Review of the Applicability of Discrete Event Simulation for Process Optimization in Mining

Abstract

Although DES has become a well-accepted tool for decision-making in the mining industry, the time consuming modelling work and high programming effort is normally only considered feasible for large-scale projects examined in project-based case studies. This is why Knauf has developed an adapted simulation tool for mining for the use on a cross-project basis. There could be achieved valuable operational goals in each of the two performed case studies by the use of the adapted simulation tool. The quarry operations have been examined for profitability of stockpile use for ore transport, influence of dumper size on ore transport and transport capacity by increasing hauling distance using variable equipment composition. Gained knowledge during the case studies has been transferred to a simulation procedure suited for simulation studies in quarry mining. Important phases of simulation have been identified and strategies for successful completion including pre-designed spreadsheets have been developed. The procedure highly values the verification and validation of achieved results. Consistently, suitable test methods are proposed for each phase result, which include statistical tests for the data collection and data preparation phase as well as fixed value test and internal validity test for implementation and execution of the simulation model. The comparison of results achieved by simulation approach and deterministic spreadsheet approach has revealed that simulation is beneficial when interactions of equipment are hardly predictable due to their dependence on dynamic processes. Although the profitable use of stockpiling could be generally determined by both analytical methods, the DES model could benefit from reproduction of the decision-making behavior of the real stockpiling system and thus could determine most effective equipment composition. On the other hand, ineffective use of a bigger sized dump truck by increased hauling distance could be easily analyzed by deterministic calculation by leading only to small deviations compared to the simulation model. Although modeling effort of the simulation model is low by the helpful use of pre-defined modules, work effort of experimental evaluation, excluding data collection, is still six times higher than spreadsheet analysis due to the need of verification and validation methods and creation of confidence intervals for simulation results. Thus, applicability of DES should be limited to the analysis of more complex interaction of equipment. Reduction of work effort can possibly be achieved by replacing statistical distributions by deterministic values in the simulation model. However, every simplification must be checked individually for the unbiasedness of KPI’s.