Compressor System Performance at Abnormal or Non-Design Conditions

Compressor System Performance at Abnormal or Non-Design Conditions Presenting Author: Dylan Grosscup Consultant dylangrosscup@gmail.com Co-Authors: Carl D Ramlakhan Director Engineering Atlantic LNG Company of Trinidad and Tobago cramlakhan@atlanticlng.com Christian Jarvis Operations Specialist-Project Engineering and Optimization Atlantic LNG Company of Trinidad and Tobago cjarvis@atlanticlng.com Charles Lea Senior Process Safety and Risk Management Consultant iomosaic Corporation lea.c.mn@iomosaic.com Prepared for Presentation at American Institute of Chemical Engineers 2016 Spring Meeting 12th Global Congress on Process Safety Houston, Texas April 10-13, 2016 AIChE shall not be responsible for statements or opinions contained in papers or printed in its publications

GCPS 2016 2 Abstract When attempting to optimize facility performance or determine the effectiveness of process safety systems, accurate evaluation of compressor performance at conditions deviating from design is critical. This paper presents a joint study involving the Atlantic LNG Company of Trinidad and Tobago, the iomosaic Corporation, and an independent contractor. At the Atlantic LNG facility, a large feed gas compressor was tested at varying inlet conditions and the operating data were analyzed to determine the impact on compressor performance. A comprehensive data set spanning 2 years and more than 400,000 measured conditions were used to develop a method combining empirical data and design calculations to optimize compressor performance and provide a framework for evaluating compressor safety. A validation of 3 methods of predicting compressor performance (the Isentropic Energy Balance Model, the Ideal Polytropic Model, and the Real Polytropic Model) was performed to determine which method/s are most suitable for process safety/optimization work. The analysis considered both, the fundamental and empirical bases. A mathematical model was derived for projecting the behavior of the polytropic head and efficiency curves, provided by the compressor manufacturer, based on numerical integration and data residualization techniques. Then, the data set were analyzed to find behavioral patterns which may indicate potential hazardous conditions or deviations from efficient operation. Additionally, a detailed list of hazardous scenarios, design considerations, and operating practices that have resulted in safety incidents in current industry applications are given for consideration during compressor system design and evaluation. It was found that, under optimal conditions, the Isentropic Energy Balance Model and the Ideal Polytropic Model predict exactly the same results for compressor performance. It was found that the polytropic head and efficiency curves furnished by the vendor are only accurate at certain, optimal, suction pressures and that these optimal pressures are only consistent with the reference pressure on the vendor supplied curves at very specific operating points. Furthermore, it was found that deviations from optimal suction pressure result in a reduced head and flow capacity of the compressor. The pattern of inefficiency is consistent with operating conditions that have been shown to lead to equipment failure and loss of containment from vibration induced fatigue in existing compressor system applications. The final result of the study found that it is possible to use a combination of operating data and design calculations to generate suction pressure curves that will improve safe operations, optimal efficiency, and can be used by operators while the system is online.

GCPS 2016 3 1. Introduction and Previous Work Since 1698, when British inventor Thomas Savery patented the first steam engine, compressors have been integral components of virtually every process facility on the planet. As such, compressor performance has been widely studied from both the theoretical and measured operational data aspects. The foundation of modern compressor assessment techniques was published in 1850, in the paper, On the Moving Force of Heat by Rudolf Julius Emanuel Clausius. Today Clausius theories are known as the First Law of Thermodynamics and the Second Law of Thermodynamics and they drive more than just compressor system analysis. A few decades later, in 1869, J. Homer Lane, credited with first publishing the polytropic model in his paper, On the Theoretical Temperature of the Sun Under the Hypothesis of a gaseous mass Maintaining it s Volume by it s Internal Heat and Depending on the Laws of Gases Known To Terrestrial Experiment. The Polytropic Model, with Ideal Gas assumptions, is the method most commonly used today to assess compressor performance. In 1962, John Shulz wrote, The Polytropic Analysis of Centrifugal Compressors in which he derives a modification of the Ideal Polytropic equation, the polytropic work factor, intended to correct for deviations due to non-ideal conditions. Additionally, he develops two compressibility factors, X and Y, to supplement the traditional Z factor, and applies the factors using a method known as the Shulz method. ASME PTC 10-1997, Performance Test Code on Compressors and Exhausters, first published in 1965, gives guidance on how to determine appropriate compressor testing methods as well as several dimensionless parameters for using mechanical design criteria to predict compressor performance. Methods for designing compressors to meet specific performance criteria have been well explored in the current body of work; however, there is little direction on safe design criteria, and almost nothing related to a more practical holistic approach for performance analysis of an entire system. This paper presents information, tools, and methods for design/validation of compressor safety systems as well as methods for optimizing performance using a combination of empirical data and design calculations.

