Energy Review Guideline

Transcription

1 分享最佳实践创造低碳未来 iipnetwork.org Energy Review Guideline Institute for Industrial Productivity(IIP)

2 About this Energy Review guideline This guideline is designed to assist organizations who wish to implement a formal Energy Management system in order to drive their energy consumption and costs down. The guide takes the organization step by step through the Energy Review or Plan part of the implementation process including: Data Collection and Analysis Challenging and Auditing of Significant Energy Users Objectives, Targets and Action Plans The appendices include further information on the requirements for Senior Management prior to the Energy Review, a guide to Regression Analysis, and examples of the outputs from each stage of the process. The authors wish to acknowledge the contribution of materials prepared by the United Nations Industrial Development Organization (UNIDO) and Sustainable Energy Authority of Ireland (SEAI) to this guide. 2

3 Contents 1. Background - why Energy Management Systems (EnMS) and what have they achieved? Energy Review - why bother? Energy Review - the process... 5 Steps 1&2: Data collection & analysis (SEUs, regression, drivers, baselines, EnPIs)... 6 (a). Data collection:... 6 (b) Trend Analysis... 9 (c) Drivers, Baseline and EnPIs Steps 3&4: Challenge SEUs & Perform Energy Audits Step 5: Objectives, Targets & Action Plans References Appendix1: Management Commitment a. Energy Policy b. Scope & Boundaries c. Energy Team Roles and Responsibilities Appendix 2: Regression Analysis - single variable and multivariate Single Variable Regression Multivariate Regression Regression Analysis in Excel Appendix 3 Typical Outputs from Energy Review

4 1. Background - why Energy Management Systems (EnMS) and what have they achieved? According to the International Energy Agency (IEA) Energy efficiency is the only fuel that simultaneously meets economic, energy security and environmental objectives (1). Industry is currently responsible for 1/3 of global energy consumption and Greenhouse Gas emissions and will be one of the main drivers of growth in global energy demand over the coming decades. Industry faces multiple issues relating to energy consumption including: Rising energy prices Increasing energy shortages in many countries Pollution caused by energy use Corporate social responsibility to deal with pollution and climate change issues Global security issues caused by the rising demand for energy Many industries already have formal systems for managing essential issues e.g. Quality, Health & Safety, Environment, Food Safety - which have succeeded in addressing these challenges through a formal structured approach based on the Plan Do Check Act (PDCA) virtual circle. This can also be applied to Energy Management. Figure 1 PDCA Cycle 1

5 This formal approach to Energy Management has led to sustainable improvements in energy efficiency in many industries around the world over the past 2 decades. For example, businesses who switched from an ad hoc method of managing energy, to a formal Energy Management Program with an Energy Management System (EnMS) as part of the US Department of Energy Superior Energy Performance (SEP) Program increased their savings from 3.6% annually to 13.7% annually. Companies in this program had an average payback period of 1.7 years for their energy saving investments, with 2/3 of savings generated by low cost operational change, and 1/3 by capital investment (2). Several household names have participated in the SEP program Volvo, Dow, 3M, Nissan, Bridgestone. Deloitte studied energy management in 600 companies across the US in 2013 and found that those with formal Energy Management approaches had savings of 3 times those with informal approaches (3). Many studies have found that along with the energy savings achieved through EnMS there are also indirect benefits (4) : higher productivity improved machine reliability reduced noise better working conditions reduced Capital Investment requirements improved quality improved health & safety Studies in Denmark found that these Non Energy Benefits are worth up to 150% of the energy savings (5). The potential for improved energy management to have a significant effect on the bottom line can be greatest where energy costs make up a large part of the overall costs. In some instances where energy costs make up 30% of total costs, and existing net margins are approximately10%, net margins could be increased to 13% with a 10% cut in energy costs. The formal Energy Management System requires 5 essential steps Step 1 is Management Commitment followed by the 4 Plan Do Check Act steps. 2

6 The idea of an Energy Management system (EnMS) is to improve the energy performance of an organisation via commercially sound organisational, technical and behavioural improvements. The traditional ad hoc approach tends to lead to initial gains in energy efficiency when the business focuses on the problem. However, these gains are often not sustained. The formal structured approach tends to lead to increased savings that are sustained and built on over time. 3

7 2. Energy Review - why bother? The Energy Review is, in effect, the Plan part of the PDCA process. It is carried out once management commitment to the process has been secured. Management show their commitment via: Drawing up and signing an Energy Policy Defining the scope and boundaries of the planned Energy Management system what processes, what departments, what buildings, what energy sources Appointing an Energy Team Committing to provide the necessary resources required to implement the EnMS See Appendix 1 for details of the management commitment requirements. To improve Energy Management it is necessary first to understand how energy is currently consumed on site. what energy sources are consumed, how much of each source is consumed where are they consumed what drives consumption what are the trends in energy usage and what is the likely future use Once this is understood the Energy Team can then begin to identify how this energy consumption can be reduced and put together a plan to achieve this. It is critical to the success of the Energy Management process that the Energy Review be carried out effectively as it is the basis for all Energy Management activities carried out over the following period. 4

