cause blood disorders and to impact the central nervous system and the reproductive system.  Toluene may affect the reproductive and central nervous systems.  Ethylbenzene and xylene may have respiratory and neurological effects [1]. BTEX is present in natural gas streams and is being picked up in glycol dehydration and amine sweetening units.

In the United States HAP emissions from glycol dehydration units are regulated under 40 CFR, Part 63, Subpart HH.  Glycol dehydration units processing more than 3 MMscfd (0.85 10⁶ Sm³ per day) and having benzene emissions greater than 900 kg/year (1 ton/year) are required to control HAP emissions.

This problem is one which requires careful attention in the design phase. The purpose of this Tip of the Month (TOTM) is to discuss the primary factors affecting the absorption of BTEX components in glycol dehydration systems.

In gas dehydration service, triethylene glycol (TEG) will absorb limited quantities of BTEX from the gas. Based on the data from reference [2], predicted absorption levels for BTEX components vary from 5-10% for benzene to 20-30% for ethylbenzene and xylene. Figure 18.18 in reference [2] shows approximate ab­sorption percentages for BTEX components as a function of TEG circulation rate and contactor temperature at 6895 kPa (1000 psia). Absorption is fa­vored at lower temperatures, higher pressure, increasing TEG concentration and circulation rate.

The bulk of absorbed HAPs will be vented with the water vapor at the top of the regenera­tor. The most common emission mitigation strategies are to:

1) Condense the regenerator overhead vapor in a partial condenser and combust the remaining vapor.  The uncondensed vapors are typically routed to an incinerator or, if a direct-fired reboiler is used, routed to the reboiler fuel gas. The liquid hydrocarbons are collected and disposed of by blending into a crude oil or condensate stream.  The condensed water is typically routed to produced water disposal.

2) Route the regenerator overhead vapors to another process stream in the facility.   This is typically a low pressure stream such as flash vapors from the last stage of a crude or condensate stabilization system.

In this TOTM, we will revisit Figure 18.18 of reference [2] for estimating absorption of BTEX in the glycol dehydration systems using the experimental vapor-liquid equilibrium data reported in the Gas Processors Association Research Report 131 (GPA RR 131) [3]. The objective of this TOTM is to reproduce similar diagrams covering wider ranges of pressure and temperature. First we demonstrate the accuracy of ProMax [4] and the Peng-Robinson [5] equation of state (PR EOS) of the same software to generate the required data. Finally, for ease of use the generated results are presented graphically.

Verification of Thermodynamic Model:

A series of flash calculations for the reported experimentally measured pressures, temperatures and synthetic feed gas compositions were performed. The mixtures consisted of methane, benzene, toluene, ethylbenzene, o-xylene, TEG and water. The pressure ranged from 20 to 1000 psia (138 to 6895kPa) and temperature ranged from 77 to 400°F (25 to 204°C). These ranges cover the normal operating conditions of contactor, flash tank, and regenerator in a TEG dehydration plant. The calculated liquid (x) and vapor (y) phase compositions for the four BTEX components are compared with the corresponding experimental values and presented in Figure 1.

Figure 1. Comparison of calculated BTEX mole fractions in the liquid and vapor phases  by ProMax with the experimental values reported in GPA RR 131.

Results and Discussion:

For the purpose of this study, a contactor column with three theoretical stages and with the feed composition shown in Table 1 was simulated. The concentration of the lean TEG stream was 99.0 weight % TEG, and it was assumed the TEG temperature was 5°F (2.8°C) warmer than the feed gas. The feed gas was saturated with water at feed conditions. For each contactor pressure and temperature, the lean TEG circulation ratio was varied from 1 to 7 US gallon of TEG/lb_m of water removed (8.3 to 58.4 liters of TEG/kg of water removed).

Three temperatures and three pressures, covering typical contactor operation ranges were studied. Figures 2 to 5 present the results of simulations using ProMax. Absorption of BTEX components is plotted as a function of temperature, pressure and glycol circulation rate.

Table 1. Dry-basis composition of feed gas

Figure 2. Absorption of benzene as a function of temperature, pressure, and circulation ratio

In Figure 2, benzene absorption is plotted as a function of circulation ratio (liquid volume rate per gas standard volume rate) for two temperatures (77 and 122 °F or 25 and 50 °C) and two pressures (500 and 1000 psia or 3447 and 6895 kPa). Absorption increases with decreasing temperature and increasing circulation ratio. The effect of pressure on absorption is small but is more pronounced at 500 psia than at 1000 psia.  The likely reason for this is that at the lower pressure, the water content of the feed gas is higher and the heat of absorption effect increases the gas outlet temperature which, in turn, decreases the solubility of benzene in the TEG. This effect will be not as significant at higher pressures.

