Slow-varying batch process monitoring based on canonical variate analysis

Most industrial batch processes display normal slow changes over batches because of the influence of external factors. The question of how to take this slow-varying behaviour into account during dynamic process monitoring remains a challenge for researchers. To address the above issue, a slow-varying batch process monitoring method is proposed for multiphase batch processes. It makes subtraction between two different batches with a certain interval for pre-treating the original data and subsequently utilizes a canonical variate analysis (CVA) algorithm with kernel density estimation (KDE) for fault detection. For each subphase, the proposed method can extract a variable-wise dynamic characteristic with the time evolution for every single batch and capture statistical features of slow variations along batch direction simultaneously. It is less sensitive to normal gradual changes with a low false-positive rate. The performance of the proposed method for batch process monitoring was tested on a numerical simulation system and penicillin fermentation production process with comparison to multiphase CVA, multiphase slow feature analysis (SFA), multiphase principal component analysis (PCA), and traditional CVA without phase division. The achieved results clearly demonstrate the effectiveness of the proposed method, which is a remarkable and promising tool for modelling and monitoring batch processes with regular slow-varying characteristics.