Self-healing control of abnormal conditions for fused magnesium furnace based on data augmentation and improved JITL

Abstract

Abnormal conditions that arise during the fused magnesia smelting process (FMSP) can negatively impact product quality and increase energy consumption. However, due to the process's nonlinear and time-varying nature and the limited availability of abnormal condition data, researching effective self-healing control measures has been severely constrained. Therefore, this paper proposes a fused magnesium furnace (FMF) abnormal condition self-healing control method based on data augmentation and improved just-in-time learning (JITL). Firstly, data augmentation technology is used to generate virtual abnormal condition samples to expand the historical dataset. Then, an improved JITL model is constructed to solve the self-healing measures corresponding to abnormal conditions. Experimental results demonstrate that the proposed method can effectively restore the FMF to normal conditions and outperforms existing methods.