Meaningful Energy Performance Indices - IETC 2009

Industrial Energy Technology Conference, New Orleans (May 2009) Page 1 of 16

Meaningful Energy Efficiency Performance Metrics for the Process Industries Jimmy D Kumana, MS ChE Noshir R Sidhwa CEO Kumana & Associates, Houston, Texas

Business Development Manager Kumana & Associates, Houston, Texas

[email protected] [email protected] ABSTRACT An effective energy performance benchmarking should include a consideration of production rate, product specifications, feedstock mix, and process type, in addition to thermodynamics and economics. Unfortunately, there is no accepted industry standard for developing Energy Efficiency (EE) performance metrics for the chemical process industries, and published literature on the subject is extremely sparse. This paper will present a comprehensive system of EPIs as applied in a complex multi-product multi-plant organization in the oil and gas industry. Four categories of EPIs are recommended:

• By equipment • By process unit • By product • By business unit

It will be shown how each type of EPI fulfils a specific business objective in the organization. Successes and failures are described, and recommendations are provided. The principles and practices outlined in this paper are generally applicable, and will hopefully lead to a standard methodology for EE performance reporting. INTRODUCTION In recent years, Key Performance Indicators (KPIs) have become a popular tool for monitoring and managing how well an organization is doing in meeting its stated objectives [1, 2, 9, 10, 11]. Energy Performance Indices, or EPIs, are one such KPI. In this paper, the authors describe their recommended approach to developing a set of EPIs tailored to the company’s needs, and how they have been effectively used in a large multi-plant oil & gas company [4]. EPIs can have several different applications, each of which requires a different formulation:

• Report card, for information only • Benchmarking (historical, competitive, or absolute) • Economic Dispatch • Process Improvement, including troubleshooting • Operations optimization

In general, for a manufacturing company with multiple plants, one would need 4 different types of EPIs, depending on the application:


Type/Name Application Corporate energy KPI Report card for CEO’s dashboard Product EPIs Dispatching production among similar plants Process Unit EPIs Benchmarking, process improvement Equipment EPIs Operations optimization

While simple in concept, the actual implementation of a company-wide performance measure-ment program is usually fraught with challenges, many of which are often political and cultural rather than technical [6]. A good KPI should have the following characteristics [7]:

1. Direction of movement is consistent with change in actual performance 2. Magnitude of movement is consistent with of change in actual performance 3. Provides actionable information 4. Performance criterion is related to output (results) rather than input (resources expended) 5. Should be automated for online display and reporting.

A college economics textbook by Paul Samuelson provides an actual case study of what happens when performance metrics are not formulated correctly: In the former Soviet Union, steel mill performance was measured by how much steel was processed in the facility, and the manager was rewarded on the basis of capacity utilization, measured as “tons processed”. Now it so happened at one remote mill that, there was a chronic shortage of raw material supply due to transportation problems, and so the mill was operating well below its capacity. In his performance review, the manager was penalized for falling below the performance target, although it was due to no fault of his. His solution to improve the KPI was to re-process some of the mill’s product (output) using the full amount of labor and fuel. His KPI improved and he was rewarded accordingly, but his personal gain occurred at the expense of society’s loss. It is vitally important to ensure that performance targets and KPIs do not create perverse incentives. EQUIPMENT EPIs Common equipment used in process plants can be broadly classified into two groups:

Energy Consumers Energy Converters Pumps Boilers Compressors Steam turbines Process heaters (steam or direct fired) Gas turbines Dryers – steam or direct fired Internal-combustion reciprocating engines Distillation columns (in reboilers) Electric motors Misc rotating machinery Electric generators


Of the various types of EPIs, Equipment EPIs are the simplest and easiest to formulate. The proper measure of energy efficiency for energy consumers is:

SEEE 12

SuppliedEnergy Utility AbsorbedEnergy UsefulEfficiency −

==

The proper measure of energy efficiency for energy converters is:

A

B

EE

==InputEnergy

OutputEnergy Efficiency

Figure 1. Energy Flows through (a) Consumers and (b) Converters

Alternative formulations of equipments EPIs are as an “intensity” or as an “approach to target”. Both are acceptable, depending on which formulation is more useful.

