关键词:

独立元分析, 过程监测, 特征空间, 青霉素发酵过程

Abstract:

Updating the monitoring model timely to address the time-variant characteristics has vital significance in detecting abnormalities of chemical process and equipment breakdown exactly.The conventional ICA-based methods used to update the model have a high computational load and low efficiency.RUFS-ICA algorithm was proposed to model and monitor a fed-batch penicillin fermentation process on line.Compared with conventional ICA methods, the proposed method reduced the false alarm rate to 1.67% and basically overcame alarm failures.Compared with other methods, the algorithm could greatly decrease the computation time by about 54.1%, and saved the memory.

Key words:

独立元分析, 过程监测, 特征空间, 青霉素发酵过程