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3E STRATEGY
STRATEGY
EFFICIENCY
ENERGY
EARNINGS
G u i d e B o o k 1
H
ow
to
save
energy
and
m
oney
THE 3E STRATEGY
EUROPEAN COMMISSION
N e t h e r l a n ds M i n i s t e r y o f E c o n o m i c A f f a i r s
TSITechnical Services International
MY
IN GRE ER
A NL ES DAN
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HOW TO SAVE
ENERGY AND MONEY:
THE 3E STRATEGY
This booklet is part of the 3E strategy series. It provides advice on practical
ways of how to save energy and money in companies and the ways of
going about it.
Prepared for the European Commission DG TREN by:
The Energy Research Institute
Department of Mechanical Engineering
University of Cape Town
Rondebosch 7701
Cape Town
South Africa
www.eri.uct.ac.za
This project is funded by the European Commission and co-funded by the
Dutch Ministry of Economics, the South African Department of Minerals
and Energy and Technical Services International, with the Chief contractor
being ETSU.
Neither the European Commission, nor any person acting on behalf of
the commission, nor NOVEM, ETSU, ERI, nor any of the information
sources is responsible for the use of the information contained in this
publication
The views and judgements given in this publication do not necessarily
represent the views of the European Commission
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H O W T O S A V EE N E R G Y A N D M O N E Y :
T H E 3 E S T R A T E G Y
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HOW TO SAVE
ENERGY AND MONEY:
THE 3E STRATEGY
Other titles in the 3E strategy series:
HOW TO SAVE ENERGY AND MONEY IN STEAM SYSTEMS
HOW TO SAVE ENERGY AND MONEY IN ELECTRICITY USE
HOW TO SAVE ENERGY AND MONEY IN BOILERS AND FURNACES
HOW TO SAVE ENERGY AND MONEY IN COMPRESSED AIR SYSTEMS
HOW TO SAVE ENERGY AND MONEY IN REFRIGERATION
HOW TO SAVE ENERGY AND MONEY IN INSULATION SYSTEMS
Copies of these guides may be obtained from:
The Energy Research Institute
Department of Mechanical EngineeringUniversity of Cape Town
Rondebosch 7701
Cape Town
South Africa
Tel No: 27 (0)21 650 3892
Fax No: 27 (0)21 686 4838
Email: 3E@eng.uct.ac.za
Website: http://www.3e.uct.ac.za
ACKNOWLEDGEMENTS
The Energy Research Institute would like to acknowledge the following for their
contribution in the production of this series of guides:
. Energy Technology Support Unit (ETSU), UK, for permission to use information
from the Energy Efficiency Best Practice series of handbooks.
. Energy Conservation Branch, Department of Energy, Mines and Resources, Canada.
. The IEA CADDET Energy Efficiency Energy Management in Industry booklet is a
major source for this guide.
.
Wilma Walden for graphic design work (Walden@grm.co.za).
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T a b l e o f C o n t e n t s1. INTRODUCTION ....................................................................................................................... ............................................................ 5
2. A COMPANY 3E STRATEGY ..................................................................................................................... .................................... 6
2.1 Commitment and Organisation ................. ............................................................................................................................... 6
2.2 Common problems associated with Energy Cost Reduction Programmes ...................................................... 6
2.2.1 Uneven Distribution of Knowledge ............................................................................................................................ 6
2.2.2 Lack of Accountability ........................................................................................................................................................ 6
2.3 Cost Reduction Programme ................................................................................................................... .................................... 7
2.4 Achieving the Savings: In-house Expertise and Consultants ....................................................................................... 9
2.4.1 Fee Based Consultants ....................................................................................................................................................... 9
2.4.2 Performance Based Consultants ..... .............................................................................................................................. 9
2.5 Energy Audits ...................................................................................................................................................................................... 9
2.5.1 Walk Through Audit ........................................................................................................................................................... 9
2.5.2 Diagnostic Audit .................................................................................................................................................................... 10
3. ENERGY CONSUMPTION AND COSTS .............................................................................................................................. 113.1 Consumption and Costs ........................................................................................................................... .................................... 11
3.1.1 Invoice Data ............................................................................................................................................................................. 11
3.1.2 Annual Energy Input and Site Performance Indicators ..................................................................................... 12
3.1.3 Instrumentation and Closer Investigation ................................................................................................................. 13
3.2 Fuel Purchase and Tariffs .............................................................................................................................................................. 13
3.2.1 Pipe Line Gas .......................................................................................................................................................................... 13
3.2.2 Electricity ....................................................................................................................... ............................................................ 13
3.2.3 Liquid Oil Products .............................................................................................................................................................. 14
3.2.4 Coal .............................................................................................................................................................................................. 143.2.5 Liquefied Petroleum Gases .............................................................................................................................................. 14
4. MONITORING AND TARGETING (M & T) ....................................................................................................................... 15
4.1 Characteristics of Processes Determined from M&T Data ........................................................................................ 16
4.2 Process Energy Linked to Production .................................................................................................................................... 17
4.3 Approximating Multivariable Situations ....................................................................................................................... .......... 24
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4.4 Building Heating linked to Degree Days ............................................................................................................................... 25
4.4.1 Degree Days ............................................................................................................................................................................ 25
4.4.2 Building Cooling linked to Degree Days .................................................................................................................. 28
4.5 Processes linked to Time Through Activities ..................................................................................................................... 28
4.6 Processes with No Relation to Other Variables or Time ........................................................................................... 30
4.7 Monitoring Data as an Indicator of Efficiency .................................................................................................................... 30
4.7.1 Non-productive and Activity-unrelated Energy Consumption ..................................................................... 31
4.7.2 Production-related Efficiency ........................................................................................................................................... 32
4.7.3 Building Heating Efficiency ................................................................................................................................................ 33
5. USING INFORMATION ON ENERGY USE FOR MANAGEMENT CONTROL .......................................... 36
5.1 Introduction ......................................................................................................................................... ................................................. 36
5.1.1 Non-productive Consumption .............................................................................................................................. ......... 36
5.1.2 Production-related Efficiency ........................................................................................................................................... 36
5.2 CUSUM Technique .......................................................................................................................................................................... 37
5.2.1 The Control Chart .............................................................................................................................. ................................. 395.2.2 Non-parametric Forms of CUSUM and Control Chart .................................................................................. 41
5.2.3 Application of CUSUM ...................................................................................................................................................... 41
6. FACTORY SERVICES ............................................................................................................................................................................. 43
6.1 Motors and Drives ............................................................................................................................................................................ 43
6.1.1 Check List .................................................................................................................................................................................. 43
6.2 Compressed air ......................................................................................................................... ......................................................... 44
6.2.1 Check List .................................................................................................................................................................................. 44
6.3 Refrigeration ......................................................................................................................................... ................................................ 446.3.1 Check Lists ................................................................................................................................................................................ 45
6.3.2 Refrigeration Cold Stores ................................................................................................................................................. 45
6.4 Chilled and Cooling Water .......................................................................................................................................................... 45
6.4.1 Check Lists ................................................................................................................................................................................ 46
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7. INDUSTRIAL HEATING PROCESS ............................................................................................................................................ .. 47
7.1 Boilers and Boilerhouse Management .................................................................................................................................... 47
7.1.1 Check List ................................................................................................................................................................................. 48
7.2 High Temperature Processes ................................................................................................................................................... .. 48
7.2.1 Check List ................................................................................................................................................................................. 49
7.3 Low Temperature Processes ...................................................................................................................................................... 49
7.3.1 Check List ................................................................................................................................................................................. 49
8. BUILDING SERVICES ............................................................................................................................................................................ 50
8.1 Space Heating ..................................................................................................................................................................................... 50
8.1.1 Check List ................................................................................................................................................................................. 50
8.2 Air Conditioning and Ventilation .............................................................................................................................................. 51
8.2.1 Check List ................................................................................................................................................................................. 51
8.3 Hot Water and Water Supply ................................................................................................................................................. . 51
8.3.1 Check List ................................................................................................................................................................................. 51
8.4 Lighting .................................................................................................................................................................................................... 51
8.4.1 Check List ................................................................................................................................................................................. 52
9. CAPITAL EXPENDITURE .................................................................................................................................................................. 53
9.1 Financial Criteria ..................................................................................................................... ........................................................... 53
9.2 Raising Capital ..................................................................................................................................................................................... 53
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1. INTRODUCTION
The 3E's are 'Energy Efficiency Earnings' and this
booklet lays out the how to of implementing the
strategy in companies. Energy is one of the largest
controllable costs in most organizations and there
is considerable scope for reducing energy con-
sumption and hence cost. The benefits arereflected directly in an organization's profitability
but they also contribute to improving the global
environment. The essentials of implementing the
3E strategy are detailed in what follows.
