Materials transport: Racing to net zero-comparing haulage for reduced greenhouse gas emissions and costs

The objective of this analysis is to compare five primary haulage options for the mining sector in terms of greenhouse gas (GHG) emissions using the inventory methodology defined by the Greenhouse Gas Protocol Scopes 1 and 2 for direct and indirect emissions. This analysis also includes Scope 3 (Category 2) for embedded emissions in the manufacturing of the equipment and associated infrastructure. Additionally, this analysis compares operating and capital costs associated with the five haulage options. The scope of this analysis is limited to GHG emissions and operating costs related to fuel, maintenance and operation of equipment, as well as upfront capital costs associated with equipment purchase and installation. GHG emissions boundaries are direct emissions from the burning of fuel (Scope 1), indirect emissions from the consumption of electricity (Scope 2), and upstream embedded emissions associated with the manufacturing of the equipment (Scope 3, Category 2), as well as the materials and construction of infrastructure such as roads, powerlines and rail. The calculated emissions factors published here can be used to estimate highlevel emissions of a mining operation. A company’s operations can have one or many haulage options to account for variation in distance, topography, geography and other site conditions. The range of haulage methods analyzed are not all considered direct replacements of one another. Instead, the emissions factors specific to complementary long distance and short distance haul methods are meant to estimate different portions of the supply chain. The emissions factors in this study (GHG emissions per tonne-km) are designed as a comparative estimating tool and should not be the primary source for accounting or disclosure. It is also important to note that these emissions and operating cost factors are for a zero percent grade scenario, for simplicity in comparing applesto- apples. For a full and complete analysis, grade, schedule and power source are aspects that need to be considered. Special consideration should also be given to different haul lengths and how operating expenses like fuel, labor and maintenance would scale (which is not linear). Methodolgy, assumptions and frameworks for comparing haulage systems In evaluating GHG emissions associated with each haulage option, the study team used the internationally recognized Greenhouse Gas Protocol and ISO 14064 for Greenhouse Gas Inventories and Reporting for Scopes 1 and 2 and ISO 14044 for Life Cycle Assessments (LCAs) for the embedded GHG emissions associated with the equipment and infrastructure. All emissions factors were derived from peer-reviewed databases such as the U.S. Environmental Protection Agency (EPA) GHG Emission Factors, the Chalmers University LCA Database and other published LCAs. Some emissions factors such as infrastructure GHG emissions per kilometer are sourced from LCAs, whereas others such as direct emissions for diesel haul trucks were calculated from the fuel economy values provided by the equipment manufacturer and EPA emissions factors to arrive at a kg of CO2e per tonne-km. The study team developed a complete cost and emissions factor calculator for each haulage option. The scenario variables are distance and tonnes/year. The calculator was used to develop the hypothetical scenario for comparing each system apples-to-apples. The team used a limited set of variable cost assumptions (which are subject to market fluctuations) to develop cost calculations. Spot pricing for Feb. 22, 2022 was used, with diesel prices at $1.22/liter, and carbon at $50/tonne. Diesel emissions are converted from metric tonnes (equal to 1,000 kg) of CO2e per gallon to metric tonnes of CO2e per liter, derived from the mobile combustion emissions for diesel EPA factors table. The emissions factor used in this study for diesel is 0.0027 tonnes CO2e/liter. All Scope 2 emissions factors are calculated using an average energy grid emissions factor for the United States, Canada and Mexico, at 0.33 kg CO2e per kWh. The study team used North American emissions factors for electricity, as many of the clients and projects the team works with are in North America. All calculations are based on a kWh/tonne-km value, which could have any region’s emission factor applied. Table 1 shows the factors from each country and data sources for each emission factor. Scope 1 and 2 emissions factors have been presented in kilograms (kg) CO2e, as the metric tonne kilometer (tonne-km) is the international standard for evaluating various haulage systems. While GHG emissions are frequently presented in metric tonne (MT) of C02e, in this case the values (tonne of CO2e per tonne-km) are very small and therefore it is hard to discern relative differences, hence kilogram (kg) is used in lieu of tonnes. Table 1 Emissions factors for diesel and electricity. Scope 3, Category 2 is being taken into account for embedded emissions in materials and infrastructure. Scope 3, Category 3, the emissions associated with the extraction and distribution of energy consumed, is not being taken into