AU2003265723B2 - Energy performance monitoring system - Google Patents

Description

WO 2004/029557 PCTiAU2003/001262 -1- ENERGY PERFORMANCE MONITORING SYSTEM Field of the invention s The invention relates generally to monitoring resource use and especially relates to monitoring the efficiency of energy resource use.

Background Industrial energy users have been conducting energy audits of their operations for many years. Energy audits, usually involving an ad hoc analysis of consumption data, can in some cases identify causes of energy wastage, and such wastage can often be minimized to improve the efficiency of energy usage. Few users that do-conduct energy audits identify findings that lead to any sustained improvement in energy consumption.

As well as energy audits, various systematic attempts have been made to improve the energy efficiency of operations. As an example, European Patent Publication No 0 581 273 Al, relating to a patent application made 28 July 1993 by Praxair Technology, Inc, describes a method of reducing energy requirements for a chemical production process by forecasting an optimum production schedule to meet a total required production demand.

Systemic efficiency measures are, however, in no way addressed.

Regardless of previous advances that have been made in the~field of improving energy efficiency for industrial operations, a prevailing perception is that energy is a commodity that is used as required, without concern for potential savings, Consequently, the vast majority of energy users do not fully understand their energy cost of operations.

Even if users calculate their annual' energy cost of operation, most users are unable to identify any causes of energy wastage or potential savings. Economically, energy costs are treated as fixed rather than variable costs. Accordingly, in view of these and other observations, a need clearly exists for an improved manner of monitoring energy performance.

Summary A major reason for the above-mentioned inaction of energy users in implementing energy efficiency measures is believed to be the lack of accurate monitoring and determination of the actual savings (or losses) achieved through the implementation (or of lack thereof) of energy efficient measures. Energy audits, as described above, only identify energy efficiency improvement projects, and do not define a benchmark for subsequent analysis.

As described herein, energy efficiency can be monitored by recording data relating to resource consumption (such as natural gas, electricity and water) in conjunction with production data, or other related data relevant to the industrial or commercial application under consideration. Resource consumption data is normalised with respect to the production or other data to provide a performance indicator that is displayed, in graphical or tabular form, in contrast with notional "baseline" reference values representative of benchmark efficiency levels. These reference values are defined for corresponding levels of the production or other data to provide a reasonable comparison that accounts for system non-linearities. Performance indicator values can be indexed for display in relation to time (for example, for each shift), or by production volume (so that efficiency at different production volumes can be compared).

The techniques and systems described herein define performance indicator values, and associated reference values, most typically in the context of a resource consumed against output generated. A reference is defined, against which the relative efficiency of energy or other resource usage can be assessed.

Therefore, the invention provides a method for monitoring the efficiency of resource use comprising the steps of: receiving data relating to a resource variable and data relating to a related variable; calculating performance indicator values by normalising values of the resource variable data with corresponding values of the related variable data; determining a mathematical relationship that approximates a relationship between the performance indicator values and the related variable values; determining the reference values based upon the determined mathematical relationship; and [R:\LIBK]599387AU.doc:TCW 2a Sdisplaying graphically, with respect to the related variable, instances of the performance indicator values in contrast with a curve representative of the corresponding ;reference values.

s The invention further provides computer software, recorded on a medium, for monitoring the efficiency of resource usage, the computer software comprising software code means for performing steps of: t accessing data relating to a resource variable and data relating to a related IDvariable; calculating performance indicator values by normalising values of the resource variable data with corresponding values of the related variable data; determining a mathematical relationship that approximates a relationship between the performance indicator values and the related variable values; determining the reference values based upon the determined mathematical relationship; and displaying graphically, with respect to the related variable, instances of the performance indicator values in contrast with a curve representative of the corresponding reference values.

The invention yet further provides a computer system for monitoring the efficiency of resource use, the computer system comprising computer software means for performing steps of: storing data relating to a resource variable and data relating to a related variable; calculating performance indicator values by normalising values of the resource variable data with corresponding values of the related variable data; determining a mathematical relationship that approximates a relationship between the performance indicator values and the related variable values; determining the reference values based upon the determined mathematical relationship; and displaying graphically, with respect to the related variable, instances of the performance indicator values in contrast with a curve representative of the corresponding reference values.

The invention yet further provides a computer system comprising: [R:\LIBKj599387AU.dcc:TCW 2b O a memory for storing data relating to a resource variable and data relating to a Srelated variable; ;a processor for calculating performance indicator values by normalising values of the resource variable data with corresponding values of the related variable data, determining a mathematical relationship that approximates a relationship between the performance indicator values and the related variable values; determining the reference values based upon the determined mathematical relationship; and IDa display for displaying graphically, with respect to the related variable, 1o instances of the performance indicator values in contrast with a curve representative of Sthe corresponding reference values.

