Best Practice
SABP-A-002
Load Management for Energy
Efficiency: Pumps and Compressors
Document Responsibility: Energy Systems Unit, CSD
26 October 2005
Load Management for Energy Efficiency:
Pumps and Compressors
Developed by: Energy Systems Unit
Consulting Services Department
Issue Date:
October 2005
Previous issue: None
Next Planned Update: 1 November 2006
Primary Contact: jimmy.kuamana@aramco.com, phone +966 (3) 874-6157
Copyright©Saudi Aramco 2009. All rights reserved.
Page 1 of 49
Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Table of Contents
1.0
Introduction
1.1
Purpose
1.2
Scope
1.3
Intended Users
1.4
References and Related Documents
Page
5
5
5
5
2.0
General
2.1
Definitions
2.2
Principles and Concepts
2.3
Degrees of Freedom
2.4
Affinity Laws
2.5
Drivers
2.6
Data Quality
6
6
6
7
7
8
3.0
Pump Networks
3.1
Flow Profile
3.2
Number of Operating Trains
3.3
Recycle Minimization
3.4
Best Efficiency Point
3.5
Load Allocation by Efficiency
3.6
Composite Characteristic Curves
3.7
System Curve
3.8
Controls and Instrumentation
14
15
17
23
28
29
30
34
37
4.0
Compressor Networks
4.1
Thermodynamics of Gas Compression
4.2
Performance and System Curves
4.3
Control Strategies
4.4
Process Modifications
38
38
41
42
46
ATTACHMENTS
none
Page 2 of 49
Copyright©Saudi Aramco 2009. All rights reserved.
Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
List of Exhibits
Exh. No
2.1
2.2
2.3
2.4
2.5
2.6
2.7
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
3.10
3.11
3.12
3.13
3.14
3.15
3.16
3.17
3.18
3.19
3.20
3.21
3.22
3.23
3.24
3.25
3.26
3.27
3.28
3.29
3.30
3.31
Title
The Affinity Laws
Simplified Schematic of CDU in an Oil Refinery
Raw PI Data for Crude Oil Flow rates
Calculated Yield Trends for Crude Oil Product
Schematic Diagram of Butane Vapor Recovery System
Measured PI Data for Butane Vapor Recovery System
Schematic Diagram of Oil Storage and Loading Facility
Typical Pump Network and Control System
Fluid Flow Historical Data (sample)
Fluid Flow Profile Histogram
Determination of Ideal Trigger Points for Pump Switching
Ideal Operating Policy for AM Shipper Pumps
Indicative Relationship between Trigger Point and Reliability
Basic Pump Data
Pump Operating Status and Flow Data
Estimating Power Savings from Minimizing No of Operating Trains
Actual Pump Trains in Operation versus Minimum Required
Power Cost Savings Potential vs Trigger Point
Impact of Sampling Interval on Calculated Savings
Typical Pump Control System
Typical Variation of Power Consumption with Flow Rate
Power Savings Potential from Minimizing Recycle
Inferring Recycle Requirements from Flow Profile
Estimating Power savings from Minimizing Recycle
Operation at Best Efficiency Point vs Minimizing Pump Trains
Operation at Best Efficiency Point vs Minimizing Recycle
Load Allocation by Equipment Efficiency
Generic Network of 3 Pumps in Series/Parallel
Characteristic Curves for Individual Pumps
Correlation of Pump Characteristic Curve Data
Composite Characteristic Curve for Pumps in Series
Composite Characteristic Curve Data for Pumps in Series/Parallel
Composite Characteristic Curves for Entire Network
Schematic Diagram of Simple Pumping System
System Data at Design Conditions
Static and Dynamic Heads at Design and Maximum Flow
System and Characteristic Curves
Recommended Control Philosophy for Parallel Pump Trains
Page
7
9
10
10
11
11
13
14
15
16
17
18
19
20
20
21
22
22
23
24
24
25
26
27
28
29
30
31
31
32
32
33
33
34
35
36
36
37
Page 3 of 49
Copyright©Saudi Aramco 2009. All rights reserved.
Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Typical Data for Centrifugal Compressors
Typical Centrifugal Compressor Operating Curves
Compressor Performance with Variable-Speed Drive
Suction Throttling Control of Fixed-speed Parallel Compressors
Proportional Loading of Parallel Compressors with ASDs
Control of Parallel Compressors (One ASD and Rest Fixed Speed)
Power Consumption for Suction vs Discharge Throttling
Process Modifications to Reduce Compressor Load
Power Conservation by Minimizing Compressor Discharge Pressure
Shedding Fan Load vs Minimizing Compression Ratio
40
41
42
43
44
45
46
47
48
49
Page 4 of 49
Copyright©Saudi Aramco 2009. All rights reserved.
Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
1.0
Introduction
1.1
Purpose
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Large industrial plants commonly use multiple parallel equipment trains for improved reliability.
Very often, installed equipment capacity far exceeds normal production requirements. This
excess capacity can be translated into energy cost savings through “optimum load
management”. The purpose of this New Best Practice is to describe ways in which energy
efficiency improvement can be achieved for different kinds of equipment.
1.2
Scope
Many types of equipment commonly used in Saudi Aramco plants are significant energy
consumers and amenable to operational optimization through Load Management, including:
•
•
•
•
•
•
•
Pumps
Compressors
Fired Heaters (furnaces)
Boilers – fired and unfired
Process Coolers – air, water, refrigerant
Steam turbines
Gas turbines
This Best Practice manual focuses on methods to determine the optimum load management
policies for pumps and compressors only. The rest are covered in other complementary Best
Practice manuals.
1.3
Intended Users
This Best Practice manual is intended for use by the engineers working in Saudi Aramco
plants, who are responsible for efficient operation of their facility.
1.4
References and Related Documents
SAES G-005: Centrifugal Pumps
SAES K-402: Centrifugal Compressors
SAEP-14: Project Proposal Requirements
SAER-5968: Detailed Energy Assessment at Safaniya Onshore Plants, TIC library, Dhahran
(January 2005)
Electrical Power Savings in Pump and Compressor Networks Via Load Management, Proc of
27th National Industrial Energy Technology Conference, New Orleans, La, USA (May 2005)
No conflict is expected between the optimum load management policy and other standard
Saudi Aramco operating practices with respect to reliability, safety, etc.
Page 5 of 49
Copyright©Saudi Aramco 2009. All rights reserved.
Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
2.0
General
2.1
Definitions
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Best Practice: A process or method that, when correctly executed, leads to enhanced system
performance.
Load Management: An operating policy that distributes the load among multiple machines or
equipment installed as series-parallel networks in a way that minimizes their energy (fuel +
power) consumption, without compromising safety or reliability.
2.2
Principles and Concepts
The first priority in any energy conservation program should be to capture the “Easy Pickings”,
that is, energy cost savings that can be achieved with little or no investment. Managing the
load on various items of energy-consuming equipment falls into this category. The
fundamental concept is to extract some operating cost savings in the form of reduced energy
consumption from the capital that has already been invested in equipment assets, but is not
being utilized for production capacity. The objective is to operate the equipment at the lowest
total cost while still meeting the process objective.
Several general principles and strategies apply in all cases:
•
•
•
•
Minimize number of machines being operated in parallel
Reduce the rate at which individual machines are being run, through minimizing
recycle flows
Operate equipment at conditions that will maximize the system efficiency of the
network, even if it means that individual items are operating away from their maximum
efficiency points
Assign maximum duty to the most efficient equipment (in a parallel set), and use the
least efficient equipment as the “swing” machine
It must be recognized, though, that there is always a trade-off. The fewer the number of
parallel machines that are running at any given time, the less redundancy there will be, with
consequent loss of some operating flexibility. The analysis procedure outlined in this manual
will help establish the quantitative relationship between operating flexibility and energy costs,
thereby enabling the operating engineers and foremen to jointly make intelligent choices about
what the optimum operating policy should be.
2.3
Degrees of Freedom
Optimization implies that one has multiple choices to accomplish the desired objective, and the
only problem remaining is to choose the best option. The range of options available is limited
by constraints – which can be either “hard” or “soft”. A hard constraint is one which we cannot
or are unprepared to violate at any cost – e.g. the laws of physics, market realities, or the
directives of upper management. A soft constraint is one that we have imposed on ourselves,
and which could be relaxed at our discretion upon penalty of incurring some additional costs
Page 6 of 49
Copyright©Saudi Aramco 2009. All rights reserved.
Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
elsewhere. An example of a soft constraint is the requirement for redundancy in installed
equipment in order to increase the level of operator comfort. It follows that the range of
available options can be increased by relaxing soft constraints, and by finding some other way
to alleviate the problem that the constraint was intended to prevent/mitigate.
The range of options can be increased by introducing new Degrees of Freedom, which are
parameters or design features over which one has some control. For example, in an existing
pumping network, one can increase the range of options available for optimizing operating
policies by adding inter-connective piping (eg. “headers”) between parallel trains, retrofitting
fixed speed motors with variable frequency drives, etc.
Basically, one must keep an open mind. Think “out-of-the-box”. Do not accept the existing
plant configuration as inviolate; try to think of the ideal solution, and then systematically add
features to the existing design that will help to reach that ideal solution. Learn to recognize the
difference between hard and soft constraints.
2.4
Affinity Laws
Sometimes it is necessary to determine the performance of an existing pump or compressor
for a different impeller diameter or speed. The pump performance at off-design conditions can
be estimated using what are known as the Affinity Laws, sometimes also called the Fan Laws,
as summarized in Exhibit 2-1.
Exhibit 2-1: The Affinity Laws
2.5
Constant Impeller Diameter
Constant Impeller Speed
Capacity
Q1 N1
=
Q2 N 2
Q1 D1
=
Q2 D 2
Head
H1  N1 