An energy audit is an essential activity for any
organisation wishing to control energy and utility
costs. This booklet describes the five fundamental
aspects of an energy management strategy:
. Section 2 details the need for a Company
3E Strategy or energy plan and outlines the
basis for a cost reduction program;
. Section 3 relates to purchase and cost
control as well as a consumption audit of
primary energy usage;
. Section 4 gives the framework and meth-
odology for monitoring and targeting
energy savings;
. Sections 6, 7 and 8 covers savings in energy
usage through positive practical methods
for improving the efficiency of plant and
industrial processes and
. Section 9 is concerned with the financial
appraisal of energy efficiency.
This booklet is intended to act as a practical manual
to enable Works Engineers, Energy, and Engineer-
ing Managers to make savings in site energy costs.
Accordingly the major sections are sub-divided into
the smaller sub-sections:
. the audit and use of energy for typical
industrial plant and processes:
. a checklist of potential methods for
reducing costs.
In this way, depending on individual experience and
site requirements, only the relevant parts need to
be read in detail.
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2. A COMPANY 3E STRATEGY
A Company's 3E strategy or energy plan forms the
basis for minimizing purchase costs and use of
energy and related utilities such as water, tele-
communications and transport. The main organiza-
tional aspects are outlined below while the
technical and practical aspects are detailed in theremainder of the booklet.
2.1 COMMITMENT AND
ORGANISATION
Effective energy management requires the commit-
ment of senior management. This provides theauthority to take action, to utilise people skills, to
provide finance, other resources and, most im-
portant, motivation.
The organisation of an energy management plan
can then be determined. This can vary from a
committee or working party approach to the
assignment of additional responsibilities to specific
staff. The energy programme will depend on a
number of factors, including: company size; relative
importance on energy costs; technical expertise;
and management style. The important aspect is that
energy is integrated as a management function and
is managed in the same way as any other resource
in the company.
2.2 COMMON PROBLEMS
ASSOCIATED WITH
ENERGY COST
REDUCTION
PROGRAMMES
2.2.1 UNEVEN DISTRIBUTION OF
KNOWLEDGE
Figure 1 overleaf represents a typical situation.
Technical and engineering staff are often aware of
effective energy and cost saving measures. This
knowledge, often does not get implemented byoperational staff, as middle and top management
are not aware of the potential energy and cost
savings.
2.2.2 LACK OF ACCOUNTABILITY
It is often the case that strategies to save energy are
not considered by all the sections of a factory. A
utilities section is responsible for supplying various
forms of energy elsewhere on a plant for
production.
By simple changes in production or maintenance,
large savings can very often be made. These savings
may not interfere with the process or outputs.
They are in many cases not considered because
there is an absence of an energy and cost reduction
programme that involves various levels of manage-ment and plant sections involved.
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2.3 COST REDUCTION
PROGRAMME
Energy saving projects may be divided into four
categories:
(i) Housekeeping. Simply improved housekeeping,
making sure that equipment operates properly,
cleaning fouled surfaces and pipes and having
regular maintenance can save much energy and
money.
(ii) Low Cost. Many energy improvements may be
made with low cost modifications and improve-
ments.
(iii) Retrofits. Retrofitting existing systems with
new parts and equipment can bring great benefits
in energy efficiency.
(iv) Major Capital expenditure. This is the most
costly option and should only be considered last.
Often the money saved through options (i) to (iii)
can finance (iv).
The basis for reducing site energy costs is shown in
flow chart form in Figure 1, together with a
reference to the relevant part of this booklet for
each stage.
. Energy consumption and costs
Auditing and monitoring are linked as components
of an overall strategy for effective energy manage-
ment and these are discussed in Sections 3, 4 and
5. In effect this preliminary audit is to identify the
main areas of expenditure and to minimize utility
purchase costs.
Monitoring provides management control of utility
costs in the same way as control of labour or raw
material costs.
. Factory services and industrial processes
The understanding of energy use in industrial
processes can be assisted by preparing an energyf low diagram as part of an audit based on
examining current practices and patterns of use.
Figure 1: Effective use of information. (source: CADDET)
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In this way improvement in operation and the
potential for energy saving projects can be
identified.
Opportunities for cost savings with the main
industrial processes and factory services are
presented in checklists in Sections 6, 7 and 8.
Figure 2: Flow chart for energy audits. (source: ETSU)
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. Capital investment and project imple-
mentation
Proposals for high levels of capital expenditure
should conform to the Company's accepted
methods of financial appraisal. An overview of
cost/benefit analysis is given in Section 9 together
with alternative means of financing projects such as
leasing and Contract Energy Management.
2.4 ACHIEVING THE
SAVINGS: IN-HOUSEEXPERTISE AND
CONSULTANTS
With the relevant staff, time and expertise, most
savings can be achieved in-house. If in-house
manpower is not available consultants can be
employed. In the area of cost reduction paying for
consultants generally falls into two categories:. fee based
. performance based on savings achieved
Whichever option is chosen it is worth carrying out
simple checks to ensure value for money. This
should include:
. asking for and taking up references;
. meeting the engineers or at least obtaining
CVs;
. obtaining more than one quotation;
. using a member of a recognized body.
2.4.1 FEE BASED CONSULTANTS
This has been the traditional way of employing
energy consultants, usually on a fixed fee basis but
sometimes on a day rate. The main consideration is
to ensure clear terms of reference. In addition today rates, time and work delivered need to be
carefully controlled. Experienced and competent
staff will undertake work in far less time than
inexperienced staff, however well qualified,
although the daily rates may be double.
2.4.2 PERFORMANCE BASED
CONSULTANTS
Some consultants now work on a performance
basis, with all fees coming from savings achieved.
The fees are usually based on a percentage of
savings for an agreed period of time, typically 50%
for periods ranging from one year to five years.Performance Contracts need to be checked in the
same way as those for fee based work.
Contract Energy Management (CEM) companies
generally provide finance for capital intensive work
as well as management of site utility services.
Contracts are usually fairly long term, typically from
five to ten years.
2.5 ENERGY AUDITS
An energy audit involves the identification of areas
throughout a facility where energy may be wasted
because of nonexistent, or inadequate insulation.
The audit may be applied to the facility as a whole,
or may be concentrated on specific pieces ofprocess equipment or piping systems.
