3E Guidebook1

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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: [email protected]

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 ([email protected]).


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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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19

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

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