In TEG dehydration process, the common unit of circulation ratio is in gallons of TEG per pound of water absorbed (liters of TEG per kilogram of water absorbed). In Figures 3, 4, and 5 the circulation units on the x-axis were changed to these units.

Figures 3 to 5 can be used to estimate the absorption of BTEX components in a glycol dehydration system for a given pressure, temperature and circulation ratio.

Experimental solubility data for BTEX components in TEG at pressures greater than 1000 psia (6895 kPa) are not available in open literature. Figure 5, which presents BTEX absorption at 1500 psia (10344 kPa) has not been validated with experimental data. In addition, 1500 psia (10344 kPa) is above the cricondenbar of the feed gas used in this study and hence falls in the dense phase region. The solubility behavior of dilute vapor components in solvents such as TEG can be significantly different in the dense phase; therefore, caution should be taken in extrapolating these correlations above 1000 psia (6895 kPa).

Figure 3A. Absorption of benzene and toluene in TEG at 500 psia (3447 kPa)

Figure 3B. Absorption of ethylbenzene and o-xylene in TEG at 500 psia (3447 kPa)

Figure 4A. Absorption of benzene and toluene in TEG at 1000 psia (6895 kPa)

Figure 4B. Absorption of ethylbenzene and o-xylene in TEG at 1000 psia (6895 kPa)

Figure 5A. Absorption of benzene and toluene in TEG at 1500 psia (10342 kPa)

Figure 5B. Absorption of ethylbenzene and o-xylene in TEG at 1500 psia (10342 kPa)

Figure 6 shows the effect of pressure on the absorption of each BTEX component at 95°F (35°C) at 0.2 US GPM TEG/MMSCFD of gas (1.6 m³/h TEG/10⁶ Sm³/d of gas). Be reminded that high this work has not been experimentally validated at pressures above 1000 psia (6895 kPa).

Comparison with the GRI-GLYCalc Software:

GRI-GLYCalc [6] is a relatively simple and easy-to-use software package that is widely used by operators for the estimation of BTEX emissions from glycol units.  It is accepted by most state regulatory authorities.  Table 2 shows the ProMax results in this work compared to GLYCalc for each BTEX component at 3 different operating conditions.

Conclusions:

As shown in Figure 1, PR EOS can be used to estimate VLE of BTEX compounds in glycol systems.

In reviewing Figures 2 to 5, one can conclude that the absorption of the BTEX components decreases as:

1. Temperature increases
2. Circulation ratio decreases

For pressures between 500 (3450 kPa) and 1000 psia (6895 kPa), the effect of pressure on BTEX absorption is not large.

From operational point of view, minimizing circulation ratio is the most effective way of decreasing the absorption of BTEX components. This also minimizes reboiler duty and the size of the regeneration skid.  Lower TEG circulation rates require more theoretical stages in the contactor to meet outlet water content specifications, but the additional cost of a taller contactor is often offset by savings in the regeneration package.  Care should be taken that the glycol circulation rate is sufficient to ensure adequate liquid distribution over the packing.  Packing vendors can provide minimum circulation guidelines.

Finally, it should be noted that in the operation of a glycol dehydration unit, the desired outcome is to meet the water content specification for the outlet gas, e.g. 7 lbs H₂O/MMSCF (111 kg/10⁶ Sm³).   When using the graphs in this TOTM, different operating points (T, P and circ ratio) will produce different outlet water contents. Make sure that the operating points you are using to estimate BTEX absorption are can also meet the water specification.

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) and G5 (Gas Conditioning and Processing-Special).

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 Mahmood Moshfeghian and Robert A Hubbard

Figure 6. Impact of pressure on BTEX absorption at 95 F (35 C) and 0.2 US GPM TEG/MMSCFD of gas (1.6 m³/h TEG/10⁶ Sm³/d of gas)

Table 2. Comparison between GRI-GLYCalc and ProMax BTEX absorption at

1000 psia (6,895 kPa), 99.0 weight % lean TEG, and 3 theoretical trays

* gallons TEG/lb_m of water removed (liters TEG/kg of water removed)

Reference:

1. http://www.earthworksaction.org/BTEX.cfm, 2011.
2. Campbell, J. M. “Gas conditioning and processing, Volume 2: The Equipment Modules,” John M. Campbell and Company, Norman, Oklahoma, USA, 2001.
3. Ng, H. J., Chen, C. J., and Robinson, D.B.: RR-131, “The Solubility of Selected Aromatic Hydrocarbons in Triethylene Glycol,” Gas Processors Association (Dec. 1991).
4. ProMax 3.2, Bryan Research and Engineering, Inc, Bryan, Texas, 2011.
5. Peng, D. Y., and Robinson, D. B., Ind. Eng. Chem. Fundam., Vol. 15, p. 59, 1976.
6. GRI-GLYCalc 4.0, Gas Research Institute, Des Planes, Illinois, 2000

Three methods of preventing hydrate formation in pipelines and processing facilities are commonly used in our industry.  These are:

1)      Maintain the T & P of the system outside of the hydrate formation region.