The invention yet further provides a computer system comprising: means for storing data relating to a resource variable and data relating to a related variable; means for calculating performance indicator values by normalising values of the resource variable data with corresponding values of the related variable data, determining a mathematical relationship that approximates a relationship between the performance indicator values and the related variable values, and determining the reference values based upon the determined mathematical relationship; and means for displaying graphically, with respect to the related variable, instances of the performance indicator values in contrast with a curve representative of the corresponding reference values.

Resource types that are typically of particular interest are natural gas and electricity, as considerable economic savings can be made through reducing consumption of these resources. The resource to which the performance indicator values relate, however, may be any resource. Water and air are common examples. Similarly, the main related variables with which the performance indicators are concerned are typically production measures. Such production measures may be, for industrial users, production output by weight, length or production units. Commercial users, typically building owners, may use normalisation parameters such as occupancy levels, square meters of floor space, or total-degree-days. Further, the related variable to which a performance indicator relates may [R:\L1BKI599387AU.doc:TCW WO 2004/029557 PCT/AU20031001262 -3also be any suitable intermediate product, by-product, or waste product. Such related variables might be adopted to analyse environmental performance, and so might include volume of greenhouse gases, or volume of waste water.

s In any case, the resource type and related variable selected for any application are typically those which are of interest for maximising output or minimising consumption.

While a primary application is minimising energy consumption per unit of total production, the techniques and systems described herein encompass various other applications.

If a performance indicator for a particular shift or period is worse than a baseline reference value, then a predetermined procedure can be initiated to identify the underlying cause of any unnecessary wastage. Such a predetermined procedure can include check lists to investigate possible problems, and steps known to rectify such problems.

Subsequent analysis of such data can reveal further causes of inefficiency in the equipment or its operation.

Accordingly, procedures can be improved as more knowledge is gained concerning plant and equipment. An automated system enables users to monitor their performance each shift or day. Alarm limits can be set by the user to provide local feedback when the set targets for energy consumption are exceeded. Inefficiencies can be identified and rectified immediately, or as required.

Performance indicators can also be established for total site energy consumption, for site sub-systems, or for individual appliances such as furnaces, driers, kilns, boilers, and compressors. Appropriate metering of equipment under consideration is of course required.

Description of drawings Fig. 1 is a schematic representation of a system architecture for monitoring energy usage in an industrial plant.

WO 2004/029557 PCT/AU2003/001262 -4- Fig. 2 is a schematic representation of a computer system of a type suitable for use in the energy monitoring of Fig. 1.

Fig. 3 is a flowchart of steps that are performed using the system described herein.

Figs. 4 to 14 are graphs that represent examples that illustrate use of the techniques and systems described herein.

Detailed description The techniques and systems described herein allow users to actively monitor the efficiency of resource consumption. The term Energy Performance Indicator (EPI) is used herein to refer to the performance indicator values described herein, as the resource type with which performance indicator values are concerned is typically natural gas or electricity, or another energy-related resource.

Resource types and related variables Resource types selected for analysis are commonly energy resources such as natural gas and electricity. Liquid petroleum gas and diesel fuel are other examples of possible resource types. Other "non-energy" resource types, however, can also be analysed using the techniques described herein. As an example, water is another resource that may be of interest to usrs Consumption (or generation) of these various resource types is measured in appropriate units. Natural gas, liquid petroleum gas, and diesel energy are each measured in units of gigaJoules Electricity is measured in units of kiloWatt-hours (kWh). Water usage is measured is measured in kilolitres Instead of measuring natural gas, electricity or water in units of GJ, kWh or kL, a corresponding dollar value representative of the cost of the resource type can be used.

Related variables selected for use in calculating the performance indicator values in conjunction with the resource type are typically related to a production output.

WO 2004/029557 PCT/AU2003/001262 Performance indicator values are accordingly specified in units appropriate to the component resource type and the related variable. Thus, performance indicator values can be represented in different forms. Unit of gigaJoules of natural gas per tonne of production output (GJ/T) may be used for natural gas applications, and kiloWatt-hours of s electricity per tonne of production output (kWh/T) may be used for electrical processes.

Other units that could be used for different applications are kilolitres of water per tonnes of production output (kL/T).

Production output need not be measured by weight. A more appropriate measure rather than weight may in some cases be volume, or length. Examples include volume in kilolitres (kL) of fruit juice produced, or length in metres of carpet produced. Other production measures such as "per unit" or "per barrel" might also be adopted, if appropriate. As an example, tyre production is an application in which a "per unit" measure might be used.

