Simulation of Rock-Handling Systems for Sub-Level Stoping

INTRODUCTION The selection of trackless equipment for underground mining can be a complex engineering problem due to the wide range of equipment sizes and operating modes. Computer simulation is particularly useful in estimating the performance of different systems for specific rock- handling problems. A hybrid simulator, incorporating some of the features of deterministic and stochastic simulation, was developed in order to handle not only queuing delays at loading and dumping points, but a1 so traffic interferences. Any number of vehicles can operate in any one of four basic modes (LHD, LAF, FEL, HFC) in parallel or in series. If the units use common roadways, loading and dumping points, certain operating delays will occur and be registered by the simulator and thus give a better evaluation of the marginal productivity of each additional unit. Based on a typical layout of drawpoints and ore passes for the sub-level stoping method, productivity and operating costs of different rock-handling systems will be examined. 1 . COMPUTER SIMULATION Numerous applications of computer simulations are reported in the 1iterature for various mining problems. Depending on the complexity of the system to be studied, simulation models were conceived with different degrees of sophistication. Three different types of simulators are generally recognized: stochastic, deterministic, and hybrid. Stochastic or Monte Carl o simulation randomly generates items, transactions, or events from some population defined by a frequency distribution and produces some expected future situations. Because this type of simulation is governed by the input of probability distributions, it requires a detailed knowledge of the system to be simulated; it implies expensive and time-consuming studies and reports to gene- rate this input information. A major short- coming of stochastic techniques is in new equipment evaluation, where the lack of data is unavoidable, and in new system design where the conditions are outside the range of the known historical behavior of the equipment. However, probabilistic simulation is almost essential for the study of cyclic queues and traffic problems. Deterministic simulation studies a system by generating performances on the basis of the mechanical capabilities of the vehicles and the physical limitations of the mining scheme. It is based on the engineering principle that the engine converts its energy into a rimpull at the wheels, which is in turn opposed by the rolling resistance and the grade of the ground; the machine is accelerated or decelerated until the tractive and resistive forces are in equilibrium, at which point it moves at constant speed. The information required by this technique, such as rimpull charts and equipment weight, is readily available from equipment suppliers. However, equipment performance at the mining site is also dictated by human and environmental conditions and changes with time and usage. For this reason, deterministic simulation generally overestimates the system capabilities; these must then be adjusted by efficiency factors based on observation and experience. Recently, hybrid simulation models, using both stochastic and deterministic techniques, have been built with some of the events generated stochasticly and others being deterministic. This compromising approach originated at the Pennsylvania State University. O'Nei1 (1966) designed a simulator for truck-and- shovel operations that allows for transportation from multiple faces to mu1tiple destinations. Each truck performs according to its mechanical capabilities while its loading and dumping time and its load fluctuate according to specific probability distributions. A major advance in the simulation of mining systems is due to Sanford's model (1969) of underground coal mining operations. The originality of the model is in the use of an Executive System Control which sets up the initial system, de- fines the operation conditions , and instructs continuously four sub-assemblies representing shuttle cars, trains, continuous miners , and conveyors, which in turn generate a feed-back of their movement to the Executive Control System for further instructions. The model has enough flexibility to simulate simultaneous and sequential jobs that characterize any dynamic system. Sanford's early work evolved slowly to what is known today as the Under- ground Materials Handling Simulator for coal mining (Manula, 1974).