2)      Dehydrate the gas to remove the water.

3)      Inhibit hydrate formation with chemical inhibitors.

Option 3 is commonly used when it is impractical or uneconomic to install dehydration facilities, typically glycol dehydration.

There are a number of chemicals used to inhibit hydrate formation, but generally fall into one of two types: equilibrium (sometimes called thermodynamic) inhibitors or kinetic inhibitors.  Equilibrium inhibitors lower the equilibrium hydrate formation point and include polar chemicals such as alcohols, glycols and salts.  Kinetic inhibitors (often referred to as Low Dosage Hydrate Inhibitors, LDHIs) do not change the equilibrium hydrate formation condition but instead modify the rate at which hydrates form or the ability of hydrate crystals to agglomerate into a plug that could block flow.  These will not be discussed in this article and more detail can be found in June and July 2010 tip of the month.

The most commonly used equilibrium inhibitors used in the upstream and midstream sectors of the oil and gas business are:  monoethylene or diethylene glycol (MEG or DEG) and methanol.  In general, glycols are more commonly used in systems requiring continuous inhibition.  The glycol is typically recovered, regenerated and recirculated.  Methanol is more commonly used in systems that do not require continuous inhibition, i.e. systems only requiring inhibition during cold weather or upset conditions.  In addition, methanol is not commonly recovered and reused. This is due to the difficulty of separation of the methanol from water. There are obviously exceptions to this, methanol is used as a continuous inhibitor in a few offshore installations and in a handful of gas processing facilities. Another significant disadvantage of methanol relative to glycol is the high methanol losses to both the liquid hydrocarbon and vapor phase.

The purpose of this article is to review experimental VLE data for methanol-hydrocarbon systems and to show correlations that may be used to estimate methanol losses to the vapor phase.

The total injection rate required to inhibit hydrate formation is the sum of the inhibitor in the

liquid water (aqueous phase) plus the inhibitor in the vapor phase plus the inhibitor in the liquid hydrocarbon phase, if any.

As described in Chapter 6, Volume of 1 of Reference [1], the Hammerschmidt [2], Nielsen and Bucklin [3], or Maddox et al. [4] equations can be used to estimate weight percent of methanol or glycol in the rich solution (aqueous phase) required to inhibit hydrate formation. The actual inhibitor injection rate to satisfy the aqueous phase inhibitor concentration needed is found by material balance and is a function of the amount of water to be inhibited as well as the lean inhibitor concentration.

Figure 6.20 on page 191 of reference [1] provides reliable estimates of vaporization loss for pressures less than about 4830 kPa (700 psia) and water phase methanol concentrations less than about 40 weight %. At higher pressures methanol losses to the vapor phase may be significantly higher than indicated in Figure 6.20, particularly at high methanol concentrations.

In this Tip Of The Month (TOTM), we will revisit Figure 6.20 of reference [1] for methanol loss to the vapor phase using the experimental vapor liquid equilibrium data reported in the Gas Processors Association Research Report 117 (GPA RR 117) [5]. The objective of this TOTM is to reproduce the same diagram covering wider ranges of pressure, temperature and weight percent of methanol in the aqueous phase. First we demonstrate the accuracy of ProMax [6] and then the polar version of Peng-Robinson [7] equation of state (PR EOS) of the same software to generate the required data. Finally, for ease of use the generated results are presented graphically.

Results and Discussion:

GPA RR 117 presents experimental equilibrium phase compositions for systems containing methane and n-heptane, methane and methylcyclohexane (MCH), and methane-toluene in the presence of 35 and 70 weight % methanol solutions. The experimental conditions for these data are shown in Table 1. In order to evaluate the accuracy of the ProMax software for these systems, we predicted the ratio of vapor to liquid composition in terms lb_m of methanol per MMSCF/(weight % methanol in aqueous phase) or kg of methanol per 10⁶ Sm³/(weight % methanol in aqueous phase). The results of this comparison are also shown in Table 1. The same comparison results are also shown graphically in Figures 1 and 2.

Table 1 indicates that the average absolute percent deviation for the 18 cases tested is about 15% with a maximum deviation of 23%. Considering the fact that the experimental data has some inherent error and some scatter in the data, the accuracy of ProMax is reasonably good for determination of methanol loss to the vapor phase.

