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Flotation modelling based on floatability distributions regressed from routine data

机译：基于常规数据回归的浮性分布的浮选模型

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摘要

Flotation models based on parameters derived from sampling campaign data degrade over time if left unattended. This problem can be addressed by continuously regressing flotation rate constant distributions, using measurements that are available online. Different distributions were investigated, to determine how the modelling accuracy of concentrate flows from individual cells are affected by more complex distributions. Rate constant distributions were regressed using a comprehensive data-set based on a detailed sampling campaign, as well as combined concentrate-flows, to approximate results achievable using routinely available data on industrial plants. Two circuit configurations were used to show that parameters regressed for one configuration is also valid for another, hence being representative of feed characteristics.
机译：如果不加注意，基于采样活动数据得出的参数的浮选模型会随着时间推移而降低。可以通过使用在线提供的测量值连续回归浮选速率常数分布来解决此问题。研究了不同的分布，以确定更复杂的分布如何影响来自单个单元的精矿流的建模精度。使用基于详细采样活动的综合数据集以及合并的精矿流量对速率常数分布进行回归，以近似使用工业工厂常规数据可获得的结果。使用两种电路配置来显示针对一种配置回归的参数对另一种配置也有效，因此代表了馈电特性。

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