2.2 Monitor and track energy performance

The diverse range of equipment and operating processes on a mine site can make it difficult to understand where and how efficiently energy is being consumed. Many dynamic factors also influence energy consumption, such as mine design and layout, the age and efficiency of equipment and the motivation and skills of personnel. One important reason that energy management has received limited attention in the past is that accessible and reliable data has simply not been available. This reflects the relatively low cost of energy and the limited focus on greenhouse gas reduction when many mines were first established.

The term ‘energy information system’ is used here to describe the development of a system that supports the collection, interpretation and reporting of energy data in order to measure and verify energy performance and to locate opportunities for reducing energy consumption and cost. Since leading practice energy management is built on evidence rather than assumptions, addressing data limitations is essential.

However, improving energy data is not straightforward and typically requires investments of financial and human resources over a number of years to progressively improve the mining operation’s energy information system. Therefore, it is important to establish the current state of any existing energy information system and then to develop an informed plan for improvement. Strategies for improvement may include enhancing data capture as well as improved modelling and analysis.

Taking stock of your energy information system

To determine the current state of the energy information for your site start by mapping out the energy data that is currently available. Energy data can be categorised in a hierarchy (Figure 5). At the coarsest level, it is appropriate to establish total energy use at the site and then identify what energy data is available by area of use and at the level of key processes and significant individual items of equipment. For each of those levels, document the frequency at which the energy data is available. This may vary from annual or quarterly down to frequent intervals for data captured by software every few minutes. The following section considers the types of questions that can be used to prompt an understanding of energy consumption and potential opportunities for improvement.

Figure 5: Energy use data hierarchy

Figure 5: Energy use data hierarchy

Total energy use

Identify how much energy is consumed at the site by reviewing invoice data from your energy suppliers. Answer the following questions:

  • What is the proportion of energy costs relative to the overall cost of operating the mine?
  • How has that proportion changed over the past few years?
  • What is the energy productivity of the mine (for example, energy per tonne of product)?
  • Is energy productivity increasing or decreasing? Why?
  • What are the overall trends in energy use?
  • What are future trends likely to be?

It is also important to understand the quality of the data. Consider:

  • Who is responsible for reviewing billing data?
  • How is billing data checked?
  • What meters are used and how accurate are they?
  • How frequently are meters checked and calibrated?

Energy consumption by area of use

Identify the proportion of energy used in each part of the operation. At this point, it may become more difficult to obtain the necessary data. To determine what additional information is needed, explore the following questions:

  • Where is energy being used—which areas, processes, vehicles, plant?
  • When is it being used?
  • Does energy use in any area appear excessive or high compared with the area’s function?
  • Are some areas suited to specific key performance indicators (such as GJ/tonne of ore quarried or processed)?
  • Is there a clear area or grouping of equipment in one of the areas that should also be metered?
  • How has the consumption of each energy source changed from last year, and what are the causes (such as increased because of mine depth, less rain so less dewatering, varying quality of ore, change in excavators or trucks or change in procedures or operator training)?

Working through these questions can help to identify opportunities for improvement. It can also help to highlight the areas in which additional energy data is likely to be valuable. This can inform the priorities for improvements to the energy information system.

Energy consumption by time of use

Consider what information you have that allows for the examination of energy consumption by time of use. Once again, this is a top-down process. Start with annual or monthly data. Examine trends in energy use over those periods.

If it is available, daily or hourly consumption data can provide more fine-grained insights. For example, if the operating process is considered to be continuous and relatively unchanging, major spikes and other anomalies in energy use might be identified and provide important insights into areas of the process that should be investigated in more detail. The data may also highlight the proportion of ‘baseload’ energy consumption. By examining energy consumption during production downtimes, unnecessary energy loads can be identified.

In comparing energy consumption against production over time, consider the following questions:

  • What are the components of the baseload?
  • How does the baseload compare with that of other similar operations?
  • How does the baseload compare with the theoretical limits of the process?
  • Can these loads be better controlled so that they only operate when they are contributing to production?

