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首页> 外文期刊>Transactions of the Institutions of Mining and Metallurgy, Section B. Applied Earth Science >Predicting finished product properties in mining industry from pre-extraction data
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Predicting finished product properties in mining industry from pre-extraction data

机译：根据提取前的数据预测采矿业的成品属性

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An essential requirement of product quality control in the mining industry is to be able to reliably predict key quality properties of finished product from the data available before the extraction of the ore. From a production viewpoint, the unit of data collection is generally the input and output data set for each shift of crusher production but could be any period where mine pre-crusher data can be reliably matched with product data. Linear regression models can be used to predict crush grades from blast grades, even where the crush material is blended from multiple sources or pits for each of which differing regression models might apply. The best model for any application will be a balance between required predictability, available data and the tolerance of the business for complex models. The regression modelling approach has several advantages over the classic method of run of mine crusher trials. The models can use any predictor variable such as grade, geotype and in situ density provided the pre-extraction data can be reliably matched with post-crusher data and is significant as a predictor. The models have been used extensively in the generation of the daily crusher plan with the aim of maintaining finished product grade. This approach has also been used associated with exploration drilling and long term planning. It is acknowledged that there are inherent problems in fitting lump and fines grade to a linear model. However, these problems are minor when such information is used for interpolation within the window spanned by the shift blend records used to produce the model. This paper discusses some of the issues limiting linear regression models in this application, and suggests methods enabling consistent models to be formulated.
机译：采矿业中产品质量控制的一项基本要求是，能够从提取矿石之前的可用数据中可靠地预测成品的关键质量特性。从生产的角度来看，数据收集的单位通常是破碎机生产的每个班次的输入和输出数据集，但是可以是矿山预破碎机数据可以与产品数据可靠匹配的任何时期。线性回归模型可用于根据高炉品位来预测压碎品位，即使是从多种来源或矿坑混合了压碎材料的情况下，每种方法可能都适用不同的回归模型。对于任何应用程序而言，最佳模型将是所需的可预测性，可用数据与复杂模型的业务容忍度之间的平衡。与经典的矿山破碎机试验方法相比，回归建模方法具有多个优势。这些模型可以使用任何预测变量，例如坡度，地理类型和原位密度，前提是提取前的数据可以与破碎后的数据可靠地匹配，并且对于预测具有重要意义。该模型已在日常破碎机计划的生成中得到广泛使用，目的是保持成品等级。这种方法也已与勘探钻探和长期计划结合使用。公认的是，将块和细度等级拟合到线性模型中存在固有的问题。但是，当此类信息用于在用于生成模型的班次混合记录所跨越的窗口内进行插值时，这些问题很小。本文讨论了在此应用中限制线性回归模型的一些问题，并提出了能够制定一致模型的方法。

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