Table 1. Accuracy of ProMax for calculating methanol loss to vapor phase

1. Methane-n-Heptane-Methanol-Water
2. Methane-Methylcyclohexane-Methanol-Water
3. Methane-Toluene-Methanol-Water

1. (lb_m of methanol per MMSCF)/(Weight % methanol in aqueous phase)
2. (kg of methanol per 10⁶ Sm³)/(Weight % methanol in aqueous phase)

Figure 1. Comparison of predicted methanol loss to the vapor phase by ProMax with the experimental values  reported in GPA RR 117.

Figure 1 (SI). Comparison of predicted methanol loss to the vapor phase by ProMax with the experimental values reported in GPA RR 117.

Figure 2. Comparison of predicted methanol loss to vapor phase by ProMax with the experimental values reported in GPA RR 117.

Figure 2 (SI). Comparison of predicted methanol loss to vapor phase by ProMax with the experimental values reported in GPA RR 117.

Figure 3 presents the effect of pressure and temperature on the methanol loss to the vapor phase. This diagram is generated for a mixture with total composition of 33.63 mol% methane, 22.42 mol% n-heptane, 24.95 Mol% methanol, and 19 mol % water. A three phase calculation on this mixture produces, at various pressure and temperature, vapor phases containing about 98 mol% methane and aqueous phases containing about 70 weight percent methanol. A similar diagram was generated for another mixture with composition of 29.43 mol% methane, 19.62 mol%, n-heptane, 11.86 Mol% methanol, and 39.09 mol % water. The later mixture produces an aqueous phase containing about 35 weight percent methanol. The calculated corresponding methanol losses were close for the two mixtures.

Figure 3. Variation of methanol loss to vapor phase with pressure and temperature over methanol concentration of up to 70 weight % methanol in the aqueous phase

Figure 3 (SI). Variation of methanol loss to vapor phase with pressure and temperature over methanol concentration of up to 70 weight % methanol in the aqueous phase

Conclusion:

As shown in Table 1 and Figures 1 and 2, the limited experimental data of GPA RR 117 indicate that ProMax can be used to estimate the methanol loss to the vapor phase. Therefore, this software was used to reproduce Figure 6.20 in reference 1 and presented here in this work as Figure 3.  This figure covers wider ranges of pressure, temperature, and methanol weight percent (up to 70 weight %). Even though the methanol loss to the vapor phase shown on the x-axis of Figure 3 depends on the gas composition, the effect of composition is small and can be negligible for the planning purposes and facilities calculations.

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) and G5 (Gas Conditioning and Processing-Special).

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. Campbell, J. M. “Gas conditioning and processing, Volume 1: Fundamentals,” John M. Campbell and Company, Norman, Oklahoma, USA, 2001.
2. Hammerschmidt, E. G., Ind. Engr. Chem., Vol. 26 (1934), p. 851.
3. Nielsen, R. B. and R. W. Bucklin, “Why not use methanol for hydrate control?,” Hyd. Proc., Vol. 62, No. 4 (Apr. 1983), p. 71
4. Maddox, R.N., M. Moshfeghian, C. H. Tu, A. Shariat, and A. J. Flying “Predicting Hydrate Temperature at High Inhibitor Concentration,” Proceeding of Laurence Reid Gas Conditioning Conference, March 4 – 6, 1991.
5. Ng, H. J., Chen, C. J., and D. B. Robinson, D.B.; RR-117, “The Solubility of Methanol or Glycol in Water – Hydrocarbon Systems,” Gas Processors Association (Mar. 1988).
6. ProMax 3.1, Bryan Research and Engineering, Inc, Bryan, Texas, 2010.
7. Peng, D. Y., and Robinson, D. B., Ind. Eng. Chem. Fundam., Vol. 15, p. 59, 1976.

In this Tip of the Month, we explore how process safety competency can be identified and developed using a simple model for guidance.

This TOTM is the paper that was developed by JMC Instructor/Consultant Clyde Young for a poster presentation at the Center for Chemical Process Safety (CCPS) 7^th Global Congress on Process Safety in March, 2011.

Commit to Process Safety is the first pillar mentioned in “Guidelines for Risk Based Process Safety Management”, published by the Center for Chemical Process Safety (CCPS).  This pillar is supported by five elements.  One of the elements is Process Safety Competency, which is described as being associated with efforts to maintain, improve and broaden knowledge and expertise.

Competency defined

Competency is a word that is used a great deal by human resources departments, training departments and even government agencies.   Many organizations have expended tremendous amounts of resources identifying and documenting competency levels.  The documentation is sometimes referred to as competency maps that describe the skills required for specific levels of expertise or competency.  In general, these skill levels include awareness, fundamental application, skilled application and mastery competencies.   Training programs and courses usually deliver competencies at the awareness and fundamental application level, but it takes work experience and time in position to acquire the higher level competencies.  Achieving the mastery level competencies of a skill may take years of work and academic achievement.

