Multivariable Collaborative Modeling With Knowledge Transfer and Its Application in Soft Sensing of Iron Flotation Grade

In the iron flotation production process, production stages often undergo updates due to equipment upgrades, changes in raw materials, and other reasons. The operating condition prediction model established based on data from previous production stages may not meet the requirements of the new stage, resulting in a significant waste of collected datasets. Data-driven models established using small samples collected during the current stage may lack accuracy due to the limited sample size. This study proposes a method based on knowledge transfer to effectively leverage a large amount of outdated data. It allows for the rapid establishment of a new model that aligns with production requirements while minimizing the need for additional data collection. In previous tailings grade soft sensors, more emphasis was placed on quality parameters such as flotation froth features, often overlooking production process parameters. To enhance model accuracy, we introduce a multivariate collaborative modeling approach. The experimental results and industrial applications validate the effectiveness of this method.