8 3. Energy Review - the process The ideal process is shown in the diagram below The Energy Review seeks to answer the following questions: How much energy and what types of energy are we using What is the trend in usage Where is it being used what are the Significant Energy Users (SEUs) which account for >80% of the total energy cost and who are the people who influence the energy use What is driving the energy use i.e. what causes energy use to rise or fall for the site and for the SEUs How can I measure improvement What opportunities for improvement exist How can I avail of these opportunities 5

9 Steps 1&2: Data collection & analysis (SEUs, regression, drivers, baselines, EnPIs1) (a) Data collection: The first step is to collect available information on how energy is currently consumed on site. Data should be collected on energy consumption and output for the previous 24 months. Some organisations will also collect and analyse data on water usage due to the fact that pumping, heating and treating water can consume a lot of energy, and water is becoming a scarce resource which could benefit from being managed in a similar way to energy. This data is then used to develop an energy balance for the site showing energy consumed by the site, and where it is consumed. This should be done in cost and energy terms to identify the energy users that are costing the most money. Figure 5 Site Energy Balance (tonnes of coal equivalent) 1 Energy Performance Indicator: quantitative value or measure of energy performance, as defined by the organization 6

10 Figure 6 Site Energy Balance (China Yuan) From the diagram we now know the main energy sources in kwh and cost Coal and Electricity - and we know the main energy users in kwh and cost. The main energy users are usually termed the Significant Energy Users (SEUs) and it is these SEUs which typically consume over 80% of the energy cost which will receive the focussed analysis in order to identify opportunities for improvement. In the above example the SEUs in terms of cost are, Process Drying, Boiler Losses Process 1 Electrical and Process 2 Electrical. Energy cost is critical to many organisations. However, it must be remembered that while the organisation can exert control over their energy consumption, the unit cost of energy is mainly influenced by factors outside of their control, and significant changes in the energy unit cost can have a significant effect on which users are the SEUs in cost terms. Note that while the SEUs should be the main focus of attention, other smaller energy users should not be ignored where they can be easily improved. For example take security lights that are left on 24x7x365 which should only be during hours of darkness. While these lights may not be a SEU, they are potentially a Quick Win to generate early savings both energy and maintenance savings - with low cost, so they should not be ignored. 7

11 In an ideal world you will have metered data for all of the SEUs for the past 24 months. However, in many cases this is not available. Therefore you may have to start with the information that is available to identify SEUs. Once the SEUs are identified you can put in place a metering system to give accurate data. Electrical loads are, in general, easy and cheap to meter. However, where metered data is unavailable, estimates must be made. If the load on the motor is consistent, a grip on meter can give a spot reading which can be multiplied by the run hours to generate kwh readings. Where the load varies from minute to minute, but doesn t vary much day to day, a kwh meter can be used to measure for 1 day, or for a week, and multiplied by the number of running days/weeks to indicate annual consumption. Alternatively, estimates can be made based on the run hours multiplied by the nameplate kw rating multiplied by the % loading in order to identify the significant motors. Once the biggest loads are estimated, meters can then be installed to generate accurate data. For lighting loads the wattage drawn by the lamp and it s control circuit can be multiplied by the run hours to get a kwh estimate. Thermal loads are more difficult to meter without clamp on heat meters. However, often there is adequate information available from existing meters to enable an estimate of annual energy use to be made. For example consider a process consuming thermal energy in the form of hot water. We don t have a heat meter on the process, but we know that hot water enters the process at 80oC and leaves it at 40oC. We also have information from a flow meter that tells us that 4m3 pass through the process daily for 260 days per annum. Therefore we can calculate the thermal energy consumed by this process: Thermal Energy Consumed p.a. = Flow through the process x specific heat capacity of the fluid x (Change in Temperature) = 4,000kg/day x 260 days x 4.18 kj/kg o C x 40 o C = 173,888,000 kj = 48,302 kwh This process is continued until you have collected data to account for >80% of your energy cost. 8