=
H 2  N 2 
Horsepower
BHP1  N1 

=
BHP2  N 2 
2
H1  D1 
= 
H 2  D2 
3
2
BHP1  D1 
= 
BHP2  D2 
3
Drivers
Pumps and compressors are usually driven by electric motors, but not always. Sometimes the
motive power is provided by steam turbines (usually in the 500-10,000 HP range) or by gas
turbines (usually >10,000 HP).
Electric motors generally operate at fixed speeds. For 60 Hz power supply, these are usually
around 1200, 1800, or 3600 rpm. For 50 Hz a/c power supply the corresponding speeds are
1000, 1500, or 3000 rpm. When speed variation is desired for either process reasons or for
power savings, they have to be fitted with some sort of a speed control, such as a belt & pulley
system (obsolete technology), a hydraulic clutch and gear box, or a variable frequency drive.
Page 7 of 49
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Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Steam and gas turbines, on the other hand, are inherently variable speed devices, and
elaborate controls are required to make them operate at constant speed.
The correct choice of driver – whether motor or turbine – depends on whether speed control
would be beneficial in that particular application, and on the size (power consumption) of the
pump or compressor. The overall site steam and power balance also has a considerable
influence on the economics of driver selection (especially for the larger sizes), and should not
be ignored.
A separate Saudi Aramco Engineering Procedure (still under development at the time of
writing) addresses the issue of how and when to select adjustable speed drives (ASDs).
2.6
Data Quality
Data quality refers to both the consistency and accuracy of measured values. Consistency is
necessary; accuracy (within the specified limits) is sufficient. {The terms necessary and
sufficient are used here in accordance with their strict mathematical definitions.}
It should be common sense that “bad” data will lead to the wrong decisions no matter how
brilliant the quality of the analysis. But how do we define “Bad” and “Good”? There is no such
thing as perfect accuracy. An acceptable level of error in data accuracy is that which will not
lead to the wrong process design or operating decision. As long as the correctness of the
decision is not affected, the data quality can be considered to be “Good”.
2.6.1
Data Validation for Consistency
Data Validation is the process of checking the various related values measured and recorded
in the DCS/PI systems against independent sources and found to be in agreement. In
general, we need to ensure the measured data are consistent of with the laws of mass and
energy conservation, and with known physical and thermodynamic properties.
The
methodology is best explained using illustrative examples.
Example 1: Material Balance check
Consider the Crude Distillation Unit shown in Exhibit 2-2, with the following measured data.
Stream
Feed
P1
P2
P3
P4
Measured flow (MBD)
100
10
25
35
30
Density
Mass flow (Klb/h)
0.85
0.70
0.80
0.90
0.95
6198
510.4
1458.3
2296.9
2078.1
Sum of product flows on volume basis = 10 + 25 + 35 + 30 = 100 MBD, which appears to be in
exact agreement with the feed rate of 100 MBD.
Page 8 of 49
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Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 2-2: Simplified Schematic of CDU in an Oil Refinery
CRUDE OIL
DISTILLATION
UNIT
P1
P2
Feed
P3
P4
Would it be right to conclude that the data are consistent? No, because what is conserved is
mass, not volume. This is a common mistake that should be avoided.
Sum of product flows on mass basis = 510.4 + 1458.3 +2296.9 +2078.1 = 6343.7 Klb/h.
It is not possible for the flow out to be more than the flow in. So strictly speaking the measured
data should be considered to be inconsistent. However, if we look at the magnitude of the
error, it is 146 Klb/h, or 2.4% of the feed rate, which is within the accuracy of the meters, and
so we would accept the data as being acceptable despite being inconsistent; in effect we
deem the data to have acceptable consistency. If, on the other hand, the error was found to
be greater than the meter accuracy, then the data would be determined to be unacceptable,
and some action would be required to reconcile the discrepancies before analysis can begin.
Example 2: Material Balance check
Let us say that we want to check the quality of flow data for the AM and AH product shipment
pumps from one of the GOSPs. Sample raw data from the PI system are shown in Exhibit 2-3,
columns 4 and 5. How can we check for consistency?
One way is to calculate the “yield”, which we shall define as the ratio of product flow to feed
flow. Because a certain (variable) amount of vapor flashes off in the wet crude receiving tank,
the yield is expected to be less than 100%. The computed values shown in columns 6 and 7
reveal that the yield for AM crude is fairly steady throughout the year at around 94%, indicating
the data are consistent. The computed values of yield for AH crude, on the other hand are
often in excess of 100%, and occasionally in excess of even 200% (see circled areas in
Exhibit 2-4).
Page 9 of 49
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Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 2-3: Raw PI Data for Crude Oil Flow Rates
Exhibit 2-4: Calculated Yield Trends for Crude Oil Product
Material Balance Check
250
AH Crude
200
Dry/Wet Yield, %
AM Crude
150
100
50
0
1
31
61
91
121
151
181
211
241
271
301
331
361
Day of Year (2003)
Page 10 of 49
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Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Since it is impossible for the yield to be higher than 100% (see numbers highlighted in brown
in Exhibit 2-3), we conclude that the measured data for AH crude feed and product flow rates
are inconsistent with each other, and there was obviously some problem with the metering
system for the first 5 months that appears to have been fixed subsequently.
Example 3: Properties check
Consider the product recovery system in Exhibit 2-5, in which vapor from a liquid-butane
storage tank is compressed and condensed against air before being returned to storage.
Exhibit 2-5: Schematic Diagram of Butane Vapor Recovery System
P2
T2
Air Cooler
Compr
Sat. Butane
Vapor
P1
P3
Receiver
T3
T1
Exhibit 2-6: Measured PI Data for Butane Vapor recovery System
Point #
Measured
Pressure (psia)
Measured
Temp (F)
1
2
3
13
110
107
30
169
160
Tsat at
measured
Pressure
25.5
155
153
Psat at
measured
Temp
14.3
130.1
116.7
Let us examine whether the measured pressures and temperatures make sense. The first
thing to do is list the equilibrium temperatures and pressures for the measured pressures and
temperatures.
Page 11 of 49
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Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
For point 1, which is known to be a saturated vapor, the measured temperature of 30°F is 4.5°
higher that the equilibrium (or saturation) temperature of 25.5°F . This is too great a
discrepancy. So either the temperature or pressure measurement must be wrong. Of course,
it is also possible that they are both wrong. Generally temperature measurements are more
reliable, so we would probably choose T=30° and P=14.3 psia for point 1 as the reconciled
values.
For point 2, which is known to be a superheated vapor, the measured temperature would be
expected to be higher than the saturation temperature, which is in fact the case. So we would
accept the data for point 2 as being consistent.
For point 3, which is known to be a saturated vapor, there is once again a discrepancy
between the measured pressure and temperature. If we assume, as for point 1, that the
temperature reading is more reliable, we get a pressure value in the receiver of 116.7 psia,
which is more than the measured value of P2. Since that is not possible, we choose P3=107
and T3=153°F. This gives the following reconciled values:
Point
1
2
3
Pressure, psia
14.3
110
107
Temperature, °F
30
169
153
An alternative possibility is that the measured pressure reading of P2 is also wrong. If we
postulate that both the temperature readings are right and both readings are wrong, then we
get a pressure drop in the air cooler of 130.1 – 116.7 = 13.4 psi. This is too high. Therefore
we conclude that our first reconciliation decision is probably correct, and these are the values
that should be used for design.
2.6.2
Data Validation for Accuracy
Accuracy means that the measured values are equal to the true values. Checking for
accuracy is much more difficult than for consistency. Consider this example of an oil storage
and loading station, depicted schematically in Exhibit 2-7.
The flow is 10,000 gpm for 2 hours. The meter has been calibrated recently and certified as
accurate by the maintenance department. The same pump is used for both loading (filling)
and unloading a fuel storage tank, according to the following operating policy:
Filling
Unloading
Valve A
open
closed
Valve B
closed
open
According to the meter, the amounts of oil that flow in and out are each 1,200,000 gallons
(28,571 barrels). So the data are consistent. But are they accurate?
For this we need independent verification.
Page 12 of 49
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Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 2-7: Schematic Diagram of Oil Storage and Loading Facility
FI
B
Level 2
A
A
Level 1
Truck or Ship
B
Let us say that the tank is a vertical cylindrical type with a diameter D = 80ft. Let us say that
the difference between the initial and final level in the tank (after filling) is 30 ft. Then, the
volume of oil pumped in is
V = (πD²/4) x ∆h = 150,797 ft³ = 1,130,976 gal.
This is 6% less than what the meter reading shows, and so would be considered inaccurate,