Figure 2. Alternative Equipment EPI Formulations

Taking the example of a process pump, the overall efficiency (for a Newtonian fluid and assuming the driver is a 3-phase induction motor) is given by:

M

12

.cos I.V. 3.982(psi) ]P[P (gpm) Q Pumpηφ

x −=η

where Q = process fluid flow, gpm

P1, P2 = suction and discharge pressures, in psia or psig I = measured current, amperes

V = applied voltage (known) cos(φ) = power factor, expressed as a decimal, typically 0.85-0.92 ηM = motor efficiency (from data sheet), typically 94-97%


Normally V is known, but I is not, unless the motor is large enough to be included in the plant’s power monitoring system. Therefore on-line monitoring of pump efficiency is generally feasible only for larger sizes. An alternative off-line diagnostic tool to assess pump performance and mechanical condition is to plot the actual pump characteristic curve (flow rate versus ∆P) and compare it against the design curve. If the pump is delivering less TDH (total discharge head) than design at the measured flow, it means that the performance has fallen off and needs corrective action. The efficiency loss can be estimated very roughly as the ratio of actual head to design head at that particular flow rate. For compressors, use the adiabatic (isentropic) efficiency as a KPI for process efficiency, and the polytropic efficiency as an indicator of mechanical condition. Also keep in mind that although it may be an excellent KPI for overall process efficiency, power consumption is not necessarily a good indicator of compressor equipment efficiency. As an example, at a gas processing plant, total compression power was reduced drastically (50%) by making relatively simple piping and controls modifications. The overall process efficiency improved dramatically, even though some of the individual compressor efficiencies actually dropped slightly (due to operation at off-design conditions).

Trend line for compressor power consumption

0

2000

4000

6000

8000

10000

12000

1400016000

18000

Figure 3. Compressor Station Power Consumption at Constant Process Conditions

For fired heaters, the fuel efficiency is most simply calculated by the heat balance method, also known as the “direct” method:

HHVFhHW

.).( 12 −=η

where W = process fluid flow rate in lb/h; h1, H2 = inlet and outlet enthalpies of the process stream, Btu/lb; F = fuel flow rate; and HHV = higher heating value (in Europe, the lower heating value is preferred). While simple in concept, the heat balance method has a major deficiency – it gives accurate results only when the data quality is extremely good (generally less than 1% error), which is


hardly ever the case. A more accurate estimate of furnace efficiency can be obtained by the heat loss method (also known as the “indirect“ method) as follows:

LossesdutyAbsorbedduty Absorbed+

=

η or alternatively HHVF

LossesHHVF.

. −=η

Choose the formulation according to which measured value (absorbed duty or fuel input) is likely to be more accurate. For energy conversion devices, the formulation is slightly different, even though there could be several variations, as illustrated by the example of single-exhaust back-pressure turbine.

Figure 4. Schematic of Back-Pressure Steam Turbine

Here again, two kinds of efficiency are applicable. One is the isentropic efficiency of the turbine, which is an indicator of how well the machine was designed to begin with, and its present mechanical condition. This “machine” efficiency is calculated as:

'21

21

HHHH

−−

=η

where H1 = Enthalpy of HP inlet steam, Btu/lb H2 = Actual enthalpy of exhaust LP steam, Btu/lb

H2’ = Enthalpy of exhaust LP steam assuming isentropic expansion, Btu/lb The other kind of efficiency is the overall or “cycle” energy efficiency, which is calculated as a ratio of the useful energy output as a fraction of the energy input:

1

2

W.HW.H kw x 3413 +

=η

Since enthalpy cannot be measured directly, it must be inferred from pressure and temperature measurements using an electronic steam properties database. Some plants have “extraction”

