Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, United States.
Department of Civil and Environmental Engineering, University of Missouri, Columbia, MO 65211, United States.
Sci Total Environ. 2021 Jan 20;753:141922. doi: 10.1016/j.scitotenv.2020.141922. Epub 2020 Aug 22.
Algal productivity in steady-state cultivation systems depends on important factors such as biomass concentration, solids retention time (SRT), and light intensity. Current modeling of algal growth often ignores light distribution in algal cultivation systems and does not consider all these factors simultaneously. We developed a new algal growth model using a first principles approach to incorporate the effect of light intensity on algal growth while simultaneously considering biomass concentration and SRT. We first measured light attenuation (decay) with depth in an indoor algal membrane bioreactor (A-MBR) cultivating Chlorella sp. We then simulated the light decay using a multi-layer approach and correlated the decay with biomass concentration and SRT in model development. The model was calibrated by delineating specific light absorptivity and half-saturation constant to match the algal biomass concentration in the A-MBR operated at a target SRT. We finally applied the model to predict the maximum algal productivity in both indoor and outdoor A-MBRs. The predicted maximum algal productivities in indoor and outdoor A-MBRs were 6.7 g·m·d (incident light intensity 5732 lx, SRT approximately 8 d) and 28 g·m·d (sunlight intensity 28,660 lx, SRT approximately 4 d), respectively. The model can be extended to include other factors (e.g., water temperature and carbon dioxide bubbling) and such a modeling framework can be applied to full-scale, continuous flow outdoor systems to improve algal productivity.
稳态培养系统中的藻类生产力取决于重要因素，如生物质浓度、固体停留时间 (SRT) 和光强。目前的藻类生长模型通常忽略了藻类培养系统中的光分布，并且没有同时考虑所有这些因素。我们使用第一性原理方法开发了一种新的藻类生长模型，该模型考虑了光强对藻类生长的影响，同时考虑了生物质浓度和 SRT。我们首先测量了室内藻类膜生物反应器 (A-MBR) 中培养的 Chlorella sp. 的深度处的光衰减（衰减）。然后，我们使用多层方法模拟了光衰减，并在模型开发中关联了衰减与生物质浓度和 SRT。通过划定特定的光吸收率和半饱和常数来使模型与在目标 SRT 下运行的 A-MBR 中的藻类生物量浓度相匹配，从而对模型进行了校准。最后，我们应用该模型预测了室内和室外 A-MBR 中的最大藻类生产力。室内和室外 A-MBR 的预测最大藻类生产力分别为 6.7 g·m·d（入射光强度为 5732 lx，SRT 约为 8 d）和 28 g·m·d（阳光强度为 28,660 lx，SRT 约为 4 d）。该模型可以扩展到包括其他因素（例如，水温和二氧化碳鼓泡），并且这种建模框架可以应用于全规模、连续流动的室外系统，以提高藻类生产力。
