When it comes to selecting the right equipment in open pit mining, bigger is not necessarily better. And the initial mobile equipment fleet selection is one of the most important as it will help determine the development of ramp gradients, cross-sections, the electric power network, and other factors.

As no two mines are alike, equipment selection can be an arduous process to get right. There are five basic categories influencing equipment selection. These are tonnage requirements, material and waste, topography and property line consideration, resource control requirements, physical characteristics, and climate conditions. For the purposes of this blog, we will focus on a general overview about the considerations needed for truck and loading unit selections for a mine site.

Equipment selection criteria

There is a range of criteria to consider when deciding on equipment selections. These include, but are not limited to:

• Health and safety legislation
• The type of shovel/excavator dragline (mechanical drive or electrical drive)
• Loading unit size and capacity
• Truck size and capacity
• Equipment capability
• Haulage time and costs
• Pit geology (through seam mining or conventional mining)
• Manoeuvring space
• Climate
• Maintenance capacity
• Equipment parts availability
• Fuel efficiency.

Equipment selection

Selecting an appropriate set of loaders and trucks is subject to various objectives and constraints. The methods for doing this vary, as does the assumptions and types of constraints in the models.

Integer programming

While integer programming methods are well known in mining and construction operations, much of the focus is on project scheduling, dispatching or completion. Constraints include:

• There is assumption of a given equipment type (instead of models selecting with fleet size).
• It may not be practical for fleet homogeneity and restricted passes between the loader and truck.
• Production schedules are assumed to be provided in equipment selection models, which means early completion is not usually a consideration.

Other methods, or formulations, may have considered budgeting constraints regarding ownership and operating costs (with the maximum predicted) or given a certain exclusivity to one equipment choice.

Shovel-truck productivity

Accurately predicting a truck and loader fleets productivity is linked to equipment selection, and it is an important problem to solve. A focus needs to be on predicting times of travel for the haul and return portions of the truck’s cycle, and on the shovel and truck at loading.

Simulation

Simulation is most effectively used in earth-moving system selection, though there are other simulation models for other equipment selection solutions as well, albeit they are not perfect. There are academics who have noted there is a gap between opportunity driven models and industry need-based models.

Artificial intelligence

Common methods include expert systems (often for complex systems) and decision support systems. Although the expert systems approach is not entirely ideal, an important highlight is that the equipment will be dependent on mining conditions.

Match factor

In the match factor model, the productivity of the loader and truck fleet does not surpass the lowest capacity of the loader or trucks. This model works effectively when the equipment type is given, and effective equipment numbers need to be selected. There is a simple formula to calculate the match factor:

The optimum match factor number is 1 (it is also called Perfect Match). So, in the equipment selection process, achieving an MF number as close as practicable to number 1 would result in more equipment productivity.  

The match factor model assumes that the most economical fleet will also be the most efficient and productive. As it assumes maximum efficiency, the match factor can therefore be misleading from a cost perspective.

Queuing theory

Queuing theory studies waiting times, lengths, as well as other properties relating to queues. There has been some shovel-truck productivity research on the waiting times for trucks and loaders, and while it has not resulted in suitable equipment selection solutions, it may deliver an upper bound on the truck capacity or size.

Bunching theory

Bunching models capture the likelihood of how moving objects will bunch together when they are moving in a line. Some objects might operate more efficiently than others, or there could be unpredictable delays. Bunching happens in a system of a load and trucks, and it reduces the fleet’s ability to meet its maximum capacity. This means that the slowest truck dictates all other trucks cycle times which directly impact the fleet productivity. 

How we can help

By using relevant data, studies, and methods, mine planners at Atlantech assist miners to identify the most cost-effective opportunities to maximise productivity and equipment life at open pit mining operations. We are skilled at equipment selection, drawing on modern approaches. Please get in touch with us today if we can be of assistance.

Additional sources:

• org.au (Curtin University of Technology, Rio Tinto Technical Services)
• MineWiki
• Optimization Online
• 911 Metallurgist