2.5.1 WALK THROUGH AUDIT
The initial action is aWalk Through Audit, which is
a tour through the facility looking for obvious signs
of energy waste. The walk through audit is generally
more meaningful if an individual who, though notassociated with the facility operation, and who is
familiar with both the subject of process insulation
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and the concept of energy management conducts
it.
Typical items which could be noticed during a walk
through audit would include missing or damaged
insulation, hot or cold surfaces, wet insulation,
deteriorating insulating coverings or protective
finishes, missing or damaged vapour retarders, gaps
in insulation at expansion/contraction joints, ex-
cessive heat radiating from insulated surfaces and
other similar items.
2.5.2 DIAGNOSTIC AUDIT
Once items have been identified in the walk
through audit, a diagnostic audit is required to
determine the existing energy loss, the reduction in
energy loss which would result if new or additional
insulation or covering were installed and the
installed cost of the added material. The reduction
in energy consumption establishes the rand savings.
With this information, simple payback calculations
can establish the financial viability of the opportu-
nity.
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3. ENERGY CONSUMPTION
AND COSTS
To be effective, energy and utility management
must address three essential areas:
. Purchasing;
. Management;
. Engineering.
This Section covers the first two areas.
The first step in identifying areas for potential
savings is to establish the quantity and cost of the
energy and utilities used on the site. This includes
fuel oil, coal, gas and electricity but also water and,
on some sites, vehicle fuel usage.
Having completed this analysis it is then essential to
investigate whether the utilities are being purchased
competitively. It is pointless investing capital in
engineering projects unless the energy or utility is
being bought at the right price.
Management control is an essential element in any
cost reduction programme. Apart from the need to
monitor and maintain savings brought about byimproved purchasing and engineering projects,
there are often savings available simply by managing
resources more effectively using standard monitor-
ing and targeting techniques.
3.1 CONSUMPTION AND
COSTS
It is necessary to obtain an accurate picture of
current consumption: how much is spent on energy
in different forms and the unit costs; what it is used
for; which uses are essential and which are not. This
information should be obtained from the following:
. utility invoices for fuel, electricity and water
for at least one year;
. site energy records and sub metering;
. production information.
3.1.1 INVOICE DATA
Data should be checked carefully to ensure that
there is a complete record and that it can beidentified with known supply points. The numbers
required are energy units for each month as well as
tariff charges and structure. Note any estimated
readings; additional earlier invoices should be
collected for comparison if there are more than
one or two estimates in the audit period.
A summary table should then be prepared for each
fuel, electricity and water showing consumption
and costs. The monthly trends in consumption arecorrespondingly plotted. In this way variations
during the year can be seen and the trend
examined to determine any untoward pattern of
consumption.
. A seasonal or cyclical pattern could
indicate major seasonal loads such as space
heating.
. General upward or downward trends can
reflect changes in load or efficiency. They
could also be attributed to changes in
operating practice.
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. The lack of a clear pattern where variations
are normally expected might suggest a lack
of control.
. Where boiler plant serves a mixed load, a
steady base load can be identified, usually
due to domestic hot water, standing losses
and any continuous process load.
3.1.2 ANNUAL ENERGY INPUT
AND SITE PERFORMANCE
INDICATORS
The total annual energy use on a site can be used
to calculate a Performance Index, to assess the
energy performance and indicate whether there is
likely to be a good opportunity for improvement.
These indices provide useful guidance in setting
priorities, but actual settings will depend on
production and process plant.
The annual consumption for each energy type
should be converted to a standard unit (e.g.
gigajoules, GJ) using the conversion factors in
Appendix 1. After calculating the percentage
breakdown of total energy consumption and cost
of energy type, a table can be prepared.
The next stage is to obtain information on energy
use by the various types of activity in the
organization, which can then be audited separately
to establish consumption and costs. Effort can then
be directed to the major areas and opportunities
for savings can then be more carefully examined, asset out in Sections 6, 7 and 8.
The first step is to establish a list of main services
and/or end users. Try to identify specific areas of
consumption such as:
. factory services (e.g. motive power; com-
pressed air; refrigeration etc.);
Figure 3: Simple energy account for a small factory. (source: ETSU)
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. heating processes (boilers; furnaces; kilns
etc.);
. building services (space heating; domestic
hot water; lighting etc.).
Initially consumption and, therefore, costs can be
estimated on the basis of installed load, operating
hours and utilization factor. Consumption informa-
tion can be presented in the form of a Sankey
diagram, as illustrated in Figure 3.
A Sankey diagram is useful in that it gives an
immediate visualization of energy flows and thusenables priority areas to be identified and tackled.
3.1.3 INSTRUMENTATION AND
CLOSER INVESTIGATION
More detailed information on consumption can be
obtained in a number of ways:
. demand profile recording;
. metering selected items of plant/factory
areas.
There is usually a great deal to be learnt from a
study of the energy profile.
Initially meters can be read manually, but the use of
instrumentation makes data collection more
straightforward. Electrical demand profiles can be
monitored with clip-on instrumentation and this
may well identify scope for savings through the
control of Maximum Demand. Gas and water
meters without built-in pulsed outputs can be read
automatically using optical couplers. Data transfer
to a personal computer and the use of a
spreadsheet or similar program will ease analysis.
Installation of meters on an area or individual plantbasis can be used to record consumption. By
comparing energy use and production, an analysis
of the efficiency of the plant can be obtained. The
cost of submetering can usually be justified on
major loads, particularly where little information on
energy use is currently available.
Once installed, meters should read on a regular
basis to establish trends. The impact of energy
saving initiatives, or process changes, can then
readily be determined.
3.2 FUEL PURCHASE
AND TARIFFS
Obtaining the best energy price depends on
market knowledge and negotiating skills. If in-house
expertise is not available there are numerous
consultants and advisers able to assist.
3.2.1 PIPE LINE GAS
Currently pipeline gas is sold by SASOL. Various
tariffs are available subject to consumption vo-
lumes. While not yet in place, it is likely that
imported natural gas will supplement the existing
network and new networks may be installed in
Cape Town.
Large boiler plant can operate on dual-fuel supplies
and it is important to ensure that the most cost
competitive fuel is used, wither interruptible gas or
fuel oil.
3.2.2 ELECTRICITY
The electricity market is becoming more complex
with a range of fixed tariff options available for
consumers. Contracts can be on a fixed unit cost
basis, similar to tariff structures, or electricity can be
purchased on a pool-based contract with prices
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varying throughout the day, depending on supply
and demand. In this climate, market intelligence and
negotiating skills are essential and companies must
keep in touch with what is on offer.
When large load shifts to off-peak tariff times are
possible, they may be made more viable by
renegotiating the time of use tariff. This may be
beneficial to the supplier as it would increase off
peak demand and help increase his load factor.
3.2.2.1 ELECTRICITY TARIFFASSESSMENT
Supply capacity, Maximum Demand and, where
appropriate, power factors should be checked to
ensure that these costs are minimized. The tariff
structure most appropriate to the site operating
pattern should be selected. The demand profile
should be monitored and the various tariff options
costed to determine the optimum choice.
3.2.3 LIQUID OIL PRODUCTS
Liquid fuels are available from a number of
suppliers, and it is therefore possible to negotiate
for the best deal. Prices depend primarily on
market conditions, but also vary with quantity
purchased, season and supplier. For example, if
storage facilities are adequate, oil can be purchased
at lower costs during the summer months for use
at the start of the winter season.
3.2.4 COAL
It is important that coal prices are assessed on the
basis of delivered energy and not weight when
comparing competitive quotes. Bulk purchases can
provide additional savings.