12 (b) Trend Analysis Once the SEUs have been identified we need to discover how energy use for the overall site and for the SEUs - changes over time is it rising/falling? Are there seasonal effects? What is likely to be the energy use for the coming 12 months without intervention? The data for energy use of the overall site and of the SEUs that has been collected for the past 2 years is graphed in order to discern trends and look for anomalies. Careful examination of the trends may reveal opportunities. For example, a large hotel graphed it s gas usage on a monthly basis and found at the end of January that usage had spiked. They investigated the cause of the spike and found that a control valve was stuck in the open position, leading to wasted heat energy. The problem was resolved 2 weeks after it arose. However, without trend graphing the problem may have gone on a lot longer without being noticed. In some cases it may be useful to graph 12 month rolling total energy usage to remove seasonal effects. For example, figure 7 below shows monthly gas consumption by a process. It is clear than usage goes up in winter and down in summer, but is unclear whether there is any other usage trend. Figure 7 Monthly Gas Consumption 9

13 Figure 8 shows the same data graphed as a 12 month rolling total graph. In this case each point represents the total gas consumption for the previous 12 months. In this graph it clearly shows that consumption is trending upwards. Figure 8 Rolling 12 month total Gas Consumption (c) Drivers, Baseline and EnPIs The main objective of implementing an Energy Management System is to effect improvement. In order to be able to effect improvement, you must be able to measure it. One of the most popular metrics for measuring performance improvement relating to energy is Specific Energy Consumption (SEC) or energy consumption per unit output. SEC is also used by many industries to benchmark energy performance against other companies in the same industry. While it is a useful indicator, for many industries there are difficulties in using it for benchmarking or for measuring performance if their energy consumption has a significant Baseload. The Baseload is the energy consumed when the driver of the energy consumption in most cases Throughput is zero. Even though production has stopped, the factory is still consuming energy for certain loads unrelated to Throughput - lighting, HVAC, hot water, pumps etc. Therefore the Energy Consumption is made of a fixed element the Baseload - plus a variable element which is usually proportional to the Throughput: Energy Consumption = Baseload + (Throughput x Factor) Therefore 10

14 Specific Energy Consumption = Energy Consumption Throughput = Baseload + (Throughput x Factor) Throughput = Baseload + Factor Throughput Therefore, as throughput increases, the factor typically remains constant, and the Baseload is divided by a bigger throughput so that the SEC falls even though no improvements have been made. This is the danger with using SEC for benchmarking. If the baseload is low, then this problem is not significant and SEC is a valid indicator. However, if the Baseload is significant, then using SEC as an indicator can be misleading as it may be falling purely due the baseload being divided by a bigger throughput energy performance may actually be getting worse. Therefore you need to work out what is currently driving your energy use (i.e. the driver) and then work out the current relationship between your energy use and it s drivers (i.e. the baseline). You can then use this Baseline relationship in the future to predict what your consumption would have been without any improvement measures, and compare it to your actual consumption in order to measure improvement. This Baseline relationship can also be used by the finance department to forecast energy costs for budgetting purposes. Figure 9 shows a graph of energy consumption for a SEU graphed against output. Regression analysis can be performed via Excel to generate an equation which describes the line of best fit through the points on the graph. In this case the equation generated by Excel is: y= x where the y is gas consumed, x is the output, the Factor is and the Baseload is i.e. Gas Consumption = ( x Output)

15 The R 2 value is which indicates that output levels are responsible for over 95% of the variation in energy use for this process. Therefore it is output that drives the energy consumption and we can use the equation as our Baseline i.e. the formula for how the energy consumption of the process relates to the output. Figure 9 Gas Consumption vs Output On this particular site they implemented an EnMS and started to take some low cost measures to save energy. They then measured energy consumption for the following months and compared it with the baseline energy use i.e. how much energy the process would have consumed if they had not introduced any improvements. They used the relationship between the actual energy usage and the predicted energy usage as an indicator of their performance i.e. an Energy Performance Indicator (EnPI). The results are shown in table 1. Month Output (tonne) Gas Consumption (kwh) Predicted Energy Use (kwh from Baseline Equation) March % April % May % June % July % Table 1 Energy Performance Indicator 12 Actual Consumption / Predicted Consumption (EnPI)

16 In order to measure energy performance for the site there should be an EnPI for each significant energy source. Ideally there should also be an EnPI for each SEU. The graphs used to generate the baseline equations also highlight the Baseload In figure 9 the baseload is kWh. The Baseload is often a significant area of opportunity for energy saving: What is causing the baseload? Do the activities which cause the Baseload add value? If not, are they avoidable? In the above case Energy Consumption is driven by output. This is true in most industrial cases. However, there may be other factors also driving energy use. To identify drivers you need to consider what could be the factors that may be driving the energy consumption of a process up or down. Typically tonnage processed is a driver. For thermal loads the heat consumed by the process will generally be driven by the tonnage processed and the temperature lift. For some thermal processes the average external temperature is a driver as it affects the amount of energy required to heat the air used in boiler combustion. The condition of the raw material may also be a driver for example the % moisture may affect the thermal energy required to dry the material. For some processes the % of recycled material is a driver as it often consumes less energy to process than virgin material. Once the possible drivers are identified then Regression Analysis can be used to determine whether there is a strong relationship between the driver and energy consumption. See Appendix 2 on regression analysis for further instruction on identifying drivers and baselines. 13

