Fault detection and diagnosis in industrial fed-batch fermentation

This paper applies multivariate statistical process control (MSPC) techniques to pilot plant fermentation data for the purpose of fault detection and diagnosis. Data from ten batches, nine normal operating conditions (NOC) and one failed, were available. A principal component analysis (PCA) model was constructed from eight NOC batches, while the remaining NOC batch was used for model validation. Subsequently, the model was used to successfully detect (both offline and online) a process abnormality in the failed batch and diagnose the factors contributing to the fault. These monitoring results agree with the observed biological phenomena encountered during this batch.