because the standard of accuracy for custody transfer meters is usually less than 0.5%.
Page 13 of 49
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Next Planned Update: 1 November 2006
3.0
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Pump Networks
The methodology used for estimating savings potential will be described for a single
representative pumping system only (see Exhibit 3-1), as all systems can be evaluated in an
identical manner.
Exhibit 3-1: Typical Pump Network and Control System
There are three principal steps:
1. Develop and assess flow profile
2. Determine optimum operating policy for each of the four load management strategies
noted in section 2.2
3. Estimate the power cost savings potential by comparing the costs of operation under
the prevailing operating practices against the those from following the optimum
operating policy
Each of these will now be described in more detail.
Page 14 of 49
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Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
3.1
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Flow Profile
First and foremost, we have to develop a histogram of the load profile from raw PI data, as in
Exhibits 3-2 and 3-3. PI data should be recorded as daily averages for a period of at least 12
months to capture seasonal variations.
Exhibits 3-2a and b: Fluid Flow Historical Data (sample only). Note:
AM CRUDE OIL PRODUCTION RATE
1400
1200
Design
capacity
800
600
400
200
A M Crude
12/1
11/1
10/1
9/1
8/1
7/1
6/1
5/1
4/1
3/1
2/1
0
1/1
Flow , MBD
1000
DATE
Page 15 of 49
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Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 3-3: Fluid Flow Profile and Histogram
AM Crude production profile
140
D ays per year
120
100
80
60
40
20
0
550
600
650
700
750
800
850
900
950
Flow, MBD
HELP NOTE
For those of you using Microsoft Excel®, there is a useful feature that enables you to generate
histograms easily from tabular data. However, this feature is not part of the basic installation
of MS Office®, and must be loaded manually. If you do not see Data Analysis on the Tools
menu, you will need to load the Add-In as follows:
Click on Tools > Add-Ins
Check the box named Analysis ToolPak
After a few seconds, you should see Data Analysis on the Tools menu. Click on that, select
Histogram, and follow the instructions.
Page 16 of 49
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Document Responsibility: Energy Systems Unit, CSD
Issue Date: 26 October 2005
Next Planned Update: 1 November 2006
3.2
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Minimize Number of Operating Trains
One of the biggest sources of energy savings is to minimize the number of pump trains being
operated in parallel. This section describes the criteria for determining this number, and
developing an operating policy that balances energy savings versus equipment integrity,
operating flexibility, and reliability. Two important considerations must be kept in mind:
(a) There is a certain minimum flow required through each pump below which cavitation
could occur and damage the pump. This type of cavitation (as opposed to the type
caused by inadequate NPSH) is due to eddy formation in the pump suction/discharge
nozzles at low flows, and generally begins when the flow falls below 60% of flow at the
best efficiency point. Short term episodes of low flow are not a problem; damage occurs
only if low-flow operation is sustained for several weeks or months. However, if the pump
flow falls below 30% of the “best-efficiency” flow, the fluid could overheat due to low pump
efficiency, and reach its bubble point inside the pump casing. If this happens, the pump
will seize, and stop working altogether due to internal mechanical damage. The recycle
line is designed to prevent these types of problems.
(b) In general, the flow achievable by using N pumps in parallel will be less than N times the
flow through a single pump. This is because there is a non-linear relationship between
flow and number of pumps, which is determined by the intersection between the system
curve and the composite pump characteristic curve.
As a matter of principle, we should never operate more than the minimum number of trains
needed to satisfy the production target set by the corporate dispatching department. On the
other hand, if the required throughput is bordering between the capacities of one pump and
two, or between 2 pumps and 3, it is not good practice to frequently start and stop the “extra”
pump.
Exhibit 3-4: Determination of Ideal Trigger Points for Pump Switching
AM Crude Booster/Shipper Pumps
2000
1800
1600
Head, ft of oil
1400
1200
1000
system hd, 3 p/l
800
system hd, 2 p/l
600
1 train
400
2 trains
200
3 trains
0
0
500
1000
1500
2000
2500
3000
Total Flow , MBD
Page 17 of 49
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Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
The fractional load at which we make the switch between N and N+1 pumps has been named
the “Trigger Point”. Numerically, the Trigger Point is expressed as the ratio (%) of actual flow
at which the switch is made to the “ideal” flow at which the switch should be made. The flow
rate at which we should ideally switch from N pumps to N+1, and vice versa, is when the
composite characteristic curve for the pump network intersects the system curve (inclusive of
required minimum control valve ∆P), as in Exhibit 3-4. This corresponds to a Trigger Point of
100%.
The pump characteristic curve is obtained from the data sheets, and verified against operating
data in the PI (plant data historian) system. For pumping networks consisting of multiple
pumps connected in series and/or parallel, we have to construct a composite characteristic
curve from the individual pump curves, according to the procedure explained in section 3.6.
For pumps connected in series, we must add the individual heads at a given flow rate. For
pumps connected in parallel, we must add the flows at a given head.
The system curve can either be determined from the data sheets, or from PI data, as
illustrated in section 3.7. One should keep in mind that the design manual and data sheets are
usually based on new pipe, for which the pressure drop per linear foot is less than for old pipe,
and make the necessary adjustments. Another potential complicating factor is that sometimes
there could be more than one pipeline available for use. In the case of the AM and AH crudes,
there are several pipelines that could be used interchangeably. For AM crude either 2 or 3
pipelines are normally used, depending on the flow rate.
The “ideal” operating policy at a Trigger Point of 100% as derived from intersection of the
system curves with the pump composite curves in Exhibit 3-4 is shown in Exhibit 3-5.
Exhibit 3-5: Ideal Operating Policy for AM Shipper Pumps (at Trigger Point = 100%)
Unfortunately following the “ideal” policy runs the risk of having to throttle back production
during the time it takes to get the extra pump/train up and running. In practice, therefore, it is
safer to start up the N+1th pump a little bit before it is needed, and to keep it running a bit
longer after it is no longer needed. In effect, therefore, the optimum Trigger Point for fixed
speed motors drives is somewhat less than 100% (see Exhibit 3-6).
The approximate relationship between Trigger Point and reliability (measured in terms of lost
production during the switchover period) is shown semi-quantitatively in Exhibit 3-6. The
optimum operating zone is around the sharp bend in the curve, when reliability falls off rapidly
for small increases in Trigger Point. For fixed speed motors the optimum range of Trigger
Page 18 of 49
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SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Points centers around 95%, which is the number recommended, and is the basis for
estimating the energy savings potential compared to existing operations.
Significantly greater power savings can be realized if the Trigger Point is raised from 95% to
100% or 105%. This can be achieved if the pump driver has over-speed capacity, eg. if the
motor is fitted with a variable frequency drive (VFD), or the driver is a steam- or gas turbine.
Exhibit 3-6: Indicative Relationship between Trigger Point and Reliability
Reliability vs Trigger Point
120
Reliability Index
100
80
Optimum
Zone
60
40
f ixed spd motor
var spd drive
20
0
0
20
40
60
80
100
120
Trigger Point, %
To calculate the energy savings potential, one has to compare the cost of the current
operating practice versus the cost of following the optimum policy. The energy consumption
and cost of actual operation can be obtained either from the power meters (if the pumps have
them), or by following the methodology described below.
Step 1: Prepare a summary of the pumps data.
Step 2: Determine pump on/off status over a period that represents typical operation.
Step 3: Calculate minimum number of pump trains required for each operating interval, for a
range of Trigger Points, say 85% to 105%.
Step 4: Estimate power savings potential on the basis of shutting down the excess pumps
during each operating interval, and sum these savings for all intervals within the
selected period of interest.
Step 5: Prepare a table and plot of power/cost savings potential vs Trigger Point.
Page 19 of 49
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Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 3-7: Basic Pump Data (Example)
From the PI system, determine the on/off status (and flow rate if ON) of the pumps at the mid
point of the selected period. For example, if the year is divided into 365 24-hour periods, and
