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),...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | International Journal of Mining Science and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095268622001021 |
| _version_ | 1811162777105989632 |
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| author | Christian Both Roussos Dimitrakopoulos |
| author_facet | Christian Both Roussos Dimitrakopoulos |
| author_sort | Christian Both |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T06:34:37Z |
| format | Article |
| id | doaj.art-eafc326a0338467b92c3a8abbf13cdce |
| institution | Directory Open Access Journal |
| issn | 2095-2686 |
| language | English |
| last_indexed | 2024-04-10T06:34:37Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Mining Science and Technology |
| spelling | doaj.art-eafc326a0338467b92c3a8abbf13cdce2023-03-01T04:31:25ZengElsevierInternational Journal of Mining Science and Technology2095-26862023-02-01332185199Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexesChristian Both0Roussos Dimitrakopoulos1Corresponding author.; COSMO – Stochastic Mine Planning Laboratory, Department of Mining and Materials Engineering, McGill University, Montreal H3A 0E8, CanadaCOSMO – Stochastic Mine Planning Laboratory, Department of Mining and Materials Engineering, McGill University, Montreal H3A 0E8, CanadaThis 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.http://www.sciencedirect.com/science/article/pii/S2095268622001021GeometallurgyStochastic optimizationShort-term open pit mine production schedulingMeasurement while drillingNon-additivityHardness |
| spellingShingle | Christian Both Roussos Dimitrakopoulos Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes International Journal of Mining Science and Technology Geometallurgy Stochastic optimization Short-term open pit mine production scheduling Measurement while drilling Non-additivity Hardness |
| title | Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes |
| title_full | Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes |
| title_fullStr | Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes |
| title_full_unstemmed | Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes |
| title_short | Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes |
| title_sort | integrating geometallurgical ball mill throughput predictions into short term stochastic production scheduling in mining complexes |
| topic | Geometallurgy Stochastic optimization Short-term open pit mine production scheduling Measurement while drilling Non-additivity Hardness |
| url | http://www.sciencedirect.com/science/article/pii/S2095268622001021 |
| work_keys_str_mv | AT christianboth integratinggeometallurgicalballmillthroughputpredictionsintoshorttermstochasticproductionschedulinginminingcomplexes AT roussosdimitrakopoulos integratinggeometallurgicalballmillthroughputpredictionsintoshorttermstochasticproductionschedulinginminingcomplexes |