17 Steps 3&4: Challenge SEUs & Perform Energy Audits Once the SEUs have been identified, they must be challenged to see: Are they essential to the process - do they add value? If not, can they be eliminated? If the process is essential can the outcome of the process be achieved via a more energy efficient process? Is the current process using best practice and the best available technology? If the current process is the appropriate one, is it controlled appropriately? Do the operational, maintenance staff have the appropriate skills, training, experience and motivation to run and maintain the process optimally? Are the SOPs optimal? What are the critical operating parameters? This process is illustrated in the Onion diagram of figure 10. It will usually require a detailed energy audit to answer these questions. Companies can also apply Lean methodologies at this stage including Value Stream Mapping in order to identify opportunities. Housekeeping Operation & Maintenance Control Systems Plant Design Process Technology Energy Service Figure 10 Energy Opportunity Onion Diagram A detailed Energy Audit for a process will typically follow these steps: 1. Preparation of Energy Balance for the process what energy sources are being consumed and how much of each, and where is the energy going see Sankey Diagram below where the SEU is a boiler: 14

18 Figure 11 - Boiler Sankey Diagram 2. Identify energy performance improvement opportunities: Measures to reduce or recover energy losses insulation, fixing leaks, waste heat recovery Equipment replacement, improvement, re- design variable speed drive, LED lighting Improved operation new operating procedure, new set points, improved control system Improved maintenance total productive maintenance procedures, predictive maintenance Training and Awareness identification of areas where employees can positively effect energy performance Energy Management monitoring, metering, EnMS 3. Division and Prioritisation of Opportunities Opportunities may be divided into People, Technical and Organisational opportunities Opportunities should be prioritised based on Savings, Cost and Difficulty of Implementation The output from step 4 of the Energy Review process should be a list of energy saving opportunities similar to table 2 below. 15

19 No Description Etimated Annual Savings kwh elec kwh fuel CO2 $ Capital Cost($) Potential payback (years) Ease of Implementation Downtime Required Owner Date Entered Target Completion Date Fit VSD to hammer mill 1 main drive Easy 1 day JB 01/12/ /04/2014 Approved 2 Fit VSD to rotary mill Easy 1 day KL 01/01/ /05/2014 A waiting approval Train maintenance in 3 Energy Efficient Easy None JB 01/01/ /12/2014 In progress Maintenance Train key operators in 4 Efficient Operations Easy None JB 01/02/ /02/2014 Complete Replace air compressor 5 with screw type with VSD Easy 1 day JB 01/12/ /03/2014 Complete Approved- Recycle heat generated awaiting Difficult 2 days GD 01/10/ /10/2014 from mill to kiln summer shutdown Status Table 4 Opportunities List Step 5: Objectives, Targets & Action Plans Once steps 1-4 have been completed, the organisation will have developed a good understanding of how energy is consumed on site, and of the opportunities which exist to save energy. Step 5 involves evaluating these opportunities and establishing Objectives for the organisation in relation to energy to be achieved over the coming 3-5 years, along with more specific targets to be achieved over the coming months, and specific action plans designed to deliver the targets within the month timeframe. Figure 12 Objectives, Targets and Action Plans (UNIDO) 16

20 Examples of Objectives would be: Reduce Overall Site Energy Usage per tonne by 15% by Replace all coal usage with natural gas by Achieve ISO50001 certification by 2016 Become the most energy efficient cement plant in China by 2020 The Targets should then be the stepping stones to deliver these objectives. Examples of Targets would be: Reduce lighting energy consumption by 10% by end of year Reduce steam consumption by 3% by end of year Train all staff in relevant aspects of Energy Management by end September Each Target must then be supported by specific action plans as shown below in Table 5 ID Description of Opportunity Fit VSD to hammer mill main drive Fit VSD to hammer mill main drive Fit VSD to hammer mill main drive Fit VSD to hammer mill main drive Train maintenance in Energy Efficient Maintenance Train maintenance in Energy Efficient Maintenance Train maintenance in Energy Efficient Maintenance Service Elec Elec Action Prepare spec. for drive Purchase drive as per spec Person Responsible Target Completion Status PG 01/06/2014 Complete MA 15/06/2014 Elec Fit drive and test JB 03/06/2014 Elec Maint Train hammer mill operator in operation of VSD Prepare training material BJ 03/09/2014 JB 01/06/2014 In progress Maint Deliver training JB 01/07/2014 Maint Measure performance ober 6 month period JB 31/12/2014 Notes,Barriers,Risks Performance will be measured by change in dowmtime Table 5 Action Plans Once Step 5 is complete the organization has completed the Plan phase and is ready for the Do phase which involves putting the Plans into action and generating savings. 17

