Vapor Combustor / Flare System Improvement Project Analysis
1. Vapor Combustor / Flare System
Improvement Project
Old Dominion University
Engineering Management and Systems Engineering Department
Krimmel, Timothy
Tkrimm1@gmail.com; Tkrim001@odu.edu
ENMA 605: Capstone Course
January 2016
2. Disclaimer
All numbers used in this presentation were
adjusted so that they do not reflect actual plant
operation.
This model is purely a demonstration of the
skills, knowledge, and techniques learned
through Old Dominion University’s Masters of
Engineering Management program.
3. Executive Summary
Baker Hughes Chemical Facility is a specialty chemicals batch plant which produces hundreds of millions
of pounds of chemicals each year for the oil and gas industry. The purpose and objective of this project
is to systematically and methodically analyze the vapor combustor/flare system at Baker Hughes Plant
and propose solutions using data, modeling, and simulation to improve the system.
This project delivers a model, simulation, risk/probabilities of system loading, and proposal for
improving the system. The general approach to achieving these goals was to first identify the sources of
pressure including system limitations by walking out the entire system and studying the system
documentation and specifications. The system was then sketched into isometric drawings be used for
calculating pressure drop and ensuring all tie-in points are known. A process flow diagram was
prepared using the isometrics and P&ID’s in order to create a user friendly single page diagram. The
probabilities of specific processes being online (risk) and potential loading of each of these processes
was thoroughly discussed and identified with plant engineering and applied to the model. A nonlinear
programming model was developed in Excel and combined with a Monte Carlo Simulation to analyze
system performance over multiple changing variables based on known probabilities. Data was collected
in the field and used to validate and calibrate the model.
This project has demonstrated that a nonlinear model combined with good statistics, and a Monte Carlo
Simulation can offer valuable contributions to any process. All of these tools came together to give a
comprehensive picture and recommendation with a highly specific prediction on improved system
performance.
4. Model Basis
The basis of this project is a functional model of the vapor combustor system using Microsoft Excel. The model uses
Nonlinear Programming techniques combined with the processing power of my laptop to model this highly complex
multi-variable system. The model was developed using the fluid flow equation from Crane Technical Paper No. 410 for
isothermal simplified compressible flow for long pipe lines.
Crane Technical Paper No. 410 Equation 3-7a:𝜔2 = 0.1072 ∙
144∙𝑔∙𝐴2
𝑉2′∙𝛫
∙
𝑃′1
2− 𝑃′2
2
𝑃′1
This function is applicable because the piping is un-insulated and very long, ensuring the gas will be at or near ambient
temperatures and each section would have a pressure drop of less than 10% of P1 or the pressure into the line. This
equation was rearranged in the model to solve for ω2 (mass flowrate), P’1 (inlet pressure), and P’2 (outlet pressure) as
needed depending on what system information is given for each line.
The ideal gas law, PV=nRT is used to convert between mass flow and volumetric flow, because the system is near
atmospheric pressure and ambient temperature. The gas is assumed to be N2 with a molecular weight of 28.02
because this gas is used to inert all of our tanks and reactors.
The objective function for this model is minimized and equal to the sum of the absolute differences between mass
balances around each node as well as the sum of absolute differences between calculated pressures from each line.
When the error is minimized, the model is an accurate representation of the system. Microsoft solver was run using
the GRG Nonlinear method as well as the Evolutionary method to reduce the error to 0.2 or less for each run.
The decision variables were chosen as the pressures at key nexus nodes in order to minimize the number of decision
variables as well as minimize the complexity of the math in the Excel spreadsheet. One additional decision variable
was chosen to adjust the resistance flow coefficient at the end of the system to account for the variable flowrate of the
blowers and natural gas at the vapor combustor unit.
The constraints were chosen to prevent division by zero errors and to constrain the decision variables to realistic values.