Options to improve energy information systems

While it might seem ideal to invest in a large number of energy meters in order to better understand energy consumption on a site, that might not always be the most appropriate course of action. Other options include:

  • using manual records or electronic records from fuel dispensing systems
  • using temporary data loggers to monitor pulses from existing billing or private meters
  • using temporary metering or transducers on existing meters
  • arranging the installation of a time-interval meter with the energy retailer
  • reading an existing meter (for example, a billing meter) at the same time each day for a month.

The software systems that are used to collect and analyse the data are also critical. There is no point in obtaining additional data if it cannot be effectively accessed and utilised. Also consider other costs (for example, meters require ongoing calibration to ensure that the data being monitored is accurate).

Software systems are typically used to convert raw data into meaningful information. The sophistication of the software used can vary from a simple spreadsheet to customised energy management software linked to financial and operational data. The level of detail within the software system is likely to vary.

Balanced against the need for additional data is the difficulty of justifying investment in improved energy monitoring systems. Successful strategies might leverage other business drivers:

  • There is a legislative requirement.
  • Improved metering is a means of addressing production issues.
  • There is enough information to show that there is a significant opportunity to save energy and contribute to production, but additional monitoring will improve the outcome.
  • Specifications for the procurement of plant and equipment can include components of metering and feedback systems.

Where data is not available, it may still be possible to adequately quantify and evaluate opportunities by undertaking more detailed energy analysis, using temporary or spot measurements to test the accuracy of the analysis under different conditions. Manufacturers or suppliers may also have data or simulation models that can be used to examine specific opportunities. Sensitivity analysis can be used to determine whether a project is justifiable, especially when the project may be relevant to other parts of the business.

Box 3 provides an example in which the energy-efficiency benefits were just one part of the overall case for improved energy metering at the Yandicoogina Mine. The case study highlights the importance of:

  • linking energy projects to current business priorities, such as improving power quality, reducing plant downtime and meeting compliance obligations
  • involving the right technical expertise as the business case proposal is being developed to ensure that all costs, benefits and risks are considered and accounted for.

Box 3: Upgrading energy metering at Yandicoogina Mine

Yandicoogina is an open-cut mine in the Pilbara region of Western Australia. Iron ore is processed onsite and then transported 450 km by train to the port of Dampier for export. As part of the site energy management team’s preparation for an energy efficiency assessment under the former Energy Efficiency Opportunities (EEO) Program, gaps in electrical energy data across the site were identified.

The investment required for electrical metering can be difficult to justify because the benefits are hard to quantify. At Yandicoogina the business case for metering was developed over a six-month period. An experienced electrical engineer, who was also the site energy champion, played a key role in developing the business case. His knowledge of power quality issues meant that he was able to demonstrate in the proposal the production benefits that the metering could help deliver through reduced plant downtime. The cost to install the meters was approximately $600,000.

The benefits presented in the business case included

  • improved power quality and less unplanned plant downtime
  • compliance with EEO requirements
  • identification and evaluation of energy efficiency projects that were likely to have been rejected due to a lack of energy data
  • development of key performance indicators at the process level, which allows ongoing area-specific plant inefficiencies to be highlighted and investigated
  • increased awareness of energy consumption in the workplace by communicating energy-related performance data more frequently
  • streamlined review and analysis of energy data by linking the meters to the site’s SCADA system.

Rio Tinto’s corporate commitment to energy efficiency and regular briefings on energy efficiency risks and opportunities to the site management team were factors that also contributed to the final approval of the business case proposal for energy meters.

Fifty-six Schneider ION meters have been installed. The model of the ION meter varied depending on the engineering requirements: major substations had a higher end model installed so that power quality analysis could be done. The ION meters were specifically selected due to the ease with which they integrate into the site SCADA system and the consequent ease with which data can be interrogated.

A number of opportunities identified through the assessment that may have been rejected due to a lack of energy data have been evaluated more closely. For example, electricity consumption data from the meters was used to estimate the potential savings from modifying conveyor belt realignment and the idler replacement program.

Source: Department of Industry, Building the Business Case Project, Canberra, 2011, http://eex.gov.au/case-study/rio-tinto-iron-ore-investing-in-energy-metering-at-yandicoogina-mine/.