consideration as it was not readily available, particularly for comparing the emissions associated with transporting hydrogen. However, the emissions associated with the production of hydrogen was assigned as a Scope 2 calculation, assuming that hydrogen would be produced onsite. It is important to note that the embedded emissions associated with transportation infrastructure is being counted here to better compare these various haulage options. This is due to the fact that some haulage options, like conveyors, do not have separate equipment apart from the infrastructure. Some of the embedded emissions associated with roads, rail and conveyors can be counted either as a Scope 1 or 2, or Scope 3 for reporting, depending on who constructs and/ or owns the infrastructure. In most scenarios, associated transportation infrastructure is not counted, unless calculated by the reporting company. The following section summarizes the comparative emissions and cost calculations, and is intended for use in developing high-level estimates for costs and emissions in various haulage scenarios. While this analysis may be a tool for helping companies estimate GHG emissions and costs for various scenarios, it is not designed to be a tool for reporting. Note: When referring to vehicle capacity, both imperial tons and metric tonnes are used, depending on which equipment is being referenced. The study team kept the haulage capacity definition in its original terms as a descriptor of the equipment, but converted any imperial tons to metric tonnes for calculating emissions, cost and energy consumption. Haulage systems GHG emissions and cost comparison summary Diesel-electric haul trucks are estimated to have the highest GHG emissions of the specified hauling methods at 0.096 kg CO2e/ tonne-km. This is directly related to the high emissions factor of diesel fuel and limited haul capacity per haul truck. The hauling method with the lowest emissions is the Railveyor system, with an estimated 0.012 kg CO2e/tonnekm. Fuel switching from diesel to electricity along with scalable haul capacity allows Railveyor to surpass all other haul methods in terms of efficiency. All electricity-fueled vehicle emissions factors are site specific as the emissions from electricity generation depends on location. Based on average diesel and electricity prices, the hauling method with the highest fuel operating cost is estimated to be dieselelectric haul trucks at $0.039/t-km. Railveyor is estimated to have the lowest fuel operating cost at just $0.003/t-km. The hauling method with the highest nonfuel operating cost is estimated to be diesel-electric trucks at $0.053/t-km. The lowest nonfuel operating cost is estimated to be Railveyor at less than $0.01/t-km, considerably lower than any other hauling method included in this analysis. This is due to autonomous operations and relatively low maintenance costs. The hauling method with the highest Scope 3 emissions per tonne of vehicle capacity is estimated to be the battery-electric haul truck (BEV) at 2.6 t CO2e/tonne vehicle capacity. This is a result of emissions associated with the production of the battery used to power the vehicle. However, it is important to note that the Scope 2 emissions during the use phase of a BEV haul truck are half that of the dieselelectric haul truck. The system with the lowest Scope 3 emissions per tonne of vehicle capacity is Railveyor, estimated to be 0.5 t CO2e/tonne vehicle capacity. This is a result of the material efficiency used in the rail cars, along with the high haul capacity of each vehicle. The Scope 3 emissions associated with the infrastructure required for each hauling method is the highest for conveyors, at 742 t CO2e/km of infrastructure. This high emissions factor is heavily influenced by the amount of raw material and extensive installation operations required by the conveyor infrastructure. The lowest emissions associated with infrastructure is estimated to be gravel roads (typical of a mine haul road) at 699 t CO2e/km (Dillon Consulting, 2017). This makes sense, as grading requires no steel or other materials and the emissions associated with the gravel road is largely due to direct emissions from grading equipment. Figure 1 Haulage system GHG emissions and OPRX per tonne-km. Figure 2 Haulage system embedded emissions and CAPEX Diesel-electric haul trucks Diesel-electric powertrain is an industry standard today due to its start/stop capability and motor efficiency. An electric drive means fewer mechanical components and less maintenance, which can help improve truck availability and lower lifecycle cost (Komatsu, n.d.). Fuel efficiency for these vehicles is dependent on a number of factors such as grade resistance, traction and altitude. Average fuel use estimates based on load factors can be calculated for these vehicles, but actual values will vary from site to site. Estimated fuel consumption for three payload capacity trucks are summarized in Table 3, assuming a moderate consumption scenario. Scope 1 emissions for diesel-electric vehicles were calculated using 2020 EPA emissions factor of 0.0027 MTCO2e/liter for diesel fuel and the estimated fuel consumption (hourly fuel and