The mining industry continuously struggles to keep produced tonnages and grades aligned with targets derived from model-based expectations. Deviations often result from the inability to characterise short-term production units accurately based on sparsely distributed exploration data. During operation, the characterisation of short-term production units can be significantly improved when deviations are monitored and integrated back into the underlying grade control model. A previous contribution introduced a novel simulation-based geostatistical approach to repeatedly update the grade control model based on online data from a production monitoring system. The added value of the presented algorithm results from its ability to handle inaccurate observations made on blended material streams originating from two or more extraction points. This contribution further extends previous work studying the relation between system control parameters and algorithm performance. A total of 125 experiments are conducted to quantify the effects of variations in measurement volume, blending ratio and sensor precision. Based on the outcome of the experiments, recommendations are formulated for optimal operation of the monitoring system, guaranteeing the best possible algorithm performance.

中文翻译：

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测量量、混合比和传感器精度对坡度控制模型实时协调影响的研究。

采矿业不断努力保持生产吨位和品位与基于模型的预期得出的目标保持一致。偏差通常是由于无法根据稀疏分布的勘探数据准确描述短期生产单元的特征而导致的。在操作过程中，当监控偏差并将其集成回基础品位控制模型时，可以显着改善短期生产单元的特性。之前的贡献介绍了一种新颖的基于模拟的地质统计方法，可根据生产监控系统的在线数据重复更新品位控制模型。所提出算法的附加价值源于其处理对源自两个或多个提取点的混合材料流进行的不准确观察的能力。这一贡献进一步扩展了之前研究系统控制参数和算法性能之间关系的工作。总共进行了 125 次实验来量化测量体积、混合比例和传感器精度变化的影响。根据实验结果，为监控系统的最佳运行制定建议，保证最佳的算法性能。