3.2.5 LIQUEFIED PETROLEUM GASES
Butane or propane can be bought from various
suppliers either on a fixed price or on an indexed,
variable, basis. Again, knowledge of market condi-tions is important in the purchasing process.
For sites with a large water use it is essential to
carry out a detailed mass balance to identify both
supply and effluent volumes and ensure that
charges are correct, and also to detect wastage,
particularly at weekends when production is not
occurring. On water systems there are often large
savings available from preventing leaks and wastage.Initially, monitoring of use should be carried out
through hourly readings.
Where a water borehole is available this is
generally the cheapest means of supply. It can also
be cost effective to install an effluent treatment
plant as a means of reducing overall disposal costs.
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4. MONITORING AND
TARGETING (M & T)
The initial energy audit provides information on
consumption and costs on the site and can also
highlight areas where savings can be made.
M & T is a disciplined approach to energy
management, which ensures that energy resources
are used to the maximum, as well as monitoring
savings brought about by improved purchasing and
through energy saving investments.
At its simplest, monitoring involves the systematic
and regular measurement and recording of the
energy consumption of the whole organization.
The principles necessary for forming a monitoring
and targeting program are loosely pictured in Figure
4. Commitment, understanding and motivation for
the implementation of the M & T part of a 3E
program are essential in order obtain success.
Upon these the data that has been gathered must
be presented to management together with
proposed improvements.
This data can be obtained in a variety of ways for
example, from fuel invoices, which might require
adjustment to allow for different reading dates, or
from metering.
It is important that the monitoring process is tied in
with other company review processes, such as
monthly financial and production figures, so that
information on energy flows can be meaningfully
related to other performance data.
Figure 4: Monitoring and Targeting action steps. (source: ETSU)
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Figure 5: Information flows necessary for successful monitoring and targeting. (source: CADDET)
4.1 CHARACTERISTICS OF
PROCESSES DETERMINED
FROM M & T DATA
From an M & T standpoint, industrial processes
divide into two groups:
1. Processes where energy use is largely
determined by the physics of the process, i.e.
how much energy is used and to what extentthe process transforms the product. This group
comprises all heat-based processes (heating,
melting, evaporation); all chemical and electro-
chemical processes; and some processes requir-
ing physical work such as the compression of
gases and vapours (for example, refrigeration
and compressed air).
2. Processes in which physics provides a poor
indication of the energy needs or of the
extent of the process most of these
processes are mechanical in nature and com-
prise processes such as cutting, size reduction,mixing, conveying, etc.
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All the processes in the first group are sufficiently
consistent in their energy behaviour to make M&T
easily applicable: success depends mainly on the
skill with which it is applied. In the second group,
whether or not M & T has a place depends on how
far energy consumption can be meaningfully
related to some measure of production, or
whether another system of performance evaluation
can be found.
Fortunately, a very large proportion of industrial
energy use comes into the first group, and much of
the Statistical Process Control (SPC) element ofquality management has been developed to handle
processes in the second group. So, for a very wide
range of processes there is already some estab-
lished basis on which measured energy use could
be used for management control.
Within the second group there are three forms of
energy, which are difficult to handle:
. energy consumption associated with activ-
ities linked to time rather than production
this applies to many of the non-
production uses of electricity;
. energy consumption, which is not linked to
production but to the weather space
heating and space cooling;
. vehicle fuel.
4.2 PROCESS ENERGY LINKED
TO PRODUCTION
In processes where there is a strong link to
production, the first requirement is to establish the
nature of the link. This is easiest to consider in the
form of an energy vs. production scatter graph.
[te metric tonnes]
Figure 6: Energy vs. production for a glass melting furnace
the common form of graph. (source: ETSU)
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Figure 6 represents a basic pattern to which the
behaviour of most processes can be related. Such agraph contains three elements:
1. An intercept (the point where a best fit line
through the data cuts the energy axis at zero
(production) this is the energy that would be
required if this process ran but did not produce
anything. It is also energy consumption that
continues while production is in progress but
does not contribute to production.
2. A slope the amount of energy required at any
given level of production to process each
additional unit of production. The efficiency of
the process can be established from the slope.
3. The scatter the amount by which the energy
used for any one level of production varies from
one period to another. This tends to be
governed by operational factors.
4. The pattern in Figure 6 is the most commonly
observed, although this does not imply that it is
the most likely for any specific factory or sector.
The type of pattern found in a given factory isdetermined mainly by the industry sector.
Figures 7 13 show examples of other
common types of pattern.
Figure 6 is taken from a glass furnace. It has an
intercept on the energy axis, the line is straight over
the whole range of production, there is not much
scatter, and production covers a wide range. The
best-fit line to the data can be formulated as:
Energy (m production) c
Where c and m are empirical coefficients
(empirical means they are determined from the
data, whether fitting a line to the data by eye or
calculating it from the data).
I n thi s ca se, c i s 71 .5 MWh /d ay a nd m i s
1.185MWh/te so the pattern is:
energy (MWh/day) {1.185 production (te/day)} 71.5
Figure 7: Energy vs. production for an electric arc furnace
a special case where the line passes through 0,0. (source: ETSU)
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Similar patterns are found for most furnaces (for
heating or melting), ovens, kilns, some dryers and
many more processes. In the absence of other
indications, it is usual to assume a relationship of
this kind.
Figure 7 is similar to Figure 6 but has no intercept,
i.e. it is a straight line that, when extrapolated,
passes through the origin (0 production, 0 energy).
It is generally rare for this to be the case.
This example is for an electric arc furnace melting
steel for continuous casting. Our knowledge ofphysics leads us to expect the line to pass through
the origin. It would be possible to represent this
pattern by the formula:
Energy m production
Where m is an empirical constant and the c
coefficient from the previous example is 0. In
general, this should not be assumed unless there is
a good physical case for it.
It happens to be an important case because
rearranging the formula leads to:
energy
production m
In other words, the expected value of energy/
production (specific energy) is a constant, in this
case 0.511 MWh/te. This is true for this and only
one other of the known patterns. In all other cases,
specific energy depends on the level of production,and statement of the specific energy without
reference to the production rate is meaningless in
management terms.
In Figure 8, the intercept is overwhelmingly more
important than the slope of the line. This example
is for a machine for extrusion-blow moulding of
thermoplastic resins.
Figure 8: Energy vs. production for an extrusion-blow moulding machine
an example of a very high production-unrelated demand. (source: ETSU)
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There are three common circumstances which give
rise to this pattern:
1. The process has innate characteristics that give
it a high standing consumption but low
additional consumption for each unit of pro-
duction. Work-based processes in the produc-
tion of plastic extrusions are a good example. In
addition, processes with variable output driven
by fixed-speed motors also often show a high
intercept (although the line may be curved).
2. The process does not have a naturally highstanding consumption but a fault is causing a
high and continuous energy loss, e.g. faulty
steam traps on steam-heated equipment such
as sterilizes or rubber tyre moulding presses.
3. Processes where the energy consumption is
representative of a fixed duty and the produc-
tion variable used does not take adequate
account of the real duty. An example is paper
production where this shape of graph appears
when steam is plotted against weight of paper
produced. In paper machines, the actual process
is the evaporation of water and the machine has
an essentially fixed evaporative capacity. Varia-
tions in production rate represent the differentamounts of water that are evaporated for the
range of paper types produced on the same
machine.
