Batch Process Modeling and Monitoring With Local Outlier Factor

Batch processes are commonly involved by a succession of working phases with implicit non-Gaussian behaviors. Besides, in most cases, batch-to-batch processes also show similar but yet not identical running trajectory variations. To deal with these issues, this paper introduces a systematic analysis flowchart based on local outlier factor (LOF) for monitoring multiphase batch processes. First, a step-wise phase dividing algorithm is proposed with LOF to conduct phase dividing for a better understanding of batch process. Afterward, we develop the multiphase LOF for similar batch data modeling and then fault detection. The fault isolation method is proposed, where variable contributions with LOFs are induced, also with the analysis of isolability. The developed method is validated on a numerical example and the fed-batch fermentation benchmark process, both of which are compared with the multiphase principal component analysis method. Results demonstrate the feasibility and superiority of the proposed method.