the periods are counted from midnight to midnight, then you would check the on/off status at
noon every day. If the period is elected to be a shift, and the shift timings are 6 am – 2 pm, 2
pm – 10 pm, and 10 pm – 6 am, then the mid-points of the periods would be 10 am, 6 pm, and
2 am. The selection of sampling interval can be important, and is discussed in detail at the
end of this section. Sample output from the PI system for the AM Booster/Shipper pumps at
Safaniya is shown in Exhibit 3-8 for illustration.
Exhibit 3-8: Pump Operating Status and Flow Data (from PI system)
Page 20 of 49
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From the pump flow data and the ideal operating policy table (Exhibit 3-5), calculate the
number of pump trains required during each selected period for a range of Trigger Points (eg.
85% to 105%, in increments of 5%). The computational logic is as follows:
Let number of parallel trains required = NP, and assumed Trigger Point (%) = TP.
Let the minimum required flow through a pump to avoid cavitation or seizure be FM.
Then,
For FM < Flow < 825*TP, NP = 1
For 825*TP < Flow < 960*TP, NP = 2
For Flow > 960*TP, NP = 3
Exhibit 3-9: Estimating Power Savings from Minimizing No of Operating Trains
The power savings are estimated assuming that each excess pump will be operating for
exactly one full interval. While this is not strictly true, it is not a bad approximation, as there
will be some intervals during which an excess pump may be operating part of the time but
does not get recorded because it happened to be off at the sampling moment, and these
discrepancies should cancel one another on average.
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Load Management for Energy
Efficiency: Pumps & Compressors
It is helpful to also plot the “fractional” number of pump trains required against actual number
of trains in operation (as in exhibit 3-8) to get a visual feel for how much of the time excess
trains are being operated.
Exhibit 3-10: Actual Pump Trains in Operation versus Minimum Required
AM crude shipping pumps
2.5
Number pump trains in operation
Excess Pumps in use
2.0
1.5
1.0
0.5
No . trains needed
No . Trains Running
0.0
12/10
1/29
3/20
5/9
6/28
8/17
10/6
11/25
1/14
3/4
Date, 2003
Exhibit 3-11: Power/Cost Savings Potential vs Trigger Point
AM Booster/Shipper Pumps
1200
Savings, K$/yr
1000
800
600
400
200
0
80%
85%
90%
95%
100%
105%
Trigger Point
It can be seen that savings can be substantially higher for pumping systems with adjustable
speed (variable frequency) drives on the motors. In the case of the AM Booster/Shipper
pumps at Safaniya Onshore Plants, an additional $520-760 K/yr of savings could be realized
by fitting the fixed speed motors with VFDs, and operating at a higher Trigger Point. The
economics of installing VFDs are very attractive because it is possible to design a control
system such that only one VFD is needed for any number of parallel trains. For other types of
adjustable speed drives, eg. hydraulic gears, this is not the case.
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When evaluating the existing load management practices of a set of parallel equipment it is
important to ensure that the data sampling technique is an accurate representation of reality,
because in order to have an accurate estimate of savings potential, we need to get the closest
correlation possible between the average flow rate over the sampling period, and the on/off
status of the pumps (which is an instantaneous measurement) and the average flow rate
during the interval. This would argue for the shortest possible interval, say 15 minutes.
However, since it normally takes at least 2 hours to get a pump fully operational from a cold
start, there is unlikely to be a disconnect between average flow rate and pump status for
sampling intervals less than 2 hours. In order to reduce the computational effort, we can limit
the number of samples to 365 by making the assumption that average flow rates over the
sampling period are representative of the average flow rates for the whole day, and calculate
estimated savings accordingly. The relationship between sampling time interval and calculated
savings is shown in Exhibit 3-12, which confirms that for savings from optimizing the number
of running pumps, the sampling period does not have a statistically significant impact on the
results.
Exhibit 3-12: Impact of Sampling Interval on Calc’d Savings from Minimizing Excess Pumps
Savings from Excess Pumps
129
128
Savings, K$/yr
127
126
125
124
123
122
0
4
8
12
16
20
24
Sampling Interval, hr
3.3
Minimize Recycle Flows
Flow control can be achieved in many different ways – by throttling the main discharge line, by
running the pump at full throttle and recirculating the excess flow, or by using an adjustable
speed drive. Flow recirculation is also employed for protecting the pump against mechanical
damage that could occur at low-flow conditions, as explained at the beginning of section 3.1.2.
A typical pump installation showing the piping and control scheme is illustrated in Exhibit 3-13.
Page 23 of 49
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SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 3-13: Typical Pump Control System
Pump power consumption is a function of flow (Exhibit 3-14). Recirculation through the
bypass line increases flow rate and wastes energy; therefore it should only be employed when
the net process flow falls below the minimum flow requirement of the pump. The opportunity
for energy savings arises when some flow is being recirculated through the by-pass line even
when it is not needed. This usually happens when the pumps are grossly oversized for the
required service, a consequence of excessive conservatism during the project planning and
design phase.
Exhibit 3-14: Typical Variation of Power Consumption with Flow Rate
Pum p Pow er Consum ption
900
Total
800
PV pow er
Power, BHP
700
Pw r to Heat
600
500
400
300
200
100
0
0
200
400
600
800
1000
1200
1400
1600
Flow , gpm
Page 24 of 49
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SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
“PV” power is the useful energy absorbed into the process for increasing pressure or driving
the fluid. However, a certain amount of input power is lost to heat due to friction. Observe that
the pump efficiency (useful energy divided by input power) is not constant but in fact goes
through a maximum over the pump’s operating range, falling off to near zero at extremely low
flow rates.
In general, there are two situations that we could encounter:
(a)
(b)
Required Process Flow > Minimum Pump Flow
Required Process Flow < Minimum Pump Flow
In case (a), there should be no recycle; in case (b) some recycle is unavoidable, but should be
kept to the minimum.
Exhibit 3-15: Power Savings Potential from Minimizing Recycle
Pump Power Consumption
1000
MINIMUM
Power, BHP
800
REQUIRED
ACTUAL
600
SAVINGS
400
200
RECYCLE
0
0
200
400
600
800
1000
1200
1400
1600
Flow , gpm
Pump Power Consumption
1000
REQUIRED MINIMUM
Power, BHP
800
ACTUAL
UNAVOIDABLE
RECYCLE
600
SAVINGS
400
200
∆ RECYCLE
0
0
200
400
600
800
1000
1200
1400
1600
Flow , gpm
Page 25 of 49
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SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Consider Exhibit 3-15, which shows the power-flow curve for a typical pumping system. Let us
adopt the following nomenclature:
Parameters: F =Flow, HP= Power
Subscripts: A=Actual, R=Required, M=Minimum
Then the potential power savings for a given time interval are:
∆HP = HPA – max ( HPR, HPM )
where BHP =
Flow (gpm) x Head (psi) Flow (gpm) x Head (feet) x sp gr
=
1714 x pump eff
3964 x pump eff
To calculate the power consumption for each case (actual, required, minimum), use the
average flow rate and head for that time interval. Pump efficiencies at the relevant flow rates
should be obtained either from the pump manufacturer’s data sheet/curve or from the
efficiency data generated during the most recent pump performance test.
The power savings for each time interval must be added up for all intervals during the year to
get the total annual savings. It is recommended to use either 365 intervals of 1-day each, or
730 intervals of 12 hours each.
The pump flow profile histogram is a very good indicator of whether there is significant cost
saving potential from elimination or minimization of recycle.
Exhibit 3-16: Inferring Recycle Requirements from Flow Profile
Flow Distribution Histogram
140
100
Days per year
Design
Capacity
Minimum
Flow
120
80
60
40
20
0
550
600
650
700
750
800
850
900
950
1000 1050
Flow , gpm
Page 26 of 49
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Load Management for Energy
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Specific steps to be taken are listed below and illustrated in Exhibit 3-17.
(a)
Develop correlation for pump characteristic curve (from factory test or data sheet)
(b)
Develop correlation for pump efficiency curve (from factory test or data sheet)
(c)
Establish minimum flow requirement per pump (if not specified on pump data sheet,
assume 35% of flow at “best efficiency” point)
(d)
Gather PI data for net process flow and actual flow through pump (= process flow +
recycle flow)
(e)
Calculate power consumption and potential savings from recycle flow elimination or
minimization using the formulas given above
Pump Curve and Efficiency correlations:
Head-flow correlation (pump characteristic curve)
h (ft) = a + bQ - cQ^2, where Q = gpm/100
a
b
c
1702
15
2.5
Pump Efficiency correlation
eff (%) = a + bQ - cQ^2, where Q = gpm/100
a
b
c
14.48
14
0.8
Operating data:
liquid sp gr
Cost of power
Interval duration
0.86
26.7
24
$/MWH
hours
Minimum flow (surge point)
Head (from char curve)
Efficiency, %
Power consumption
571