For the first two cases, the simple intercept
formula, energy (m production) c, is
appropriate, although in the second case the cause
of the high standing loss needs investigation. In the
third case, monitoring will be worthwhile only ifthere is a change in the way the production variable
is measured.
Note that, in this case, the specific energy is more
closely related to production rate than is energy
consumption.
Figure 9 is similar to the third variant of the
previous case. It is a process with a fixed productive
capacity producing an essentially uniform product,
so both the energy use and production fall
consistently within a narrow range.
Figure 9: Energy vs. production for an electric arc furnace
an example of the impact of a very narrow range of production
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This example is for another arc furnace for steel. In
this case, although the data should fit a straight line
of the form energy (m production) c, c may
be difficult to determine empirically from the data
the long extrapolation back to zero production
makes any error in the slope too significant in
determining the value of the intercept by purely
statistical means. The dotted line can only be
established either by specific tests to establish c and
find m, or by calculation of m, and using this to
estimate c. If there is significant scatter, considera-
tion may need to be given as to whether the
variables being used, especially for production, are
appropriate.
Figure 10 is a pattern in which the line is curved,with the slope rising as consumption increases. This
is for a milk manufacturing plant making butter and
milk power. Increasing slope means that the energy
consumption per additional unit of output rises
with production.
The most common causes of this shape of chart
are when:
. as in this example, the data refer to the
whole factory and production at different
levels is achieved by a changing mix of plant
of different efficiencies:
. the data refer to a part of the factory or
accounting centre which covers more than
one use of energy, and there is a relation-
ship between these which is not a simple
ratio, e.g. a combination of a seasonallydependent production rate and space
heating, which is common in breweries.
Figure 10: Energy vs. production for a milk manufacturing depot an example of a curved chart
created by plant with different efficiencies being operated in a merit order. (source: ETSU)
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A suitable formulation of the pattern is then:
energy {(m1F1 m2F2 m3F3 ...) production} c
Where F1, F2, etc. are the fractions of the
production in each period, accounted for by each
item of plant, and m1, m2, etc. are empirical
constants specific to those items.
In Figure 11 the graph curves with reducing slope
to become straight at higher production rates. This
tends to be rather unusual. In a single process, the
range of production that produces this effect israrely encountered in practice, and in multiple
processes it implies that most inefficient plant has
priority. This data is taken from a shaft furnace used
for melting aluminium. A feature of the process is
the way heat in the exhaust is recovered to preheat
the material entering the process; this is less
effective at low throughput. In the straight section
of the line, the relationship is exactly the same as
for Figure 1.
The precise relationship in the curved section is
usually not known, or not easily calculated. A useful
modification of the formula that achieves a good
empirical fit for most circumstances is:
energy (1 expk production) (m production c)
Where m is the slope of the straight section of the
chart, c is the intercept found by extrapolating thestraight section to zero production and k is an
empirical constant (sometimes called an approach
coefficient). Note: (1 expk production) i s a
common mathematical expression for approximat-
ing curves.
Figure 11: Energy vs. production for a shaft furnace an example of a curved chart caused by
efficiency varying with throughput due to internal recycling of heat. (source: ETSU)
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In Figure 12 the scatter is so great that it
overwhelms an underlying pattern. There are five
common reasons for this type of chart:
1. The variable used to represent production is
entirely inappropriate explore other variables.
2. More commonly, the times at which energy
meter readings and production records are
taken are different, so there is a mismatch in the
periods covered by the data. The shorter the
data collection interval, the greater the impact,
so it is most common in systems that use daily
or weekly data.
3. The metered energy is serving more uses than
just that measured by the production variable
chosen this is not unusual when energy
includes building heating as well as production-
related energy.
4. It has not been noticed that the energy and/or
production scale does not extend to the origin
(0.0) and the process is really the type shown in
Figure 4.
5. The data cover a long period of time and there
has been a steady change in the energy required
for a given range of production over time,
which has not been taken into account.
The data (Figure 12) are actually for compressed
air compared to production in a steel rolling mill. A
combination of the above factors is involved. It is
usually possible, by further analysis, to obtain a
clearer picture of the factors at work and attribute
the chart to another type.
The characteristic feature of Figure 13 is a negative
slope. In physical terms, however, it is far more
significant because of the interpretation of the
Figure 12: Compressor power vs. volume of compressed air in a
hot rolling steel mill an example of poor control. (source: ETSU)
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slope. As production increases, less energy is
required and it appears, therefore, that marginal
increases of production could be producing energy.
This is the clue to understanding this behaviour it
normally involves some heat recovery or recycling
of heat, although it can involve a reduction in the
extent of processing as production throughput
increases. This example is for a brewery and shows
the total fuel used compared to total throughput.
Similar behaviour is found in the injection moulding
of polymers.
4.3 APPROXIMATING
MULTIVARIABLE
SITUATIONS
If there are more variables controlling the energy
use than are incorporated in the x-variable then it is
not possible to represent these adequately on a
two dimensional graph. It is, however, still possible
to formulate energy mathematically as:
Energy (m1 P1) (m2 P2) (m3 P3) c
Where P1, P2 etc. refer to the other production or
other parameters and m1, m2 etc. are constants
related to these parameters. A common, more
generally representative, formulation is:
energy (h H) (m1 P1) (m2 P2) (m3 P3) (d DD) c
Where H is the productive hours in the period andh is an empirical coefficient, m1 and P1 have the
same meanings as before, DD stands for degree
days (a measure of the weather) and d is an
empirical coefficient. If the usage pattern of plant is
very variable, it may even be worthwhile extending
this formulation to:
energy (h1 H1) (m1 P1) (h2 H2) (m2 P2) (d DD) c
Figure 13: Energy vs. production for a brewery an example of a line of negative slope. (source: ETSU)
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Where the h1 and H1 refer to individual processes.
Approaches of this kind have been developed for
textile finishing. These coefficients can be deter-
mined by multiple regression, the method of
residuals or sometimes by statistical factorisation
methods. They may also be based on standard
values an approach used successfully in the
Flowline method in textile finishing, and in the
paper industry where one machine produces many
grades of paper.
4.4 BUILDING HEATINGLINKED TO DEGREE
DAYS
The most appropriate measure of the weather for
monitoring the heating and cooling needs of
buildings is the degree day.
4.4.1 DEGREE DAYS
Degree days are a measure of the variation of
outside temperature and enable building designers
and users to determine how the energy consump-
tion of a building is related to the weather. They
quantify how far, and for how long, the external
temperature has fallen below set base tempera-
tures (normally 18oC or 15.5oC for heating
applications). This daily data can then be totalled
for any required period a week, month, year, etc.
and compared with energy data.
There are four common base patterns found inindustrial buildings, shown in Figures 14 to 17. The
basic pattern is shown in Figure 14. This is exactly
analogous to the process case of a straight line with
a positive intercept, but with heating degree days as
the x-variable. This example is for a textile spinning
mill with close control of the environmental
conditions, and therefore shows little scatter.
Figure 14: Energy vs. degree days for a textile spinning mill
an example of a chart for well-controlled heating. (source: ETSU)
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It is adequately represented by the expression:
Energy (m degree days) c
The pattern in Figure 15 is a variant, which has the
intercept on the degree day axis.
This is interpreted as indicating that energy is not
required until the outside temperature falls to a
certain level of degree days, in this case, either:
. the building is maintained at a lower
internal temperature than the degree day
base temperature or
. the building is receiving heat from else-
where, e.g. process plant, which maintains
the temperature.