BPST

HP Steam

LP Steam

PROCESS

WORK


steam turbines with two exhausts – at medium and low pressure. The thermodynamic efficiency is calculated for each stage, and the overall efficiency is derived from the stage-efficiency results. The isentropic efficiency is of more practical use from an operational viewpoint, because it provides a warning of developing mechanical problems. The cycle energy efficiency is more useful for design and decision-making purposes, such for calculating the process unit energy balance when conducting plant energy audits or choosing between project alternatives. PROCESS EPIs EPIs for Process Units and Process Areas are intended to measure the energy efficiency of an entire processing system, rather than single items of equipment. Examples of process units in an oil refinery are the Crude Distillation, Fluid Catalytic Cracking, Reforming, Hydro-treating, etc. Examples of process units in gas processing plants are Sweetening, Dehydration, and Sulfur Recovery. Examples of process units/areas in a Kraft pulp mill are Woodyard, Cooking, Pulp washing, Bleaching, and Chemical Recovery (could be further broken down into sub-units such as evaporation, recausticizing, etc). Some industries have developed standard metrics for their plant performance. A notable example is the Solomon Energy Intensity Index (EII) for Oil Refining, which builds up the overall plant energy index from the energy indices for individual process units. All that each refinery has to do is ensure that the data being used are accurate, and then link them into the EII calculation spreadsheet for each process unit to automatically display/print trend charts. Although attempts have been made to formulate a similar Energy Efficiency Index (EEI) for the gas processing industry, they have not been widely accepted, largely because the simplistic approaches used do not yield actionable information. For other process industries, there is no established standard at all for computing usable energy indices. The methodology described in this paper was developed specifically to fill this glaring unmet need. It is generally applicable to any type of process plant and for any industrial sector. In general, each process unit or area will have only two energy indices: a fuel index and a power index:

rameterCapacityPaptionFuelConsumIndexFuel =. and

rameterCapacityPamptionPowerConsuIndexPower =.

In cases where the process unit uses both steam and direct fuel, it may be desirable to have three indices for added insight – a process fuel index, a steam index (= boiler fuel index), and a power index. The energy consumed in the Utilities area of the plant and by common facilities (eg admin buildings, perimeter lighting) must be properly allocated to the process units. The consumption indices could also be combined into a single energy cost index. The “process area” indices are designed to monitor the energy efficiency of groups of process units that together produce recognizable intermediate products or perform a value-adding function on the feed – and whose capacities can be measured by the flow rate of those products


or feeds. A single “capacity parameter” should be chosen to represent the operating rate of each process area. For a typical gas-processing plant (Figure 5) suitable capacity parameters for the various units might be: combined feed gas flow, hydrocarbon condensate flow, sweet wet gas, sour off gas, product gas, etc. The recommended procedure is as follows:

• Divide the plant into major process areas (as illustrated in Figure 5) which have clearly defined feeds coming from other process units and clearly defined intermediate “products” that go to other units. These process areas may include more than one process unit, or may be only part of a process unit.

• Use feed and product data (flow rates, compositions, temperatures, pressures) and flow rates + concentrations of key intermediate streams from the plant information (PI) system to run an overall plant HMB simulation model. Extract the capacity parameters.

• Extract the steam and fuel consumption calculated by the simulation model. Convert steam usage to proportional fuel usage in the boilers. Add the fuel for steam plus direct fuel use in the process unit (area) to get the total fuel consumption. Divide this number by the selected capacity parameter to get the Fuel Index.

• HMB models typically do not currently calculate the plant power consumption. A simple but approximate method to get around this problem is as follows. (a) Estimate the net total plant power consumption (= import + generation - export) and call this X. (b) Calculate the power consumption of the major compressors and pumps from PI data using the equipment spreadsheet models. Call this Y. (c) Allocate the balance of power usage (X-Y) megawatts to each process unit based on installed HP of non-major running equipment. (d) Add the allocated and calculated power consumption for each unit and use the total to compute the Power Index.

Figure 5. Major Process Areas/Units for a Typical Gas Processing Plant


Overall process area EPIs following this approach are presented in Table 1. These indices clearly indicate which units are the major energy consumers, pinpointing specific areas of the plant that need attention both in terms of operating problems and opportunities for major cost reduction; in short they provide actionable information. Table 1. Process Area EPIs for a Gas-Processing Plant

Consider the process EPI trend chart shown for the Amine Gas Treating Unit over a 3-month period. What does it tell us? During the first two months steam usage is relatively stable, although a slight uptrend is discernible. Then around the beginning of the third month, it starts to rise rapidly. The area process engineer came up with three possible explanations:

• Faulty flow meter • Defective/damaged control valve • Colder feed to stripper (eg. due to HX fouling)

Upon investigation, it was discovered that there was a small but growing steam leak across one of the stripper feed pre-heater tubes. Without this process EPI, and the ability to interpret it correctly, the problem would not have been so readily discovered.