GCPS 2016 4 2. Compressor Performance Prediction Models Before abnormal operating conditions can be effectively, and quantitatively, assessed, it must be shown that there exists a mathematical model capable of predicting meaningful and sensible values for compressor performance across a wide range of conditions. Three calculation methods were analyzed to determine their suitability for predicting compressor system performance at abnormal conditions: 1. Isentropic Energy Balance Model 2. Ideal Gas Polytropic Model 3. Real Gas Polytropic Model The Ideal Gas Polytropic Model and the Real Gas Polytropic Model are outlined in ASME PTC-10 [1]. This section provides a fundamental description of each model as well as the applied calculation method used for this analysis. The Isentropic Energy Balance Model approach defines the system boundaries using and analyzes the system based on the 1st and 2nd laws of thermodynamics [2]. Simplifying assumptions are made and a method is produced to generate predictions of the compressor discharge pressure for given suction conditions, composition, and compressor speed. 2.1 Stagnation Pressure and Temperature Translation In order to apply the calculation methods mentioned within this paper to operational data it is necessary to convert measured pressure and temperature values to stagnation pressures and temperatures [1]. Per Bernoulli s equation [2] : P stagnation = P static + P velocity + P gravitational potential (2.1-1) Pressure sensors typically measure static pressure. Static pressure does not account for velocity pressure from kinetic energy of a flowing fluid, nor the impact of differences in height along the cross sectional area of the pipe (gravitational potential pressure is normally assumed to be radially constant). Temperature sensors typically measure a temperature that is somewhere between the static temperature and the stagnation temperature. The difference between the measured temperature and the static temperature is defined by a recovery factor (r f ) [1] Manufacturers often publish these values for specific sensors. If the r f is unknown a value of 0.65 [1] can be used based on most sensor configurations. 2.1.1 Calculation Methodology 1. Find ke measured using P measured and T measured. r f = H measured H static H stagnation H static (2.1-2)

GCPS 2016 5 ke = 1 2 mv2 (2.1.1-1) 2. Find H static using: H static = H measured r f ke static (2.1.1-2) 3. Using a fixed pressure (P static ), vary T until the enthalpy satisfies the condition in step 2 4. Now that you have P static and T static, find, P stagnation and T stagnation by performing isentropic translations (it is assumed that S stagnation is constant) from P static and T static until the following condition is satisfied: H stagnation = H static + ke stagnation (2.1.1-3) 2.2 Isentropic Energy Balance Model 2.2.1 Derivation General Energy Balance [2] d(mu) cv dt Steady State Energy Balance:* = [(U + 1 2 v2 + gz) m] fs + Q + W (2.2.1-1) cv = control volume fs = flowing stream 0 = [(U + 1 2 v2 ) m] fs + Q + W (2.2.1-2) *assumes the following: 1. Changes in potential energy due to changes in elevation are negligible such that gz = 0. 2. Accumulation of energy within the system is negligible (all the energy moving into the system is moving out of the system) such that d(mu) cv = 0. Definition of Enthalpy [2] Isentropic Manipulation of Steady State Energy Balance:* H U + PV (2.2.1-3) 0 = [(H is + 1 2 v2 ) m] fs + W is (2.2.1-4) is = isentropic *isentropic manipulation assumes the following: 1. The work is reversible (i.e. no work is lost to inefficiency) such that in H U + PV, PV = 0 therefore U = H 2. The system is adiabatic (i.e. no heat transfer into or out of the compressor system) such that Q = 0 3. Kinetic energy effects should not be assumed to be negligible per ASME PTC 10-1997.E.3.1 which states The isentropic work for the purposes of this Code is the work done in an isentropic process between the inlet stagnation state and the discharge stagnation state.

GCPS 2016 6 Energy Balance Base Equation:* W is = [(H 2 + 1 2 v 2 2 ) m 2 (H 1 + 1 2 v 1 2 )] m 1 (2.2.1-5) *due to leakage rates, often m 1 m 2 Leakage Rate [1] Compressor systems exhibit leakage such that m 1 m 2. The compressor test code requires estimation of the leakage ratio [1]. If the design/tested leakage rates are known this should be accounted for in the calculations and, if possible, an estimation of a potentially increased leakage rate at abnormal conditions should be considered as this may lead to increased pressure. If the calculations are being done to assess an existing system for which there is no information about the design/tested leakage rates, and no leakage information is available it may be necessary to assume the leakage rate is 0. Simplified Energy Balance Equation:* H 2 = H 1 + W is + ke (2.2.1-6) *a value from the polytropic head curves may be used here if W is is multiplied by the mass flow rate m. Validity of this assumption is shown in section 3.1. 2.2.2 Calculation Methodology 1. Convert measured/static conditions to stagnation conditions using 1.2 2. Perform isentropic translations from P 1 and T 1 to P 2 and T 2 until the condition in 1.3-6 is satisfied. (T 2 is the temperature that is coincident to the isentropic flash from suction conditions to discharge pressure, P 2.) 2.3 Ideal Gas Polytropic Model 2.3.1 Derivation The ideal gas polytropic method is based on, as the name implies, approximating the polytropic path of a reversible compression process. A polytropic process must satisfy the following equation [3] : W ke = H (2.3.1-1) *ke = kinetic energy The polytropic path describes the relationship between Pressure (P) and Specific Volume (υ) for a polytropic process and can be characterized by the following function [3] (n is defined as the polytropic exponent): 1 n = P υ ( P υ ) S (2.3.1-2) Rearranging 1.4-2 and integrating (assuming n is constant) gives:

