This work describes a multivariate monitoring and control concept for bioprocesses based on historical process data. The concept is demonstrated for a Saccharomyces Cerevisiae (baker’s yeast) fermentation process executed in a small-scale bioreactor, which is equipped with common probes to analyze the broth and off-gases. The data of “in-control” fermentation processes were evaluated by means of a principal component analysis to define confidence limits for subsequent fermentations. A violation of these limits indicated that a process had to be classified as “out-of-control”. Fault diagnosis was provided by the components of the squared prediction error, which can also be used to determine the appropriate counteractions, e.g. via an expert system control strategy as described in this study. The sensitivity of fault diagnosis was demonstrated via various erroneous runs. The duration of bioprocesses can vary distinctly, which complicates the definition of time dependent control limits. Therefore, this study utilizes a three-component partial least squares regression model to quantify the current batch maturity during the process. This maturity is then used to reference current data to the appropriate historical data and the assigned control limits.
- Automated fault diagnostic
- Multivariate process monitoring
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Artificial Intelligence