Observability analysis of biochemical process models as a valuable tool for the development of mechanistic soft sensors.

By enabling the estimation of difficult-to-measure target variables using available indirect measurements, mechanistic soft sensors have become important tools for various bioprocess monitoring and control scenarios. Despite promising higher process efficiencies and increased process understanding, widespread application of soft sensors has been stalled by uncertainty about the feasibility and reliability of their estimations given present process analytical constraints. Observability analysis can provide an indication of the possibility and reliability of soft sensor estimations by analyzing the structural properties of first-principle (mechanistic) models. In addition, it can provide a criteria for selection of suitable measurement methods with respect to their information content; thereby leading to successful implementation of soft sensors in bioprocess development and manufacturing environments. We demonstrate the utility of observability analysis for two classes of upstream bioprocesses: the processes involving growth and ethanol formation by Saccharomyces cerevisiae and the process of penicillin production by Penicillium chrysogenum. Results obtained from laboratory-scale cultivations in addition to in-silico experiments enable a comparison of theoretical aspects of observability analysis and the real-life performance of soft sensors. By taking the expected error of measurements provided to the soft sensor into account, an innovative scaling approach facilitates a higher degree of comparability of observability results among various measurement configurations and process conditions.