percent load) per tonne-km (U.S. EPA , 2020). These emissions assume a 150 ton haul capacity truck. Diesel haul trucks do not use grid electricity, therefore avoiding Scope 2 emissions. The embedded emissions associated with the production of large haulage vehicles has been studied by Swedish heavy-duty vehicle manufacturer Scania (Burul & Algesten, 2021). Based on the Scania study, the team calculated an emissions factor of 1.03 MTCO2e/tonne of vehicle weight. This factor is used for the Scope 3 estimates of MTCO2e/tonne vehicle capacity. These emissions include the production of materials and use of equipment during the construction of the road (Dillon Consulting, 2017). Table2 Haul system GHG emissions and cost comparison. Battery-electric haul trucks The use of BEVs significantly increases energy efficiency by better using energy stored in batteries rather than energy stored in fuel. BEVs more effectively convert stored energy to electrical motors compared to combustion engine vehicles, while eliminating tank-to-wheel emissions. According to Sandvik, the BEV truck (fully loaded) was able to climb an eight percent grade at nearly twice the speed of the diesel truck. At zero grade, the speed was 20 percent faster. BEVs substitute the traditional on-truck diesel power plant with batteries containing stored electrical power. As there is no combustion associated with BEVs, direct, Scope 1 emissions are avoided entirely. Direct energy emissions during the use phase for BEVs are Scope 2, indirect emissions: the use of electricity purchased to charge the BEV. Scope 2 emissions will vary based on the carbon intensity of the electricity generation sources, and the mix of energy sources varies significantly around the world. The Scope 2 emissions are based on 150-ton haul capacity BEV trucks and an average electricity emissions factor for North America of 0.33 kg CO2e per kWh (eGRID, n.d.; government of Canada, n.d.; climate transparency, n.d.). Within the energy generation mix, the higher the renewables factor, the lower the emissions intensity. Therefore, the Scope 2 emissions from powering BEVs could be eliminated if 100 percent renewable generation serves as the power source. The presence of batteries in a BEV haul truck adds nearly 0.595 MTCO2e per tonne vehicle weight to the emissions factor of the BEV haul truck. Lifecycle assessments (LCAs) of heavy-duty vehicles used for long-haul transportation and distribution were used as base assumptions for the comparisons between diesel and battery powered vehicles that are used in mining operations. Scania published an LCA showing that the emissions resulting from the production of a heavy BEV is 2.6 t CO2e/tonne of vehicle weight compared to 1.26 from the production of heavy diesel vehicles (using a European energy mix). The increase in emissions during the production phase of BEVs are a result of the manufacturing of the battery used in the vehicle. Scania concluded that while the production of BEVs results in higher emissions than that of diesel vehicles, there are major reductions in lifecycle emissions during BEVs use phase, providing a return on carbon. The source of electricity for BEVs also affects Scope 3 emissions. Emissions required to produce utility grid infrastructure or renewable and microgrid materials contribute to total Scope 3 emissions of BEVs. The emissions associated with the road infrastructure are assumed to be the same as the diesel-electric haul truck. Hydrogen fuel cell haul trucks Hydrogen is more energy dense than diesel, allowing for the same amount of energy to be stored with less mass. Specifically, the energy density of hydrogen is equal to 33.6 kWh of usable energy per kg, where diesel only holds about 12-14 kWh per kg (Molloy, 2019). Fuel cells alone provide a steady supply of electricity but do not handle demand changes well, such as truck acceleration. Improved vehicle performance comes from combining fuel cells with batteries. Hydrogen fuel cell systems enhance the performance of batteries by addressing distance and refueling time issues that have prohibited the adoption of BEVs for some applications. Because of hydrogen fuel cells’ similarity in range and fueling time to diesel, hydrogen fuel cell haul trucks can be implemented without needing to drastically adapt current scheduling and routing, allowing for a more seamless transition to an emissions efficient, cost-effective hauling system. Hydrogen fuel cell haul trucks replace the traditional diesel power plant with fuel cells and batteries. As there is no fossil fuel combustion associated with fuel cell trucks, Scope 1 emissions are eliminated depending on how and where the hydrogen is produced. Assuming hydrogen is produced on site as part of mining operations, electricity is needed to perform electrolysis. This is the process of using energy to break water molecules into oxygen and hydrogen. The larger the percentage of renewable energy used to power electrolysis, the lower this emissions factor will be. If a fuel cell haul truck were to run on green hydrogen (hydrogen produced from electrolysis that is powered by 100 percent renewable energy) the Scope 2 emissions factor would be zero. The Scope 3 