21 References 1. IEA World Energy Outlook 2013, available at /#d.en Assessing the costs and benefits of the Superior Energy Performance program, available at paper13.pdf 3. Deloitte resources 2013 Study - The Power Shift: Businesses Take a New Look at Energy Strategy, available at UnitedStates/Local%20Assets/Documents/Energy_us_er/us_er_reSour ces2013business_july2013.pdf 4. Energy Conservation Also Yields: Capital, Operations, Recognition and Environmental Benefits, available from energy.org/white- paper/energy- conservation- also- yields- capital- operations- recognition- and- environmental 5. Importance of co- benefits and how these can be measured, IEA Paris 2009, Gudbjerg E et al. 18

22 Appendix1: Management Commitment A successful EnMS relies on maximising the low cost energy savings available throughout the organisation. It requires a cultural change to ensure that all staff are aware of the importance of energy savings to the organisation. For staff who can significantly affect the energy consumption of a SEU operators, planners, supervisors, maintenance, process design, purchasing - it requires constant attention. For other staff it requires very little beyond turning off office lights/heating/cooling when not needed. But it requires some level of attention from all staff. To achieve this management must be committed and be seen to be committed to the EnMS. In order to demonstrate this commitment there are a number of issues that the Senior Management must address prior to the implementation of an EnMS. a. Energy Policy Drafting and approving and Energy Policy forces Senior Management to consider their energy consumption strategically: How important is energy cost to the business, what is the potential to save etc. Publishing the policy sends a message to all staff that Senior Management is committed to EnMS and starts the cultural change that is required. The Policy should include certain elements: high level objective for energy saving commitment to continuous Improvement in Energy Efficiency commitment to provide the resources needed to improve energy efficiency commitment to consider Energy Efficiency in new process and building design, and purchasing of energy consuming equipment For organisations that wish to attain certification of their EnMS to ISO50001 they must also include: commitment to address Legal and other requirements in relation to energy consumption Figure 13 below shows an example of an Energy Policy which conforms to the requirements of ISO

23 b. Scope & Boundaries Sample Energy Policy XYZ Cement Company operate in an highly energy intensive sector and are very aware of our responsibilities with regard to energy. We are striving to become the most energy efficient cement producer in China by We are committed to: Reduce energy consumption per tonne by 10% by 2015 and 20% by 2020 Educate all key staff in optimum energy management Dedicate the necessary resources to achieve our energy aims Uphold legal and other requirements relating to energy consumption Figure 13 Energy Policy It is up to the Senior Management team to decide what will be managed by the EnMS what processes, what sites, what buildings, what energy sources. The scope generally refers to the energy sources - gas, electricity, heating oil, coal, transport diesel - whereas the boundaries generally refer to the physical limits within which we choose to manage energy. For example, in defining the scope the business may decide to exclude energy sources that are insignificant for the business. It may also decide to exclude transport energy use that is consumed off site. In defining the boundaries it may decide to exclude processes involved in R&D, or an administration building etc. c. Energy Team Roles and Responsibilities For the Energy Team to be effective they must contain the right personnel including a sponsor in the Senior Management team and they must know their roles, responsibilities and levels of authority. This must be approved by Senior Management in order to be taken seriously. Figure 14 below shows an example of an energy management team. Figure 14 Energy Team 20

24 Appendix 2: Regression Analysis - single variable and multivariate Regression analysis is a statistical tool for investigating relationships between variables e.g. between energy use and output. To achieve this you must collect data on the variables in question both the dependent variable (the variable being influenced in our case this is usually energy consumption) and the independent variable (the variable that may be influencing the dependent variable e.g. output). Single Variable Regression For example in the table below we have electricity consumption of a SEU and output from this SEU. Out put (tonne) Electricity Consumption (kwh) Table 6 We can plot this data on a 2 dimensional Scatter diagram where each point represents a particular output and corresponding energy consumption. 21

25 Figure 15 Scatter Diagram It is clear from the diagram that as output increases so does energy consumption, so there appears to be a relationship between the 2. We can also see that for similar levels of output there are similar levels of energy consumption. Therefore it appears that the relationship between these 2 factors is close i.e. Output appears to drive energy consumption we term output a driver of energy consumption. Regression analysis establishes an equation which attempts to describe the relationship between the independent variable and it s driver(s). In the example above, it attempts to plot a line of best fit through the scatter diagram, and the equation which describes this line is the best equation for describing the relationship between the independent variable and the dependent variable. The line of best fit is the line which generates in the minimum error between the predicted result and the actual data. Figure 15 shows the same data with a line of best fit, the equation for the line and the R 2 value for the data. Figure 16 Regression Analysis 22

