Marine Bioengineering Group, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.
University of Chinese Academy of Sciences, Beijing, China.
Biotechnol Bioeng. 2018 Feb;115(2):359-370. doi: 10.1002/bit.26483. Epub 2017 Nov 22.
Biodiesel produced from microalgae has been extensively studied due to its potentially outstanding advantages over traditional transportation fuels. In order to facilitate its industrialization and improve the process profitability, it is vital to construct highly accurate models capable of predicting the complex behavior of the investigated biosystem for process optimization and control, which forms the current research goal. Three original contributions are described in this paper. Firstly, a dynamic model is constructed to simulate the complicated effect of light intensity, nutrient supply and light attenuation on both biomass growth and biolipid production. Secondly, chlorophyll fluorescence, an instantly measurable variable and indicator of photosynthetic activity, is embedded into the model to monitor and update model accuracy especially for the purpose of future process optimal control, and its correlation between intracellular nitrogen content is quantified, which to the best of our knowledge has never been addressed so far. Thirdly, a thorough experimental verification is conducted under different scenarios including both continuous illumination and light/dark cycle conditions to testify the model predictive capability particularly for long-term operation, and it is concluded that the current model is characterized by a high level of predictive capability. Based on the model, the optimal light intensity for algal biomass growth and lipid synthesis is estimated. This work, therefore, paves the way to forward future process design and real-time optimization.
由于微藻生物柴油相对于传统运输燃料具有潜在的显著优势，因此已经对其进行了广泛研究。为了促进其工业化并提高工艺盈利能力，构建能够预测所研究生物系统复杂行为的高度精确模型对于工艺优化和控制至关重要，这也是当前的研究目标。本文介绍了三个原创性贡献。首先，构建了一个动态模型来模拟光强、养分供应和光衰减对生物量生长和生物脂生产的复杂影响。其次，将叶绿素荧光（一种可即时测量的变量和光合作用活性指标）嵌入模型中，以监测和更新模型准确性，特别是为了未来的过程最优控制，并量化了其与细胞内氮含量之间的相关性，据我们所知，目前为止还没有涉及到这一点。第三，在不同的场景下（包括连续光照和光/暗循环条件）进行了彻底的实验验证，以证明模型的预测能力，特别是对于长期运行，得出的结论是，目前的模型具有很高的预测能力。基于该模型，估算了藻类生物量生长和脂质合成的最佳光照强度。因此，这项工作为未来的过程设计和实时优化铺平了道路。
