A novel MDFA-MKECA method with application to industrial batch process monitoring

For the complex batch process with characteristics of unequal batch data length, a novel data-driven batch process monitoring method is proposed based on mixed data features analysis and multi-way kernel entropy component analysis ( MDFA-MKECA ) in this paper. Combining the mechanistic knowledge, different mixed data features of each batch including statistical and thermodynamics entropy features, are extracted to finish data pre-processing. After that, MKECA is applied to reduce data dimensionality and finally establish a monitoring model. The proposed method is applied to a reheating furnace industry process, and the experimental results demonstrate that the MDFA-MKECA method can reduce the calculated amount and effectively provide on-line monitoring of the batch process.