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混养、营养限制条件下微藻生长过程中淀粉和脂质形成的动力学建模。

Kinetic modelling of starch and lipid formation during mixotrophic, nutrient-limited microalgal growth.

机构信息

School of Chemical Engineering and Analytical Science, Biochemical and Bioprocess Engineering Group, The University of Manchester, Manchester M13 9PL, UK.

School of Earth and Environmental Sciences, The University of Manchester, Manchester M13 9PT, UK.

出版信息

Bioresour Technol. 2017 Oct;241:868-878. doi: 10.1016/j.biortech.2017.05.177. Epub 2017 Jun 2.

Abstract

Microalgal starch and lipids, carbon-based storage molecules, are useful as potential biofuel feedstocks. In this work, cultivation strategies maximising starch and lipid formation were established by developing a multi-parameter kinetic model describing microalgal growth as well as starch and lipid formation, in conjunction with laboratory-scale experiments. Growth dynamics are driven by nitrogen-limited mixotrophic conditions, known to increase cellular starch and lipid contents whilst enhancing biomass growth. Model parameters were computed by fitting model outputs to a range of experimental datasets from batch cultures of Chlamydomonas reinhardtii. Predictive capabilities of the model were established against different experimental data. The model was subsequently used to compute optimal nutrient-based cultivation strategies in terms of initial nitrogen and carbon concentrations. Model-based optimal strategies yielded a significant increase of 261% for starch (0.065gCL) and 66% for lipid (0.08gCL) production compared to base-case conditions (0.018gCL starch, 0.048gCL lipids).

摘要

微藻淀粉和脂质是碳基储存分子,可用作潜在的生物燃料原料。在这项工作中,通过开发一个多参数动力学模型来描述微藻的生长以及淀粉和脂质的形成,结合实验室规模的实验,确立了最大化淀粉和脂质形成的培养策略。生长动态由氮限制混合营养条件驱动,这种条件已知可以提高细胞淀粉和脂质含量,同时促进生物量生长。通过将模型输出拟合到一系列来自莱茵衣藻分批培养的实验数据集来计算模型参数。该模型针对不同的实验数据建立了预测能力。随后,该模型用于计算基于营养素的最佳培养策略,涉及初始氮和碳浓度。与基准条件(淀粉 0.018gCL,脂质 0.048gCL)相比,基于模型的最佳策略使淀粉(0.065gCL)和脂质(0.08gCL)的产量分别显著增加了 261%和 66%。

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