emissions of a hydrogen fuel cell haul truck are driven by the vehicle and the infrastructure it operates on. Emissions associated with this vehicle include emissions from hydrogen fuel production, raw materials extraction and processing, vehicle assembly, distribution and disposal. LCA comparisons between internal combustion automobiles and 100 percent fuel cell automobiles show the emissions associated with materials production, assembly of the vehicle, distribution and disposal are nearly identical (Hussain, Dincer & Li, 2007). Lacking manufacturers’ materials lists, the study team assumed that for diesel-electric haul trucks and a 100 percent fuel cell-electric haul truck, the total embedded emissions would be similar. This is the case for the adapted Komatsu haul truck being tested by Anglo American that includes 800 kW of fuel cell power and 1.1 kWh of lithium ion batteries. Table 3 Fuel consumption by capacity and load factor for diesel haul trucks.*Load factor is “the ratio of the average load to total vehicle freight capacity, in tonnes or volume” (EEA, 2001), where the load is expressed as tonne-km (t-km) and capacity as vehicle-km (v-km). Railveyor Small gauge (or light) rail has been used in mining since the 18th century, with modern versions still in use today. Railveyor is an autonomous, electrically controlled and powered light-rail materials haulage system that uses a connected set of trough cars. Railveyor is powered by electric drive stations placed along a track throughout the length of the system. Railveyor is an electrically driven and controlled haulage system and thus, does not contribute direct Scope 1 emissions. Scope 2 emissions are from the generation of electricity required for operations. Electricity is either generated by a utility and distributed through grid infrastructure or generated onsite. Scope 2 emissions vary based on the carbon intensity of the electricity generation sources, and the worldwide mix of energy sources varies greatly. Like all haul systems, electricity required by a Railveyor system depends on the profile of the haul. For a flat surface haul profile, the average power demand is 10.5 MW to move 1,300 t/hour a total of 310 m at 4.5 m/ second. These performance metrics, along with the average North American grid mix emissions intensity of 0.33 kg CO2e per kWh, are used to calculate the Scope 2 emissions of kg CO2e/ tonne-km for Railveyor. Within the energy generation mix, the higher the renewables factor, the lower the emissions intensity. Therefore, the Scope 2 emissions from powering Railveyor would be eliminated if 100 percent renewable generation serves as the power source. Embedded emissions associated with the infrastructure of the system were calculated based on the capital costs of the system and Scope 3 commodity emissions factors from the EPA (Ingwersen & Li, 2020). The embedded emissions for the Railveyor system are estimated to be 738 t CO2e per km compared to the 941 t CO2e per km for the average heavy rail system. Railveyor’s modular design and low downforce requirement allows it to be installed with small-scale equipment that includes mini- excavators and boom trucks or fork trucks. This, combined with a short installation period, limits the fuel consumed and emissions generated during construction. Conveyor Like light rail, conveyors have been present since the earliest days of mining. Today, a system can be adapted with multiple types of configurations, including buckets, pipes and open trough belts. The system is driven by electric motors, therefore it contributes no Scope 1 emissions. The electricity required by a conveyor system depends on the profile of the haul. For a flat surface haul profile, a conveyor moving 1,550 t/hour a total of 100 km would have an average power demand of 10.5 MW. These performance metrics, along with the average North American grid mix emissions intensity, are used to calculate the Scope 2 emissions of kgCO2e/t/km for a conveyor system. Within the energy generation mix, the higher the renewables factor, the lower the emissions intensity. Therefore, the Scope 2 emissions from powering the conveyor would be eliminated if 100 percent renewable generation serves as the power source. Embedded emissions associated with the conveyor system were calculated using cradleto- gate emissions factors for the materials used in the conveyor belt system, the majority being steel and rubber. The estimated emissions were calculated using an assumption that the system is roughly 75 percent steel and 25 percent rubber, with the rubber emissions factor nearly four times that of steel (Circular Ecology, 2020). Embedded emissions associated with the infrastructure of the system were calculated based on the estimated capital expenditure for a conveyor system and Scope 3 commodity emissions factors from the EPA (Ingwersen & Li, 2020). Estimated capital costs are based on a statement made by Beumer Group, estimated between 1,000 and 3,000 euros per meter (Gleeson, 2021). Figure 3 Hypothetical mine scenario OPEX, CAPEX and carbon costs. Hypothetical apples-to-apples scenario To compare these haulage scenarios apples to apples, the study team designed a very basic