For commercial applications, the related variable can be selected as total-degree-days (TDD). Accordingly, for natural gas applications, performance indicator values might be quantified in united of gigaJoules per total-degree-days (GJ/TDD). Similarly, units of kiloWatt-hours per total-degree-days (kWh/TDD) might be adopted for some applications.

Due to the environmental effects of resource use, particularly energy use, many industrial and commercial sectors are facing increasing pressure to monitor their greenhouse gas emission levels. Accordingly, a related variable may be selected as a related "environmental" variable such as the volume of carbon dioxide emissions resulting from the operation of a particular plant or site. Carbon dioxide emissions might be quantified as a kilogram (kg) equivalent of carbon dioxide, as an example.

Operational architecture Fig. 1 schematically represents a particular operational architecture that can be used to monitor resource use for an industrial plant 120. An industrial plant 120 is effectively connected to a central server 155 to collect metered data concernilg resource WO 2004/029557 WO 210411)9557PCTIAU2003/001262 -6consumption. The central server 155 independently obtains data concerning a related variable, commonly production data.

As an example, the plant 120 has inputs of gas 105, electricity 110 and water 115. The plant 120 measures resource consumption using suitable meters. In this case, consumption is metered using a gas meter 125, an electricity meter 130, a water meter 135, a steam meter 140, and a compressed air meter 145. Meters may be used for individual appliances, subsystems, or systems, and also for resource types other than those indicated in Fig. 1.

Each of these meters 125 to 145 is operatively connected to a data collection unit, such as a Remote Telemetry Unit (RTU) 150, which is in turn connected to a wireless data network, in this case a General Packet Radio Service (GPRS) network 380. The meters 125 to 145 periodically transmit metered consumption data via the RTU 150. The RTU 150, in turn, transmits this data via the GPRS network 380.

The central server 155 is connected to the GPRS network 380. The central server 155 receives data from the meters 125 to 14 via the GPRS network 380. Similarly, the central server 155 is connected to a plant server 180 at the plant 120 via a data or communications network, such as the Internet 385. Production data from the plant 120 can be transmitted from the plant server 180 to the central server 155 via the Internet 385.

Alternatively, production data may be entered using a keypad provided on the RTU 150, and transmitted with the meter data via the GPRS network 380.- The remote telemetry unit (RTU 150 can be of any type suitable for recording meter data for subsequent transmission. One suitable device is the ETM9500 model produced by ETM Pacific Pty Ltd of Sydney, Australia. The ETM9500 model. includes GPRS capability. The ETM9 500 is a GPRS terminal with a programmable microcontroller, which can be used for data logging applications. The ETM9500 model also includes a serial communication port and 7 programmable 110 lines.

Similarly, the meters 125 to 145 described above can be of any type for recording consumption of a particular resource. Examples of equipment suitable for meters 125 to 145 are as follows. Different meters may be suited to different appl ications. Typically, WO 2004/029557 PCT/AU2003/001262 -7meters produce a pulse output indicative of their metered values. Pulse outputs from the above-mentioned meters 125 to 145 are transmitted to the RTU 150 either via a 2-core screened and shielded instrument cable or via a wireless Local Area Network (LAN) communication link, Standard wireless LAN units have a communication range of about 200 meters. Such a wireless LAN is a low frequency radio communication system, which can typically be operated without a telecommunications licence. A remote radio transmitter is installed at the meter 125 to 145 and a radio receiver at the RTU 150 if a wireless LAN system is used to collect meter data.

Gas meter 125 may be a Krom Schroder natural gas flow meter, as supplied by Krom Schroder of Germany. These meters have an electronic counter head that has a pulse generator for remote indication of the quantity of natural gas consumed.

Electricity meter 130 may be a MultiCube Power Meter supplied by Northern Design is (Electronics) Ltd, of England. A MODBUS Communications Options Module for the MultiCube power meter provides multi-drop serial communications to the MultiCube meter. This meter uses a high-speed microprocessor to extract information from the meter interface to an industry standard MODBUS system.

Water meter 135 may be a Kent Helix 4000 water meter supplied by Kent Meters Ltd, of England. This meter produces a pulse output, and requires an external power supply.

Steam meter :t40 may be a YF100/MASS Intelligent Vortex Mass Flow Meter, as supplied by Yokogawa Electric Corporation of Japan. This meter outputs a pulse signal directly proportional to mass flow rate without the need for pressure and/or temperature compensation.

Fig. 1 describes only one example of an operational architecture that can be used. Other systems or architectures can be used to collect data relating to resource use and a relevant related variable.

WO 2004/029557 PCT/AU2003/001262 -8- Computer hardware Fig. 3 is a schematic representation of a computer system 300 of a type suitable for use as the central server 155 and the plant server 180.

