Mine production index(MPI)-extension of OEE for bottleneck detection in mining

Although mining production depends on various equipments, significant amount of production loss can be attributed a specific equipment or fleet. Bottleneck is defined not only by production loss but also by our satisfaction from the equipment. The user satisfaction could be measured as machine effectiveness.Mining literatures on performance improvement and optimization of equipment operations assert importance of availability, utilization and production performance as key parameters. These three parameters are useful for evaluating effectiveness of equipment. Mine production index(MPI), which can represent the effect of these factors, has been applied for continuous operation in mining. MPI uses Fuzzy Delphi Analytical Hierarchy Process to determine importance of each three parameter for individual equipment. A case study in a Swedish open pit mine was done to evaluate the field application of MPI.The results reveal that crusher is the bottleneck equipment in studied mine. As a methodical approach,an algorithm which uses MPI and detects bottleneck in continuous mining operation has been proposed.

Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes

This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes.The datasets for the throughput prediction model include penetration rates from blast hole drilling(measurement while drilling),geological domains,material types,rock density,and throughput rates of the operating mill,offering an accessible and cost-effective method compared to other geometallurgical programs.First,the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates.A regression model was constructed to predict throughput rates as a function of blended rock properties,which are informed by a material tracking approach in the mining complex.Finally,the throughput prediction model was integrated into a stochastic optimization model for short-term production scheduling.This way,common shortfalls of existing geometallurgical throughput prediction models,that typically ignore the non-additive nature of hardness and are not designed to interact with mine production scheduling,are overcome.A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8.By integrating the prediction model and new stochastic components into optimization,the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill.Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7%per period can be mitigated this way.