Propagation of measurement accuracy to biomass soft-sensor estimation and control quality
VerfasserHerwig, Christoph In der Gemeinsamen Normdatei der DNB nachschlagen ; Steinwandter, Valentin ; Zahel, Thomas ; Sagmeister, Patrick
Erschienen in
Analytical and Bioanalytical Chemistry, Berlin ; Heidelberg 2016, Jg. Article not assigned to an, S. 1-14
ErschienenSpringer 2016
Published version
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)Bioprocess / Biomass estimation / Soft-sensor / Accuracy / Error propagation / Bioprocess control
URNurn:nbn:at:at-ubtuw:3-1928 Persistent Identifier (URN)
CC-BY-Lizenz (4.0)Creative Commons Namensnennung 4.0 International Lizenz
 Das Werk ist frei verfügbar
Propagation of measurement accuracy to biomass soft-sensor estimation and control quality [3.26 mb]
Supplementary material [0.63 mb]
Zusammenfassung (Englisch)

In biopharmaceutical process development and manufacturing, the online measurement of biomass and derived specific turnover rates is a central task to physiologically monitor and control the process. However, hard-type sensors such as dielectric spectroscopy, broth fluorescence, or permittivity measurement harbor various disadvantages. Therefore, soft-sensors, which use measurements of the off-gas stream and substrate feed to reconcile turnover rates and provide an online estimate of the biomass formation, are smart alternatives. For the reconciliation procedure, mass and energy balances are used together with accuracy estimations of measured conversion rates, which were so far arbitrarily chosen and static over the entire process. In this contribution, we present a novel strategy within the soft-sensor framework (named adaptive soft-sensor) to propagate uncertainties from measurements to conversion rates and demonstrate the benefits: For industrially relevant conditions, hereby the error of the resulting estimated biomass formation rate and specific substrate consumption rate could be decreased by 43 and 64 %, respectively, compared to traditional soft-sensor approaches. Moreover, we present a generic workflow to determine the required raw signal accuracy to obtain predefined accuracies of soft-sensor estimations. Thereby, appropriate measurement devices and maintenance intervals can be selected. Furthermore, using this workflow, we demonstrate that the estimation accuracy of the soft-sensor can be additionally and substantially increased.