Biotechnology and Bioprocess Engineering, Ostwestfalen-Lippe University of Applied Sciences and Arts, Campusallee 12, Lemgo, Germany.
Fraunhofer IOSB, Lemgo, Germany.
Bioprocess Biosyst Eng. 2022 Jan;45(1):15-30. doi: 10.1007/s00449-021-02627-2. Epub 2021 Oct 22.
Kinetic growth models are a useful tool for a better understanding of microalgal cultivation and for optimizing cultivation conditions. The evaluation of such models requires experimental data that is laborious to generate in bioreactor settings. The experimental shake flask setting used in this study allows to run 12 experiments at the same time, with 6 individual light intensities and light durations. This way, 54 biomass data sets were generated for the cultivation of the microalgae Chlorella vulgaris. To identify the model parameters, a stepwise parameter estimation procedure was applied. First, light-associated model parameters were estimated using additional measurements of local light intensities at differ heights within medium at different biomass concentrations. Next, substrate related model parameters were estimated, using experiments for which biomass and nitrate data were provided. Afterwards, growth-related model parameters were estimated by application of an extensive cross validation procedure.
动力学生长模型是更好地理解微藻培养和优化培养条件的有用工具。此类模型的评估需要实验数据,而在生物反应器环境中生成这些数据非常繁琐。本研究中使用的实验摇瓶设置允许同时运行 12 个实验,每个实验有 6 个单独的光强度和光照持续时间。通过这种方式,为培养普通小球藻生成了 54 组生物质数据集。为了确定模型参数,应用了逐步参数估计程序。首先,使用在不同生物质浓度下介质中不同高度的局部光强度的额外测量值来估计与光相关的模型参数。接下来,使用提供了生物质和硝酸盐数据的实验来估计与基质相关的模型参数。之后,通过应用广泛的交叉验证程序来估计与生长相关的模型参数。