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Truck simulations validate mine design decisions, researcher argues

SIMULATING MINE MATERIAL HAULAGE Simulation proves to be a powerful tool in estimating realistic system behaviour as it is capable of producing unbiased information

SYED MOHSIN ALI To evaluate an ideal fleet selection, the primary parameters that are subject to variability are the truck cycle time and equipment availability

21st August 2015

By: Ilan Solomons

Creamer Media Staff Writer

  

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An exact prediction about the future of truck haulage systems is difficult owing to the diversity and dynamic nature of all the factors involved. Nonetheless, mine management must use various strategies and methods to reduce the overall cost of the haulage system, says University of the Witwatersrand School of Mining Engineering postgraduate student Syed Mohsin Ali.
He states that, among the various techniques, simulation proves to be a powerful tool in estimating realistic system behaviour, as it is capable of producing unbiased information about the likelihood and possible future of the project.

Ali points out that existing simulation programs demonstrate the power of computerised simulation in addressing these complexities by integrating GPSS/H, P5 and VBA software programs.

“Discrete event simulation associated with animation and user-friendly interface can effectively represent the variable and interlinked nature of truck haulage operations, which helps in validating design decisions,” he notes.

Ali explains that the truck haulage system is the most common method of transporting material and offers comparative advantages in relation to other systems, such as conveyer systems and railway tracks, owing to its high mobility, flexibility, climbing ability, small turning radius and its minimal investment in infrastructure.

However, he points out that it can also be very costly and may constitute up to 60% of total project costs.

Ali says that the high operating costs of truck haulage systems force mine management to keep evaluating the strategies and reduce the overall cost of materials handling.

“If we look closely at the truck haulage system, we see that the cycle functions in a nonstationary, unpredictable and outdoor environment. The efficiency of a load-haul-dump cycle depends on many interdependent and dynamically changing variables.”

He adds that there are certain important considerations, which must be taken into account while modelling the truck haulage system. These include the uncertainty of the system, heterogeneity in the fleet equipment operational parameters, several locations for loading and dumping, bunching of trucks on their travel routes and truck dispatching policies.

Ali further notes that the truck haulage system involves many uncertainties in the technical, operational and economic fields. It is dynamic in nature and generally faces considerable changes.

Therefore, he says, to evaluate an ideal fleet selection, the primary parameters that are subject to variability are truck cycle time and equipment availability.

“Simulation of a truck haulage system requires data that closely describes the real system. The input data is often difficult to collect owing to variations in the operational parameters of the fleet and different managerial policies. To accurately describe the system, it is necessary to generate random observations throughout the production duration.”

Ali states that considerable work has been done by many researchers to determine the probabilistic properties related to equipment cycle times. These works present a range of distribution types applicable to haulage cycle variables.

He explains that, in a truck haulage system, different loader types may be operating at different locations and, similarly, the trucks operating between the loaders and dump sites may also be of mixed types.

Ali says that mixed fleets generally arise when newly bought equipment joins the older fleet to meet productivity targets. He states that, as the heterogeneity of a fleet increases, the model becomes more complex, owing to an increase in number and the interdependence of variables.

The mixed types of trucks affect the simulation model by representing different travel speeds and payloads. This, in turn, may result in an increase in bunching and nonlinear production output.

He posits that the haulage system may represent a complicated model, owing to the presence of multiple loading locations, multiple dumpsites, or multiple haulage routes.

Ali states that a single loader sending material to a single dumpsite through a single haul road is the simplest haulage system.

However, he notes that the model becomes complex when one loader connects to several dump sites or when several loaders supply material to one dump site through a haul road network.
The complexity further increases when there are ‘many-to-many’ relationships between various loaders and dumpsites.

Additionally, Ali highlights that the behaviour of a truck operating in an outdoor environment can vary significantly, owing to the variations of its travel speeds, interaction with other trucks en route and delays at road junctions.

The greater the number of trucks in the system, the greater the chance of bunching or interference of trucks.

“Bunching increases the cycle time, fuel consumption and emissions, and reduces the use and productivity of equipment. Identifying the factors causing bunching is easy, but the challenge lies in the incorporation of these factors into the simulation model.”

He says that many previous studies assumed that the data of statistical distributions is independent and identically distributed; often the models accounted for it by incorporating a simple reducing factor. However, in the case of a truck haulage system, truck cycle time cannot remain independent, owing to bunching of trucks behind slow or stopped trucks.

Moreover, Ali highlights that the truck haulage system functions through fixed or variable allocation.

In fixed allocation, the trucks are locked in a fixed loader-dump route during the shift. This form of dispatching is cost effective, simple and easy to implement, as the decision is to be taken at the start of a shift. However, it is inefficient in production and does not cater for breakdowns.

The variable allocation, on the other hand, dispatches the truck to a destination in accord-ance with the system state at the allocation time. The variable allocation is comparatively more efficient in improving equipment use.

Ali adds that, at the same time, it is a costly solution and the benefits are dependent on data communication effectiveness, processing speed, haulage network configuration, fleet size and the use of a specific dispatch policy.

He says that the selection of the best dispatching policy is a site-specific problem, particularly where grade considerations and production targets exist.

Edited by Martin Zhuwakinyu
Creamer Media Senior Deputy Editor

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