Yousefi-Darani Abdolrahimahim, Paquet-Durand Olivier, Hinrichs Jörg, Hitzmann Bernd
Department of Process Analytics and Cereal Science University of Hohenheim Stuttgart Germany.
Department of Soft Matter Science and Dairy Technology University of Hohenheim Stuttgart Germany.
Eng Life Sci. 2020 Dec 4;21(3-4):170-180. doi: 10.1002/elsc.202000058. eCollection 2021 Mar.
Real-time information about the concentrations of substrates and biomass is the key to accurate monitoring and control of bioprocess. However, on-line measurement of these variables is a challenging task and new measurement systems are still required. An alternative are software sensors, which can be used for state and parameter estimation in bioprocesses. The software sensors predict the state of the process by using mathematical models as well as data from measured variables. The Kalman filter is a type of such sensors. In this paper, we have used the Unscented Kalman Filter (UKF) which is a nonlinear extension of the Kalman filter for on-line estimation of biomass, glucose and ethanol concentration as well as for estimating the growth rate parameters in batch cultivation, based on infrequent ethanol measurements. The UKF algorithm was validated on three different cultivations with variability of the substrate concentrations and the estimated values were compared to the off-line values. The results obtained showed that the UKF algorithm provides satisfactory results with respect to estimation of concentrations of substrates and biomass as well as the growth rate parameters during the batch cultivation.
底物和生物质浓度的实时信息是生物过程精确监测和控制的关键。然而，对这些变量进行在线测量是一项具有挑战性的任务，仍然需要新的测量系统。一种替代方法是软件传感器，它可用于生物过程中的状态和参数估计。软件传感器通过使用数学模型以及来自测量变量的数据来预测过程状态。卡尔曼滤波器就是这类传感器的一种。在本文中，我们使用了无迹卡尔曼滤波器（UKF），它是卡尔曼滤波器的非线性扩展，用于基于不频繁的乙醇测量在线估计生物质、葡萄糖和乙醇浓度以及分批培养中的生长速率参数。UKF算法在三种不同的培养过程中进行了验证，这些培养过程中底物浓度存在变化，并将估计值与离线值进行了比较。获得的结果表明，UKF算法在分批培养过程中对底物和生物质浓度以及生长速率参数的估计方面提供了令人满意的结果。
