Development of an Online Updating Stochastic Configuration Network for the Soft-Sensing of the Semi-Autogenous Ball Mill Crusher System

The overflow slurry concentration (OSC) of a hydrocyclone is a key performance indicator (KPI) of a semi-autogenous ball mill crusher (SABC) system. Accurate modeling and prediction of the indicator can improve the grinding efficiency and product quality of the process. However, the mechanism of this process is complex, and the gradual wear of key equipment leads to data drifts in the measured results. To address these problems, an online updating soft sensor that combines a stochastic configuration network (SCN) with a dynamic forgetting factor sliding window (SW) technique was proposed. First, an SCN was used as the basic learner to build an offline model with the initial dataset. Second, a new data stream was continuously obtained and divided into different SWs according to the time sequence. Third, a dynamic forgetting factor approach that assigned different factors to diverse SWs was designed to reduce the redundancy in the historical data and fully utilize data information at different times. Finally, the effectiveness of our approach was verified through a public case and an actual industrial SABC case. In the SABC case, our approach improved the prediction mean square error (PMSE) by 25% and 37%, prediction mean absolute error (PMAE) by 17% and 23%, and
$R^{2}$
by 6% and 10%, compared with the state-of-the-art just-in-time learning (JITL) and the time difference (TD) methods, respectively. Comparative results demonstrated the effectiveness and superiority of the proposed approach.