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

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

扫码查看
原文链接
  • 万方数据
  • 维普
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 pre-diction 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 behav-ior of the orebody was geostatistically simulated by building additive hardness proportions from pene-tration 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 pro-duction 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 pre-dicted 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.

GeometallurgyStochastic optimizationShort-term open pit mine productionschedulingMeasurement while drillingNon-additivityHardness

Christian Both、Roussos Dimitrakopoulos

展开 >

COSMO-Stochastic Mine Planning Laboratory,Department of Mining and Materials Engineering,McGill University,Montreal H3A 0E8,Canada

National Sciences and Engineering Research Council of Canada(NSERC)under CDR Grant CRDPJNSERC Discovery GrantCOSMO mining industry consortium

500414-16239019

2023

矿业科学技术学报(英文版)
中国矿业大学

矿业科学技术学报(英文版)

CSTPCDCSCD北大核心EI
影响因子：1.222
ISSN：2095-2686
年,卷(期)：2023.33(2)
  • 2