GCPS 2016 7 n υ υ + P P = 0 n ln υ + ln P = const Pυ n = const (2.3.1-3) The polytropic work equation [3] is an approximate solution of the work of compression integral for a reversible (or ideal) and adiabatic work process: Alternatively [1] : W = P 2 P 1 VdP n 1 = ( n n 1 1υ 1 [( P 2 ) P 1 1] (2.3.1-4) n 1 W = ( n n 1 1RT 1 [( P 2 ) P 1 1] (2.3.1-5) *R = universal gas constant divided by molecular weight Rearranging 1.4-3, the polytropic exponent (n) can be found using the following equation [3] : for calculational ease use: n = lnp2 P 1 ln υ 2 υ 1 (2.3.1-6) υ = 1 ρ n = lnp 2 P1 ln ρ 1 ρ2 (2.3.1-7) (2.3.1-8) Solving equation 1.4-5 for P 2 gives the following equation: P 2 = P 1 [( n ) W + 1] n n 1 Z 1 RT 1 n 1 (2.3.1-9) 2.3.2 Calculation Methodology: 1. Convert measured/static conditions to stagnation conditions using 1.2 2. Find Z 1, ke 1, and ρ 1 using P 1, T 1, MW 1

GCPS 2016 8 3. Perform an isentropic translation from P 1 and T 1 to P 2 (= P 1 * 1.1 for the initial calculation) to find ρ 2 4. Calculate n calculated using equation 1.4-5 with P 2initial and ρ 2 5. Find P 2calculated using equation 1.4-8, work, W p - Δke (W p is from the compressor design curves), and set n guessed = n calculated 6. Continue replacing n guessed with n calculated until n calculated = n guessed meaning P 2calculated converges to a constant value 7. Perform a new isentropic translation from P 1 and T 1 to P 2calculated and redo steps 4-7 until P 2calculated converges on a constant value (this method involves iteratively solving steps 5-6 in an inner calculation and then iteratively solving 4-7) 2.4 Real Polytropic Model [3] 2.4.1 Derivation: In order to compensate for potential deviation of the polytropic exponent (n) from a perfect isentropic path, the polytropic equation can be modified with a polytropic work factor (f) as follows: n 1 W = f ( n n 1 1υ 1 [( P 2 n ) P 1 1] (2.4.1-1) f = H 2 H 1 n n 1 (P 2υ 2 P 1 υ 1 ) Applying this to the polytropic equation results in the following: (2.4.1-2) W = (H 2 H 1 ) Solving equation 1.5-3 for P 2 results in the following: P 1 υ 1 P 2 υ [( P n 1 2 n ) 1] (2.4.1-3) 2 P 1 υ 1 P 1 P 2 = P 1 [( P 2υ 2 P 1 υ 1 W ) P 1 υ 1 (H 2 H1 ) + 1] n n 1 (2.4.1-4) 2.4.2 Calculation Methodology: 1. Convert measured/static conditions to stagnation conditions using 1.2 2. Find H 1, ke 1, and ρ 1 using P 1, T 1 3. Perform an isentropic translation from P 1 and T 1 to P 2guessed (= P 1 * 1.1 for the initial calculation) to find ρ 2 and H 2

GCPS 2016 9 4. Find P 2calculated using equation 1.4-8 and work, W p - Δke (W p is from the compressor design curves) 5. Continue guessing a new P 2guessed and performing steps 3 and 4 until P 2calculated = P 2guessed

GCPS 2016 10 3. Numerical Integration Method of Compressor Mapping Projection of the compressor polytropic head and efficiency curves is critical in accurately predicting abnormal compressor performance. Due to the irregular nature of polytropic head and efficiency curves, it is difficult to accurately interpolate between speeds. Extrapolation of system behavior to speeds above or below the normal system boundaries is even more difficult. It is often necessary to analyze power requirements during start up conditions as well as safety system design requirements at over-speed conditions. Traditional methods of analyzing the behavior of compressor curves look at changes in the X and Y axis to project or interpolate polytropic values. The traditional methods are time and labor intensive to develop and the predicted values are generally inconsistent with operational data. The Numerical Integration method of compressor mapping is based on mathematical analysis and can be automated and give stable values at speeds well above the mechanically achievable range. 3.1 Derivation To assess the mathematical behavior of a system of curves it is necessary to determine both the direction and magnitude of a change in the system. A compressor map (as shown in Fig. 3-1) appears to be a traditional X-Y plot of inlet actual volumetric flow rate and polytropic head or polytropic efficiency. Similar to a topographical map, changes in the X-Y directions of the compressor map are related to 3 variables: inlet actual volumetric flow rate (X), polytropic head/efficiency (Y), and compressor speed (shown in terms of X and Y). Figure 3-1. An example compressor map depicting the typical regions that define rotating multi-speed compressor performance behavior. The fundamental concept of this analysis method is rooted in the idea that the shape and length of the individual curves are defined by continuous functions bounded by the same limits. These limits are the surge line and the stonewall line. The surge line is defined as the inlet volumetric flow rate at which the