Both of these are common circumstances in
buildings and this is a frequently encountered
pattern. For M&T purposes it is represented by the
expression:
for degree days < DD0 energy 0
for degree days > DD0 energy (m degree
days) C
where DD0 is the intercept on the degree day axis
and c will be negative.
Figure 16 shows energy vs. degree days for a
building in which the line is curved and levels out to
horizontal at extreme degree days.
At the point where the line is horizontal, the
heating system is not accepting more fuel, despitefalling outside temperatures (usually because it is
working at full capacity). As degree days increase,
Figure 15: Energy vs. degree days for an engineering works an example of the effect of
an internal temperature maintained below the degree day base temperature or where the
building gains heat from elsewhere, e.g. process plant or other machinery. (source: ETSU)
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so more heat is added which results in a falling
internal temperature. The simplest mathematical
representation of this pattern is:
Energy c (Emax c)(1 ek degree days
)
which is easily formulated on computer spread-
sheets. It is a convenient formula because it
contains only three empirical constants. Emax andc are interpolated directly from the chart. k is
obtainable either by successive approximations on
a spreadsheet (to produce a curvature recognisable
as this case within a range of 500 degree days, k
tends to have a value between 0.002 and 0.01) or
directly by mathematical techniques. (This curve is
not amenable to evaluation by least squares
regression. To use this formulation in an M&T
system it must be programmed into the software.)
Figure 17 shows curvature in the opposite
direction.
In this particular case, which is the commonest
form of curvature in this direction, energy is a good
fit to:
Energy C m (degree days)2
and is due to temperature stratification in the
building cold air ingress forcing warm air to rise
and temperatures in the roof of the buildingbecoming much warmer than at floor level. It is
common in dispatch warehouses.
There are other patterns relating to building
heating and degree days. Detailed discussion of
these is beyond the scope of this Guide. Broadly,
these divide into two groups:
. patterns which arise from a combination of
a weather-unrelated demand and one of
the patterns already discussed:
. patterns in which the me followed by the
points on the graph changes with season
Figure 16: Energy vs. degree days for a building with limited heating capacity. (source: ETSU)
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so that the line moving from winter to
summer or summer to winter produces
loops when the individual points are joined
up in time series order.
4.4.2 BUILDING COOLING LINKED
TO DEGREE DAYS
For cooled buildings, behaviour is not quite the
same as for heated buildings. At precisely the right
cooling degree day base temperature, solar gain
causes a curve, which can be shown to be a goodfit to:
Energy (1 expk degree days) (c m
degree days)
This is exactly analogous to the curve in Figure 6
but with cooling degree days substituted for
production. A fortunate coincidence in this
relationship and a rule associated with changes in
the case temperature for degree days, however,
means that this curve can be straightened by the
simple expedient of using degree days to a different
base temperature.
4.5 PROCESSES LINKED TO
TIME THROUGH
ACTIVITIES
For some processes it is difficult to establish anindependent variable (such as production or
degree days) against which to monitor energy
consumption. Some processes, however, are
associated with activities that are strongly linked
to time. Time can therefore be used as the
comparator to identify characteristic patterns. It is
not necessary to know what the activity is in order
to use time as a basis for monitoring.
Figure 17: Energy vs. degree days for a building in which temperature stratification is occurring.
(source: ETSU)
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Example
Figure 18 shows the fuel use in a large vehicle fleet.The fuel consumption of vehicles depends on
environmental conditions, on the nature of the
load and on road conditions. It is not necessarily
very easy to establish all of these. In Figure 18 there
is clearly a pattern which is seasonally dependent
and which offers a basis for comparison of one
period with another in a previous year.
Figure 19 shows a half-hour electricity demand
profile for a factory producing domestic consum-
ables. There are clear features in the profile on
weekdays, which are repeated each day without
much variation. This kind of information is now
available routinely at the whole-site level for large
numbers of industrial sites, and there is justification
in extending it selectively to the sub-meter level
now that the cost of metering technology has
reduced.
In the specific case of Figure 19, a range of
questions of interest to management are raised by
the profile:
. What causes the differences from day today?
. Why does the afternoon demand on
Friday tail off early?
. Why is the lunchtime dip not more
noticeable?
. What activities are being supported by the
load at night and over weekends?
There is a wide range of techniques for handling
this information and this is only one form of
presentation of data for one week. The normal
format for this information at the whole-site level is
as a 48 365 array (365 days and half-hourly
energy data sometimes shown pictorially as con-
tour mapping). Without restructuring the array in
any way it is possible to compare one day with
another, compare one time over many days and
compute averages on an hourly, daily or weekly
basis. However, the data require processing to
produce a chart like Figure 19.
Figure 18: Fuel consumption in vehicles as an example of a seasonal pattern
which is not related directly to temperature. (source: ETSU)
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4.6 PROCESSES WITH NO
RELATION TO OTHER
VARIABLES OR TIME
Processes, which seem to have no relation to other
variables or time lead to an expectation of the
same value each time they are measured. There is
no need to discuss the analysis of these in detail in
this Guide; they are a standard case within the
scope of Statistical Process Control and can be
treated as an extreme case with zero slope.
True examples of this type of behaviour are found
from time to time in energy management. They are
usually due to machinery that is running uncontrolled
and therefore left running when not needed a
source of immense waste. On-off controls and
simple alarms are usually cheaper than fitting
meters and collecting data.
Example
In a textile spinning mill, measurement of the
electricity consumption of vacuum pumps, used to
remove stray fibre from the machines, was found
not to vary at all. Timers to shut down pumps
reduced running hours of 20 kW motors from 90
to 55 hours a week, reducing annual consumption
by 35,000 kWh worth 1,580 a year.
4.7 MONITORING DATA AS
AN INDICATOR OFEFFICIENCY
Monitoring data is both a useful indicator of the
efficiency of processes and a means to gauge the
scale of potential savings.
Figure 19: The half-hour electricity demand profile of a factory making domestic consumables.
(source: ETSU)
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4.7.1 NON-PRODUCTIVE AND
ACTIVITY-UNRELATED
ENERGY CONSUMPTION
The intercept on a chart of energy vs. production,
i.e. the point where the line is extrapolated back to
zero production, represents energy, which the
process uses even though it produces nothing. It is
a fair question to ask how much of this is necessary.
The same applies to nighttime electricity loads in
factories that do not operate at night.
The first step is to quantify non-productive energy.On a chart of the form:
Energy (m production) c
the non-productive energy is the intercept divided
by the total for average production:
p r op ort i on of n on- p rod uc t iv e e n e rg y
cm average production
1007
In Figure 1 the best fit to the data is:
Energy (1.185 production) 71.5
and the average production is 107 te a day.
So proportion of non-productive energy 71:5
71:5 1:185107 0:360 367
This is a key element of the Avoidable Waste style
of approach.
ExampleA glass melting furnace comprises a refractory-lined
insulated tank of molten glass which is kept
constantly topped up with raw material as molten
glass is pulled from one end, and a system of large
tower regenerators for recovering heat from the
hot exhaust gases. In this furnace, the ducts
between the glass furnace and the regenerators
were found to be contributors to non-productive
heat loss. Insulating the ducts reduced heat loss by1.3 MWh/week.
Figure 20: Combustion air fan power compared to gas consumption for a steel reheat furnace
showing the high production-unrelated demand of a fixed-speed drive. (source: ETSU)
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Figure 20 shows an example of electricity use in a
combustion air fan. Extrapolation of electricity
consumption shows a production-unrelated de-
mand of 300 kW. This is because, although this is a
variable load application, the motor attached to the
fan is a fixed-speed motor in which variable air flow
was achieved by throttling using a damper. Installing
variable-speed control on the motor matches the
speed to the load and, in this case, achieved a
reduction in standing consumption of 100 kW.