26 The equation for a straight line is given by y = mx+ C where y is the value on the Y axis, m is the slope of the line, x is the value on the X axis, and C is the y value where the line cuts the Y axis. In this case the equation is y = x i.e. Electricity Consumption = *Output The R 2 value in this case is This is a measure of how closely the line of best fit and the equation that describes this line fits the data. The closer the points are to the line, the higher the R 2 value. In effect a high R 2 value means that this driver is likely to be the main controller of the variation in the independent variable. So for a R 2 value of 0.9, the driverappears to be responsible for 90% of the variation in the independent variable. Multivariate Regression In many cases there are more than 1 significant variable that drive energy consumption. If so you need multivariate regression to establish an equation describing the relationship. For example take a bakery which consumes gas to provide heating and to heat the ovens which bake the bread. Gas consumption is likely to be driven by the oven throughput. It is also likely to be driven by the average external temperature the lower the temperature, the more gas will be required to heat the ovens and heat the factory. Therefore the relationship is likely to be in the form of y = m 1 x 1 + m 2 x 2 + C i.e. Gas consumption = (factor 1*throughput) + (factor 2*average external temperature) + Constant With multiple regression you can no longer picture a line of best fit given that there are 2 drivers and an independent variable it requires 3 dimensional representation and the line of best fit becomes, in effect, a plane of best fit which is described by the equation. Table 7 below shows the relevant data collected over 8 months for the bakery: 23

27 Month Gas Consumption (kwh) Production (tonne) Jan Feb Mar Apr May Jun Jul Aug Table 7 Heating Degree Days Heating Degree Days is a measure how far the average external temperature for the month was below the level recommended for space heating (15.5 o C). If the average temperature each day was 10.5 o C for 30 days, then the HDD is calculated by the number of days x the difference between the average temperature and the temperature at which heating is required. In this case: Heating Degree Days = 30 x ( ) = 150 HDD Analysing this data in Excel yields the following results in table 8: Regression Statistics Adjusted R Square Coefficients P- value Intercept Production (tonne) Heating Degree Days Table 8 This suggests that 93.8% of the variation in gas consumption is driven by these 2 drivers - Production and Heating Degree Days. The equation which describes this relationship is obtained from the co- efficients in the table: Gas Consumption =737.2*Production *Heating Degree Days The P value is a measure of the likelihood that there is a relationship between the driver and the independent variable. If the P value is <0.1 then there is likely to be a relationship. In this case both P values are way below 0.1 so we can be confident there is a relationship. 24

28 Regression Analysis in Excel a. Single Variate Regression Step1 Enter data for independent variable and suspected driver into 2 adjacent colums Step 2 Highlight the 2 columns Step 3 On the Insert tab highlight scatter in the charts section and select scatter with only markers Step 4 Right click on one of the data points in the graph and select add trendline Step 5 Under format trendline pick trendline options and select linear and check the boxes that say Display equation on chart and display r squared on chart b. Multivariate Regression in Excel You must have the data analysis toolpak enabled to carry out multivariate regression. To activate the Data Analysis Toolpak: Step 1: Click on the office symbol in the top left hand corner of the screen Step 2: Select Excel Options Step 3: Click on Add Ins and select Analysis Toolpak from the Inactive Application Add Ins Step 4: Click Go at the bottom of the screen and check the box that says Analysis Toolpak To carry out multivariate regression analysis: Step 1: Enter the data for the independent variable and the suspected drivers side by side in columns Step 2: In the Data tab click on Data Analysis and select regression box Step 3: Input Y range is the independent variable include the title Step 4: Input X range is the drivers again include the titles Step 5: Check the labels box so that it sees the labels as separate form the data Step 6: Click the box saying new worksheet The spreadsheet will look like table 9 below with key data highlighted: 25

29 26

30 Appendix 3 Typical Outputs from Energy Review This Energy Review guideline is supplied in electronic form with a spreadsheet tool to assist organisations in preparing their EnMS Plan. The table and graphs below are outputs from an example organisation that has used the spreadsheet for their Energy Review. a. Data Collection The first step in the Energy Review is the collection of relevant data on energy consumption for at least the previous 24 months. The relevant data on energy consumption is entered here. Example data are shown below: ELECTRICITY GAS OIL Month kwh Cost($) kwh Cost($) kwh Cost($) Jan ,000 53, , Feb ,000 58, , Mar ,000 36, , Apr ,000 70, , May ,000 58, , Jun ,000 53,200 62, Jul ,000 53,200 55, Aug ,000 61,500 52, Sep ,000 70,500 49, Oct ,000 87, , Nov ,000 97, , Dec ,000 63, , Jan ,000 60, , Feb ,000 74, , Mar ,000 79, , Apr ,000 85, , May ,000 72,600 69, Jun ,000 66,000 54, Jul ,000 59,400 61, Aug ,000 66,000 64, Sep ,000 79,200 82, Oct , , , Nov , , , Dec ,000 74, , Table 10 Data 27