GCPS 2016 11 back pressure in the machine exceeds the developed head and a cyclic reversal in flow occurs (this coincides with the point of minimum inlet volumetric flow at which the compressor develops the maximum head for a specific speed). The stonewall point is defined as the point at which the compressor develops a choked or critical flow region at the flow limiting diameter of the machine. At the stonewall point there is a pressure discontinuity on the compressor discharge that acts as a physical barrier to flow based on the sonic velocity of the fluid at the conditions in question (this coincides with the maximum inlet actual volumetric flow rate at a specified speed). It is assumed that limitations based on thermodynamic properties will consistently define the relationship between inlet volumetric flow rate and polytropic head at constant speed. The functions governing the mathematical tendencies of a set of irregular can be expressed as: f(polytropic head, inlet volumetric flow rate, compressor speed) = W p V ( W p ) V Speed (3.1-1) Integration of 3.1-1 yields: V stonewall W p V ( W p ) V Speed f(inlet volumetric flow rate, compressor speed) = V surge (3.1-2) The Numerical Integration method of compressor mapping assumes 3.1-2 can be solved numerically by the common distance equation at very small intervals to find the length of a fixed speed best fit line on the compressor map. V length = stonewall V (V 2 V 1 ) 2 + (W p2 W p1 ) 2 surge (3.1-3) Using equation 3.1-3 to estimate the direction of the curve assumes that any point on curve A has a corresponding behavior point on curve B that occurs at the same length ratio at every speed. More basically stated, a point on curve A that occurs at 10% of the total length of curve A is equivalent to the point on curve B that occurs at 10% of the total length of curve B. This relationship becomes more accurate at predicting the direction axis as the length of the segments calculated in equation 3.1-3 decreases, so it is essential to use a large number of points (on the order of several thousand) to assess system behavior. By creating a series of best fit line functions, from the numerically integrated, direction axis, a speed length axis is generated. These unusual axes comprise a unique coordinate system for each compressor map. The created coordinate system is functionally similar to the radial-angular axes in the radial coordinate system (r-θ). One of the axes used to project the compressor maps in the Numerical Integration method is magnitude of speed. The other axis is a combination of polytropic head/efficiency and actual volumetric flow in one parameter that indicates direction. Often it is convenient to use the polynomial model for the best fit lines as well as the contours that characterize curve behavior. When doing so it is necessary to limit the order of the polynomial by the number of data points minus one. For example, a 3 rd order polynomial cannot be characterized by less than 4 data points, and a 6 th order polynomial cannot be characterized by less than 7 data points. This becomes relevant when considering the number of speed curves available.

GCPS 2016 12 As stated earlier, this method can be automated in spreadsheet software, and performs high/low speed projections and interpolations that are consistent with historical data. Figure 3.2 depicts the application of this method used in the Atlantic LNG Feed Gas Compressor analysis, projected to 130% of the design speed. Even at 30% above the design speed, the projection is extremely stable. The contour lines shown perpendicular to the vendor supplied curves are used to characterize the mathematical behavior of the entire curve set. These curves are generated from points at a specific distance ratio along the integrated curve. The green line represents a projection of the polytropic head values at 6071 rpm, which is 130% of the design speed. Figure 3-2. Polytropic head curves for Atlantic LNG feed gas compressor. With curve sets that behave in a similar manner at each speed, using the correct best fit model is less important as the contour lines used to characterize the polytropic head/actual volumetric flow ratio exhibit a less dynamic behavior. In this case the contours can be characterized by the same model and a 2 nd or 3 rd order polynomial is often used used on a consistent basis. When the curves become more complex, as in the polytropic efficiency curves shown in Figure 3-3 below, it is important to characterize each curve and contour on an individual basis. This becomes especially significant when projecting values above or below the limits of the vendor supplied curves. Best fit models that are extremely accurate within the supplied boundaries may change dramatically when even slightly above or below so it is necessary to project at 10-15% above the desired speed when choosing the appropriate method. The below figure displays the polytropic efficiency values, as given by the compressor vendor, as well as the contour lines

GCPS 2016 13 that were generated to predict the mathematical behavior of the curve system. Polytropic efficiency maps are often more complex in behavior and require more complex contour lines. The green line represents a projection of the behavior to 5700 pm, which is over 120% of the design speed. 3.2 Calculation Method Figure 3-3. Actual Volumetric Flow Rate vs Polytropic Efficiency. 1. Gather numerical data points for each set of curves from the Polytropic Head or Polytropic Efficiency maps. Normally it takes 10-15 data points per curve to create a curve with adequate accuracy. Plot the points on a chart. 2. Develop best fit lines for each compressor speed. Normally 5-8 compressor speeds should be used for increased accuracy. If you do not have at least two speeds, use one speed and the origin for the most conservative assumption. 3. Use the best fit lines from step 2 to calculate 5000-10000 individual data points for each curve in order to maintain the inaccuracy below 1%. 4. Find the surge point, 10%-90% length lines, and the stonewall point for each speed. 5. Create a best fit line equation from all of the surge points on each curve. Do the same for the 10%-90% points and then create a best fit line from the stonewall points on each curve. This results in best fit curves based on speed. 6. Chose a speed, solve each best fit line from step 5 for the chosen speed.

GCPS 2016 14 7. Create a best fit line from the points generated in step 6. 4. Comparison of Calculation Methods To assess the accuracy of the calculation methods in Section 2, in predicting compressor behavior at abnormal conditions, an array of data points was chosen for analysis. More than 7,000,000 data points, at more than 415,000 conditions, across two years of operating data, including shut downs, startups, instrumented trips, and normal operating conditions were sorted through to find the most stable/repetitive conditions experienced as they are expected to be fully representative of the compressor system. Discharge pressure was chosen as the basis for this analysis since increasing pressure is the fundamental purpose of a compressor. Data points at compressor speeds 3040 rpm, 3965 rpm, 4685 rpm, and 4904 rpm were analyzed, grouping the data to 5 rpm increments (or 0.1% of the design speed). Within each speed, representative points were taken in a range from the highest suction pressure to the lowest suction pressure experienced by the compressor within the span of the data. The data points at each suction pressure were grouped by inlet pressure, inlet temperature, discharge pressure, discharge temperature, and recycle opening. Then the largest grouping of points at each suction pressure was chosen in order to ensure the stability of the data. To determine the accuracy of the calculations, the predicted discharge pressure and the predicted compressor differential pressure were compared to the operating data values. Suction Pressure: 27.1-54.2 barg Suction Temperature: 18.1 29.3 C Mass Flow Rate: 784,717 1,315,437 kg/hr Inlet Volumetric Flow Rate: 24,272 41,177 m3/hr Compressor Speed: 3040 4904 rpm (65 105%) Recycle Valve Opening: 5 100% The reference conditions set on the as tested design compressor curves are as follows: Suction Pressure: 37.2 barg Suction Temperature: 15.9 C Compressor Speed: 3770 rpm (80%) Recycle Valve Opening: 0%

