The golden batch-driven root cause analysis for anomalies in bioreactor fermentation process

Abstract

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Bioreactors are essential for the production of biopharmaceuticals and bioproducts, requiring continuous monitoring to ensure quality assurance. Manual processes in manufacturing plants often lead to anomalies such as out-of-trend and out-of-spec incidents, necessitating extensive root cause analysis that typically takes 2–8 weeks. This paper introduces an innovative methodology that uses the golden batch profile as a benchmark to identify deviations and root causes in subsequent industrial batches. The methodology involves normalizing the data and calculating the variances of a specified batch from the golden batch profile. By examining the contribution of each critical process parameter to these variances, the study highlights their importance in root cause analysis. The application of this methodology to the IndPenSim dataset demonstrated its effectiveness by significantly reducing false positives and negatives compared to traditional PCA-based methods. Emphasis on the deviations of critical quality attributes and critical process parameters from the specified batch compared to the golden batch profile offers valuable insights into industrial process analysis. This approach not only enhances anomaly detection accuracy but also improves the efficiency and reliability of biopharmaceutical and bioproduct manufacturing processes.

Keywords

• process analytic technology (PAT)
• golden batch profile
• root cause analysis (RCA)
• anomaly detection (AD)
• deviation analysis (DA)