1706
68.3
309
gpm
feet
%
HP
Exhibit 3-17: Estimation of Power Cost Savings from Minimizing Recycle
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3.4
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Operation at Best Efficiency Point
The efficiency of pumps is a function of flow rate. Sometimes, the efficiency can be
significantly lower at flow rates beyond the “best efficiency” point, and one has to check to
make sure that minimizing the number of operating pumps will in fact minimize power
consumption. The procedure for doing so is illustrated in Exhibit 3-18. If this is not the case,
then the operating policy developed for minimizing pump trains in operation (as recommended
in section 3.2) must be revisited and revised as necessary. Real efficiency curves are seldom
as extreme as the one shown in Exhibit 3-18, but it makes the point.
Exhibit 3-18: Operation at Best Efficiency Point vs Minimizing Pump Trains
100
1600
80
1200
60
800
40
Head
Efficiency
400
200
400
600
Process Flow, gpm
Number // pumps
Flow per pump, gpm
Head, feet
Efficiency, %
BHP per pump
Total power, HP
Case 1
2700
2
1350
1449
57.7
735
1471
normal
Case 2
2700
3
900
1635
75.7
421
1264
best eff
20
0
0
Efficiency, %
Head, ft of liquid
Pump Head and Efficiency
2000
800
1000
1200
1400
1600
1800
0
2000
Flow, gpm
In the case or recycle elimination/minimization (as recommended in section 3.3), however,
there is never a case to be made for operating at higher flow than the minimum, because the
increased power consumption due to higher flow always exceeds the savings from efficiency
improvement, as illustrated in Exhibit 3-19.
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Exhibit 3-19: Operation at Best Efficiency Point vs Minimizing Recycle
100
1600
80
1200
60
800
40
Efficiency, %
Head (ft), or Power (HP)
Pump Head and Efficiency
2000
Head
Power
400
20
Efficiency
0
0
200
400
600
800
1000
1200
1400
1600
1800
0
2000
Flow , gpm
3.5
Load Allocation by Equipment Efficiency
So far we have assumed that all pumps in parallel are identical, and have identical efficiencies.
In fact, this can never be strictly true; at best, it can only be approximately true. In some
cases, it may not even be approximately true, eg. if one machine suffers mechanical
deterioration at a faster rate than another.
The appropriate operating policy, when we have parallel machines of unequal efficiency, is
both simple and obvious:
Use the most efficient machines for base load,
and the least efficient machines for swing loads.
The calculation procedure is straight-forward. Consider the case of three equal-sized pumps
of varying efficiency of which only two are normally operated in parallel (see Exhibit 3-20).
The best combination is pumps 1+3, while the worst is pumps 2+3. The savings potential
between best and worst combinations is $1039 - $996 = $42K per year. While this may not be
very great compared to some of the other savings, it can be achieved easily with zero capital
investment.
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Load Management for Energy
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Exhibit 3-20: Load Allocation by Equipment Efficiency
Process Flow, gpm
Head, feet
sp gr
Pump efficiency, %
Pump power, HP
Pump #1 Pump #2 Pump #3
10000
10000
10000
800
800
800
0.9
0.9
0.9
75
69
72
2421
2631
2521
Alternative pump combination options:
Option
1
2
3
Pumps ID
1+2
1+3
2+3
Σ HP
5052
4942
5152
K$/yr
1018
996
1039
n
In general, the number of combinations to be evaluated is Cm, calculated using factorials as
follows:
n
Cm =
n!
n(n − 1)(n − 2)...x2x1
=
m! x (n − m)! [m(m − 1)...x2x1] x [(n - m)(n - m - 1)(n - m - 2)...x2x1]
where n = total number of installed parallel pumps, and m = number required to be in
operation simultaneously.
3.6
Composite Characteristic Curves for Pump Networks
The head-flow relationship of pumping networks consisting of multiple pumps connected in
series and/or parallel is described by the composite characteristic curve, which must be
constructed from the individual pump curves. For pumps connected in series, we must add
the individual heads at a given flow rate. For pumps connected in parallel, we must add the
flows at a given head.
Consider the series/parallel network shown in Exhibit 3-21, with individual pump characteristic
curves as shown in Exhibit 3-22.
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Load Management for Energy
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Exhibit 3-21: Generic Network of 3 Pumps in Series/Parallel
to Process
Wet Crude
Storage
Tank
Storage
Tank
Pump 2
Pump 3
Pump 1
Exhibit 3-22: Characteristic Curves for Individual Pumps
Individual Pump Characterictic Curves
2000
Pump 1
1600
Pump 2
Head, feet
Pump 3
1200
800
400
0
0
500
1000
1500
2000
2500
3000
3500
4000
Flow, gpm
The construction procedure for the composite characteristic curve is illustrated in Exhibits 3-23
to 3-26.
Step 1: Develop quadratic correlation (use curve fitting utility within Excel) for each
pump curve in the form h (ft) = a + bQ - cQ^2, where Q = gpm/100.
It should be noted that the quadratic formulation is a good fit only for heads less than
95% of the shut-off value. In the very low flow region, when head is between 95 and
100% of the shut-off value, the relationship is more accurately correlated as a linear
function: h = a - dQ. (The shut-off head is the value at zero flow)
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Step 2: Add the a, b, c and d parameters of the pumps connected in series (#2 and
3 in the example) to get the composite values for the two together.
Exhibit 3-23: Correlation (Curve-Fit) of Pump Characteristic Curve Data
Exhibit 3-24: Composite Characteristic Curve for Pumps 2 & 3 in Series, and Pump 1 by itself
Composite Characteristic Curves for Pumps in series
2000
Head, feet
1600
1200
800
Pump #1
400
Pumps 2/3
0
0
500
1000
1500
2000
2500
3000
3500
4000
Flow , gpm
Step 3: Construct a new table showing the flow for pump 1 and the 2/3 combination
at the same head. Then add the two flows together at each value of head, which gives
the composite characteristic curve for the whole network.
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Exhibit 3-25: Characteristic Curve Data for Pumps 1 and 2 & 3 (in Series) in Parallel
Flow, gpm
Pumps 2/3
Head, ft
1550
1545
1540
1520
1500
1495
1485
1450
1400
1300
1100
800
Pump 1
0
0
0
0
0
250
750
1212
1477
1870
2440
3076
in series
0
147
294
882
1338
1360
1402
1536
1701
1975
2407
2911
1 + 2/3
0
147
294
882
1338
1610
2152
2749
3178
3845
4846
5987
Exhibit 3-26: Composite Pump Characteristic Curves for Entire Network
Composite Characteristic Curves
1800
1600
1400
Head, feet
1200
1000
800
600
P ump 1
400
P umps 2/3
Co mpo site
200
0
0
1000
2000
3000
4000
5000
6000
7000
Flow, gpm
Observe that if system head is greater than the shut-off head for Pump 1 (= 1500 ft), it will not
be able to contribute any flow, and the combined network flow will be equal to the flow from
Pumps 2/3 (in series) only. Both parallel lines can contribute flow only when the system head
falls below the shut-off head for the lower one of the two.
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3.7
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
System Head Curve for Pump Networks
System head is the total head that the pump must overcome at any given flow. It has two
principal components – static and dynamic. The static head consists primarily of the potential
energy difference between the suction and discharge points. The dynamic head consists
primarily of kinetic energy (fluid momentum) differences and frictional losses in the piping
network.
Exhibit 3-27: Schematic Diagram of Simple Pumping System
P2
P1
Static Head (feet of liquid) = (P2 - P1)/ρ + (h2 - h1)
It should be recognized that the “static” head is not necessarily constant. It will fluctuate
somewhat as a consequence of variations in vessel pressure at the suction and discharge
ends, as well as due to fluctuations in liquid level. If frictional losses dominate the system,
then the static head may be considered to be approximately constant, but if not, then
variations in static head would have to be taken into account in the analysis.
Dynamic head (feet) =
(α 2 V22 − α1V12 ) Σ(∆Pf )
+
2g
ρ
In normal industrial piping systems, the kinetic energy (V2/2g) term is generally small, and can
be safely neglected. Strictly speaking, the frictional term in the Bernoulli equation includes
pressure losses in the piping, equipment, instruments and the pump itself (bearings, seals,
etc). It is common practice, however, to separate pump losses from piping/equipment losses.
Internal losses within the pump are accounted for as pump efficiency, and only the piping,
equipment and instrument losses are included in the dynamic head component of system ∆P.
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Frictional pressure drop in turbulent flow (Reynolds numbers > 10,000) can be very closely
estimated by the equations
∆P = 2ρ f LV2 / gD
and
f = 0.0029 (ρDV/µ)-0.2
Because the “Moody” friction factor f is itself a function of velocity, the net proportionality
between frictional pressure drop and pump flow works out to be approximately
∆P α Q1.8
With a proper understanding of these basic principles, it becomes easy to develop the system
curve from available data.
If the engineering design contractor and the procurement group have done their jobs right, the
static and dynamic heads at the design condition will be recorded on the pump documentation
supplied by the manufacturer. Only four items of information are needed – design flow rate,
liquid density, static head, and piping/equipment frictional drop at design flow – to calculate the
system curve over its entire range of operation:
(∆Pf ) d
H = HS +
ρ
 Q

