Energy Efficient Mines: Consistent Decisions and Reliable Processes Create Energy Efficient Opportunities
Mining is an energy intensive industry with large amounts required to extract the ore, process and ship it to the end customer. The industry, traditionally, has focused more on productivity improvements rather than energy efficiency but the developments in artificial intelligence, data science and machine learning are providing new insights into these processes to help mining companies be more energy efficient in their operations.
Decisions made by mining companies usually have an energy impact, whether it be fuel consumption, explosives use, electricity consumption or energy sourcing. Mining processes are complex and decisions are often made with incomplete information, such as expected ore grade, and a new workforce operating on old equipment, sometimes with different levels of training. Often people will make different decisions with the same information provided to them.
This inconsistency in decisions becomes an issue. Having consistent, reliable processes offer new ways to gain insight into the energy needs and use across the mining value chain.
Variance in decisions
Some of the decisions made in the value chain were made many years ago when the technical feasibility plans were created and the construction parameters were undertaken. These decisions, such as mining methods, processing fundamentals and energy sources, have a legacy that may be difficult to change.
There are also many more recent decisions that were made yesterday, will be made today and are still to be made tomorrow. These decisions may not be made all in the same way and mining has addressed some of the variance through initiatives such as Six Sigma and lean manufacturing.
There is also an opportunity to use machine learning and data science to gain insight into the decisions and provide better intelligence and feedback to operations.
Energy use across the value chain may not be consistent and leads to energy efficiency. Some of the causes of energy variance include the following:
- People factors: Not everyone will make the same decision about how to operate equipment efficiently or effectively given the same information with training and skill levels could be different between operators or crews.
- Process optimization: The process set points do not change to adapt to the conditions of the process. Seasonal changes, such as weather, temperature and humidity, may impact the energy requirements.
- Material: The feed material may require different processing set points or recipes. Ore characteristics, such as hardness or contaminants, may require different processing.
- Maintenance: Poorly maintained equipment will often have higher energy use. Higher energy use could even be used to trigger maintenance requests.
- Age of Equipment: As equipment ages, the energy profile may change and become less efficient. More efficient equipment using different technologies will require less energy.
These points highlight some of the issues where the energy consequences of decisions are not always available or apparent to those making them.
Extracting insights from data
The increase in available data about the equipment, processes and systems provide data that often has energy insights hidden within it. The data may be explicitly measured using energy meters, fuel rates, or power consumptions or be more implicit in measurements about the process.
An energy efficiency initiative that looks at direct energy measurements and measurements with energy consequences will provide opportunities to understand the areas where energy can be saved.
Some examples of using data science to provide value include:
- Energy prediction: This is when the planning team can predict the energy requirements and make informed decisions. These include decisions to defer the use of energy to a time when the cost is lower, such as off-peak, or selecting an alternative energy source.
- Process state classification: Using the data around the process will allow for the process to be better understood. Are there particular conditions, process parameters or modes of operation that cause a lower energy efficiency through either higher energy use or lower productive output?
- People analysis: Do different teams or operators have different energy profiles?
- Equipment analysis: Higher energy use may be an indication that equipment requires maintenance and could lead to one of the factors in a condition-based maintenance scenario.
However, getting the right data, at the right time, at the right resolution for energy efficiency projects can be a challenge. An assessment of the data quality, completeness and availability of energy data is critical to ensure that value can be realized from data science and its tools.
Energy driver tree provides focus
An energy driver tree provides areas to focus on to save energy. The value from energy can be measured in understanding the amount of energy to extract a final product, such as Giga Joules (GJ) per gram of gold or Kilowatt Hours per tonne. See image above.
Understanding the different aspects of the driver tree will provide insight into the different levers that may or may not be controllable.
The energy price driver ($/GJ) will indicate the cost of the energy being sourced with different costs associated with different sourcing options. Newer equipment may have different energy sources with more equipment being electrified as technology improves. Energy price may fluctuate depending on the market prices or the time of day.
The grade driver (gram/tonne) will be dependent on the ore-body and cut-off grade assessments. Technologies such as grade engineering will allow some control of the grade that may have downstream impacts. As ore grades decrease, the energy required to process the ore will increase to extract the same amount of product.
The energy efficiency driver (GJ/tonne) will be where most of the focus will be. There are actually more drivers associated here, the amount of energy being consumed and the amount of productive work being undertaken (tonnes, etc). Being energy efficient is using less energy to achieve the same or a greater level of productive output. Energy performance is using the minimum amount of energy to meet the business objectives. Energy efficiency is about using energy smarter, such as controlling the process better, by turning equipment off and/or moving into reduced energy modes.
Energy efficiency may also be about using the energy in a different way. For example, selective blasting with an optimal blast pattern and power will fragment the rock and potentially require less energy in grinding.
Energy efficiency examples
Some examples of energy efficiency in mining include ventilation optimization, haul truck fuel analysis and selective smart blasting.
- Ventilation optimization: Ventilation can be a major user of electrical energy. By analyzing the unground needs and location of people and machines, operations can save energy by turning off ventilation in non-operational parts of the mine.
- Process control: Process control systems can be tuned and reconfigured to save energy by slowing down or turning off auxiliary equipment, such as drives, fans and pumps, when the main process is not operating.
- Selective smart blasting: Selective smart blasting can reduce the feed size to the primary crusher and requires less energy to crush the ore to the same product size.
- Conveyor control: Conveyors left running when no material is being transported can be optimized to be switched off or put into low power modes to save energy.
- Haul truck fuel analysis: Improved driver practices and real-time feedback about fuel use can impact on the safe and efficient use of equipment. This can include acceleration and braking analysis as well as increased fuel use required to stop haul trucks unnecessarily.
- Operator alerts and training: Operator alerts and training can be used to ensure that processing circuits are not operated in manual mode, bypassing energy efficiency algorithms in the control system.
- High energy alerts: Worn or old equipment can often require more energy to perform the same work. Energy-based alerts for equipment can indicate that maintenance is required and will need to be scheduled to save energy.
- Tire management systems: Poorly maintained tires can increase the rolling resistance of vehicles which corresponds to higher fuel use. A tire management system can help maintain the condition of the tires, saving fuel and tire life.
About this author
Joseph Plant leads the Global Operations Improvement Practice as part of Wipro Limited’s Natural Resources vertical and has experience in creating, designing and implementing operational improvement solutions for mining and metals customers. He has a background in industrial automation and software development, and experience in operational systems such as Manufacturing Execution Systems (MES), and leading data science initiatives and analytics for operational systems. Joseph has been involved in creating and delivering numerous energy management initiatives for the mining and metals industry. He has worked on projects and opportunities Australia, South Africa, China, Brazil, India, the United States, the United Kingdom, France, Belgium, Finland and Canada.