GCPS 2016 15 Table 4-1, shows 19 conditions supported by approximately 14,000 data points, upon which each of the 3 calculational methods were performed. The data were chosen to represent the entire range of inlet pressures, inlet temperatures, inlet densities and mass flow rates experienced by Atlantic LNG s feed gas compressor during a period of two years for the speeds considered in the analysis. Speed Inlet Pressure Measured Pressure Discharge Pressure Delta P Table 4-1. Calculation Method Comparison. Calculation Method Comparison Isentropic Energy Balance Model Discharge Pressure Delta P Ideal Gas Polytropic Model Discharge Pressure Delta P Real Polytropic Model Discharge Pressure Delta P RPM barg barg bar barg bar barg bar barg bar 3043 43.4 54.0 10.7 54.2 10.8 54.2 10.8 53.0 9.6 3043 43.9 54.3 10.4 54.6 10.7 54.6 10.7 53.3 9.4 3043 46.1 55.7 9.6 56.8 10.7 56.9 10.8 55.6 9.5 3043 47.3 56.3 9.0 58.5 11.1 58.4 11.1 57.2 9.9 3043 50.7 58.2 7.5 60.5 9.8 60.5 9.8 59.3 8.6 3043 50.8 57.9 7.1 60.8 10.0 60.8 10.0 59.6 8.8 3043 51.8 59.7 7.9 62.2 10.3 62.2 10.3 61.0 9.2 3043 52.7 60.8 8.1 63.0 10.3 63.0 10.3 61.8 9.1 3043 54.2 62.8 8.7 65.1 11.0 65.1 10.9 63.8 9.6 3964 37.2 53.3 16.0 53.7 16.5 53.7 16.5 52.2 15.0 3964 38.8 55.4 16.6 56.3 17.5 56.3 17.5 54.9 16.0 3964 41.3 55.6 14.3 58.1 16.8 58.1 16.8 56.7 15.4 4683 33.5 54.4 20.9 55.2 21.7 55.2 21.8 53.6 20.1 4683 34.4 54.9 20.6 56.8 22.4 56.8 22.4 55.1 20.7 4683 35.7 56.1 20.4 59.4 23.7 59.4 23.7 57.7 22.0 4904 27.1 45.7 18.5 46.3 19.1 46.3 19.2 44.6 17.5 4904 32.3 53.9 21.5 55.9 23.5 55.9 23.5 54.2 21.8 4904 33.7 55.0 21.3 57.9 24.2 57.9 24.2 56.2 22.5 4904 34.0 55.6 21.7 58.6 24.6 58.5 24.6 56.8 22.9 Average Deviation 3.0% 12.4% Max Deviation Min Deviation Range of Deviation 5.5% 0.3% 5.2% 28.9% 1.5% 27.5% 3.0% 5.5% 0.3% 5.2% 12.4% 28.9% 1.4% 27.4% 0.5% 2.8% -2.4% 5.3% 3.1% 19.2% -11.5% 30.6%

GCPS 2016 16 4.1 Discharge Pressure Prediction As shown in Figure 4-1., provides the comparative information regarding predicted discharge pressure for each point in Table 4-1., for each calculation method and the measured discharge pressure for reference. The Isentropic Energy Balance model and the Ideal Gas Polytropic Model unexpectedly return the same values for every point. The largest deviation calculated was less than 0.2% across the data set and the majority of the time the agreement is on the order of 99.9%. For this reason these two methods are considered to be equivalent for the remainder of this paper. The highest deviation from predicted discharge pressure by the energy balance method is +5.5% while the lowest deviation is +0.3% giving a range of 5.2%. These methods produce results that are close to, but consistently above, the measured discharge pressure. Figure 4-1. Comparison: Predicted Discharge Pressure vs Measured Discharge Pressure. The real polytropic method returns results that are more closely grouped around the measured pressure such that the average deviation is smaller; however, the range of deviation is larger. The highest deviation from the measured discharge pressure is +2.8% while the lowest deviation is -2.4% giving a range of 5.3%.