The components of the computer system 300 include a computer 320, input devices 310, 315 and video display 390. The computer 320 includes: processor 340, memory module 350, input/output interfaces 360, 365, video interface 345, and storage device 355.

The computer system 300, as represented, is connected using the input/output (I/O) interface 365, to the GPRS network 380 and the Internet 385.

The processor 340 is a central processing unit (CPU) that executes a operating system and computer software executing under the operating system. The memory module 350 includes random access memory (RAM) and read-only memory (ROM), and is used under direction of the processor 340.

The video interface 345 is connected to video display 390 and provides video signals for display on the video display 390. User input to operate the computer 320 is provided from input devices 310, 315 consisting of keyboard 310 and mouse 315. The storage device 355 can include a disk drive or any other suitable non-volatile storage medium.

Each of the components of the computer 320 is connected to a bus 330 that includes data, address, and control buses, to allow these components to communicate with each other via the bus 330.

Computer software executed by the computer system 300 may be stored on the storage device 355. Alternatively, the computer software can be accessed directly from the network 380 by the computer 320. In either case, a user can interact with the computer system 300 using the keyboard 310 and mouse 315 to operate the computer software executing on the computer 320.

The computer system 300 is described only as an example for illustrative purposes. Other configurations or types of computer systems can be equally well used for the central server 155 or plant server 180.

WO 2004/029557 PCT/AU2003/001262 -9- Computer software The central server 155 of Fig. 1 can be a computer system 300 as described above. The plant server 180 can also be a computer system 300 of the same type. The central server s 155 executes computer software for receiving and collecting resource data from the plant 120, and related variable data from the plant server 180. This software executing on the central server 155 can also calculate performance indicator values as described below, and can display the performance indicator values. More commonly, the performance indicator values are of interest to operators of the plant 120, and are thus also displayed on the plant server 180 following transmission to the plant server 180 via the Internet 385.

The computer software on the central server 155 can include two major components, namely a database application and a data processing application. A suitable example of a database application is Industrial SQL Server 8.0, which can be used to collect and store data on the central server 155. A suitable example of a data processing application is Active Factory 8.0, which provides a framework for generating data reports using the data stored on the central server 155. Both Industrial SQL Server 8.0 and Active Factory are developed and sold by Wonderware Incorpartion of Lake Forest, California, United States of America. Both products function on the Microsoft Windows Server platform, which can be installed as an operating system in the central server 155. The data processing application can calculate various reports, the data for which can also be stored in the database application.

An external interface to the data stored in the database application can be provided by a World Wide Web (WWW) site hosted by the central server 155, and accessible by the plant server 180 via the Internet 385. Any suitable Web server software can be used to host the Web site on the central server 155, and any suitable Web browser used to display the Web site on the plant server 180. The Web site on the central server 155 can provide for secure communications with the plant server 180. A secure means of registering with the Web site, such as by using a usemame and password arrangement, can be provided.

Any desired interface for accessing and displaying performance indicator values, and related reference values can be provided on the Web site. Tabular and graphical reports are favoured for representing the data. Reference values can be represented as absolute values (typically in a graphical format) or as values that indicate a variation, either in WO 2004/029557 PCT/AU2003/001262 absolute or percentage terms, from the corresponding performance indicator values (typically in tabular format). Other forms of representation are also possible.

The Web site is also accessed from the plant server 180 to record related variable data, most usually production data, with the central server 155. Again, any desired interface can be adopted for permitting data to be entered at the plant server 180 for storage in the database application at the central server 155. Users can record production data at the end of each shift, or total-degree-days at the end of each month.

Reporting Reports relating to performance indicator values can be represented in various ways.

Tabular and graphical reports are favoured for providing details of recorded figures, as well as an overview of any trends. Performance indicator values, when represented in tabular form, may be indexed with respect to time. That is, performance indicator values are displayed on a shift-by-shift basis. A similar form of representation can also be adopted for graphical reports, if required. In any case, performance indicator values are presented with reference values for a corresponding level of production.

Graphical reports favour the display of performance indicator values indexed with respect to production. In other words, performance indicator values are displayed for different levels of production. Such reporting allows for identification of relative efficiencies or inefficiencies at different levels of production -olume. Underlying causes of such observations can consequently be postulated, and tested on site.

Table 1 below presents, in tabular form, an example report of performance indicator values for consecutive shifts.