hypothetical scenario. It is important to note that variance in an operation, site, production schedule, ore body and local infrastructure will limit which haulage options make sense for a site. In many operations, multiple haulage technologies will be employed for transport across different parts of a site. Moreover, there are certain grade-based nuances that are not captured in this analysis. For example, a fully loaded electric truck is able to climb an eight percent grade at nearly twice the speed of its diesel counterpart. At zero grade, the speed is estimated to be 20 percent faster, improving production (Sandvik Coromant, 2022). Based on conversations with operators who have deployed electric haulage systems and the original equipment manufacturers that have extensive field tests, in nearly every setting, electric equipment outperforms diesel equipment on speed, efficiency and maintenance costs. In this hypothetical applesto- apples scenario, the five different haulage technologies are compared using the same annual tonnage hauled (10,000,000 t/yr), hauling distance (10 km), and haulage route grade (0 percent) over a 10-year mine life. Scopes 1 and 2 emissions vary greatly across the five haulage systems and the costs of that fuel source is reflective of the efficiency of each haulage technology. Scope 3 emissions from the upstream cradle-to-gate for each technology and infrastructure are relatively similar with the exception of diesel-electric heavy rail. This is particularly interesting in light of the common misconception that electric vehicles are more emissions intensive than combustion powered vehicles due to the lifecycle cost (LCC) related emissions (often associated with batteries). Emissions from the extraction of raw materials for batteries, like nickel, cobalt and lithium, and battery manufacturing are thought to be so high that it takes years of product usage to break even with combustion alternatives. While any manufacturing process produces waste and emissions, the LCC emissions of BEVs are no worse than that of extracting, refining and transporting fossil fuels. This same emissions intensity relationship was determined in a Yale University study. The transition to electric vehicles, including production, demonstrates a net decrease in overall emissions (Wolfram, Weber, Gillingham & Hertwhich, 2020). Table 4 Hypothetical haulage scenarios CAPEX, OPEX, emissions and total life of mine net present cost. Acknowledgments This report was made possible through the contributions of individuals in the form of interviews, data, case studies, advice, review of calculations and content and editing. The authors would like to recognize and thank the following people for their support and contributions to this report: Jim Fisk, James Hawkins, Matt Walton, Lisa Youngblood, Eric Zanetti, Mark Stockton, Mike Spicher, Andree St. Germain, Tom Dyer, Ehsan Vahidi, John Gardner, Deborah LaSalle, Alaina Sisco, Aly Dritenbas and Heather Colwell. References Circular Ecology. (2020). Embodied carbon footprint database. Retrieved from https://circularecology.com/ embodied-carbon-footprint-database.html. Climate Transparency. (n.d.). Mexico. Retrieved https:// www.climate-transparency.org/countries/americas/mexico. Dillon Consulting. (2017). Manitoba infrastructure remote road operations: Greenhouse gas follow-up assessment. Retrieved from https://www.gov.mb.ca/mit/hpd/ environment/pdf/project6/baseline/ followup.pdf. Government of Canada. (n.d.). Canada’s official greenhouse gas inventory. Retrieved from https://www. canada.ca/en/environment-climate-change/services/climatechange/ greenhouse-gas-emissions/inventory.html. Hussain, M., Dincer, I., & Li, X. (2007). A preliminary life cycle assessment of PEM fuel cell powered automobiles. Retrieved from https://doi.org/10.1016/j. applthermaleng.2007.01.015. Ingwersen, W., & Li, M. (2020). Supply chain greenhouse gas emission factors for U.S. industries and commodities. Retrieved from https://cfpub. epa.gov/si/si_public_record_Report.cfm?dirEntryId= 349324&Lab=CESER. Komatsu. (n.d.). 730E-10 Electric Drive Mining Truck. Retrieved from https://www.komatsu.com/en/products/ trucks/electric-drive-mining-trucks/730e-10/. Komatsu. (2021). Komatsu announces collaborative customer alliance to advance zero-emission equipment solutions. Retrieved from https://mining.komatsu/docs/ default-source/press- releases/komatsu-announcescollaborative- customer-alliance.pdf?sfvrsn=5ed6a06b_6. Molloy, P. (2019). Run on less with hydrogen fuel cells. Retrieved from https://rmi.org/run-on-less-with-hydrogenfuel- cells/#:%7E:text=A%20truck%20can%20be%20 fil led,advantage%20is%20hydrogen%E2%80%99s%20 energy%20 density. Sandvik Coromant. (2022). Presentation at Electric Mine Conference in Stockholm. Retrieved from https://www.theelectricmine.com/event/43b430e6-9fdd-49b2- 83a4-66e21ec9a8b5. U.S. Environmental Protection Agency. (2020). Emissions factors for greenhouse gas inventories.Retrieved from https://www.epa.gov/sites/default/files/2021- 04/ documents/emission-factors_apr2021.pdf. U.S. Environmental Protection Agency. (n.d.). Emissions & Generation Resource Integrated Database. Retrieved from https://www.epa.gov/egrid.