To become a well rounded professional in the oil and gas industry, no matter what specialty position a person works in, requires varying degrees of competency in many different areas of expertise.  Obtaining higher level competencies is a continuous process of training and performing tasks, sometimes under the direction of a mentor or coach.

Workforce challenges

The workforce in the oil and gas industry is getting older and younger.  Demographics indicate that a large number of workers may be retiring soon and a large number of younger workers will be hired to replace them.  There is and will continue to be, for a considerable time, a large gap in knowledge that can only be partially filled through training.

Newer, less experienced workers can be trained to a certain level of competency.  Working on assigned tasks under the direction of a mentor is supposed to help them achieve the higher level competencies, which make them more productive and valuable to the organization.  With the age gap that exists in our business, these more experienced people are increasingly being asked to perform the technical work that the less experienced people are not competent to do.  This means they have less time to mentor or coach and younger employees are sometimes left to build their higher level competencies independently.  For some applications, this may not be a big problem.  This can lead to a higher level of risk in the workplace especially when it comes to process safety.

Development of personnel within the process safety profession requires skills and levels that are not easily obtained.  People working in other areas of expertise, sometimes called disciplines, will certainly require process safety competencies at appropriate levels to be able to identify and manage the risk associated with the processes we operate in our business.

Being able to describe how and why a person achieves necessary competencies can be difficult.  Choosing applicable competencies from maps or tools is sometimes confusing.  Understanding the relationship among all the skills required is vital to developing personnel in the process safety function.  Perhaps a simple, graphic will help.

Competency pyramid

The pyramid above illustrates a progression for process safety competency.  Throughout the progression, a person may decide to specialize in certain areas.  That does not mean the other skills should be ignored, only that they need to be developed to the appropriate level.   Specialists will require skilled application and in some cases mastery level competency to be proficient.  Everyone in an organization requires at least an awareness level or higher competency in all elements of the pyramid so that when faced with an abnormal situation, the proper action is taken.

Building a base

Consider someone recently hired by a company.  The new hire must receive training and apply the training to all of the company policies and procedures that exist to work for that company.  It’s critical for a person to know things like:  when to come to work, who their supervisor is, how to fill out a time card correctly, how to file an insurance claim, how they fit into the organization’s risk management system, how to work in teams, how much risk the company is willing to accept, and ultimately how to exhibit leadership skills.  New hires who struggle with the day to day procedures of working for an organization will not provide value and may eventually leave.  (See Company Policy level of pyramid)

The training and guidance required for a new hire to become proficient at all the things included in the base of the pyramid can be delivered in several ways.  Formalized orientation training and mentored guidance are some of the most effective methods, but generally a person achieves proficiency here by working for a period of time and gathering knowledge as it is presented.

Define normal

Oil and gas are hydrocarbons.  In order to perform technical work in this industry, people will require a certain level of competency about the physical properties of hydrocarbons.   This includes how they act at different temperatures, pressures and flow rates.  Hydrocarbon products will burn and release energy at different pressures and temperatures.  There is a certain amount of risk associated with hydrocarbons and managing this risk requires that all personnel obtain a competency level associated with hydrocarbons that is commensurate with the job. (See Properties of Hydrocarbons level of pyramid)

Working with hydrocarbons requires equipment.  Equipment includes piping systems and their components, pumps, compressors, turbines, columns, heat exchangers, control loops and hundreds of other things.  Personnel need to understand that to reduce the risk associated with working with hydrocarbons, equipment suitable for the application must be chosen.  There is also a certain amount of risk associated with the different types of equipment involved.  The risks associated with centrifugal pumps are different than the risks associated with positive displacement pumps.  Now consider the types of jobs associated with equipment and the competency levels required to perform those jobs.  Engineers might need to have a skilled application competency level to choose the equipment suitable for the application, a fundamental application competency level may be sufficient for operations personnel and a person performing maintenance may require a mastery level competency, depending on the complexity of the equipment and the job to be performed.  (See Equipment level of pyramid)

All processes within the oil and gas industry consist of equipment, designed to contain, move and process the raw materials.  The composition of the raw materials and the desired output determines the type of process used.  This could include dehydration to remove water, separation and stabilization, or fractionation to separate the components of natural gas liquids.  In addition to the main processes, several utility functions will exist in a facility to provide air, fuel and electricity according to the specifications required.    (See Processes level of Pyramid)

One of the key basic principles of oil and gas is that they consist of many components and can be processed into products that can be sold.  This is done by controlling flow, temperature, and pressure within the chosen equipment that makes up the processes.  To do this safely and efficiently normal operating parameters must be established and operating procedures must be implemented.  To ensure normal operations, equipment must be installed and maintained according to best practices in the industry.  Because there are inherent hazards associated with hydrocarbons it is crucial that the products be contained within the equipment of the process.

