A comparative study of truck cycle time prediction methods in open‐pit mining

Abstract

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to compare the predictive capability of three methods of truck cycle time estimation in open-pit mining: computer simulation, artificial neural networks (NNs), and multiple regressions (MRs). The aim is to determine the best method. The most common method currently used is computer simulation. Design/methodology/approach -Truck cycle times at a large open pit mine are estimated using computer simulation, artificial NNs, and MRs. The estimated cycle times by each method are in turn compared to the actual cycle times recorded by a computerized mine monitoring system at the same mine. The errors associated with each method relative to the actual cycle times are documented and form the basis for comparing the three methods. Findings -The paper clearly indicates that computer simulation methods used in predicting truck cycle times in open-pit mining underestimate and overestimate the results for short and long hauls, respectively. It appears that both NN and regression models are superior in their predictive abilities compared to computer simulations.Research limitations/implications -The cycle time prediction models developed apply to a specific mine site and one has to be careful not to directly apply these models to other operations. Practical implications -The paper describes the implementation of regression and NN modelling. An opportunity exists for mines to utilise the large volumes of data generated to predict truck haulage cycle times more accurately and hence, improve the quality of mine planning. Originality/value -The paper addresses an area of need in the mining industry. Accurate prediction of cycle times is critical to mine planners as it impacts on production targets and hence, the budgets.

Rationalization of Road Transport Park for the Carriage of Mining Rocks in the Open Mines

“…These losses are happening mainly due to the differences in movement modes of dump trucks of various types in a particular installation. They are not taken into account in the planning of roadexcavation complexes, while fixing and determining the required number of dump trucks of various types of concrete excavated to ensure the smooth of its work [10]. This leads to deviations intervals movement, violation of the rhythm of the excavator downtime dump trucks and excavators in anticipation of the start of loading operations.…”

Analysis of Waste-Rock Transportation Process Performance in an Open-Pit Mine Based on Statistical Analysis of Cycle Times Data

“…Thus, the waste rocks loaded, hauled and dumped may range in size at a waste dump from clay particles to boulders (e.g., less than 1.0 mm to larger than 1.0 m in diameter), which influence the loading and dumping times. Approaches for selection of haulage trucks, routes to dumps on the basis of minimizing the haulage cost is one of the main parameters in optimization studies conducted in the past [5] [7] [8], which is supported by availability of cycle time data.…”

“…Where cycle times (or service times) are exponentially distributed, service rates are the inverse of mean service times [5]. Defining the back-cycle time as the truck cycle time minus the excavator waiting and loading times, leads to another important parameter in cycle time analysis, which require investigation.…”

“…For most surface mines, truck haulage represents as much as 60% of their total operating cost [5], so that it is desirable to maintain an efficient haulage system. During aexcavator-truck mine operations, trucks cycle between their assigned excavators and dumps or crushers, through a network of haul roads.…”

“…This study went deeper on analyzing cycle time data for seven components, using statistical analysis and Pareto principle. Development of effective and efficient material handling systems can only be developed through detailed analysis of these subsystems [5], such as the work reported in this paper. The effect of road characteristics was assessed via empty-and full-haul cycle time analysis [4].…”