 Qd
1.8

 ,

where subscript “d” refers to Design conditions.
A more accurate method is to obtain this same information from PI data over a suitably wide
flow range. Unfortunately, there is seldom sufficient instrumentation installed to enable
disaggregation of the control valve drop and the frictional drop. The appropriate procedure in
such instances is to estimate the piping and equipment frictional drops (using the equations
and methods described in most engineering handbooks and college-level textbooks on fluid
mechanics).
The first step is to list the pump data (from drawings and design manuals or plant data), as in
Exhibit 3-28. The next step is to calculate the static and dynamic heads according to the
equations provided above, as in Exhibit 3-29.
Exhibit 3-28: System Data at Design Conditions
Liquid level (elevation), ft
Source/destination pr, psig
Equipment ∆P psi
Piping ∆P, psi
Instrument (meters) ∆P, psi
suction discharge
24
86
12.0
31.1
3.9
25.7
2.0
293
0.0
4.3
Page 35 of 49
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Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 3-29: Calculation of Static and Dynamic Head at Design & Maximum Flow
Flow, gpm
Liquid density (specific gravity)
Static head, ft
Dynamic head, ft
System head, ft
Delivered head, ft
Control valve DP, ft
, psi
, % of TDH.
Design Maximum
825
1070
0.8605
0.8605
100
100
884
1419
984
1519
1656
1576
672
58
250
21
41%
4%
The control valve drop is the difference between the TDH of the pump and the system head.
For good control, this should generally be about 1/3 of the total pump delivered head. Even in
the fully open position, the control valve incurs some pressure drop, equal to 21 psi (58 ft) in
the illustrative example, which defines the maximum flow possible from the pump and piping
system. The maximum flow must be found by trial and error until the system head + control
valve drop (in fully open position) equal the delivered head.
Exhibit 3-30 shows the system curve in relation to the pump characteristic curve.
Exhibit 3-30: System and Characteristic Curves
2000
Pump Curve
System Curve
Head, feet of oil
1600
Control
Valve ∆P
1200
800
Dynamic head
(frictional ∆ P)
400
Static head
0
0
500
1000
Flow, gpm
1500
2000
Page 36 of 49
Copyright©Saudi Aramco 2009. All rights reserved.
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Next Planned Update: 1 November 2006
3.8
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Controls and Instrumentation
For effective operation of pumping networks, it is important that they be controlled properly.
The control scheme shown in Exhibit 3-1 (section 3.0) is typical of Saudi Aramco facilities, but
is not optimal for effective load management. A superior control scheme, which works equally
well for both identical and non-identical pumps, is shown in Exhibit 3-31. [ Ref. Bela G. Liptak,
Optimization of Industrial Unit Processes, 2nd ed, CRC Press, Boca Raton, Florida, USA
(1999), pp 394-401.] There is no sacrifice in operating reliability; in fact the illustrated scheme
features improved flexibility.
Exhibit 3-31: Recommended Control Philosophy for Parallel Pump Trains
The combined total flow may be set on either flow control (shown) or level control (not shown),
depending on process requirements. If the stream is a process feed, we would normally prefer
flow control, as this makes for better operating stability. If on the other hand, it is a product
stream going to a pipeline or bulk storage facility, we may prefer to use level control.
The flow controller output signal passes through a hand switch, controlled by the operator,
which is routed to one of the three control valves in the individual pump discharge lines. Only
one of the valves should be controlled at any given time; the other two would be either fully
open or fully closed, depending on whether the pump is running or not. The valves should be
set to the “fail-open” mode. Check valves, block valves, bleed valves, pressure gages, and
other details of standard piping and instrumentation are not shown.
Page 37 of 49
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Next Planned Update: 1 November 2006
4.0
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Compressor Networks
The methodology for estimating savings potential from load management of compressors is
similar to that for pumps, except that several important differences must be taken into account:
(a) Gases are compressible while liquids, for all practical purposes, are incompressible.
Physical properties such as specific heat and compressibility can vary significantly at
high compression ratios, affecting power consumption.
(b) Density variations (due to composition and suction pressure drift) are more prevalent.
(c) Compressors are generally more difficult to startup and shut down (normal startup
period is on the order of 4 hours vs less than 1 hour for even very large pumps) partly
because they usually operate between their first and second critical speeds, and partly
because they have to be properly purged every time when compressing flammable
hydrocarbon gases.
(d) The system curve is generally dominated by static head, as opposed to dynamic head
for most pump applications.
(e) The surge limit generally occurs at 50% of the design flow at the design speed.
4.1
Thermodynamics of Gas Compression
The pressure-volume-temperature behavior of real gases is described by the equation:
Pv = ZRT
where P = pressure, psia
v = specific volume, ft3/mole
T = temperature, °R
R = universal gas constant = 1545 ft-lb/mole°R = 1.987 Btu/mole°R
= 10.729 for the units of measure indicated above
and
Z = compressibility factor (must be obtained from data charts for each particular gas)
For diatomic gases at low pressures, Z is approximately 1.
The power consumption of a compressor, in horsepower, is given by