WO 2004/029557 PCT/AU2003/001262 -11- TABLE 1 Production Gas EPI Reference Variation CO 2 Shift Period (GJ/T) (GJ/T) savings (tonnes) 1 Jan 21 1458.90 1.21 1.37 -11.05 -1059.7 -15.4 Shiftl 2 Jan21 1101.43 1.78 1.63 9.15 750.1 10.9 Shift2 3 Jan 21 1607.94 1.15 1.27 -9.86 -876.0 -12.7 Shift3 4 Jan 22 1788.84 1.15 1.18 -2.05 -243.6 Shiftl Jan 22 2023.40 1.06 1.06 -8.41 -826.8 12.0 Shift2 6 Jan 22 1040.87 2.09 1.68 24.41 1937.5 28.2 Shift3 Recorded consumption, as measured according to a relevant performance indicator, can be checked against previously defined reference values. This reference can be based on past performance and enables a user to assess, for a particular shift or day, whether performance is better or worse than the standard reference. The reference values are represented in absolute terms, as a percentage variation, as a corresponding monetary to saving (or loss), and as equivalent carbon dioxide gas saving (or loss).

Performance of resource consumption can thus be assessed in view of an established reference, and allows plant personnel to take corrective action if necessary. The described system allows the user to implement efficiency measures to provide better use of existing plant and equipment.

Data can be presented in predetermined time intervals, or on a shift-by-shift basis.

Tabular reports can be established to display data in the same format as the above graphical reports, or can be used to identify when the parameters have exceeded set levels, or whenever the information is called up. Graphical reports are generally preferred WO 2004/029557 PCT/AU2003/001262 12for ease of use. Graphical reports delivered through an interactive interface allow particular parts of the graph to be queried, thus allowing convenient access to actual numeral values.

Graphical and statistical techniques When performance indicator values are plotted against the related variable, a curve can be fitted to the plotted values to represent an overall trend of the graph. An equation for the representative curve can be derived through any appropriate technique. Statistical techniques commonly available in computing software applications may be used for convenience. The equation derived for the empirical data is then used to represent the relationship between production in tonnes and the performance indicator values and, consequently, to predict the performance indicator values if only the production is known.

The favoured technique for comparing the performance indicator values versus production output or total degree-days is a suitable regression analysis technique.

There are many different kinds of curves one can use to fit to empirically observed data.

The graphs of linear, exponential, logarithmic, and power functions are all useful curves.

A regression line of best fit can be determined using any suitable function.

An important question that arises in determining a curve to fit our data points is: How scattered can the values be and still have a shape that is represented by a curve? The concept of correlationjneasures this phenomenon. The Pearson correlation coefficient R can be determined to provide a quantitative indication of the "closeness" of this line of best fit to the performance indicator values. The value of R is a measure of the linear association between the horizontal variable and the vertical variable. The Pearson correlation coefficient R provides information about how tightly packed data points are about the regression line. Information is thus provided about how well the regression line fits the data. The R-values can range from -1 (strongly negative linear association) to 0 (no linear association) to +1 (strongly positive linear association).

Once one has determined a scatterplot of data, different curves can be checked for appropriateness of fit. Examples of possible curves that can be "trialed" are linear, exponential, logarithmic and power functions. One can determine which prototype curve WO 2004/029557 PCT/AU20031001262 13 provides a most appropriate "line of best fit" with a high R 2 value indicating high correlation of data values. A curve of this form can be selected as the line for a reference baseline. Selection of an appropriate baseline curve can be informed by other factors so that a minimum achievable baseline is available.

In graphical reports, such as those described below, values represented above the baseline reference are ones in which performance is relatively low, and ones under the baseline indicate and improved energy efficiency.

Procedural overview Fig. 3 presents a flowchart of steps involved in the procedure used in a plant for using the techniques described herein. Meter data for gas, electricity and water consumption might typically collected over a three-month period to collate a sufficient body of data for benchmarking purposes.

On a day-to-day basis, consumption data and production data is recorded at the end of each shift in step 310. Other relevant details relating to the shift might also be recorded, such as the shift manager, operators, or comments recorded any unusual occurrences during the shift. Performance indicator values are calculated from the consumption data and production data by simply dividing the consumption data by corresponding values of the production data. Instead of production data, any other relevant related variable may be recorded in step 310 for use in calculating performane' indicator values in step 320. The resulting collection of performance indicator values can be indexed with any other data fields such as the names of the rostered shift manager and operations staff, any plant log entries, and so on.

A report is produced or updated in step 330 based upon the performance indicator values collated over, for example, a three-month period. Tabular and/or graphical reports can be prepared as required. Finally, the reported data can be analysed in step 340 to identify gains and losses in production on a shift-by-shift basis throughout the production period.

Performance indicator values can be graphed as required against the related variable values, typically production output. A curve of best fit can be tested using available WO 2004/029557 PCT/AU2003/001262 -14statistical techniques. Often a non-linear function, such as a logarithmic function, may be concluded to best characterise the relationship between performance indicator values and the normalisation variable values. The curve of this estimated relationship of "best fit" can be conveniently adopted as a reference curve, which consequently provides a source of reference values. The baseline curve, or corresponding baseline reference values, can of course be adjusted as required.