All processes that handle hydrocarbons are designed a certain way.  Specific equipment installed to make up a process, with specific parameters for flow, pressure, and temperature.  Process safety begins with defining what normal should be.  The operations and maintenance level of the pyramid defines how an organization keeps a process within normal.  (See Operations and Maintenance level of pyramid)

What could go wrong?

Consider the definition of risk as defined in “Guidelines for Risk Based Process Safety Management”, published by the Center for Chemical Process Safety (CCPS).  Risk is a combination of three things.  What can go wrong?  How bad could it be?  How often could it happen?

Up to this point in the competency pyramid, a good base is being built for anyone working in the oil and gas industry.  Just as a firm foundation is required to build a pyramid, a good foundation is required to prepare anyone working in the process safety field to identify and mitigate hazards.  Organizations expect personnel to be able to identify a hazard and then develop a strategy to reduce the risk associated with that hazard, but many times personnel do not have the basic competencies required to even see a hazard, let alone reduce the risk.  This is the reasoning behind the competency pyramid and the concept that certain competency levels must be attained before risk can be addressed.

There are two types of hazards to be identified under the concept of, “What could go wrong?” These are process hazards and personal hazards.  While it is generally agreed that personal injury rates do not necessarily correlate to process safety, there are issues associated with personal safety that do carry over to process safety.  The most critical is identification of personal hazards while performing routine work.  Experience has shown that personnel have a tendency to focus on personal hazards when performing a job hazard analysis and fail to identify potential process hazards that could lead to an injury.  This is the reason why the foundation of the competency pyramid is important for all personnel.

Identifying process hazards is usually performed by following an established methodology, like hazard and operability study (HAZOP), bow tie analysis, or what if/checklist.  But each of these methodologies requires that a competent team of people perform the analysis.  It is unlikely that hazards associated with a process can be identified (What can go wrong?) if the team does not possess higher level competencies associated with the base of the pyramid.  (See Hazards level of pyramid)

How bad could it be?

The next question in identifying risk is, “How bad could it be?”  The next level of the competency pyramid, analysis, is associated with this.  It is fairly simple to calculate this once it has been determined what could go wrong.  There are formulas available to perform calculations on how big the fire will be, the distance associated with explosions and what a toxic cloud might look like.  In some cases, these are specialty skills and require much higher levels of competency.  It may be enough for most process safety professionals to know that these types of tools and skills are available and some knowledge of how they work.  Personnel in all disciplines should at least be aware that analysis has been performed and what the results are.  These analyses tie directly to the emergency response layer for planning purposes. (See Analysis level of pyramid)

Redefine normal

It is never appropriate to identify a hazard without doing something to eliminate or mitigate it.  The next level of the pyramid addresses competencies on choosing, sizing and installing appropriate levels of protection to eliminate the hazard or reduce the consequences of a hazard.  A process safety professional can choose from a selection of appropriate safeguards with the philosophy of hierarchy of controls in mind.  Each of the controls in the hierarchy, engineering controls first, administrative controls next and finally personal protective equipment, will require specific levels of competency to insure that the appropriate controls are chosen.  Examples include pressure safety valves (PSVs), emergency shutdown (ESD) systems, blast walls, fire walls, scrubber systems for toxic materials, and even something as simple as spacing of equipment.  (See Mitigation level of pyramid)

The next level of the pyramid addresses competencies that are required to plan for emergency situations.  It is difficult to plan for an emergency if it is not understood what could go wrong and how bad it could be.  This is why all the other levels of the pyramid must be addressed prior to emergency planning.     (See Emergency Response level of pyramid)

How often could it happen?

The incident investigation competency level helps address the third question when defining risk, “How often could it happen?”  By investigating incidents and near misses according to established procedures, data is collected that can be used to identify trends and deviations from normal.  If these trends and deviations are consistently identified  and addressed, risk is reduced.  If it is discovered that competency gaps contribute to incidents, skills in the lower regions of the pyramid can be developed to reduce the likelihood of a catastrophic incident.   (See Incident Investigation level of pyramid)

Day to day activities

Near the top of the competency pyramid, a process safety professional should now have the skills required to manage all of the activities associated with process safety in the organization.  Management of change is an example element of the overall management system that exhibits the inter relationship among all the elements and how important it is for personnel to have appropriate levels of competency in all aspects of process safety.  (See Process Safety Mgmt. level of pyramid)

If process safety is defining and keeping the process within normal parameters, all personnel within the organization should have appropriate levels of competency to work within the system that has been developed.  Contractors will be chosen, modified processes will be started and management will be monitoring the system to insure that risk has been reduced to as low as reasonably practicable.