Materials transport: Racing to net zero-comparing haulage for reduced greenhouse gas emissions and costs

Authored by:
Z. Bell, A. Bieg, M. Samson, J. Schuyler, A. Zinsser

To view the past issues of the UCA magazine you must be a member of SME/UCA.

From sessions to strategy: Revealing long-term and emerging innovation trends in data analytics, intelligent systems and automation

Mining education gets federal boost as industry faces workforce crunch

Critical minerals management on the federal seabed

From sessions to strategy: Revealing long-term and emerging innovation trends in data analytics, intelligent systems and automation

Mining education gets federal boost as industry faces workforce crunch

Critical minerals management on the federal seabed

To view the past issues of the UCA magazine you must be a member of SME/UCA.

Automation continues to move the mining industry forward

There when you need them: Mine safety rescue teams prepare for emergencies

Related Articles

Copper stockpiling, transformation capacity and industrial synchronization in the US

Historic day in Washington DC for mining education

Critical minerals: Technical and policy considerations

From sessions to strategy: Revealing long-term and emerging innovation trends in data analytics, intelligent systems and automation

From sessions to strategy: Revealing long-term and emerging innovation trends in data analytics, intelligent systems and automation

Mining education gets federal boost as industry faces workforce crunch

Critical minerals management on the federal seabed

From sessions to strategy: Revealing long-term and emerging innovation trends in data analytics, intelligent systems and automation

Mining education gets federal boost as industry faces workforce crunch

Critical minerals management on the federal seabed

Use Single Sign On