Fed-batch fermentation penicillin process fault diagnosis and detection based on support vector machine

With the increase of scale and complexity of modern chemical process, fault diagnosis and detection are playing crucial roles in process monitoring. Accidents can be avoided if faults can be detected and excluded in time. In this paper, Principal Components Analysis (PCA) and Recursive Feature Elimination (RFE) are combined with Support Vector Machine (SVM) for fault diagnosis and detection. Specifically, the original SVM, PCA-SVM and SVM-RFE are respectively utilized to identify three faults from the simulation of Fed-Batch Fermentation Penicillin (FBFP) process. Experimental results show that PCA-SVM and SVM-RFE perform better than the original SVM, and the fault detection schemes based on PCA-SVM and SVM-RFE generate satisfactory results.