5. R-5501 R-5502 R-5503 R-5504
V-5057 V-5055
q (ACFM) 3.2 q (ACFM) 5.0 q (ACFM) 5.3 q (ACFM) 4.4 30" Water Seal Leg q (ACFM) 0.0 q (ACFM) 0.0
q (ACFM) 0.0 P (in. W.C.) -0.19 P (in. W.C.) -0.19 P (in. W.C.) -0.22 P (in. W.C.) -0.22 P (in. W.C.) 23.2 P (in. W.C.) 27.0
P (in. W.C.) 10.00
T-5054
6 7
q (ACFM) 8.1 q (ACFM) 9.7
P (in. W.C.) -0.21 P (in. W.C.) -0.22 Drain
1 10
q (ACFM) 0.0 q (ACFM) 0.0 q (ACFM) 0.0
P (in. W.C.) 10.00 P (in. W.C.) -0.62 K-5701 P (in. W.C.) 0.0 13
T-5053 5 q (ACFM) 119.9 q (ACFM) 5.4
3 q (ACFM) 17.8 8 P (in. W.C.) 30.8 12 P (in. W.C.) 23.3
q (ACFM) 25.1 P (in. W.C.) -0.64 q (ACFM) 25.1 q (ACFM) 5.4
P (in. W.C.) -0.64 P (in. W.C.) -0.83 V-5701 9 P (in. W.C.) 23.2
q (ACFM) 131.3 q (ACFM) 119.9
q (ACFM) 0.0 2 P (in. W.C.) -22.78 P (in. W.C.) 30.8
P (in. W.C.) 10.00 q (ACFM) 0.0 4 V-5050 14
T-5051 P (in. W.C.) -0.62 q (ACFM) 7.3 C-5701 q (ACFM) 0.0 q (ACFM) 5.4 K-7411
P (in. W.C.) -0.51 q (ACFM) 25.1 11 P (in. W.C.) 27.0 P (in. W.C.) 23.4 q (ACFM) 5.7
Z-5503 P (in. W.C.) -0.64 K-5702 q (ACFM) 129.4 P (in. W.C.) 0.0
q (ACFM) 0.0 q (ACFM) 0.0 P (in. W.C.) 23.2 New IPAK Tote Fillers
P (in. W.C.) 0.00 V-5702 P (in. W.C.) 30.8 18
Plant 5 Tote Filler q (ACFM) 0.0 (Only One Pump On) q (ACFM) 641.6
Z-5501 Z-5502 P (in. W.C.) -0.83 (For this Simulation K-5702 is Off) 16 P (in. W.C.) 23.6
q (ACFM) 3.6 q (ACFM) 3.6 q (ACFM) 776.6
P (in. W.C.) 0.00 P (in. W.C.) 0.00 P (in. W.C.) 19.9
Plant 5 Drum Fillers V-5052 K-7412
q (ACFM) 637.2 q (ACFM) 0.0
17 P (in. W.C.) 27.0 P (in. W.C.) 0.0
q (ACFM) 782.7
P (in. W.C.) 19.0
V-6202 T-5549 RS-5
q (ACFM) 897.5 Flare q (ACFM) 0.0 21 q (ACFM) 0.0
P (in. W.C.) 0.3 q (ACFM) 897.4 19 P (in. W.C.) 19.0 q (ACFM) 0.0 P (in. W.C.) 15.5
P (in. W.C.) -66.3 q (ACFM) 784.3 P (in. W.C.) 15.5 RS-6
P (in. W.C.) 17.8 23 q (ACFM) 0.0
20 22 q (ACFM) 0.0 P (in. W.C.) 15.5
q (ACFM) 0.0 TS-2 q (ACFM) 0.0 P (in. W.C.) 15.5
Z-1102TS P (in. W.C.) 15.5 q (ACFM) 0.0 P (in. W.C.) 15.5 TS-1
q (ACFM) 0.0 P (in. W.C.) 15.5 q (ACFM) 0.0
P (in. W.C.) 17.8 29 P (in. W.C.) 15.5 RS-1
q (ACFM) 786.6 25 26 q (ACFM) 0.0
V-6714 P (in. W.C.) 10.4 24 q (ACFM) 0.0 q (ACFM) 0.0 27 P (in. W.C.) 15.5
q (ACFM) 897.5 q (ACFM) 0.0 P (in. W.C.) 15.5 P (in. W.C.) 15.5 q (ACFM) 0.0 28
P (in. W.C.) 0.3 P (in. W.C.) 15.5 P (in. W.C.) 15.5 q (ACFM) 0.0
P (in. W.C.) 15.5
34 32 RS-4 TS-3 RS-3
37 36 35 q (ACFM) 855.2 q (ACFM) 833.4 q (ACFM) 0.0 q (ACFM) 0.0 q (ACFM) 0.0 RS-2 TS-4
