A soft sensor for measuring product quality in the Bayer process has been developed. The soft sensor uses a combination of historical process data recorded from online sensors and laboratory measurements to predict a key quality indicator, namely particle strength. Stepwise linear regression is used to select the relevant variables from a large dataset composed of monitored properties and laboratory data. The developed sensor is employed successfully by RUSAL Aughinish Alumina Ltd to predict product strength five days into the future with R-squared equal to 0.75 and to capture deviations from standard operating conditions.

中文翻译：

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用于拜耳工艺的软传感器

已经开发出用于在拜耳法中测量产品质量的软传感器。软传感器使用在线传感器记录的历史过程数据和实验室测量值的组合来预测关键的质量指标，即颗粒强度。逐步线性回归用于从由监控属性和实验室数据组成的大型数据集中选择相关变量。RUSAL Aughinish Alumina Ltd成功地使用了开发的传感器来预测未来五天的产品强度（R平方等于0.75）并捕获与标准操作条件的偏差。