On the basis of generated reports, a monitoring and targeting process initiated in the plant.

Throughout this process, performance indicator values are calculated and compared against corresponding reference values. When the deviation of the actual performance indicator values from the target reference values (namely the corresponding performance indicator value for the same production level from the predefined reference value) plant engineers can analyse the plant log entries, and other operations reports, to determine the reasons for the deviation.

Resource efficiency measures The system described herein enables targets to be set for performance indicator values.

Since there is a defined reference line for performance indicator values, the monitoring system can evaluate savings generated through maintaining energy efficiency measures, implementing short-term energy saving projects or long-term energy savings projects.

Examples of simple energy efficiency measures include tuning burners of gas-fired appliances to the correct air-to-fuel ratio, boiler maintenance, maintaining an appropriate proportion of total dissolved solids in the steam boiler drum water, turning off lighting and equipment when not in use, maintaining lagging on pipework, maintaining set points for room temperature and process equipment, maintaining the correct furnace pressures.

Monitoring the energy efficiency of major energy-consuming appliances such as electricity distribution sub-boards, process lines and sites with multiple occupancies or cost centres. The system provides the user with the cost per unit output from the relevant appliance and the greenhouse gas emissions per unit output from natural gas and electrical appliances, chillers and air compressors, co-generation etc.

WO 2004/029557 PCTIAU20031001262 Users can, in many cases, reduce their overall energy consumption cost per unit output by to 15%, through appropriate monitoring and targeting. This is typically achieved simply though appropriate use of basic energy efficient measures, rather than specialised energy reduction projects that can further still reduce energy consumption.

Tabular or graphical reports assist in identification of relative inefficiencies (or efficiencies) in the underlying plant, process or appliance under consideration. Experience in scrutinizing such reports allows one to investigate particular causes of equipment malfunction, or inappropriate operation. Excessive wastage can thus be brought to account and may be avoided in future. Correlating different performance indicator values with different shifts, or shift managers can assist in pinpointing efficient or inefficient operating practices adapted by particular shift mangers.

Outlined below are some basic "rules of thumb" measures that may apply in particular cases of apparent inefficiency. Addressing such general measures is a first step towards making further efficiency gains from an operating plant or appliance. As efficiency is generally improved, a new baseline reference against which future perfonnrmance can be determined and compared. Monetary savings resulting from implementing such efficiency programs can be retrospectively audited with reference to data accumulated to date.

Steam boilers Many industries use over 50% of their natural gas consumption in steam boilers to raise steam pressure for industrial processes. Example industries include food and beverage industries, latex and rubber industries, and textile industries. Measures that may improve the operational efficiency of steam boilers include: maintaining an appropriate width and length of flame in the steam boiler maintaining an appropriate air-to-fuel ratio to achieve efficient combustion 0 replacing or repairing any faulty or leaking steam traps maintaining an appropriate total dissolved solid levels in the boiler drum water returning condensate to the boiler where practicable replacing or repairing any faulty steam pressure reducing stations WO 2004/029557 PCT/AU20031001262 -16- Furnaces Some industries, such as diecasting, rolling mills, smelters, use over 90% of their natural gas consumption in melting, holding, reheating processes using heat treatment furnaces.

Measures that may improve the operational efficiency of heat furnaces include: maintaining an appropriate width and length of flame in the heat furnace maintaining an appropriate air-to-fuel ratio to achieve efficient combustion maintaining an appropriate furnace pressure above atmospheric pressure replacing or repairing faulty furnace seals S closing all furnace doors when not in use S maintaining burners on low fire when idling Air compressors As a general rule, the industrial sector as a whole tends to use around 20% of their electrical energy consumption by operating air compressors. Faulty seals and other causes of leaking air pressure commonly accounts for an estimated 25% wastage of compressed air. Appropriate measures can be taken to at least reduce such losses where identified.

Commercial buildings Commercial buildings such as office towers have their own consumption patterns. As a general proposition, over 40% of electrical energy consumption is used by air conditioning systems, and over 20% by lighting systems. Accordingly, the following measures can be used to improve the efficiency of commercial buildings: maintaining suitable humidity and temperature settings in air conditioning systems maintaining appropriate lighting levels in operating areas using minimum lighting levels when possible using economy cycles in appropriate weather conditions WO 2004/029557 PCT/AU2003/001262 -17- Natural gas case study Fig. 4 is a graph of EPIs and two EPI reference lines for two consecutive years. Natural gas consumption is presented in GJ/T, and is graphed against daily production in tonnes The diamond co-ordinates and the upper line respectively represent EPIs and an EPI reference line for a first year of operation. The square co-ordinates and lower line respectively represent EPIs and an EPI reference line for a second subsequent year of operation.