q (ACFM) 897.5 q (ACFM) 872.9 q (ACFM) 871.4 P (in. W.C.) 4.4 P (in. W.C.) 7.2 P (in. W.C.) 15.5 P (in. W.C.) 15.5 P (in. W.C.) 15.5 q (ACFM) 0.0 q (ACFM) 0.0
P (in. W.C.) 0.2 P (in. W.C.) 0.2 P (in. W.C.) 3.7 30 P (in. W.C.) 15.5 P (in. W.C.) 15.5
q (ACFM) 32.7
P (in. W.C.) 10.4
33 Z-3240
38 q (ACFM) 0.0 K-3240 31 q (ACFM) 0.0
q (ACFM) 17.1 V-6404 P (in. W.C.) 0.2 q (ACFM) 34.1 q (ACFM) 33.7 P (in. W.C.) 0.0
P (in. W.C.) 0.2 PLT 2 TS S PLT 2 Hotwell S PLT 2 Hotwell N q (ACFM) 0.0 P (in. W.C.) -5.7 P (in. W.C.) -5.0
PLT 3 Hotwell q (ACFM) 0.0 q (ACFM) 12.0 q (ACFM) 11.8 P (in. W.C.) 7.2 PLT 2 TS N
q (ACFM) 15.6 PLT 3 TS P (in. W.C.) 3.7 P (in. W.C.) 4.4 P (in. W.C.) 5.9 q (ACFM) 0.0 Z-3241
P (in. W.C.) 0.6 q (ACFM) 0.0 P (in. W.C.) 7.2 q (ACFM) 33.7
P (in. W.C.) 0.6 P (in. W.C.) 0.0
Plant 2/3 Drum & Tote Filler
Flare
Model Flowsheet
6. Model Validation
Data was collected by connecting small electronic milliamp data loggers to
pressure transmitters. These instruments can record up to 3 days (at intervals of once
per minute) of data before requiring the data be downloaded to a computer. The data
loggers were installed at two points in the system so that the total system operation
was measured and compared to the model. Flow data was collected from the system’s
main flowmeter, which measures total vapors going into the flare from multiple
process streams. The flowmeter was connected to the Plant’s distributed control
system (DCS). I worked with the DCS engineer to send historian data for dates to
coincide with the recorded data from the pressure transmitter data collection effort.
A meeting was held with the plant engineers to brainstorm sources of
system risk. Production and Process Engineers helped me to determine probabilities
that specific streams will be online as well as how much vapor these lines are likely to
be putting into the vapor combustor system.
The model was validated by analyzing the data generated by the Monte
Carlo Simulation and comparing with the field data analysis. 30 simulations were run
using the Monte Carlo method and the results were analyzed using Microsoft Excel’s
descriptive statistics function. The model’s descriptive statistics were compared to the
field data’s descriptive statistics to validate model accuracy.
9. Temperature = 46 °F Iteration # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Sources of Gas Line On/Off
Line Pressure (W.C.