In Figure 20 the total electricity consumption for
the week was 227 850 kWh. The night caseload onweekdays was 450 kW and 200 kW over the
weekend. It is unreasonable to assume that the
whole baseload can be eliminated, but it is fair to
ask what is the difference in activity that accounts
for the difference in baseload and why it takes so
long to run down on Saturday.
4.7.2 PRODUCTION-RELATEDEFFICIENCY
A straight line energy vs. production chart means
the energy required to process one additional unit
weight of material is the same over the whole
range of output. This can be used to estimate the
efficiency of the process.
Straight lines with low scatter are encountered
frequently because, for most industrial processes,
the particular transformation from raw material to
product is very much the same for every kilogram
or tonne of material passing through, and the
efficiency with which this is achieved is the same
irrespective of the rate of throughput. The slope of
such a straight-line chart can be used to calculate
the process efficiency (as shown in the box).
The shaft furnace in Figure 11 is used for meltingaluminium alloys. The metal that enters the furnace
is always aluminium at about ambient temperature.
This temperature varies a little but variations
between 5oC and 30oC are small compared to
the 600oC rise to melt it. The output is molten
alloy for gravity die casting, which requires a melt at
a consistent temperature for its pouring and
solidification characteristics; so, the input tempera-
ture, output temperature and composition of the
metal are always the same.
In some industrial processes there is a need to
include other energy inputs. In bricks, glass,
chemicals and some other processes there are
chemical reactions to take into account. These areusually described in specialist texts on the industry.
(Full data on nearly all reactions of common
interest are also given in Kubaschewski, Alcock and
Spencer's Materials Thermochemistry.)
In processes which involve heat recovery, the
efficiency 'e' may be greater than 1 and provides a
measure of the amount of heat being recycled.
The same evaluation procedure can be applied to
evaporation and distillation processes. This includes
all processes that start with a liquid and involve
vaporization, e.g. drying. Two particular considera-
tions are that:
. the specific heat capacity of a vapour (or
gas) depends on its pressure;
. evaporation processes are often engi-
neered to recycle heat, over a number of
effects, or to use mechanical vapour or
thermo-recompression.
Two of the most important vaporisation processes
occur in boilers and drying, both of which involve
vaporisation of water. Boiler efficiency can be
evaluated from a graph of steam output vs. boiler
fuel. This is an adjunct to monitoring the efficiency
from tests on the boiler flue composition andtemperature, and not a substitute. The energy-
related properties of water vapour are given steam
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tables. Steam tables are widely published in
textbooks on mechanical engineering and some
energy management reference works. A summary
steam table is available in How to save Energy and
Money in Steam Systems guide of this series.
4.7.3 BUILDING HEATING
EFFICIENCY
The slope of the line of energy vs. degree days is
also an important indicator. It is possible to show,
although the detail is beyond the scope of thisguide, that the scope m of a line of energy vs.
degree days is equivalent to:
m FUA NVCpp
e
Where:
. e is the marginal efficiency of conversion of
the energy recorded on the y-axis to heat
(marginal means that standing losses arediscounted in the case of fuel-fired
systems this essentially means the combus-
tion eff iciency); for steam heating it
acknowledges the residual heat in con-
densate.
. F is a dimensionless number known as the
degree day correspondence factor. It is a
measure of how far the degree days used
as the indicator of the weather on the x-
axis represent the difference between the
building internal temperature and the out-
side temperature expressed as degree
days.
. UA means multiply the area, A. and the
U-value, U, of each element of the outer
fabric of the bui lding wal ls , roof,
windows, etc. in turn and add up all the
results.
. N V Cp p means multiply the volume, V,
number of air changes, N, and the heat
capacity of air, Cp, for each element of thevolume of the building by the density of air,
p, and add up all the results.
The U-value is a measure of the thermal
conductivity of a structure. It can be looked up in
standard reference sources for all common fabric
types for a first estimate, the values in the table
below can be used. The slope is measurable from
the chart, e is measurable from the standardcombustion tests on boilers (which should be
measured routinely, anyway), A and V are
measurable or estimable from the dimensions of
the building and Cp p has the value 0.33 kWh/m3/
hour/oC or 0.00792 kwh/m3/hour/degree day. The
commonly used units of U-values W/m2/
oC
can be converted to kWh/m2/degree day by
multiplying by 0.024.
Table 1: U-values for common structures in an industrial building (source: Textiles industry)
U-values
W/m2/oC KWh/m2/degree day
Single-glazed windows 4.6 0.11
Roof skylights 6.6 0.16
Solid brick unplastered 3.3 0.08
Brick cavity (brick unlined) 1.4 0.03
Well-insulated wall 0.5 0.01Pitched tiled roof plaster-board ceiling 1.5 0.04
Roof with fibreglass lining 0.4 0.01
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The degree days used by most industrial energy
managers are those published for regional obser-
ving stations using a formula which measures how
long in parts of a day and by how much, in oC, the
outside temperature is below a f ixed base
temperature. For buildings that are intermittently
heated it over-estimates the heat requirements.
How much less energy is required by an
intermittently heated building depends on the
number of hours a day it is heated and what is
called its heating inertia how fast its internal
temperature falls in oC/hour for a given tempera-
ture difference between inside and out; the fasterthe temperature falls, the lower the inertia.
Figure 15 provides a chart for finding a value for F
(degree day correspondence factor) as a function
of the number of hours of heating, and a value for
the heating inertia. (F 1 for a continuously heated
building). If required, the inertia can be measured
using a thermograph, but as long as the working
day is more than eight hours, F is not very sensitive
to the inertia and can be estimated:
. A building with a heavy structure, many
internal barriers to air movement and
considerable internal mass (product in a
warehouse) has a nigh inertia, i.e. a low
value approaching 0oC/hour/oC. There-
fore, find the value of F on the left-hand
axis for the requisite heating hours per day.
. A light building with few barriers to air
movement, perhaps some mechanicalventilation and little internal mass would
have a low inertia, i.e. a higher value, say
around 0.3o
C/hour/o
C; for this the value of
F is read on the right-hand axis. In the
fortunate position of knowing the value of
the heating inertia, the appropriate value of
F can be found from Figure 21.
Figure 21: Degree day correspondence factor isopleths for the appraisal of the
heat balance of intermittently heated buildings. (source: ETSU)
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In practice, the most difficult factor to estimate in
industrial buildings is the number of air changes
(N). It is usual to simplify the calculation by
assuming a common air exchange rate over the
entire building volume.
In principle, everything is now known except N,
and the formula becomes a method or estimating
the ventilation rate, which is commonly the highest
component of building heat loss and, after
stratification, is the most cost-effective element of
significant heat loss to correct in industrial buildings.
Example
The slope of energy vs. degree days for the building
has a slope of 6.5 GJ/degree day (1.807 kwh/degree
day).
The building is 200 feet long, 120 feet wide and 60
feet high and windows represent 40% of the wall
area. One foot is 0.3048 m. U-values are estimated
as 0.024 kwh/m2/degree day for the walls, 0.11 for
the windows and 0.03 for the roof. The boiler
efficiency is known to be 7500. The building is
heated continuously, therefore F 1.