In summary, there are four important considerations in improving energy information systems:

  • Understand what data is available.
  • Use that data as well as you can to understand and improve energy use.
  • Develop a plan for improvement based on your understanding of specific needs.
  • Piggyback on other drivers to justify improvements to systems.

Establishing key performance indicators and targets

The use of key performance indicators (KPIs) is essential to evaluate the energy performance of a business, site or process and communicating when potential problems need to be addressed. The development of effective KPIs also yields insight into the key variables affecting energy efficiency, and they are essential for setting energy performance improvement targets.

It can be challenging to establish appropriate energy KPIs due to the many variables that affect energy use on mining operations. An example of how this has been addressed at Downer EDI Mining is presented in Box 4.

Box 4: The Downer EDI Mining Energy and Emissions Measure

To capture the effects of several variables in a single analysis, the Technical Services Department of Downer EDI Mining developed, trialled and implemented its own energy and greenhouse gas performance measure—the Downer Energy and Emissions Measure (DEEM). Given their leading role in mine planning, design and operations, the project engineers’ involvement ensured that the DEEM is credible, accurate and transparent. This enables the company to measure and improve its fuel and energy efficiency with confidence—using the data to track performance and identify opportunities for improvement.

The DEEM considers the sources and destinations of material mined, together with the mass and volume of material moved and the fuel used to move the material. Two separate indicators are used. The indicator for haulage (GJ/tonne-km) applies to haul trucks, road trains, water trucks, scrapers and graders. The indicator for excavation-related equipment, such as excavators, dozers, loaders and surface miners is GJ/tonne material moved. Ancillary equipment, such as lighting plants, generators and pumps, is also considered in the non-haul indicator, as it is considered to be essential equipment for the operation.

The base data that is used to calculate the DEEM is the total fuel consumed and the bulk cubic metres (BCM) moved. This is converted to the common measure of litres of diesel per tonne moved (L/tonne). The L/tonne measure is then adjusted for equipment fuel use, distance travelled and change in elevation to normalise the effect of changes in the material movement task, resulting in a fuel efficiency factor measured in L/tonne-kilometre. As a final step, the fuel efficiency factor is converted to units of energy (GJ) and greenhouse gas emissions (tonnes CO2-e).

The ‘equivalent flat haul’ (EFH) parameter was defined to describe the characteristics of the hau route travelled. The EFH is a calculated parameter that accounts for both the distance from the source to the destination and the elevation change from the source to the destination. The EFH normalises the elevation change and distance travelled, which enables a comparison of the energy consumed and tonnage moved for a mining activity.

For a given material movement task, illustrated in the figure below, the DEEM accounts for the energy required to move the material, the tonnage moved, and the EFH.

An image showing a material movement task with indicators and considerations

The DEEM compares and links the weekly to monthly haul and non-haul energy performance to onsite operational activities, decisions and conditions. Performance data can be analysed and compared for a mine site to identify trends and factors that have either positively or negatively influenced energy efficiency. The DEEM can reveal many factors that have a direct or indirect influence on energy efficiency such as changes in equipment size, the performance of different operators, the influence of production schedules or volumes on fleet efficiency, the effect of rainfall on road conditions, and many other factors. The DEEM’s variation can be accurately correlated with and explained in terms of these and other quantifiable factors. These factors and their influences are then taken into consideration in future planning and operational decision-making.

While the DEEM is a useful energy tool for performance tracking and reporting, it is also more useful for informing business improvement decisions than a simple indicator such as litres/BCM Downer EDI Mining applies this approach to identify where factors contributing to efficiency improvements at one mine and/or mine type may be measured and implemented at another.

Typical solutions include applying different mining methods, mine planning and design changes, haul road changes (length, gradients, design and materials), changing equipment operator behaviour, mining plant selection, and fuel types.

Source: Department of Industry, Analyses of diesel use for mine haul and transport operations, Canberra, 2013, http://eex.gov.au/files/2014/06/Analyses-of-Diesel-Use-for-Mine-Haul-and-Transport-Operations.pdf.

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