Gauge)
Probability
Line is in use IPAK East:K-7411 1 0.0 0 IPAK East:K-7411 OFF OFF OFF OFF OFF 0 OFF OFF OFF OFF 0 0 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 0
IPAK East:K-7411 0 0.00 50.0% IPAK West:K-7412 0 0.0 OFF IPAK West:K-7412 OFF OFF OFF OFF OFF 0 OFF OFF OFF 0 OFF 0 OFF 0 0 OFF OFF OFF OFF OFF OFF OFF OFF OFF 0 OFF OFF OFF OFF OFF
IPAK West:K-7412 0 0.00 0.0% Z-3241:31 1 0.0 0 Z-3241:31 0 0 OFF OFF OFF OFF 0 OFF OFF 0 0 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 0 0 0 OFF 0 OFF OFF OFF OFF
Z-3241:31 1 0.00 100.0% Z-3240:31 0 0.0 OFF Z-3240:31 OFF OFF OFF OFF 0 0 OFF 0 OFF OFF OFF OFF OFF 0 OFF 0 OFF OFF 0 OFF OFF OFF OFF 0 OFF OFF OFF OFF OFF OFF
Z-3240:31 0 0.00 15.0% T-5054:1 0 10.0 OFF T-5054:1 OFF OFF OFF OFF OFF 0 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
T-5054:1 0 10.00 6.3% T-5053:1 0 10.0 OFF T-5053:1 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
T-5053:1 0 10.00 6.3% T-5051:2 0 10.0 OFF T-5051:2 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 0 OFF
T-5051:2 0 10.00 6.3% Z-5503:3 0 0.0 OFF Z-5503:3 OFF OFF 0 OFF 0 OFF OFF OFF OFF 0 OFF 0 OFF 0 0 OFF OFF OFF 0 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
Z-5503:3 0 0.00 0.0% Z-5501:4 1 0.0 0 Z-5501:4 OFF 0 OFF 0 OFF 0 0 0 OFF OFF 0 0 OFF OFF OFF 0 0 0 0 OFF 0 OFF OFF OFF OFF OFF 0 0 OFF 0
Z-5501:4 1 0.00 100.0% Z-5502:4 1 0.0 0 Z-5502:4 OFF 0 0 0 OFF 0 OFF OFF OFF 0 0 OFF OFF 0 OFF OFF 0 OFF 0 0 OFF 0 OFF 0 0 0 0 0 OFF OFF
Z-5502:4 1 0.00 100.0% V-5057:12 0 27.0 OFF V-5057:12 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
V-5057:12 0 27.00 6.0% V:5055:13 0 27.0 OFF V:5055:13 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
V:5055:13 0 27.00 6.0% V-5050:18 0 27.0 OFF V-5050:18 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
V-5050:18 0 27.00 6.0% V-5052:18 1 30.0 30 V-5052:18 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
V-5052:18 0 27.00 6.0% T-5549:17 0 27.0 OFF T-5549:17 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
T-5549:17 0 27.00 6.0% RS-6:23 0 729.0 OFF RS-6:23 OFF OFF OFF OFF 30 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 26 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 29 OFF OFF OFF
RS-6:23 0 719.00 0.5% TS-1:23 0 744.0 OFF TS-1:23 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
TS-1:23 0 747.00 25.0% RS-5:22 0 719.0 OFF RS-5:22 OFF 30 OFF OFF OFF OFF OFF OFF OFF 28 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
RS-5:22 0 714.00 0.5% TS-2:21 0 724.0 OFF TS-2:21 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 25 OFF OFF
TS-2:21 0 726.00 25.0% TS-4:28 0 707.0 OFF TS-4:28 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
TS-4:28 0 735.00 25.0% RS-1:28 0 716.0 OFF RS-1:28 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 25 OFF OFF OFF OFF OFF 29 OFF OFF OFF OFF OFF OFF OFF 30
RS-1:28 0 697.00 2.3% RS-2:27 0 708.0 OFF RS-2:27 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 28 26 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