Figure 6. 3-month Trend Line for Steam Usage in the Gas Treating Unit

Process EPIs can also provide immediate feedback on process modifications. The EPI being used for a pumping station composed of multiple pumps of different capacities and a mix of driver types (Figure 7) was total pumping cost, in $ per 1000 gal of fluid transferred. Upon implementing a proposal to change the control strategy, viz. to run the fixed-speed motors in on/off mode and the turbine-driven pump in variable-speed mode, the Pumping Cost Index immediately dropped by about 30% (Figure 8), providing quantitative proof of the benefit from this recommendation.


Figure 7. Schematic Diagram of Pumping Station in an Oil Refinery

Pumping Cost Index

0

4

8

12

16

20

24

11/9 12/29 2/17 4/8 5/28 7/17 9/5 10/25 12/14 2/2

Day of year

cent

s/ K

gal

Previous

Optimized

Figure 8. Pumping Process EPI Proves Benefit of Proposed New Operating Policy

By the same token, if the index rises, it would indicate a potential problem such as fluid recirculation, mechanical failure within the pump, or some sort of obstruction in the pipe. PRODUCT EPIs The principal purpose of product EPIs is not process improvement, but cost accounting, and as a planning tool for “economic dispatch” (which means assigning production among multiple plants on the basis of economic criteria). Incidental benefits include the ability to accurately compare


production economics at one site versus another (competitive benchmarking), and to track production cost trends (historical benchmarking). A common practice is to express the Index as an Energy Intensity, eg. Btu/ton of product. It finds favor among management for its simplicity and ease of calculation, and allows mid-managers to satisfy senior management’s demand for KPI reporting, but it does nothing at all to give insight into the energy efficiency of the company’s production facilities, and is completely worthless for decision-making purposes. Consider this. If a company has two plants in different geographic regions – using different grades of raw materials, making different grades of the same product using different techno-logies, and with different energy prices, would it make any sense to compare their energy efficiencies? The obvious answer is a resounding NO. Benchmarking metrics only make sense if they are formulated to compare like with like. Here’s a real life example: A couple of oil refinery managers were resisting the deployment of product-based EPIs because of their “complexity”. We demonstrated that when the refinery yield of high-value distilled products was improved (very profitable) at the expense of additional energy, their energy intensity computed simplistically as Btu/MB actually got worse, which is directionally inconsistent with the right operational decision. The EPI formulation recommended by K&A, however, was directionally consistent.

Figure 9. Schematic Model for Product EPI Value-Added Allocation Procedure

The basic concept underlying correct formulation of Product EPIs is to properly allocate energy consumption in each of the process units to the appropriate product. There are many possible allocation strategies – by volume, by market value, by Btu content, by value-added, etc. All of them are problematic, in the sense that none of them exactly meet the criteria of directional and magnitude consistency. However, after testing them all under several real-world scenarios, the

E2E1

PROCESS UNIT 1

Feed F

Product A

Energy input E

Waste W

PROCESS UNIT 2

Inter- mediate Byproduct B


“value-added” method was found to yield the best overall results in terms of providing insights and actionable results [3]. Furthermore, it is more consistent and in alignment with most common formulations financial and other corporate KPIs. Step one, as always, is to produce simplified Process Flow Diagrams (PFDs) and a validated Heat and Material Balance simulation model. The mathematical model of the plant required to calculate the EPI does not necessarily follow the “process unit” modular structure. Rather, the goal is to set up a sequence of modules that are punctuated by splits and mixes in various intermediate streams in such a way that they can be easily attributed to a specific product. In some cases, therefore, we may group multiple process units together into a module, and in other cases, a process unit may be sub-divided into a number of sub-systems, or even individual items of equipment. Step two is to apply the “Value-Added” allocation procedure to calculate the overall product EPIs using the equations below, according to the procedure described in Reference [7].

