Mine decarbonization through haul truck analytics and engaging front-line teams in the decision-making process

A recent report from McKinsey & Co. revealed that the mining industry contributes 2 to 3 percent of global carbon dioxide (CO2) emissions and plays a significant role in emissions reduction. Within the mining value chain, haul trucks account for 25 to 30 percent of the total Scope 1 carbon emissions, making it imperative for mining companies to seek innovative strategies to reduce carbon emissions or diesel consumption. For example, an average Caterpillar 270-t (297-st) haul truck consumes 170 to 227 L/h (45 to 60 gph) of fuel. Historically, mining companies have optimized fuel consumption through short-haul opportunities and better planning assumptions. The next significant step is exploring how companies can further reduce their fuel consumption, which points toward leveraging the vast amount of sensor data and building machine-learning models to optimize fuel consumption. To address these challenges, this article will discuss a problem statement and systems implemented with two objectives: How can current operations leverage data to monitor current fuel consumption and emission levels? How can analytics be used to predict continuous improvement opportunities in reducing fuel consumption? To achieve these two objectives, airth.io’s Connect platform, integrated with a carbontracking tool, supports the above hypothesis and provides optimal solutions for mining companies in decarbonizing operations. Problem statement airth.io worked with dispatch data at an active mining operation site with approximately 25 mining trucks and a production capacity of approximately 180 kt/d (200,000 stpd) of hard-rock material. The data were leveraged to understand the various truck delays: that is, time during the day that the haul truck is operational but is not ideal. As observed in Fig. 1, the shift change, which is a necessity and common process at this mine site, contributes to approximately 25 percent of the total delay. It was observed that during shift changes, the trucks were running, resulting in additional diesel consumption. These observations, as shown in Fig. 1, represent opportunities that data can help highlight and support initiatives in reducing waste in productivity and in carbon emissions. With the data from Fig. 1, the delays within a mining project were monitored and tracked in real time. Furthermore, the amount of fuel consumed due to the delays, shown in Fig. 2, were highlighted. On average, for the dataset observed, shift change contributes to an excess of 1,324 L/d (350 gpd), which if reduced, can further reduce the carbon footprint of the mining operations. Figure 1 Analytics used to identify waste and nonvalue-added activities. Figure 2 Analytics used to identify fuel consumed from truck delays. Figure 3 Four steps from connecting to mine data to actionable information. Value case — Continuousimprovement process flow Based on the data in Figs. 1 and 2 at an operating mine, approximately 0.9 hour per truck is lost per day due to delays (as shown). The excess delays (above target) result in approximately 5,300 L (1,400 gal) of additional fuel consumption, resulting in an increase in operating expense by about $7,000/d. This excess consumption in fuel represents 59 kt/a (65,000 stpy) of CO2 emission. The first step in any continuousimprovement process is to understand the current situation, state the problem clearly, quantify it, and then map the process flow to identify tasks to reduce fuel consumption. Figure 3 shows the process flow that airth. io adopted with the Airth Connect platform, which is a data connect, cloud-based solution that retrieves the available data from a fleet management system (FMS). Once the data from the mine site are retrieved, the consumption module is executed to analyze the fuel consumption throughout the different activities: idling, driving with load, driving without load, shift changes and so on. Change management and enabling operator engagement A carbon tracking/emissions dashboard was built, as shown in Fig. 4, which provides in near real time the different sources of fuel consumption on operator screens and to the control room, enabling operators to set trigger points when wastage levels are higher than normal. The project leveraged data and analytics to track carbon emissions from mobile equipment, creating awareness among operators. As part of the pilot project, the following information was displayed: Average daily fuel consumption (site wide) in liters per hour.Carbon emissions by truck, by shift, by activity (idle, loaded and unloaded).Financial fuel cost breakdown, similar to carbon emissions. In addition to the dashboard, the Aether platform (Fig. 5) provides a real-time, 3-D visualization of current fuel consumption across the mine, which then enables supervisors and engineers to address operational changes. For Near-real-time fuel consumption and carbon emissions dashboard available to truck drivers and control-room operators. Figure 4 example, decreasing the grade of the haul road from 12 to 10 percent will reduce the fuel consumption by 5 percent for each haul truck. In addition, it provides transparency to operators and dispatchers, creating a growth mindset and operator-level engagement. Future digital improvement — Integrating machine learning airth.io’s machine learning solution, currently under development, offers more promising potential. By predicting estimated wait times or delays, the system can provide operators with recommendations to reroute to the nearest stockpile, thereby minimizing waste. These results were limited to haul trucks that contribute to 25 to 30 percent of overall CO2 emissions in mining operations. There are many more inefficiencies in the value chain. The next steps are to bring this awareness and analytics to the fixed assets: mills and conveyors. Figure 4 Near-real-time fuel consumption and carbon emissions dashboard available to truck drivers and control-room operators. Figure 5 airth.io Aether platform, fuel consumption analytics view, displaying areas in the mine where trucks consume the most fuel. References Henry Legge, Clemens Muller-Falcke and Erik Ostgren, Creating the zero-carbon mine, https://www.mckinsey.com/ industries/metals-and-mining/our-insights/creating-the-zerocarbon- mine. Scott Odell, MIT Environment Solutions Initaitve Visiting Scientist, MIT Environmental Solutions Initiative | MIT Climate Portal. Mehdi Azadi, Stephen A Northey, Saleem H.Ali and Mansour Edrakir (2020), Transparency on green house gas emissions from mining to enable climate change mitigation. Michael Bobotis, 2021, On the path to zero emissions: how the mining industry is working towards GHGfree surface mining, https://www.hatch.com/About-Us/ Publications/Blogs/2021/05/On-the-path-to-zero-emissions.

Mine decarbonization through haul truck analytics and engaging front-line teams in the decision-making process

Authored by:
Erik Anderson, Alp Bora, Erman Koc, Abhishekh Ashok Parmar

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.

BHP approves US$4.9 billion investment in stage two of Jansen potash project

Mobile crowdsourcing framework and application in mining evacuation

Related Articles

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

Critical minerals: Technical and policy considerations

Historic day in Washington DC for mining education

Copper stockpiling, transformation capacity and industrial synchronization in the US

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