Exputec GmbH, Pfeilgasse 32/20, Vienna, Austria.
Institute of Chemical Engineering, Research Area Biochemical Engineering, Vienna University of Technology, Gumpendorferstrasse 1a, Vienna, Austria.
Anal Bioanal Chem. 2017 Jan;409(3):693-706. doi: 10.1007/s00216-016-9711-9. Epub 2016 Jul 4.
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.
在生物制药工艺开发和生产中，在线测量生物量和衍生的特定周转率是生理监测和控制工艺的核心任务。然而，硬式传感器（如介电光谱、肉汤荧光或介电常数测量）存在各种缺点。因此，软传感器使用废气流和基质进料的测量来协调周转率，并提供生物量形成的在线估计，是明智的替代方案。对于调整过程，使用质量和能量平衡以及测量转化率的精度估计，迄今为止，这些转化率是任意选择的并且在整个过程中是静态的。在本贡献中，我们在软传感器框架内提出了一种新策略（称为自适应软传感器），将不确定性从测量值传播到转化率，并展示了其优势：对于工业相关条件，与传统软传感器方法相比，由此产生的估计生物量形成速率和特定基质消耗速率的误差分别降低了 43%和 64%。此外，我们提出了一种通用工作流程来确定所需的原始信号精度，以获得软传感器估计的预定精度。由此，可以选择适当的测量设备和维护间隔。此外，使用此工作流程，我们证明了软传感器的估计精度可以进一步显著提高。