ZW(T1 + 460)  1  k   P2


BHP =

 
1281.55 MW  η ad  k - 1   P1

where
T1
P1 & P2
k
Z
W
MW
ηad
=
=
=
=
=
=
=



k −1
k

− 1


suction temperature, °F
suction and discharge pressures, psia or any other units
average specific heat ratio Cp/Cv
average compressibility factor
gas mass flow, lb/hr
molecular weight, lb/mole
adiabatic efficiency, usually in the range 0.75-0.85
The constant 1281.55 = 60 (min/h) x 33000 (ft-lb/min-HP) /1545 (ft-lb/mole°R)
Page 38 of 49
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SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
It is useful to know that Cp is usually about 7 Btu/lb-mole°R for most diatomic gases, and 5
Btu/lb-mole°R for ideal monatomic gases. For a gas mixture, Cp is evaluated as the weighted
mole fraction average. It is also useful to know that for ideal gases, Cv = Cp – R, so that k can
be approximated as
k = Cp/(Cp-R)
Overall Energy Efficiency of a compressor is easy to define:
ηo =
absorbed energy into process gas isentropic HP
=
delivered energy to the driver
brake HP
Unfortunately, it is very difficult to measure. The best we can hope for is to calculate efficiency
based on measurements of other process parameters such as flow, temperature, pressure,
and estimation of gas physical properties.
For compressors, two types of efficiencies are commonly used: adiabatic and polytropic. As a
practical matter, the single-stage adiabatic efficiency can be calculated as:
ηa =
T2
T2’
=
=
T2' − T1
T2 − T1
actual discharge temperature before any cooling, °F
isentropic (adiabatic) discharge temperature, °F, calculated as:

P
T2 ' = (T1 + 460) 2
 P1





− 1 − 460


k −1
k
The polytropic efficiency is then calculated as:
 n  k − 1 


 n − 1  k 
ηp = 
where
n = polytropic constant, which is a function of gas properties only, and determined
experimentally from the equation PVn=constant, unique to each machine.
Generally n and ηp are provided by the compressor manufacturer, and can be found in the
data sheet supplied with the equipment at time of purchase. The adiabatic efficiency can be
back-calculated from this information:
η ad