Then:
Area of wall (inc. windows) (2 200 60) (2 120 60) 38,400ft2
38,400 (0.3048)2 3,567m2
Heat loss from windows 0.4 3.567 0.11 156.9 kWh/degree day
Heat loss from walls 0.6 3,567 0.024 51.4 kWh/degree day
Heat loss from roof 030482 (200 120) 0.03 66.9 kWh/degree day
So: UA 156.9 51.4 66.9 275.2
Volume, V 0 30483 (200 120 60) 40,776 m3
From the straight-line equation:
slope 275:2 40:776 0:00792 N
0:75 1:807
Therefore:
N 1:807 0:75 275:2
40:776 0:00792 3.34 air changes peer hour
From this it can be seen what proportion of the total observed weather-related energy use is lost by different
components of the building fabric and operation:
Boiler 25%Walls (51.4/1,807) 100 3%
Windows (156.9/1.807) 100 9%
Roof (66.9/1.807) 100 4%
Ventilation (40,776 0.00792 3.34/1,807) 100 59%
100%
Clearly, ventilation in this building is overwhelmingly the largest energy user, and any measures applied to the
building fabric would have minimal impact. This is not unusual in industrial buildings and a great deal of wasted
energy is due to overzealous and poorly balanced mechanical ventilation. This technique provides a means to
assess the impact.
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5. USING INFORMATION
ON ENERGY USE FOR
MANAGEMENT CONTROL
5.1 INTRODUCTION
The normal way of using information as a basis for
on-going management control is to:
. establish a performance standard, based on
what has been achieved historically, some-
times modified to give same 'incentive' and
expressed in simple terms;
. calculate the difference between actual
performance and this standard;
. respond to instances of unusually large
differences;. reduce these differences over time.
In energy M & T historic performance is used for
establishing performance standards: however, statis-
tical methods, and an understanding of the physical
laws that underlie energy consumption, are applied
to make these performance standards robust.
The success of this approach depends on beingable to recognise when the difference between
actual consumption and the standard in any one
period is exceptional. This in turn means being able
to accommodate all the factors into the calculation,
which cause these differences but are not
controllable. The smallest difference that identifies
a deviation from the standard as a significant
exception is called the resolution of the manage-
ment system. The resolution can be improved by
being able to select, from the historic information,
the data for the particular periods days, weeks,
months that provide the best standard.
A particularly powerful method for achieving this is
a combination of a technique called CUSUM and a
device taken from quality management called thecontrol chart. These techniques will be illustrated
using the data in Figure 22, taken from a factory
that produces a fried-food product.
Before applying CUSUM, consider the other
information already apparent in the data. The data
for this process appear to split naturally into two
groups, following parallel lines a short distance
apart. The one of greatest potential interest is the
lower one, as this appears to represent higher
energy efficiency. A best-fit line drawn by eye is:
energy ('000 therms) 0.26 production (te) 100
5.1.1 NON-PRODUCTIVE
CONSUMPTION
At the commonest output of around 900 te/month, this indicates that production-unrelated
energy is:
100
100 0:26 900 1007 307
5.1.2 PRODUCTION-RELATED
EFFICIENCY
This example is for a fried product in which the
process heats the raw material to the frying
temperature of 250oC, evaporates the water that
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makes up 80% of the mass of the raw material andreplaces this with cooking oil that makes up 40% of
the product. Each te of product therefore contains
0.6 te of raw material, the production of which
involves evaporation of four times as much mass of
water (80%:20% ratio), i.e. 2.4 te of water and, in an
ideal process, the heating of only 0 4 te of oil.
The energy required to evaporate water from
liquid at 30
o
C to steam not under pressure at250oC can be looked up in standard engineering
steam tables for superheated steam the value is
2.870 kJ/kg (it is important to use the right steam
table). The specific heat of the cooking oil was
obtainable from the supplier as 2 kJ/kg/oC. The
specific heat of the other solid material is not
known but it is a carbohydrate with a rigid structure
and so cannot be far from that of wood or
polystyrene, i.e. about 1 kJ/kg/oC. The accuracy of
specific heats of solid materials in this case (and
most cases involving evaporation of water) is not
found to be critical and the effect of temperature
on specific heat, in this case, is negligible. Onetherm is 105.5 MJ.
From Figure 22 we know that the slope of the line
5 260 therms/te. The production-related efficiency
of the process is the theoretical energy required to
process 1 te of product, divided by the actual
energy used per te:
Efficiency
f2:870 2:400 2 400 1 600 240 30g260 105:5 1:000 1007 267
This is poor efficiency performance for this kind of
process.
5.2 CUSUM TECHNIQUE
CUSUM stands for the CUmulative SUM ofdifferences and is a technique for measuring bias
in equal interval time series data, i.e. information
Figure 22: Fuel vs. production for a cooker/fryer in the food industry. (source: ETSU)
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of the same kind gathered at the same time each
day, week, month etc., and organised in the same
time order as it was measured (which is the way
most of most industry collects information any-
way). The differences added are those between the
actual energy used and the energy predicted by the
best-fit line on the chart of energy vs. production.
In the example of the cooker/fryer, for any given
production rate there is a wide range of energy
consumption in the data. At around 900 te/month,
energy consumption seems to vary between about
290,000 and 400,000 therms/month a variation of/-16%. If this is the normal variation in these data,
then this is about the limit of resolution of any
system based on it. In fact, it is not representative of
the true week-to-week variation at least some of
this apparent scatter is due to the way the process
has changed over time. CUSUM is a technique that
can take account of this.
The prediction formula calculated previously was:
energy ('000 therms) (0.26 production in te) 100
Calculating CUSUM from this involves four steps:
1. Use this formula to obtain a predicted energy
use for each week from the production for that
week.
2. Subtract the predicted consumption from the
actual to obtain a difference for each week.
3. Add up the differences from the first week to
each week in turn to obtain CUSUM.
4. Plot a graph of CUSUM against time.
The first three of these steps are usually carried out
in adjacent columns of a spreadsheet (or database
if proprietary software is used). This result is shown
calculated in the table below.
Table 2: CUSUM data for cooker/fryer
Production
(Tons)
Actual gas
('000 therms)
Predicted gas
('000 therms)Difference CUSUM
Feb 1992 896 334 332.96 1.04 1.04
March 1,054 371 374.04 3.04 2.00
April 678 288 176.28 11.72 9.72
May 781 332 303.06 28.94 38.66
June
July
Aug
The resulting chart is shown in Figure 23.
If the entire scatter on the CUSUM chart were only
random about the best-fit line, the compiled
differences would also be randomly positive and
negative. The resultant accumulation of these
differences, CUSUM, would also be random andnot far from zero. CUSUM would then track
horizontally on this chart.
If something happens which changes the pattern of
consumption moves to a pattern for which the
constants in the best fit relation are different from
those in the prediction then the differences will not
be random: they will be biased positive or negative
and CUSUM will track up or down from the time
of that event. The CUSUM chart therefore consistsof a series of straight sections separated by kinks,
each kink representing a change in pattern. Lengths
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of the CUSUM chart, which run parallel to oneanother, indicate the same process behaviour
pattern being followed.
The CUSUM graph, Figure 23, identifies two clear
patterns:
1. When the line runs horizontal which is:
. up to April 1997;
. from August to November 1997;
. from September10 December 1998.
2. When the line runs upward which is:
. from May to July 1992;
. from December 1992 to August 1998;
. from January 1999 onwards.
Discussing the CUSUM chart with various man-
agers in the factory brings out an explanation for
the two patterns. A few years previously the
cooker had been fitted with a heat recovery
system, partly on economic grounds and partly to
reduce the visible plume of steam over the factory
from the evaporated water. The rising trend in theCUSUM chart could be attributed to a reduction in
the performance o