Measured performance can be readily compared against reference performance lines.

Performance improvements between years are attributed to efficiency measures being identified and implemented. Accordingly, a new benchmark or baseline EPI reference line can be determined, and against which subsequent performance is measured.

Movement of the baseline in successive years due to close monitoring and targeting is clearly shown in the two graphs in Fig. 4. The top curve (y -4.9555Ln(x) 31.932, R 2 0.566) is the Year 1 curve before implementation of efficiency measures, while the bottom curve (y -6.4689Ln(x) 39.386, R 2 0.8203) is the Year 2 curve following monitoring and targeting process. The scatter of the data points (R 2 0.8903 of the bottom curve) indicate that considerable improvements were made in Year 2 following Year 1 (R 2 0.566 of the top curve), but further improvements are still possible.

S The potential for energy savings is apparent from the natural gas EPI baseline graph for Year 1. The R 2 value is a clear indication of what opportunities exist to achieve efficiency improvements through an appropriate monitoring and targeting process. As the variation from a baseline approaches diminishes (and a corresponding variance measure, such as R 2 approaches one) the plant can usually be assumed to be operating relatively efficiently, and without significant scope to improve efficiency through targeting unnecessary inefficiencies. Any further improvements are likely to be systemic projects that target the fundamental operation of the relevant plant or equipment. Examples include waste heat recovery, installation of variable speed drives on large motors, co-generation projects and similar initiatives.

WO 2004/029557 PCT/AU2003/001262 18 As an example, for production of 275T, the natural gas EPI is indicated as 5GJ/T and not equal to the target or baseline EPI of 4GJ/T. This observation provides an opportunity for identifying the underlying cause of poor performance, and to take corrective action. The graph indicates performance of 2.2GJ/T could be achieved for similar production volumes, when the reference performance is closer to 4.0GJ/T. Observations of how the plant and equipment responds to such measures often has broader applicability to future operation in general. Consequently, efficiency improvements can be achieved.

Wool producer case study Figs. 5 to 9 are graphs of various EPIs for the example of a wool producer. Monthly electricity and natural gas EPI baseline curves drawn for a wool producer are shown in Fig. 5 (y 0.0472x 112.16 and R 2 0.8774) and Fig. 6 (y 0.399x 93.667 and R 2 0.403). These linear function graphs represent the natural gas and electricity EPI baselines calculated using existing statistical techniques with one year of monthly data.

Points in the electricity EPI baseline graph are more tightly packed (hence the higher value of R 2 0.8774) than the natural gas EPI baseline graph with a relatively low R 2 value of 0.403. This is because there is more wastage, and hence more opportunity to save energy, in the case of natural gas usage compared to electricity usage. Knowledge of a specific operation, together with the R 2 value provides an ability to predict the percentage savings that can be achieved through monitoring and targeting resource usage for that operation.

Electricity, natural gas and water EPI values and baselines are depicted in Figs. 7, 8 and 9. These two graphs use either power function or logarithmic function baselines as appropriate, respectively having equations y 11529 x -07212 and R 0.9252, y 2.5608Ln(x) 11.691 and R 2 0.4874 andy 154.93 x -0.6033 and R 2 0.7757.

Electricity and. water EPI baselines are power functions whereas the natural gas EPI o3 baseline equation is logarithmic. From the natural gas EPI baseline, production output efficiency of operation is very low below 20 tonnes/day. Natural gas EPI baseline is defined for daily production output of over 20 tonnes/day has a higher R 2 value.

WO 2004/029557 PCT/AU2003/001262 19- Dairy plant case study Figs. 10, 11 and 12 represent EPI baselines defined for a dairy manufacturing plant.

These EPI baselines are provided for natural gas (y -1.4097Ln(x) 11.668, R 2 0.8311), electricity (y -4.6031Ln(x) 38.605, R 2 0.8587) and water (y -208.74Ln(x) 1753.2, R 2 0. 8901) Greenhouse gas emission EPI baseline drawn for two consecutive years are given in Fig.

13 (y -252.53Ln(x) 2248.4 and R 2 0.9469 for Year 1, and y -402.4Ln(x) 3394 and R 2 0.9988 for Year The graphs clearly shows that the emissions have increased in the Year 2 compared to Year 1.

A cost EPI baseline graphs drawn for the same plant for consecutive years are given in Fig. 14 (y 21.573Ln(x) 188.79 and R 2 0.9638 for Year 1, and y 29.108Ln(x) 247.78 and R Z 0.9722 for Year The graphs clearly show that the energy cost of production has increased from Year 1 to Year 2.