RS-2:27 0 746.00 1.0% RS-3:26 0 721.0 OFF RS-3:26 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 26 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
RS-3:26 0 730.00 0.5% TS-3:25 0 702.0 OFF TS-3:25 27 OFF 25 OFF 28 OFF OFF OFF OFF OFF 27 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
TS-3:25 0 719.00 25.0% RS-4:24 0 744.0 OFF RS-4:24 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
RS-4:24 0 721.00 0.0% Z-1102TS:29 0 727.0 OFF Z-1102TS:29 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 27 OFF OFF OFF OFF OFF OFF OFF
Z-1102TS:29 0 740.00 10.0% V-6404:33 0 0.0 OFF V-6404:33 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
V-6404:33 0 0.00 0.0% PLT2TSN:33 0 696.0 OFF PLT2TSN:33 OFF OFF OFF OFF 25 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 25 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF
PLT2TSN:33 0 703.00 1.0% PLT2HWN:34 1 40.0 40 PLT2HWN:34 OFF OFF OFF 27 OFF 29 OFF OFF OFF OFF 29 28 OFF OFF OFF OFF OFF 29 OFF 29 OFF OFF 29 28 OFF OFF OFF OFF 27 30
PLT2HWN:34 1 40.00 100.0% PLT2HWS:35 1 40.0 40 PLT2HWS:35 27 OFF 29 OFF OFF 27 29 OFF OFF OFF OFF OFF OFF 26 OFF OFF OFF OFF OFF 29 OFF OFF OFF 27 26 OFF OFF OFF 26 OFF
PLT2HWS:35 1 40.00 100.0% PLT2TSS:36 0 693.0 OFF PLT2TSS:36 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 25 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 28 OFF OFF OFF
PLT2TSS:36 0 731.00 5.0% PLT3TS:38 0 714.0 OFF PLT3TS:38 OFF OFF OFF OFF 26 OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF 27 OFF 30 OFF OFF
PLT3TS:38 0 746.00 5.0% PLT3HW:38 1 40.0 40 PLT3HW:38 OFF 28 OFF OFF OFF OFF OFF OFF 27 OFF OFF 28 OFF OFF OFF 30 OFF 29 OFF OFF OFF 30 30 OFF 25 OFF OFF OFF OFF 25
PLT3HW:38 1 40.00 100.0% 2.747 Total Error = 0.005 0.002 0.007 0.004 0.001 0.097 0.033 0.001 0.011 0.073 0.071 0.098 0.006 0.040 0.066 0.024 0.023 0.098 0.006 0.054 0.054 0.004 0.044 0.019 0.048 0.018 0.006 0.013 0.061 0.070
Total Error = 2.75 852.0 Total Flow V6202:Flare (SCFM) 195.5 207.5 186.5 162.8 228.1 197.4 158.8 135.8 161.3 174.7 187.9 223.0 160.2 177.3 145.6 226.2 124.3 200.7 153.8 191.6 124.7 188.0 220.5 196.9 193.1 159.3 182.8 176.2 188.8 225.1
0.2 Pressure at PT-2101 (in W.C. Gauge) 9.6 10.7 8.4 8.5 11.1 15.6 15.7 10.9 11.5 12.5 15.8 9.2 4.6 4.3 3.3 7.3 8.1 11.1 11.7 11.2 10.8 10.7 12.0 10.4 9.7 6.9 8.9 7.9 8.8 8.5
23.2 Pressure at PT-5701 (in W.C. Gauge) 10.8 12.0 9.6 9.3 12.8 16.7 16.4 11.7 12.3 13.7 17.0 10.4 5.5 5.4 4.2 8.7 8.8 12.1 12.6 12.2 11.6 11.7 13.1 11.4 10.7 7.7 10.0 9.0 9.8 9.8
88 Temperature (°F) 85.0 79.0 71.0 72.0 75.0 86.0 42.0 83.0 78.0 43.0 83.0 76.0 77.0 85.0 74.0 77.0 73.0 85.0 71.0 84.0 88.0 83.0 84.0 90.0 89.0 82.0 79.0 70.0 89.0 63.0
124 Flow at 9:11 (SCFM) 124.4 124.3 124.3 124.3 124.3 113.6 119.3 124.3 124.2 124.3 112.7 127.4 124.3 124.3 124.3 124.3 124.3 123.8 124.3 124.1 124.7 124.3 115.7 119.4 120.0 125.4 122.9 126.0 121.8 124.9
5 Flow at 12:11 (SCFM) 0.0 0.0 0.0 0.0 0.0 16.1 0.0 0.0 0.0 8.4 8.1 16.3 0.0 8.1 8.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.0 0.0 0.0 0.0 0.0 8.2
130 Flow at 11:16 (SCFM) 124.4 124.3 124.3 124.3 124.3 129.7 119.3 124.3 124.2 132.7 120.7 143.7 124.3 132.4 132.4 124.3 124.3 123.8 124.3 124.1 124.7 124.3 115.7 119.4 128.0 125.4 122.9 126.0 121.8 133.1
