Fault detection and diagnosis in an industrial fed-batch cell culture process.
A flexible process monitoring method was applied to industrial pilot plant cell culture data for the purpose of fault detection and diagnosis. Data from 23 batches, 20 normal operating conditions (NOC) and three abnormal, were available. A principal component analysis (PCA) model was constructed from 19 NOC batches, and the remaining NOC batch was used for model validation. Subsequently, the model was used to successfully detect (both offline and online) abnormal process conditions and to diagnose the root causes. This research demonstrates that data from a relatively small number of batches (approximately 20) can still be used to monitor for a wide range of process faults.
Department of Chemical Engineering, University of California, Santa Barbara, California 93106-5080, USA.
Jon C
JC
Gunther
Jeremy S
JS
Conner
Dale E
DE
Seborg
D000465:Algorithms; D001331:Automation; D019149:Bioreactors; D001709:Biotechnology / Q000295:instrumentation* / Q000379:methods*; D004867:Equipment Design; D007218:Industrial Microbiology / Q000379:methods*; D007221:Industry / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D009862:Online Systems; D010100:Oxygen / Q000737:chemistry / Q000378:metabolism; D025341:Principal Component Analysis; D013696:Temperature; D013997:Time Factors
D010100:Oxygen