31 b. Trend Analysis This spreadsheet maps the trends in the data entered in the data spreadsheet. Trends for Electricity are shown below: Electricity: Monthly consumption (kwh) 700, , , , , , ,000 - Jan- 12 Apr- 12 Jul- 12 Oct- 12 kwh per month Jan- 13 Apr- 13 Jul- 13 Oct- 13 Electricity: Annualised consumption, cost kwh per year 5,700,000 5,600,000 5,500,000 5,400,000 5,300,000 5,200,000 5,100,000 1,000, , , , , , , , , ,000 - $ kwh p.a. Cost p.a. Figure 17 Trend Graphs - Electrical c. SEU Identification There are 3 spreadsheets to assist with identifying SEUs: (i) Typically motors consume over 60% of electrical power in industry. This spreadsheet can be used to identify the motors which are the most significant electrical energy users in the absence of actual metered data. When these SEUs are identified, appropriate metering can be installed to generate actual data. 28

32 No Purpose Name plate (kw) Annual Run Hours Average Speed(100% if fixed) Average% of name plate load Estimated Actual Power(kW) Estimated Actual Power(kWh) 1 Hammer Mill main Motor ,880 2 Rotary Mill main motor Main conveyor drive motor Kiln drive motor Crus her motor Total Total electricity consumption Motors% of total Electrical 67% Table 11 Motor SEUs (ii) Lighting may also be a SEU. This spreadsheet can be used to identify the major contributors to the lighting load No Area Type of Fitting Number of fittings Lamp rating(w) Number of Lamps/fitting Hour per Year Control Circuit% kwh per Year 1 Perimeter Security Sodium % Factory Floor t % Offices t % Total Lighting Total Electricity Lighting% 8% Table 12 Lighting SEUs (iii) The Thermal Load spreadsheet can be used to estimate thermal SEUs in the absence of metered data. No Purpose Design Power Usage(kW) Annual Run Hours Estimated Average% of Design Load Estimated Annual Energy(kWh) % of total 1 Gas Kiln % 2 Dryer Oven % 3 Dryer Oven % 4 Gas Kiln % Total of users % Total fuel used kwh per year(from bills) Table 13 Thermal SEUs 29

33 d.energy Balance Once the Energy inflows and outfllows have been estimated they can be illustrated in a Sankey diagram. e.measurement Plan Figure 18 Energy Balance Diagram (kwh) This contains details of the measurement plan to address gaps in the current data for SEUs. SEU Hammer Mill Rotary mill Measurement Required kwh consumption per tonne cement kwh consumption per tonne cement Instrument Required kwh meter 350 kwh meter 290 Purchase Cost($) Gas Kiln 1 Gas consumption Gas meter 1200 Gas Kiln 2 Gas consumption Gas meter 1200 Boiler Gas consumption Gas meter 850 Action Plan Table 14 Measurement Plan Purchase and install by 30/9/14 Purchase and install by 30/9/14 Purchase and install by 30/9/15 Purchase and install by 30/9/16 Await budget plan for 2015 Responsible GD GD JB JB JB 30

34 f. SEU - Drivers Once SEUs are identified you must identify what factors are driving the energy consumption for each SEU. In this spreadsheet, a baseline equation is calculated using regression analysis of 12 months of energy consumption and output data for the site or for an SEU. The graph below show an example where there is just 1 main driver of Electricity consumption Output. Month Driver(Output) Electricity Consumption(kWh) Jan ,000 Feb ,000 Mar ,000 Apr ,000 May ,000 Jun ,000 Jul ,000 Aug ,000 Sep ,000 Oct ,000 Nov ,000 Dec ,000 g. EnPIs Once a Baseline has been established for a SEU or for the plant as a whole, Energy Performance for the plant or for the SEU can be measured by comparing currrent energy use with the energy use predicted by the Baseline equation. The results can be illustrated by a CUSUM graph to illustrate progress with regard to energy saving. The CUSUM is the sum of Energy Savings against the baseline to date. If the graph is going upwards then savings are being generated. If it is flat, 31