0 Flow at 20:19 (SCFM) 29.5 32.4 29.2 0.0 55.1 0.0 0.0 0.0 0.0 29.9 23.5 0.0 2.4 0.0 13.2 45.3 0.0 0.0 0.0 0.0 0.0 10.7 0.0 0.0 0.0 0.0 27.4 26.6 0.0 11.8
779 Flow at 29:30 (SCFM) 154.0 156.7 153.5 124.3 179.4 129.7 119.3 124.3 124.2 162.6 144.3 143.7 126.7 132.4 145.6 169.6 124.3 123.8 124.3 124.1 124.7 135.0 132.8 119.4 128.0 125.4 150.4 152.6 121.8 144.9
32 Flow at K-3240:30 (SCFM) 11.7 11.7 0.0 0.0 11.6 11.5 12.1 11.6 0.0 12.1 11.7 0.0 0.0 11.5 0.0 11.6 0.0 0.0 11.7 0.0 0.0 11.7 11.7 11.7 0.0 11.7 0.0 0.0 0.0 0.0
811 Flow at 32:34 (SCFM) 165.6 168.4 153.5 124.3 208.9 141.2 131.4 135.8 124.2 174.7 156.0 143.7 126.7 143.9 145.6 181.3 124.3 123.8 153.8 124.1 124.7 146.7 144.4 131.1 128.0 137.1 150.4 152.6 121.8 144.9
836 Flow at 36:37 (SCFM) 195.5 168.4 186.5 162.8 208.9 197.4 158.8 135.8 124.2 174.7 187.9 182.1 160.2 177.3 145.6 181.3 124.3 161.1 153.8 191.6 124.7 146.7 180.7 196.9 156.8 137.1 182.8 152.6 188.8 186.6
Flow at K-3240:30 (SCFM)
Flow at 32:34 (SCFM)
Flow at 36:37 (SCFM)
Total Error =
Flow at 9:11 (SCFM)
Flow at 12:11 (SCFM)
Flow at 11:16 (SCFM)
Flow at 29:30 (SCFM)
Flow at 20:19 (SCFM)
Pressure at PT-2101 (in W.C. Gauge)
Pressure at PT-5701 (in W.C. Gauge)
Temperature (°F)
Total Flow V6202:Flare (SCFM)
Monte Carlo Simulation
10. Data Analysis
The Descriptive Statistics function in Microsoft Excel was used with
each measured data set using Excel to analyze each entire data set and
return the mean, standard error, median, mode, standard deviation,
sample variance, kurtosis, skewness, range, minimum, maximum, sum,
count, and confidence at the 95% level.
A recommended improvement was delivered to the plant based on the
model results which directly related to efficiency gains in system
operation including reduction of N2 and natural gas waste.
Data analysis was used to match the model to “real” system operation
by changing the model resistance coefficient values until the model
parameters matched the data collected in the field. The data was
further used to analyze the system operation for variability and
conformance to design specifications.
11. Mean 0.04 Mean 181.81 Mean 9.85 Mean 10.90 Mean 77.20
Standard Error 0.01 Standard Error 5.29 Standard Error 0.55 Standard Error 0.55 Standard Error 2.10
Median 0.02 Median 187.18 Median 10.05 Median 11.12 Median 79.00
Mode #N/A Mode #N/A Mode #N/A Mode #N/A Mode 85.00
Standard Deviation 0.03 Standard Deviation 28.99 Standard Deviation 3.01 Standard Deviation 3.02 Standard Deviation 11.51
Sample Variance 0.00 Sample Variance 840.68 Sample Variance 9.05 Sample Variance 9.12 Sample Variance 132.51
Kurtosis -0.81 Kurtosis -0.50 Kurtosis 0.49 Kurtosis 0.51 Kurtosis 4.04
Skewness 0.67 Skewness -0.24 Skewness -0.03 Skewness -0.06 Skewness -1.88
Range 0.10 Range 103.73 Range 12.59 Range 12.78 Range 48.00
Minimum 0.00 Minimum 124.34 Minimum 3.26 Minimum 4.23 Minimum 42.00
Maximum 0.10 Maximum 228.08 Maximum 15.85 Maximum 17.01 Maximum 90.00
Sum 1.06 Sum 5454.32 Sum 295.63 Sum 326.89 Sum 2316.00