P
=  2
 P1




k −1
k

− 1



 P2
 P1




n −1
n

− 1

 1
Page 39 of 49
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Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Adiabatic efficiency varies with compression ratio, whereas polytropic efficiency is independent
of the thermodynamic state of the gas, being a function of mechanical design only. The
reason is that gas properties are implicitly included in the polytropic constant itself. Polytropic
efficiency is therefore a better indicator of compressor mechanical condition and performance,
and so load allocation decisions should be made on the basis of polytropic, not adiabatic
efficiency.
The overall efficiencies are given by:
η oa = η a .η m
η op = η p .η m
and
ηm
=
mechanical efficiency of the compressor
=
fraction of power delivered by the driver (motor) that is actually
transmitted to the gas, usually 97-98%
Exhibits 4-1a and b: Typical Data for Centrifugal Compressors
Centrigugal Compressors
79.0
12000
78.5
8000
77.5
Efficiency
6000
Speed
77.0
4000
76.5
Nominal Speed, rpm
10000
78.0
Polytropic Eff, %
where
2000
76.0
75.5
0
0
10
20
30
40
50
60
Impeller diam, in
Page 40 of 49
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SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
For multi-stage compressors, the total power requirement is simply the sum of the power for
each individual stage. From a thermodynamic viewpoint, the defining characteristic of a
compression stage is that there should be no inter-cooling between successive impellers.
Thus a compressor casing containing multiple impellers without intermediate coolers would be
considered a single stage. Confusion often arises because some manufacturers and authors
of technical articles refer to each impeller as a “stage”. These are not thermodynamic stages
unless an intercooler is provided between each impeller. In order to keep the temperature rise
within reasonable limits, the single-stage compression ratio is normally limited to about 3.0.
When calculating the power consumption of the 2nd and later stages, one should take into
account the pressure drop in the interstage cooler and piping, the new suction inlet
temperature, and differences in gas properties at the new suction conditions.
4.2
Performance and System Curves
In general, the head vs capacity curve (also called the “performance” curve) for a centrifugal
compressor operating at a fixed speed is quite flat, with the total head at the minimum
throughout (the surge point) typically being only 105-115% of the head at design throughput.
Similarly, the system curve is also relatively flat, because the static head usually dominates
frictional (dynamic) head. The operating point occurs at the intersection of the compressor
performance curve and the system curve.
Exhibit 4-2: Typical Centrifugal Compressor Operating Curves
Centrifugal Compressor Curves
32
Head, 1000 ft
30
28
Surge limit
26
24
Performance Curve
System Curve
22
20
0
1000
2000
3000
4000
5000
6000
7000
8000
Suction Flow, acfm
These characteristics are important considerations in selecting a control scheme that will result
in both stable and energy-efficient operation.
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4.3
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Control Strategies
For almost all compressor applications, some form of flow regulation is required, whether to
maintain a constant discharge pressure or a constant flow. Furthermore, the flow set point
could be in terms of volume or mass. The type of control scheme also depends on the type of
driver – whether fixed speed or variable speed. The optimal control scheme therefore varies
from case to case.
Speed control is considerably more efficient than throttling the flow with a valve (or even
worse, by employing flow recycle) at constant compressor speed, since the valve resistance
creates an unrecoverable power loss. Steam and gas turbines are inherently variable speed
machines, with speed control being easily achieved by regulating either the steam flow or
fuel/air flow. Compared to fixed speed drivers, variable speed drivers permit a much wider
range of control in a highly efficient manner.
Speed variation can be used to alter the position of the H-Q performance curve such that it
exactly intersects the system curve, as illustrated in Exhibit 4-3, with power consumption rising
and falling roughly in proportion to the process load. The performance curves at different
speeds are developed using the affinity laws.
Exhibit 4-3: Compressor Performance with Variable-Speed Drive
R92-K151
33
31
29
System Curve
1780 rpm
Polytropic Head, 1000 ft
27
1767 rpm
1630 rpm
25
1484 rpm
1362 rpm
23
Current Compr
Flow Rate,
fixed speed
21
19
17
15
0
1000
2000
3000
4000
5000
6000
7000
Process Flow (vapor from storage tank), acfm
Page 42 of 49
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Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Controlling two or more compressors operating in parallel and having identical characteristics
would be similar to the case of a single compressor. However, because of age, wear, or
design differences, no two compressors have identical performance characteristics. Slight
variations in flow can cause one compressor to be fully loaded and the other to fall below its
surge point, forcing needless recycle. The control scheme shown in Exhibit 4-4 overcomes
this problem. Typically, the suction valve that receives the lower flow is kept 100% open.
Exhibit 4-5 illustrates how two compressors can be proportionally loaded and unloaded, while
keeping their operating points at equal distance from the surge line. The lead compressor (31)
is selected either as the larger unit or the one that is closer to the surge line when the load
rises or further from it when the load falls. Improper load distribution is prevented by
measuring the total load, and assigning a variable percentage to each compressor adjusting
the set points of the flow ratio controllers. Each compressor must be provided with its own
independent surge protection system.
Exhibit 4-4: Suction Throttling Control of Fixed-speed Parallel Compressors
[Ref. Liptak, op cit]
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Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 4-5: Proportional Loading of Parallel Compressors with ASDs [Ref. Liptak, op cit]
Various alternative control strategies for different conditions and scenarios are described in the
following excellent reference texts:
[1]
“Optimization of Industrial Unit Processes”, 2nd ed, Bela G Liptak, CRC Press, Boca
Raton, Florida (1999), Chapter 4.
[2]
“Compressor Handbook for the Hydrocarbon Processing Industries”, Gulf Publishing
Co, Houston, Texas (1979), pp 103-124.
In most Saudi Aramco plants, when the compressor is not turbine driven, the electric motor is
normally operated at fixed speed (although there are a few rare cases where variable speed
capability is provided either using a hydraulic gear box or a variable frequency drive). Since
variable speed operation is one of the ways to improve energy efficiency, it is worth noting that
this is a relatively easy retrofit that can have excellent economics when dealing with networks
of parallel compressors. The reason is that only one of the compressors needs to be fitted
with a VFD; the rest can be left on fixed speed, as illustrated in Exhibit 4-6 (surge protection
controls not shown). It is not necessary, as some mistakenly believe, to install a VFD on each
and every motor in the network.
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SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 4-6: Control of Parallel Compressors (One ASD and Rest Fixed Speed)
TI
TI
FC
FI
PI
PI
M
M
TI
TI
SC
PC
FI
FI
Whether the process objective is to maintain constant header pressure or constant flow, the
fixed speed compressors must be on flow control, which can be accomplished by either
suction throttling or discharge throttling. Butterfly valves are preferred because of their lower
pressure drop. The appropriate valve location depends on whether the goal is to deliver mass
flow (eg for most process applications, including sales gas compression) or volume flow (eg.
most utility applications such as plant or instrument air).
For equal mass flow rates, discharge throttling consumes less power, and therefore would be
preferred. However, for equal volume flows, the situation is reversed.
Consider the illustrative example in Exhibit 4-7, with suction and header pressures of 80 psia
and 230 psia respectively, a k value (= CP/CV) of 1.32, and a control valve drop of 10 psi at the
design flow. A comparison of the relative power consumption for the two cases clearly
demonstrates that appropriate placement of the control valve can save a significant amount of
energy.
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Next Planned Update: 1 November 2006
SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 4-7: Power Consumption for Suction vs Discharge Throttling
(a) Constant Mass Flow control
Inlet pressure, psia
Discharge pr, psia
Compression ratio
X = (k-1)/k
(P2/P1)^X - 1
Relative power
Suction throttling
80
230
2.875
0.1113
0.1247
1.00
Discharge throttling
90
240
2.667
0.1113
0.1154
0.9248
Suction throttling
80
230
2.875
0.1113
0.1247
1.00
1.00
1.00
Discharge throttling
90
240
2.667
0.1113
0.1154
1.2112
1.2112
1.1201
(b) Constant Volume Flow control
Inlet pressure, psia
Discharge pr, psia
Compression ratio
X = (k-1)/k
(P2/P1)^X - 1
Relative inlet density
Relative mass flow
Relative power
The final point to keep in mind is that the interaction of compressor operations with the rest of
the plant must be given due consideration in the design of the control system.
For example, if the process upstream of the compressors is under constant pressure control,
then the compressor control system must be designed (or modified) in such a way that starting
and stopping a compressor will not disturb the upstream process. In short, an integrated
control philosophy is needed.
4.4
Process Modifications
The principal process parameters that affect compressor power consumption are mass flow
rate, suction (inlet) temperature, and the compression ratio, so anything we can do to reduce
these three parameters through process modifications will help to reduce power consumption.
Flow requirements are generally set by process conditions, but one should examine the overall
process flowsheet to look for opportunities to change the material balance in such a way that
the flow through the compressor is minimized.
Inlet temperature can obviously be reduced by installing a heat exchanger in the process
stream entering the compressor, but this seldom pays out, because the new cooler creates
additional pressure drop in the system that will increase the required compression ratio. The
increased power requirement from a higher compression ratio will almost always be more than
the reduction from lower inlet temperature. The solution, once again, is to examine the overall
process flowsheet, and look for places where the suction stream may be being heated.
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SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 4-8: Process Modifications to Reduce Compressor Load
From Exhibit 4-8a, it can be seen that the suction stream to the Sales Gas compressor is
being used to cool the feed gas to the Gas Treating process. Effectively, we have a “heater” in
the compressor suction line; by bypassing it, as shown in Exhibit 4-8b, we can reduce not only
the suction temperature but also the compression ratio by eliminating the heater’s ∆P. The
process stream which was being cooled against compressor suction must now be cooled
against some other stream, with consequent net energy savings.
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SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Power consumption can also be reduced by minimizing the required discharge pressure. For
example most Aramco compressors have a fin-fan cooler in their discharge line, whose
cooling capacity varies with ambient temperature. One of the power conservation strategies
used by the operators is to shed power load on the fans during cooler weather (a laudable
attempt at thermal load management) once the temperature set point downstream of the
cooler is being met. However, maintaining a constant condenser temperature is the wrong
control objective if the compressor discharge stream is going to a condenser, because the
required pressure for condensation is not constant but varies with ambient temperature. In
such cases, even greater power savings could be obtained by following a different operating
policy – viz. to maximize the fin-fan cooling capacity but save even more power by minimizing
the discharge pressure (and therefore the compression ratio). A suggested control scheme is
shown in Exhibit 4-19, with the supporting calculations presented in Exhibit 4-10.
Exhibit 4-9: Power Conservation by Minimizing Compressor Discharge Pressure
The tricky part is being able to determine when exactly we have achieved total condensation,
something very difficult to do. The proposed solution is to have two condensers in series. The
main condenser would condense only about 90-95% of the vapor, and the vent condenser
would condense the balance. The control system would be set up to maintain a fixed 10:1 or
20:1 flow ratio between the main flow and the vent flow.
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SABP-A-002
Load Management for Energy
Efficiency: Pumps & Compressors
Exhibit 4-10: Shedding Fan Load vs Minimizing Compression Ratio
The required discharge pressure in Case 2 is found by successive iteration until the calculated
condenser surface area for cases 1 and 2 are identical.
Although process modifications cannot strictly be classified as Load Management, the subject
has been presented here because it is a way to introduce new degrees of freedom that enable
optimal load allocation between the different energy consumers in the overall system.
26 October 2005
Revision Summary
New Saudi Aramco Best Practice.
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