Many organizations have begun addressing the competency gap that is created as the workforce ages and less experienced people are hired.  The boom and bust cycles of the oil and gas industry have been seen for years.  Within most facilities, there are usually a few key individuals.  These are usually older, more experienced people.  They are the ones who have the ability to see a problem, identify the cause and then do something to fix the problem.  This is the competency level that everyone in the organization should strive for.  However, this takes time and experience.  Management has a responsibility to see that appropriate resources are available to make this happen.

Capstone

The capstone of the pyramid represents a troubleshooter. This position requires a significant number of skills, at varying levels of competency to be able to design an effective process safety management system, to identify deficiencies in a system when auditing and make appropriate recommendations to address the deficiencies.   An effective process safety management system should be balanced according to the resources that are available.  A process safety troubleshooter will have the skills and experience to not only identify deviations from normal system requirements, the troubleshooter will also be able to determine the most effective way to bring the system back to normal. (See TRB level of pyramid)

Sometimes it is difficult for personnel to identify competency gaps and suggest plans for developing the skills required to fill those gaps.  This simplified, graphical pyramid of process safety competency has proven to be a key learning point when conducting training about risk based process safety.  Participants have commented that break downs in an organization’s process safety management system can be identified and addressed by using the pyramid to identify competency gaps and developing strategies to address those gaps.

“The main product of the competency element is an understanding and interpretation of knowledge that helps the organization make better decisions and increases the likelihood that individuals who are faced with an abnormal situation will take the proper action.”  This statement from the CCPS book, “Guidelines for Risk Based Process Safety”, describes anyone working in the oil and gas industry.  For those considered to be process safety professionals, taking the proper action to address an abnormal situation can insure that the risk associated within the industry is reduced to an acceptable level.

The pyramid isn’t just for process safety professionals.  All personnel within an organization will be able to use to the pyramid to obtain at the very least, a snapshot of where improvement is needed.  Senior managers, line managers, operator and maintenance personnel alike should study the pyramid to identify areas of improvement and create development plans for themselves and their direct reports.

If you would like a copy of the paper that was presented, please contact John M. Campbell & Co. and request a copy.

To learn more about managing process safety systems, we suggest attending our PetroSkills HSE course,  HS 45- Risk Based Process Safety Management or schedule a session of our two day Process Safety Case Study for Operations and Maintenance – OT 21,   To enhance process safety engineering skills we suggest any of the JMC foundation courses or our, PS 4 – Process Safety Engineering course.
By: Clyde Young

Liquid density is needed for process simulation and equipment design. For example, accurate predictions of liquid density are needed for calculating the pressure drop in piping/pipeline and vessel sizing. Accurate liquid density is also essential for custody transfer.

In November 2006, December 2006 and January 2007 tips of the month (TOTM), we presented an overview of different methods and tools for predicting liquid densities. The methods for liquid density prediction include but are not limited to the following.

• Generalized Charts

There are several generalized charts for predicting the liquid density of petroleum fluids and hydrocarbons [1]. The charts normally present the relative density of paraffinic hydrocarbon mixtures at their boiling point or bubble point temperature and pressure. These charts apply to mixtures as well as pure components. Alignment points for paraffinic hydrocarbon mixtures and pure components are located according to their molecular weight. The accuracy of these charts is generally within 3 % of the measured values. However, the accuracy is somewhat less for mixtures with molecular weights less than 30 where temperature is low, and where the methane content is high or pseudo-reduced temperatures are above 0.9 [2].

• Correlations

In order to calculate liquid density reliably, several correlations such as: COrresponding STAte Liquid Density (COSTALD), modified Rackett equation by Spencer and Danner (RSD), and Nasrifar-Moshfeghian (NM) have been developed.

COSTALD: The COSTALD correlation by Hankinson and Thomson [3] requires two parameters: w_SRK, the optimized value of the acentric factor based on the SRK equation of state (EoS) and; V*, the pure component characteristic volume.

RSD: Spencer and Danner [4] improved the liquid density correlation of Rackett [5]. The improved correlation for saturated liquid density calculation requires only Z_RA, the improved compressibility factor.

NM: Nasrifar and Moshfeghian [6] presented an equation and a set of mixing rules for predicting the liquid density of pure refrigerants and liquefied natural gas.