E.VV

VE .E;VV

VEBA

BB

BA

AA ⎟⎟

⎠

⎞⎜⎜⎝

⎛+

=⎟⎟⎠

⎞⎜⎜⎝

⎛+

=

where Vi = gross value added to the business by manufacture of product i, and Ei = amount of energy allocated to product i. The energy intensity indices are EA/A and EB/B. The methodology, in its complete form, is generally applicable to more complex scenarios with multiple feeds, multiple sources of energy, and even money-losing byproducts.

Figure 10. Product EPIs for Two Grades of Liquid Butane

Figure 10 shows the product EPIs for liquid butane product that is produced in two grades. One is pipeline grade (non-refrigerated) and the other is export grade (refrigerated for export by ship).


For more than 20 years since plant startup, both grades were priced exactly the same. It was only when the company went through the exercise of developing EPIs that they realized how much more expensive it was to produce export-grade product. One recommendation K&A made was to reduce energy costs by minimizing the residence time of product in the storage tanks through better inventory management. Another was to adopt differential pricing for the two grades. Such actionable insights could not have been gained from the feed-based indices that were being used previously. OVERALL CORPORATE ENERGY KPI A well-known international management consulting firm was retained by our client to create a complete “dashboard” of corporate KPIs, with energy being one of them. They conducted a world-wide survey of 29 major companies in the oil and gas industry which revealed that almost all of them express their overall corporate energy index as an Energy Intensity, viz. Btu/MBoe. Based on this finding, they recommended the following corporate energy KPI:

While this may be convenient and easy to calculate using readily available data, it is a very poor indicator of whether the company is becoming more or less efficient, and useless for comparing the company’s energy efficiency against it peers. The reason is that each company has many different types of operations, each with a different energy intensity. The energy intensity for each plant is affected by several uncontrollable external factors such as field aging, weather, etc. Furthermore, the composite corporate index comprises a volume-weighted average of all the different products/technologies. Over a period of several years, the company’s business shifted towards more oil refining in proportion to crude oil production, and to heavier (cheaper) grades of crude oil feedstock that require more energy to refine. Thus their corporate energy index (as formulated above) rose even though they had a highly effective energy conservation program (see Figure 11 for individual business lines), and company operations actually become more profitable overall. In short, this simplistic index failed to meet the “directional consistency” test. To ensure directional consistency, the energy KPI should be formulated as a ratio, eg:

Energy Efficiency Index (EEI) = 100 * {Actual Energy Cost/Standard Energy Cost}

where Actual Energy Cost = ( ) ( )iN

ii priceenergynconsumptioenergy ∑

Standard Energy Cost = ( ) ( )iN

ii priceenergynconsumptiodardstan ∑

and i = type of energy used, viz. gas, oil, power, etc.


Energy Intensity, Oil & Gas Industry

0

10

20

30

40

50

60

70

1999 2000 2001 2002 2003 2004 2005 2006

Cen

ts/B

OE

Oil & Gas prod'n

Oil Refining

Gas Processing

Figure 11. Energy Intensity Trends for Different Business Units

The “standard energy consumption” depends upon a number of variables, principally throughput, but also production volumes, feedstock composition, product mix, product specs, fuel mix, technological advances, and ambient temperature. Ideally, it should be calculated from first-principles using rigorous simulation models, although shortcut approximations may be used on an interim basis to get started. The calculated actual corporate EEIs for the cited example are shown as blue squares (years 2000-2006) in the Figure 12. The target EEI trajectory is shown as green triangles. In this formulation, the lower the EII, the better. The difference between the actual EEI and 100 is a measure of the % savings achieved by the energy efficiency improvement initiatives.