GCPS 2016 17 4.2 Pressure Differential Prediction Since differential pressure is a function of the work applied to the fluid by the machine, shown in Figure 4-2., deviations in the prediction of this value more accurately predict the behavior of the compressor system. The figure shows the graphical comparison of the pressure differential predicted for each point in Table 4-1, for each calculation method and the measured pressure differential for reference. Figure 4-2. Comparison: Predicted Delta P vs Measured Delta P. The highest deviation from predicted discharge pressure by the energy balance method is +28.9% while the lowest deviation is +1.5% giving a range of 27.5%. The deviations here appear to be much more significant even though the deviation in discharge pressure is only +3.3 barg. The real polytropic method returns results that, again, appear to be more closely grouped around the measured pressure, such that the average deviation is smaller; however, visual inspection of the results seems to show the difference between the ideal gas polytropic equation and the real gas polytropic equation is merely a reduction in value instead of a modification of behavior [3]. The highest deviation from the measured discharge pressure is +19.2% while the lowest deviation is -11.5% giving a range of 30.6%.

GCPS 2016 18 4.3 Reference Suction Pressure Analysis of the deviations in pressure differential from the measured values for both the Energy Balance method and the Real Gas Polytropic Equation method shows no direct correlation between varying values of suction pressure, suction temperature, suction density, mass flow rate, volumetric flow rate, recycle valve opening, nor compressor speed. All dual combinations of the aforementioned variables were considered showing no indirect correlation with the exception of one combination. Considering the combination of compressor speed and suction pressure, at each compressor speed, there is a pressure below which the deviation in predicted differential pressure levels off. This relationship is demonstrated in the Figure 4-3 below which is the same as Figure 2-1 above, with the higher pressure points removed at each speed. In order to differentiate between inefficiency and inaccuracy, the high suction data points were removed. Figure 4-3. Comparison: Predicted Polytropic Work vs Measured Polytropic Work (Low Pressure). At suction pressures on the lower end of the range of pressures considered for each speed, the Ideal Polytropic Equation method is extremely accurate. At these conditions the predicted discharge pressure deviates from the measured values by 0.3-1.6% and the predicted differential pressure deviates by 1.4-4.0%. In this range the Ideal Polytropic equation yields results that are far more accurate than those from the Real Polytropic equation. There is a range for which this same phenomenon is true for the Real Polytropic method; however, since this behavior implies a machine efficiency related to a speed sensitive

GCPS 2016 19 reference pressure, and the Real Polytropic method predicts negative efficiency values for work in reference to the ideal system work, this would violate the 2 nd Law of Thermodynamics [2], and thus does not appear to be a reliable method for determining compressor performance at abnormal operating conditions. 4.4 Compressor Efficiency vs Suction Pressure In order to determine if efficiency based on deviation from a speed sensitive reference pressure is a valid concept, a more comprehensive data set, covering 64 conditions supported by more than 50,000 data points, was analyzed using the Energy Balance method and the results were plotted on a chart of polytropic work vs actual volumetric flow rate. Both, the polytropic work predicted by the Isentropic Energy Balance method and the polytropic work as tested compressor design curves were placed are shown in Figure 4-4. This is a representation of the ideal work that should be developed per vendor documentation to the actual work developed per the measured operating data. For each speed the darker color represents the stream work predicted by the vendor supplied polytropic head curves based on inlet volumetric flow rate. The lighter color represents the work that is obtained from isentropically flashing from the measured inlet pressure and temperature to the measured discharge pressure. Figure 4-4. Comparison: Compressor Curve Polytropic Work vs Measured Work. The data points shown at lower operating speeds (3040 rpm and 3965 rpm) indicate a reduction in machine efficiency based on increased volumetric flow rate; however, data points at the higher speeds

GCPS 2016 20 (4685 rpm and 4904 rpm) contradict this concept as they are predicting polytropic work rates very close to the as tested curves across the entire range of volumetric flow rates analyzed. Consistent and smooth predictions of a reduction in the capacity to develop head at lower speeds indicate the deviation may be a characteristic of the system rather than a lack of accuracy in the calculations. The pattern potentially indicates a discrepancy between the available power supplied by the turbine and the power required for compression. It is likely the data trends are influenced by both of these phenomena. Both polytropic work and volumetric capacity have been shown to be relatively independent of pressure in Figure 4-4. As shown in Figure 4-5 below, the data points at each speed were sorted by increasing suction pressure and the higher pressure data points were removed from the analysis. The data points consistent with the highest 75-80% of the suction pressures at each analyzed speed were removed to demonstrate if there is a comprehensive relationship between elevated suction pressure and the ability of the compressor to generate head. Figure 4-5. Comparison: Design Curve Polytropic Work vs Measured Work. This manipulation of the data shows a direct correlation of polytropic efficiency with a binary relationship between suction pressure and speed. The loss of high volumetric flow data points at some compressor speeds while maintaining flow capacity at other speeds shows the compressor will operate stably at conditions where the supplied driver power deviates from the from the polytropic head maps. This

GCPS 2016 21 second point is extremely important when assessing the impact of abnormal operating conditions on safe compressor operation. The above analysis supports the following statements to be true: 1. There is a speed sensitive reference pressure, unrelated to the reference conditions reported on the as tested compressor curves, which defines the machine efficiency across a large range of operating conditions. 2. The optimal suction pressure can vary by a large amount, on a percentage basis, from the reference suction pressure from the vendor. 3. A compressor system can operate stably across a wide range of conditions, even when the supplied driver power is lower than the power required to move the flow across the compressor, as predicted by the polytropic compressor maps. 4. The deviation in efficiency is not related to deviations in compressor mass, or volumetric, flow rate. 5. The Isentropic Energy Balance Model and the Ideal Gas Polytropic Model are essentially equivalent and accurate. 6. For the purposes of process safety assessment, the Isentropic Energy Balance Model and the Ideal Gas Polytropic Model are well suited to performing process safety design calculations and optimization calculations. Since the data used in this analysis covers the operation of only one compressor system, it was not considered within the scope of this paper to determine a system independent correlation between the design suction pressure and the speed sensitive, optimal suction pressure. Additionally, it was not considered within the scope of this project to determine a system independent correlation between the decay of machine efficiency at abnormal suction pressures and the polytropic work curves presented by the vendor.