• EoS Methods and Volume Translated

The equations of state are used in commercial simulation software for predicting phase behavior and thermodynamic properties. Generally, EoSs need a few parameters (usually two or three) that are normally obtained from critical properties. The cubic equations of state (EoS) give relatively accurate results for predicting vapor-liquid equilibria, especially for non-polar or slightly polar systems. Furthermore, these equations could be used to accurately predict vapor densities, enthalpy and entropy. These advantages encourage the researchers to augment EoS ability more than before, especially liquid density, although their accuracy for liquid density prediction is generally not as good as the correlations listed above. The popular EoSs such as SRK [7] and PR [8] predict liquid density with an average absolute error of about 8%, much higher than the correlations [9]. This large magnitude of error is not acceptable by industry; therefore they are not used for this purpose. In order to overcome this deficiency, a volume translated method has been developed by Peneloux et al. [10].

In this TOTM, we will present the method of volume translation for liquid density prediction and then demonstrate its application for liquid hydrocarbons such as pure methane, n-pentane, decane, pentadecane and carbon dioxide. In a future TOTM we will extend this procedure to multicomponent mixtures.

In order to improve the accuracy of EoSs for predicting liquid density, Peneloux et al. [10] proposed the following correction.

(1)

In the above equation,is the corrected liquid specific volume,  is the liquid specific volume calculated by SRK or PR EoS, MW is the molecular weight, ρ^L is the liquid density, and the correction term or volume shift factor “c” is determined from experimentally measured liquid density. It is normally regressed against several data points. In the absence of experimentally regressed value, it can be estimated as follows:

(2)

where Z_RA, is the Rackett [10] parameter, R is the gas constant and T_C and P_C are the critical temperature and pressure, respectively.

In this work, we determined the value of “c” by minimizing the absolute error between experimentally measured liquid densities [11] and the corresponding predicted values using MathCad software. To achieve this, the following equation was defined.

(3)

where NP is the number of data points, ρ_exp is the experimental liquid density. The value of “c” was determined by minimizing  f(c). In MathCad nomenclature the appropriate command is:

(4)

Results and Discussion:

We applied the preceding procedure to several pure compounds shown in Table 1. The temperature and pressure ranges, and the optimized values of “c” for both PR and SRK EoS as well Z_RA for the RSD correlation are shown in Table 1. Figures 1 and 2 present graphical comparisons between the predicted and experimental liquid density values of pentadecane. Similar trends were observed for the other compounds. Table 2 presents the summary of error analysis for different methods for the pure compounds shown in Table 1.

Table 1. Optimized volume translated parameter, c, for different compounds

Figure 1. Comparison of predicted liquid density of C₁₅H₃₂ by PR and volume translated PR (VTPR) against experimental data [11]

Figure 1. Comparison of predicted liquid density of C₁₅H₃₂ by SRK and volume translated SRK (VTSRK) against experimental data [11]

Table 2 indicates that considerable improvements are obtained by applying volume translated correction to liquid specific volume (or liquid density) predicted by PR and SRK. However, the accuracy of the COSTALD, RSD and NM correlations are still by far much better than the volume translation applied to these two EoSs. The accuracy of EoS and its volume translated correction deteriorate as the critical point is approached.

Table 2. Summary of error analysis for different methods studied

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) and G5 (Gas Conditioning and Processing-Special).

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:

• Campbell, J. M. “Gas conditioning and processing, Volume 1: Fundamentals,” John M. Campbell and Company, Norman, Oklahoma, USA, 2001.
• Engineering Data Book, 12^th Editions, Gas Processors and Suppliers Association Data Book, Tulsa, Oklahoma, 2004.
• Hankinson, R. W., Thomson, G. H., AIChE J., Vol. 25, no. 4, pp. 653-663, 1979.
• Spancer, C. F., and Danner, R. P., J. Chem. Eng. Data, vol. 17, no. 2, pp. 236-241, 1972.
• Rackett, H. G., J. Chem. Eng. Data, vol. 15, No. 4, pp. 514-517, 1970.
• Nasrifar, Kh. and Moshfeghian, M., Fluid Phase equilibria Vol. 153, 231-242, 1998.
• Soave, G., Chem. Eng. Sci., Vol. 27, pp. 1197-1203, 1972.
• Peng, D. Y., and Robinson, D. B., Ind. Eng. Chem. Fundam., Vol. 15, p. 59, 1976.
• Nasrifar, Kh. and M. Moshfeghian, J. of Fluid Phase Equilibria, Vol. 158-160, pp. 437-445, 1998.
• Peneloux, A. E., Rauzy, E., and Freze, R., Fluid Phase Equilib., Vol. 8, pp. 7-23, 1982
• Vargaftik, N.B., Handbook of Physical Properties of Liquids and Gases (Pure Substances and Mixtures), 2^nd ed., English Translation, Hemisphere Publication, 1975.