Zhang Dongda, Chanona Ehecatl Antonio Del-Rio, Vassiliadis Vassilios S, Tamburic Bojan
Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
Plant Functional Biology and Climate Change Cluster, University of Technology Sydney, Broadway 2007, NSW, Australia.
Biotechnol Bioeng. 2015 Oct;112(10):2025-39. doi: 10.1002/bit.25610. Epub 2015 May 12.
Chlamydomonas reinhardtii is a green microalga with the potential to generate sustainable biofuels for the future. Process simulation models are required to predict the impact of laboratory-scale growth experiments on future scaled-up system operation. Two dynamic models were constructed to simulate C. reinhardtii photo-autotrophic and photo-mixotrophic growth. A novel parameter estimation methodology was applied to determine the values of key parameters in both models, which were then verified using experimental results. The photo-mixotrophic model was used to accurately predict C. reinhardtii growth under different light intensities and in different photobioreactor configurations. The optimal dissolved CO2 concentration for C. reinhardtii photo-autotrophic growth was determined to be 0.0643 g·L(-1) , and the optimal light intensity for algal growth was 47 W·m(-2) . Sensitivity analysis revealed that the primary factor limiting C. reinhardtii growth was its intrinsic cell decay rate rather than light attenuation, regardless of the growth mode. The photo-mixotrophic growth model was also applied to predict the maximum biomass concentration at different flat-plate photobioreactors scales. A double-exposure-surface photobioreactor with a lower light intensity (less than 50 W·m(-2) ) was the best configuration for scaled-up C. reinhardtii cultivation. Three different short-term (30-day) C. reinhardtii photo-mixotrophic cultivation processes were simulated and optimised. The maximum biomass productivity was 0.053 g·L(-1) ·hr(-1) , achieved under continuous photobioreactor operation. The continuous stirred-tank reactor was the best operating mode, as it provides both the highest biomass productivity and lowest electricity cost of pump operation.
莱茵衣藻是一种绿色微藻，有潜力为未来生产可持续生物燃料。需要过程模拟模型来预测实验室规模生长实验对未来放大系统运行的影响。构建了两个动态模型来模拟莱茵衣藻的光合自养和光合混合营养生长。应用了一种新颖的参数估计方法来确定两个模型中的关键参数值，然后使用实验结果进行验证。光合混合营养模型用于准确预测莱茵衣藻在不同光照强度和不同光生物反应器配置下的生长。确定莱茵衣藻光合自养生长的最佳溶解二氧化碳浓度为0.0643 g·L⁻¹，藻类生长的最佳光照强度为47 W·m⁻²。敏感性分析表明，无论生长模式如何，限制莱茵衣藻生长的主要因素是其内在细胞衰亡速率而非光衰减。光合混合营养生长模型还用于预测不同平板光生物反应器规模下的最大生物量浓度。较低光照强度（小于50 W·m⁻²）的双曝光表面光生物反应器是放大培养莱茵衣藻的最佳配置。模拟并优化了三种不同的短期（30天）莱茵衣藻光合混合营养培养过程。在光生物反应器连续运行下实现的最大生物量生产力为0.053 g·L⁻¹·hr⁻¹。连续搅拌釜式反应器是最佳运行模式，因为它既提供了最高的生物量生产力，又具有最低的泵运行电力成本。