Figure 12. Correctly Formulated Corporate EEI Gives Actionable Information


The company’s stated goal was to reach an overall corporate EEI of 50 by 2010, from a baseline of 100 (by definition) in 2000. The chart indicates that as of end 2006 (we do not have data for 2007-08) the company was ahead of schedule in meeting this goal, but that without continued sustained effort, the savings rate would level off in 2009, and fall short of the 2010 target. The beauty of this index is that not only is it an accurate KPI for past performance, but also has predictive capability, so that timely management action can be taken to make mid-course corrections. Finally, it should be noted that even though energy prices vary over time, including them in the calculation procedure will not have a destabilizing effect on the EEI because prices occur in both the numerator and denominator. To further reduce the impact of energy price fluctuations, the recommended strategy is to re-calculate the EEI for all previous years using the new price structure whenever there is a significant change, so that the numerical value of all indices will reflect the latest and most representative economic conditions, and year-to-year comparisons can be made on a common basis. It suffers from only one drawback – calculating the “standard” energy consumption requires accurate simulation models, which can expensive to develop and maintain. Unfortunately there is no other way of accounting for a dynamic business environment. There’s a huge difference between simple and simplistic. Simple is good; simplistic is bad. CONCLUSION The proposed comprehensive methodology has been proven to meet all the principal objectives important to management, and the formulation principles are generally applicable. It is recommended for adoption by all the process industries. Keeping in mind that the objective of KPIs is to effectively manage energy performance, it is critical to ensure that the following supporting infrastructure is provided: 1.) All energy consumption should be measured accurately (more/better instrumentation and

data reconciliation software may be needed).

2.) Accurate material and energy balances for all operations.

3.) All energy consumption should be associated with a specific value adding activity.

4.) Primary energy (eg. fuel, purchased power) should be priced at marginal cost; secondary energy (eg. steam, cooling water, cogenerated power) costs should be traced back to primary energy costs via the simulation model [3].

5.) A data acquisition and archival system is needed to document accumulated knowledge and apply it systematically to operational and strategic decision making.

6.) For effective results, a company-wide online Energy Management Software system should be deployed (Figure 13).


Figure 13. Architecture for OpsKPI® Energy Management Software Deployment [8]

NOTE: OpsKPI is a registered trademark of AspenTech Inc, Boston, Mass. ABBREVIATIONS

Description BL Black Liquor (Kraft pulp mill) BOE Barrels of oil, fuel equivalent Btu British Thermal Unit CEO Chief Executive Officer EEI Energy Efficiency Index EII Energy Intensity Index EPI Energy Performance Index HMB Heat & material balance HP Horsepower (for motors) HP High pressure (for steam) K Thousand KPI Key Performance Indicator LP Low pressure (for steam) MB Thousand barrels MM Million scf Standard cubic feet (of gas)


REFERENCES 1. K R Amarnath, J D Kumana and J V Shah, “Benchmarks for Industrial Energy Efficiency”,

presented at Intersociety Energy Conference, Washington, DC (Aug 1996). 2. D Farrell and J K Remes, “How the World Should Invest in Energy Efficiency”, McKinsey

Quarterly (July 2008). 3. J D Kumana and M M Al-Gwaiz, “Pricing Steam and Power from Cogeneration Systems

using a Rational Allocation Procedure”, Proc of 26th Industrial Energy Technology Conference, Houston, Tx (April 2004).

4. J D Kumana and K D Al-Usail, “Energy Performance Indices as a Process Diagnostic

Tool”, presented at Process Performance Monitoring and Data Analysis Symposium, Manama, Bahrain (Nov 7–8, 2006).

5. J D Kumana, A H Al-Qahtani, and F H Al-Farsi, “Power Savings via Load Management at

Rabigh Refinery”, presented at 2nd Saudi Arabian Energy Conservation Forum, Dammam, Saudi Arabia (Nov 28-29, 2006).

6. J D Kumana and A S Aseeri, “Success Factors for a Corporate Energy Program”, presented

at 29th Industrial Energy Technology Conference, New Orleans, La (May 9-10, 2007). Extended article republished in two parts - Insulation Outlook (Nov 2008 and Dec 2008).

7. Kumana & Associates, “Industrial Energy Performance Measurement and Monitoring” a 3-

day training course (in prep, 2009). 8. E Petela, AspenTech Ltd, Warrington, UK, personal email communication (2007). 9. S Rivoire, M A Shah, P Ranganathan and C Kozyrakis, “JouleSort: A Balanced Energy

Efficiency Benchmark”, presented at SIGMOD conference, Beijing, China (June 2007). 10. US Environmental Protection Agency, “Efficiency Metrics for CHP Systems”,

www.arb.ca.gov/cc/ccei/publications, (2007) 11. World Business Council for Sustainable Development, “Energy Efficiency as a Strategy:

GE Case Study”, www.wbcsd.org, (2008).

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Meaningful Energy Performance Indices - IETC 2009