(509h) Modeling the Effect of Self-Shading on the Spatiotemporal Dynamics of Light Distribution and Algal Growth during Mixotrophic Cultivation in Bubble-Column Photobioreactors | AIChE

(509h) Modeling the Effect of Self-Shading on the Spatiotemporal Dynamics of Light Distribution and Algal Growth during Mixotrophic Cultivation in Bubble-Column Photobioreactors

Authors 

Ghosh, G. - Presenter, IIT Kharagpur
Atta, A., Indian Institute of Technology Kharagpur, Kharagpur 721302, India.
Chakraborty, S., Indian Institute of Technology Kharagpur
This study employs modelling and simulation to quantify the effects of the radial distribution of light intensity on the spatiotemporal dynamics of mixotrophic algal growth in tubular bubble-column photobioreactors. We solve two coupled differential equations light distribution, and algal concentration to quantify the spatiotemporal variations of both light intensity and algal concentration inside the reactor. The model simulations are validated with our experimental results on mixotrophic cultivation of Chlorella sorokiniana in 25L pilot-scale bubble-column fed-batch externally-illuminated photobioreactors, in which acetic acid and Tris are used as organic carbon and nitrogen sources, respectively, and carbon dioxide is bubbled at concentrations (v/v) varying from 1% to 3%.

Our simulations also show that as the cultivation time increases and algal concentration in the photobioreactor increases exponentially, the majority of the microalgae tend to crowd near the reactor wall in order to entrap maximum light for photosynthesis. For an illumination of 10,500 lux (=195 µM m-2 s-1) at the reactor wall, this ‘self-shading’ effect renders 40% of the reactor in the ‘dark zone’ (with illumination <5230 lux) after 36 hours, and more than 30% of the microalgal cells crowd in the peripheral 20% of the reactor after 100 hours of mixotrophic cultivation, thereby restricting the light infiltration and steepening the radial gradients of light and algal biomass in the photobioreactor.

Finally, we deploy our simulation results to obtain an easy-to-use coarse-grained algebraic equation capable of accurately predicting the temporal dynamics of average biomass growth in terms of reactor input variables such as light and carbon dioxide. The modeling and simulation technique presented here is not only more accurate than most existing algal growth models, but also can be employed in all tubular photobioreactors, regardless of their mixing type or the algal species being cultured.