Conclusion The efficiency of resource use of, for example, gas, electricity and water, as well as greenhouse gas emissions and other solid or liquid waste emission levels, can be monitored. The energy performance or efficiency of major appliances such as steam boilers, air compressors, furnaces, chillers etc can also be targeted.

Maximum demand for electricity supply, and maximum daily quantity for gas supply can also be managed. The benefits of managing maximum demand and maximum daily quantity can include the minimisation of financial penalties from the gas and electricity network providers due to the user exceeding contracted consumption limits. Regulatory compliance issues may also apply.

Various alterations and modifications can be made to the techniques and arrangements described herein, as would be apparent to one skilled in the relevant art.

Claims (13)

1. A method for monitoring the efficiency of resource use comprising the steps of: receiving data relating to a resource variable and data relating to a related variable; calculating performance indicator values by normalising values of the resource variable data with corresponding values of the related variable data; determining a mathematical relationship that approximates a relationship 1o between the performance indicator values and the related variable values; determining the reference values based upon the determined mathematical relationship; and displaying graphically, with respect to the related variable, instances of the performance indicator values in contrast with a curve representative of the corresponding reference values.

The method as claimed in claim 1, further comprising the step of calculating a measure representative of the variation between the performance indicator values and corresponding values indicated by the determined mathematical relationship.

3. The method as claimed in either one of claim 1 or claim 2, further comprising the step of periodically redefining the reference values.

4. The method as claimed in any one of the preceding claims, further comprising the step of calculating notional gains or losses represented by variations between the performance indicator values and the corresponding reference values.

The method as claimed in any one of claim 1 to claim 3, further comprising the step of calculating cumulative notional gains or losses represented by variations between the performance indicator values and the corresponding reference values.

6. The method as claimed in any one of the preceding claims, further comprising the step of displaying in tabular form instances of the performance indicator [R:\LIBK]599387AUdoc:TCW -21- values in contrast with the corresponding reference values as variations from the performance indicator values.

7. The method as claimed in any one of the preceding claims, wherein the s resource variable relates to a consumed resource selected from the group comprising electricity, natural gas, water, and steam.

8. The method as claimed in any one of claim 1 to claim 6, wherein the resource variable data relates to a generated resource selected from the group comprising 0to carbon dioxide and liquid waste volume.

9. The method as claimed in any one of claim 1 to claim 6, wherein the related variable data relates to a variable selected from the group comprising production weight, production volume, production length, gaseous by-product volume, liquid by-product volume, and solid by-product weight.

Computer software, recorded on a medium, for monitoring the efficiency of resource usage, the computer software comprising software code means for performing steps of: accessing data relating to a resource variable and data relating to a related variable; calculating performance indicator values by normalising values of the resource variable data with corresponding values of the related variable data; determining a mathematical relationship that approximates a relationship between the performance indicator values and the related variable values; determining the reference values based upon the determined mathematical relationship; and displaying graphically, with respect to the related variable, instances of the performance indicator values in contrast with a curve representative of the corresponding reference values.

11. A computer system for monitoring the efficiency of resource use, the computer system comprising computer software means for performing steps of: storing data relating to a resource variable and data relating to a related variable; [R:\LIBK]599387AU.doc:TCW -22- calculating performance indicator values by normalising values of the resource variable data with corresponding values of the related variable data; determining a mathematical relationship that approximates a relationship between the performance indicator values and the related variable values; determining the reference values based upon the determined mathematical relationship; and displaying graphically, with respect to the related variable, instances of the performance indicator values in contrast with a curve representative of the corresponding reference values. I0

12. A computer system comprising: a memory for storing data relating to a resource variable and data relating to a related variable; a processor for calculating performance indicator values by normalising values Is of the resource variable data with corresponding values of the related variable data, determining a mathematical relationship that approximates a relationship between the performance indicator values and the related variable values; determining the reference values based upon the determined mathematical relationship; and a display for displaying graphically, with respect to the related variable, instances of the performance indicator values in contrast with a curve representative of the corresponding reference values.

13. A computer system comprising: means for storing data relating to a resource variable and data relating to a related variable; means for calculating performance indicator values by normalising values of the resource variable data with corresponding values of the related variable data, determining a mathematical relationship that approximates a relationship between the performance indicator values and the related variable values, and determining the reference values based upon the determined mathematical relationship; and [R:\LIBK]599387AUdoc:TCW -23- O means for displaying graphically, with respect to the related variable, instances c of the performance indicator values in contrast with a curve representative of the Scorresponding reference values. Dated 4 August, 2006 The Australian Gas Light Company Cr Patent Attorneys for the Applicant/Nominated Person r SPRUSON FERGUSON [R:\LIBK]599387AU.doc:TCW