GCPS 2016 22 5. Safe Compressor System Operation and Design The complex nature of system design, combined with the highly variable nature of compressible fluid properties presents a variety of safety considerations that, while not inherently different than the normal range of process safety considerations, do require a specific perspective. From the perspective of hazardous scenario identification, compressors are unusual in that the majority of the scenarios needing to be analyzed are more closely related to failures in the systems feeding the machine and not related to any failure in the compressor system itself. From the design perspective, abnormal operating conditions often require more careful and varied analysis even than what is required for normal machine operation. Additionally, there are many safety concerns that actually arise when the operators are not given enough system specific information to accurately characterize compressor behavior at abnormal operating conditions such as start-up, shut down, and coping with upstream and downstream system changes as well as deviations from facility design rates and conditions due to plant upgrade and optimization projects. 5.1 Hazardous Scenario Identification Compressor systems can offer unusual challenges when it comes to identifying and quantifying safety hazards. It is important to remember that, when considering overpressure, no allowance for beneficial control action should be taken unless it tends to increase the relief load [4]. This same concept can be reasonably applied to other potentially hazardous conditions such as under pressure, high/low temperature excursions, and vibration induced fatigue. Many times, instrument initiated shut downs due to pressure/temperature/liquid level deviations are used to rule out hazardous scenarios even though the SIL rating of the compressor trip does not meet the minimum requirements for a High Integrity Protection System [4]. It is vital to evaluate these systems considering the assumptions implicit to the step in the overall process hazard analysis method being used. When calculating overpressure relief load, or other safety hazards, conservative assumptions should be used and instrumentation or operations based intervention should not be considered in this step in order to properly perform the risk assessment. When performing the risk assessment and/or LOPA step in the process hazard analysis, the perspective assumptions need to be modified such that both operator and instrumentation intervention need to be assessed based on the order of magnitude of the potential safety hazard from the calculation phase. It is critical to avoid making non-conservative or realistic assumptions during relief load calculation in order to maintain the integrity of the analysis. The following scenarios should be evaluated to determine safe compressor operation: 5.1.1 Blocked Vapor Outlet Failure closed of automated control valves, inadvertent closure of manual isolation valves on the compressor discharge line, failure closed of a bleed or vent line on the compressor discharge, or loss of cooling/condensing service on the compressor discharge may lead to overpressure or surge conditions. The relief load for this scenario can be determined using the maximum normal operating mass flow at the lowest system maximum allowable accumulation pressure, as defined by the appropriate reference code or standard for the piece of equipment or piping that defines the limiting pressure. 5.1.2 Elevated Suction Pressure

GCPS 2016 23 It is critical to understand that elevated suction pressure may lead to over pressure in the compressor discharge even though no failure has occurred in the compressor system itself. This should not be considered double jeopardy as the fundamental purpose of a compressor is to increase the pressure in a system and no credit for positive instrumentation action should be taken at this stage of the analysis [4]. Many times the upstream pressure is limited to pressures that are safe for the suction of the machine, but may lead to unsafe conditions in the discharge side of a compressor due to the increased pressure differential. Most compressors have instrument initiated shut downs based on high suction/discharge pressures; however, if these shut downs do not meet the requirements of a High Integrity Protection System [4], they should be considered as possible latent failure points and therefore should not be considered as removing elevated suction pressure from analysis. The following are potential sources of elevated suction pressure: 1. Failure of Automated Controls failure open of control valves supplying normal process flow to a compressor may lead to elevated suction pressures 2. Inadvertent Manual Valve Opening inadvertently opening a manual bypass valve on a line that normally supplies process flow, or, inadvertently opening a manual valve connected to a high pressure source that is normally used during a different operating mode or configuration may lead to elevated suction pressures 3. Abnormal Vapor Generation when a compressor is being fed by a flashing liquid system, loss of cooling, loss of heating, abnormal heat input, and mixing of hot and volatile fluids scenarios may potentially result in an elevated suction pressure due to increased vapor generation or the presence of light components flashing across pressure let downs. To determine if elevated suction pressure is a concern, use the calculations from Section 2 at the maximum normal operating speed. Credit can be taken for normal volumetric vapor outflow if there is not a condenser on the discharge. If there is a condenser on the discharge then either take credit for the normal liquid outflow or perform iterative calculations considering the condensing capacity of the heat exchanger at relieving conditions to determine how much vapor will drop out to control the system pressure. If there is outflow capacity at relief conditions, either from credit for outflow or from relief valves on the compressor discharge, then determine the head by moving down the polytropic head curve until the corresponding flow point is reached. Remember the curves work on inlet volumetric flow but the relief valves are moving the discharge flow. If there is no outlet capacity or the outlet flow is less than the minimum flow at the surge point on the maximum speed curve, then the surge head should be used in conjunction with the maximum suction pressure to determine the maximum discharge pressure. Elevated suction pressures will result in high discharge pressures within the compressor casing independent of the flow capacity downstream of the discharge. Running the system to stonewall will generate a pressure discontinuity downstream of the compressor discharge which may protect downstream equipment. This should only be considered if the maximum allowable working pressure of the compressor casing is higher than the maximum pressure developed by the compressor at elevated suction conditions. If the maximum allowable working pressure of the compressor casing is lower than the calculated discharge pressure, considering the maximum suction pressure and discharge flow capacity, it is

