Design and Optimization of a Penicillin Fed-Batch Reactor Based on a Deep Learning Fault Detection and Diagnostic Model

• Published in: Industrial & engineering chemistry research 2022-04, Vol.61 (13), p.4625-4637
• Main Authors: Hematillake, Dylan, Freethy, Daniele, McGivern, Jacob, McCready, Chris, Agarwal, Piyush, Budman, Hector
• Format: Article
• Language: English
• Subjects: Process Systems Engineering
• ISSN: 0888-5885; 1520-5045
• DOI: 10.1021/acs.iecr.1c04534

The application of a supervised deep convolutional autoencoder was tested against partial least-squares-discriminant analysis (PLS-DA) for fault detection and diagnosis in a penicillin fed-batch process. In silico data was generated with a comprehensive simulator (IndPenSim) of an industrial-scale penicillin fed-batch simulator of operation under normal batch conditions and 8 fault batch conditions. A composite face-centered design response surface was applied to optimize key bioreactor design parameters based on a profit function that was directly dependent on fault detection results. The application of PLS-DA and the NN modeling resulted in an average fault detection rate (FDR) across all faults and process parameter configurations of 72.5% and 95.9%, respectively. In classifying complex fault conditions, the deep learning model greatly surpassed the PLS-DA model, and this improvement translated into a 25.0% increase in realized profit with the supervised deep convolutional autoencoder when compared to PLS-DA monitoring.