Modeling dynamic functioning of rectangular photobioreactors in solar conditions

Pruvost, Jérémy; Cornet, Jean‐François; Goetz, V.; Legrand, Jack

doi:10.1002/aic.12389

Cited by 52 publications

(87 citation statements)

References 48 publications

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“…We also note that the performance of a cultivation system (in light-limited conditions) when expressed on a surface basis is independent of the cultivation system design (Cornet 2010;Pruvost et al 2011b). We also note that the performance of a cultivation system (in light-limited conditions) when expressed on a surface basis is independent of the cultivation system design (Cornet 2010;Pruvost et al 2011b).…”

Section: Prediction Of Biomass Concentration and Productivitymentioning

confidence: 95%

10 Knowledge models for the engineering and optimization of photobioreactors

Pruvost¹,

Cornet²

2012

Microalgal Biotechnology: Potential and Production

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Spherical coordinates:The modeling of microalgal photosynthetic biomass production draws some support from the abundant literature on bioprocess modeling, in particular when mineral or CO 2 mass-transfer limitations on growth rates are considered in the same way as substrate and O 2 -transfer limitations in engineered bacterial cultivation. However, photosynthetic biomass growth exhibits highly specific features owing to its need for light energy: unlike dissolved nutrients, assumed to be homogeneous in well-mixed conditions, light energy is heterogeneously distributed in the culture due to absorption and scattering by cells, independently of the mixing conditions. As light is the principal energy source for photosynthesis, this heterogeneity alone sets microalgal cultivation systems apart from other classical bioprocesses, as they are generally limited by light transfer inside the culture media. Hence the design, optimization and control of photobioreactors (PBRs) require specific approaches.This chapter deals with developing useful knowledge models for engineering microalgal cultivation systems. Prerequisites and main concepts will be presented. Concrete illustrations will be given that use modeling to gain a deeper understanding of the complex influence of light transfer on the process, and to predict biomass productivities as a function of cultivation system engineering variables (especially depth of culture) and operating settings (residence time and incident light flux) in both artificial constant light and natural sunlight conditions. Theoretical background for radiation measurement and handling Main physical variablesGiven the crucial importance of radiative transfer description in photobioreactor modeling, it is essential to have a broad overview of the main physical quantities and definitions involved in radiation measurement and theory, together with a thorough knowledge of the conversion factors linking the two main practical systems of units (joules and micromoles of photons). There is often much confusion on these points in the microalgal growth modeling literature, conducive to misin-

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Section: Prediction Of Biomass Concentration and Productivitymentioning

confidence: 95%

10 Knowledge models for the engineering and optimization of photobioreactors

Pruvost¹,

Cornet²

2012

Microalgal Biotechnology: Potential and Production

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“… and Vonshak for analog conditions (0.0833 and 0.0866 h ‒1 ), the compensation light intensity K CI is in the range of 5–23 μmol m –2 s –1 as given by Cogne et al. , but the half‐saturation constant K SI is higher than expected .…”

Section: Resultsmentioning

confidence: 49%

The challenge of scaling up photobioreactors: Modeling and approaches in small scale

Socher

Löser

Schott

et al. 2016

Engineering in Life Sciences

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Commercial large-scale application of photobiotechnology calls for acceptable rates and high cell densities. The latter results in self-shading and a temporally declining growth rate, making the process inefficient. Upscaling phototrophic processes requires knowledge of the reaction kinetics with special attention on the μ-I relation. Model-based calculations for studying the effect of self-shading on growth were performed for three μ(I) concepts. The nonrealistic μ(I 0 ) concept results in exponential growth without limit, the widely used μ(I (x)) concept exhibits a growth behavior that significantly deviates from real processes, but the sophisticated μ(I (x)) concept describes real processes best. A Respiration Activity Monitoring System (RAMOS) with CultiLux unit was tested regarding its applicability for exploring the μ-I dependency of Arthrospira platensis. The results were not satisfactory because of self-shading due to a high cell density (which was essential for yielding detectable oxygen-evolution signals). An innovative cultivation system in which self-shading does not interfere with phototrophic growth was required. A flat-panel system was constructed (designed by CFD, short light path of 1 cm, uniform planar light supply by LED and OLED units), allowing continuous cultivation of phototrophic microbes at varied light intensities.

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“…For simplicity, the light attenuation of the cell‐free medium was assumed to be negligible and other effects, such as fluorescence and light scattering, are known to exist, but are not taken into account in this analysis. A complete analysis of light transmission and photon flux modeling in algal cell cultures can be found elsewhere . Indeed, Eq.…”

Section: Methodsmentioning

confidence: 99%

Energy conversion in an internally illuminated annular‐plate airlift photobioreactor

Jeffryes

Severi

Delhaye

et al. 2016

Engineering in Life Sciences

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Algal-derived therapeutics, bioactive molecules, and fuels produced in photobioreactors (PBRs) are of great scientific and economic interest, but the high cost of production still prevents their widespread use. Specifically, the cost of the energy inputs and the control of the photonic inputs that enable production optimization continue to be problematic. To this end, a novel 55-L annular-plate airlift PBR (APAPBR) with internal illumination was designed and characterized for the batch production of algal biomass. The APAPBR was able to convert mixing and photonic energy inputs into Chlorella pituita SG1 biomass at an efficiency of 0.064 (J biomass [J input] −1 ), or 0.27 g dry cell weight (DW) W −1 d −1 . Thanks to a high degree of photon capture and the airlift effect that provided energy-efficient mixing and mass transfer, this energy conversion is 54% of the theoretical maximum as determined in previous studies. Under these efficiency conditions, C. pituita SG1 was able to grow photoautotrophically to 3.9 ± 0.2 gDW L −1 . Additionally, a mathematical approach was used to predict the mean light intensity with the highest biomass yield per unit of photonic input and the maximum biomass concentration achievable under the given process conditions. These predictions were validated in our system by the experimental cultivation data. This APAPBR represents a simple, innovative, and energy-efficient PBR configuration that could decrease the cost of phototrophic bioprocesses and enable novel bioprocesses that require a high degree of control over the photonic input.

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Modeling dynamic functioning of rectangular photobioreactors in solar conditions

Cited by 52 publications

References 48 publications

10 Knowledge models for the engineering and optimization of photobioreactors

10 Knowledge models for the engineering and optimization of photobioreactors

The challenge of scaling up photobioreactors: Modeling and approaches in small scale

Energy conversion in an internally illuminated annular‐plate airlift photobioreactor

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