35 no savings are being generated. If it is going down, then the process is getting less energy efficient. The slope of the line represents the rate of improvement a more vertical upward line indicates a faster rate of improvement. Month Driver(Out put) Electricity Consumption( kwh) Expected Electricity Consumption from Baseline Equation = Output * Energy Intensity Index(Actual Consumption / Expected Consumption) Difference(E xpected)con sumption- Actual Consumption )(kwh) CUSUM of Savings(kWh) Jan , , ,164 17,164 Feb , , ,922 36,086 Mar , ,219 37,305 Apr , ,954 58,259 May , ,712 70,971 Jun , ,306 80,276 Jul , ,438 97,714 Aug , , ,824 Sep , , ,417 Oct , , ,097 Nov , , ,535 Dec , , ,347 h. Technical Audits Once SEUs are identified they can be audited in order to identify opportunities. This spreadsheet can be used to record results of audits carried out on SEUs. 32

36 SEU Auditor Audit Date Observation Boiler John Doe Week 23 Boiler John Doe Week 23 Hot valves uninsulated Steam leaks in boiler room Boiler John Doe Week 23 Excess air in stack Boiler John Doe Week 23 Boiler John Doe Week 23 Waste heat could pre heat boiler feed water Rewrite SOP and retrain operator Recomme ndations Install insulated jackets on hot valves Repair leaks Trim gas/air fuel mix to correct level Install heat exchanger between exhaust and feed water Rewrite SOP and retrain operator Annual Savings($) Cost($) To be completed by Week Week Week Table 15 Results of Energy Audits Include in budget for Week 25 Responsible Maint Manager Maint Manager Maint Manager Engineering Manager Production Manager i. Opportunities Matrix A list of opportunities highlighted via data analysis, technical audits, brainstorming etc. No Description Estimated Annual Savings Potential Ease of Downtime Capital Cost($) payback(y Impleme Owner Date Target kwh kwh Completion Status CO2 Required Entered elec fuel $ ears) ntation Date Fit VSD to hammer mill main drive Easy 1 day JB 01/12/ /04/2014 Approved 2 Fit VSD to rotary mill Easy 1 day KL 01/01/ /05/2014 Awaiting approval Train maintenance in Energy Maintenance Easy None JB 01/01/ /12/2014 In progress Train key operators 4 in Energy Efficient Easy None JB 01/02/ /02/2014 Complete Operations Replace air 5 compressor with screw type with VSD Easy 1 day JB 01/12/ /03/2014 Complete 6 7 Recycle heat generated from mill to kiln Complete planned items from Boiler Technical Audit D 2 day GD 01/10/ /10/2014 Approved- awaiting summer shutdown Easy None GD In progress Table 16 Opportunities 33

37 j. Objectives Objectives should be high level and medium to long term. Reduce Electricity consumption per tonne by 15% by 2020 Reduce Natural Gas consumption per tonne by 10% by 2018 They could also include a Big Hairy Audacious Goal (BHAG) an eye catching objective that catches peoples imagination e.g. Become the most energy efficient Cement producer in Asia by 2020 k. Targets Targets for the next months which will contribute to the achievement of the long term Objectives should be recorded in this spreadsheet. Reduce electricity consumption of hammer mill by 20% by end 2014 Reduce electricity consumption of rotary mill by 7% by end 2014 Reduce gas consumption of Kiln 1 by 15% by Jun 2015 Reduce gas consumption of Kiln 2 by 15% by end 2015 l. Action Plans Action plans to achieve the targets are recorded here. Action Plans should be SMART Specific, Measurable, Achievable, Relevant and Time Based. No Description of Opportunity Service Action Fit VSD to hammer mill main drive Fit VSD to hammer mill main drive Fit VSD to hammer mill main drive Fit VSD to hammer mill main drive Fit VSD to rotary mill main drive Fit VSD to rotary mill main drive Fit VSD to rotary mill main drive Fit VSD to rotary mill main drive Train maintenance in Energy Efficient Maintenance Train maintenance in Energy Efficient Maintenance Train maintenance in Energy Efficient Maintenance Elec Elec Prepare spec. for drive Purchase drive as per spec 34 Person Responsi ble Target Completion Status PG 01/06/2014 Compete MA 15/06/2014 Planned Elec Fit drive and test JB 30/06/2014 Planned Elec Elec Elec Train hammer mill operator in operation of VSD Prepare spec. for drive Purchase drive as per spec BJ 30/09/2014 Planned PG 01/06/2014 Compete MA 15/06/2014 Planned Elec Fit drive and test JB 30/06/2014 Planned Elec Maint Train rotary mill operation of VSD Prepare training material BJ 30/09/2014 Planned JB 01/06/2014 Maint Deliver training JB 01/07/2014 Maint Measure performance ober 6 month period JB 31/07/2014 Table 17 Action Plans In progress Notes Performance will be measured by change in downtime