GCPS 2016 24 necessary to limit the suction pressure the machine can see. This can be accomplished by two methods. The first method, limit the set pressure of pressure relief valves upstream of the compressor suction. These pressure relief valves are normally set to the design pressure of the system in order to reduce the size/cost of the relief system; however, it is often more cost effective to reduce the set pressure of upstream valves to the maximum safe suction pressure. The maximum safe suction pressure is defined as the compressor suction pressure at which the discharge pressure is equal to the minimum maximum allowable working pressure of the system being considered. At or below the maximum safe suction pressure it is not physically possible for the compressor to overpressure the discharge. If this approach is used, some assessment of the operating range should be performed to ensure the system will function properly at the lower set pressures. The second method is to use an appropriately SIL rated trip at the suction pressure that will prevent overpressure in the discharge using the same concept of maximum safe suction pressure. Most applications require a SIL 2 or SIL 3 to be considered equivalent to a pressure relief valve. In large capacity systems a High Integrity Protection Systems is often less costly than several large, modulating pressure, relief valves, the associated piping, and the potential implications for flare sizing. 5.1.3 Abnormal Suction Composition Since the thermodynamic properties of a fluid vary a great deal with changes in composition, a significant change in the composition of the suction gas can produce discharge pressures higher than the maximum allowable working pressure even at the normal suction pressure. Any potential sources of significant variation in inlet composition should be evaluated for their impact on discharge pressure. The following events may lead to large changes in inlet gas composition: 1. Fire on an upstream vessel - vessels under fire can produce dramatic changes in vapor composition in downstream equipment 2. Control Valve Failure when the source of flow to a compressor is from a mixture of flow sources, failure open of a control valve on the compressor suction may lead to changes in the inlet composition 3. Inadvertent Manual Valve Opening inadvertently opening a manual valve from a source of flow that does not normally contribute to the process flow may result in changes to the inlet composition 4. Tube/Core Rupture often, flow through the other side of a heat exchanger has a significantly different composition such that introduction of this flow into the normal process fluid may result in significant changes in the inlet composition Abnormal suction composition is often overlooked because it does not involve a direct failure in the compressor system. The calculation method and mitigation considerations from Section 5.1.2 should be applied for abnormal suction composition. It should be considered that elevated suction pressures and abnormal suction compositions will occur simultaneously. Each source of abnormal suction composition should be considered independently. Additionally, special consideration should be made for fire on upstream liquid handling systems that supply flashed vapor since the maximum suction pressure will be 121% of the relief valve set pressure and the composition can vary significantly. Fire on upstream liquid handling equipment can result in extremely high discharge pressures and relief loads so using fire proof insulation such that time to reach relieving conditions is longer than the length of time considered for fire

GCPS 2016 25 scenarios (usually 2-4 hours) is often a cost effective mitigation even when factoring in the cost of maintenance needed to prevent corrosion. 5.1.4 Reverse Flow When multiple compressors, with different drivers, are operating together in a system, inevitably, there are times when one machine will be in operation while the other machine is down. When this occurs there are often several paths across which flow may move from one machine to the other. Since the system is not moving flow during this mode, leaks that would normally not impact the safety of the system may result in overpressure to isolated piping or equipment in the compressor system which is down. 5.2 Safe Compressor System Design There are a variety of hazardous operating conditions that may arise more from insufficiently detailed design rather than variations in operating conditions. 5.2.1 Compressor Inlet Isolation Valve Placement Compressor recycle lines need to tie in downstream of automated isolation valves in order to reduce the likelihood of under pressure due to mechanical failure of the inlet isolation valve to the closed position. If the entire suction system piping and equipment are rated for full vacuum this is not a concern. 5.2.2 Pocketing in Compressor Recycle Lines Pocketing in recycle lines and inappropriate failure position of quench valves can lead to accumulation of liquid. Actuation of recycle/anti-surge valves with accumulated liquid may result in significant damage to the piping or an unexpected source of liquid to the compressor inlet. To reduce the risk of hazardous operation, the following configurations should be considered: 1. There should be a knock out drum with a demisting pad to remove liquid formed in suction lines due to pressure variation. A compressor knock out drum should not have a liquid supply during normal operation. 2. For applications with quench fluid injection, the appropriate failure position needs to be chosen such that liquid will not feed in upstream of the anti-surge valve and lead to slug flow concerns upon anti-surge valve actuation. 5.2.3 Pressure Relief Valve Selection Pressure relief valves on compressor suction and discharge lines need to exhibit modulating behavior across the entire range of actuation. Compressor surge is a direct function of deviation in mass balance between the compressor suction volume and the compressor discharge volume. Anti-surge valves work to maintain the appropriate mass balance across the system. Spring Loaded and Pop Action relief valves, sized to remove the normal operating mass flow of the compressor, will cause a sudden imbalance in the discharge side of the system which will interfere with the ability of the anti-surge system to operate properly. More simply, the dynamic nature of the initial actuation of spring-loaded and pop action