Count 30.00 Count 30.00 Count 30.00 Count 30.00 Count 30.00
Confidence Level(95.0%) 0.01 Confidence Level(95.0%) 10.83 Confidence Level(95.0%) 1.12 Confidence Level(95.0%) 1.13 Confidence Level(95.0%) 4.30
Mean 125.58 Mean 11.24 Mean 137.39 Mean 5.86 Mean 144.43
Standard Error 0.96 Standard Error 2.92 Standard Error 2.97 Standard Error 1.09 Standard Error 3.70
Median 124.32 Median 0.00 Median 131.05 Median 5.76 Median 143.79
Mode #N/A Mode 0.00 Mode #N/A Mode 0.00 Mode #N/A
Standard Deviation 5.24 Standard Deviation 15.99 Standard Deviation 16.29 Standard Deviation 5.96 Standard Deviation 20.26
Sample Variance 27.43 Sample Variance 255.67 Sample Variance 265.21 Sample Variance 35.50 Sample Variance 410.27
Kurtosis 4.02 Kurtosis 0.56 Kurtosis 0.00 Kurtosis -2.15 Kurtosis 2.22
Skewness 1.47 Skewness 1.24 Skewness 0.93 Skewness 0.00 Skewness 1.32
Range 27.95 Range 55.11 Range 60.18 Range 12.14 Range 87.10
Minimum 115.71 Minimum 0.00 Minimum 119.25 Minimum 0.00 Minimum 121.84
Maximum 143.66 Maximum 55.11 Maximum 179.43 Maximum 12.14 Maximum 208.93
Sum 3767.50 Sum 337.11 Sum 4121.66 Sum 175.70 Sum 4333.03
Count 30.00 Count 30.00 Count 30.00 Count 30.00 Count 30.00
Confidence Level(95.0%) 1.96 Confidence Level(95.0%) 5.97 Confidence Level(95.0%) 6.08 Confidence Level(95.0%) 2.22 Confidence Level(95.0%) 7.56
Mean 167.73 Mean 122.87 Mean 2.71
Standard Error 4.36 Standard Error 0.64 Standard Error 0.90
Median 171.58 Median 124.29 Median 0.00
Mode #N/A Mode #N/A Mode 0.00
Standard Deviation 23.86 Standard Deviation 3.49 Standard Deviation 4.92
Sample Variance 569.45 Sample Variance 12.18 Sample Variance 24.21
Kurtosis -0.84 Kurtosis 2.92 Kurtosis 1.89
Skewness -0.37 Skewness -1.86 Skewness 1.68
Range 84.70 Range 14.74 Range 16.25
Minimum 124.23 Minimum 112.67 Minimum 0.00
Maximum 208.93 Maximum 127.41 Maximum 16.25
Sum 5031.84 Sum 3686.23 Sum 81.27
Count 30.00 Count 30.00 Count 30.00
Confidence Level(95.0%) 8.91 Confidence Level(95.0%) 1.30 Confidence Level(95.0%) 1.84
Flow at 29:30 (SCFM) Flow at K-3240:30 (SCFM) Flow at 32:34 (SCFM)
Flow at 36:37 (SCFM)
Total Error = Total Flow V6202:Flare (SCFM) Pressure at PT-2101 (in W.C. Gauge) Pressure at PT-5701 (in W.C. Gauge) Temperature (°F)
Flow at 9:11 (SCFM) Flow at 12:11 (SCFM)
Flow at 11:16 (SCFM) Flow at 20:19 (SCFM)
Statistical Analysis
12. Recommendations
The model was used to make a recommendation to
management for system improvement.
The Monte Carlo Simulation was run 30 times to
determine how the “improved” system would perform.
This “improved” system was analyzed using Microsoft
Excel’s descriptive statistics function and the results were
compared to the original validated model demonstrating
quantitative performance gains.
13. Additional Benefits
A benefit of this project is that this model will be
used in the future to troubleshoot and analyze
system performance as new problems arise, the
plant is expanded, or the system is improved.
This model may also be used as a training aid for
operators; illustrating how changes in system
loading and operations will affect system
performance.