Multivariate statistical monitoring as applied to clean‐in‐place (CIP) and steam‐in‐place (SIP) operations in biopharmaceutical manufacturing

Abstract

Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning-in-place (CIP) and steaming-in-place (SIP, also known as sterilization-in-place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real-time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations.

Anomaly Analysis in Cleaning-in-Place Operations of an Industrial Brewery Fermenter

“…On the contrary, the variable-wise unfolding does not require such estimation, making it applicable for online monitoring. 9,18,19 Wold et al 20 concluded that the former was oriented to evaluating the completed batch as normal or abnormal, while the latter focused more on the monitoring of individual time points. 20 The primary objective of this study was to identify the occurrence of an anomalous event online.…”

“…Two commonly used indexes to detect anomalies or faults are the Q-statistic 18 and the Hotelling's T 2 statistic. 9,23 The Q-statistic is computed as the squared sum of residuals: 8…”

“…However, when being used on-line, it is required to estimate the future portions of variable trajectories. On the contrary, the variable-wise unfolding does not require such estimation, making it applicable for on-line monitoring 9,18,19 . Wold et al 20 concluded that the former was oriented to evaluating the completed batch as normal or abnormal.…”

“…Therefore, multivariate methods become of growing interest, which allows monitoring and analyzing multiple variables simultaneously. 9 Both methods will be introduced and developed in the proposed study with different purposes.…”

“…turbidity) enables to detect the residues in the liquid phase. Involving the quality indicators for the cleaning operation into the second level analysis will deliver more information and potentially allow to predict the end of the cleaning operation9 .…”