Cultivation of shear stress sensitive and tolerant microalgal species in a tubular photobioreactor equipped with a centrifugal pump

Michels, Michiel H.A.; Goot, Atze Jan van der; Vermuë, M.H.; Wijffels, René H.

doi:10.1007/s10811-015-0559-8

Cited by 54 publications

(28 citation statements)

References 61 publications

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“…These studies indicated that microalgal biomass cultivation is predominantly affected by two fluid dynamic factors; shear stress and liquid mixing . While rapid mixing of liquid cultures along the direction of light transmission can improve the overall light utilization, thereby enhancing photosynthetic efficiencies, it also induces intense shear stresses that can severely damage algal cells resulting in biomass death …”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

et al. 2018

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Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae‐derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modeling. However, this approach presents computational intractability and numerical instabilities when simulating large‐scale systems, causing time‐intensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative data‐driven surrogate modeling framework, which considerably reduces computing time from months to days by exploiting state‐of‐the‐art deep learning technology. The framework built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Through verification, this framework was demonstrated to have comparable accuracy to the physical model. Moreover, multi‐objective optimization was incorporated to generate a Pareto‐frontier for decision‐making, advancing its applications in complex biosystems modeling and optimization. © 2018 American Institute of Chemical Engineers AIChE J, 65: 915–923, 2019

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Section: Introductionmentioning

confidence: 99%

Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

et al. 2018

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“…It is important for us to know if or not the shear stress is limited to levels that can be tolerated by the microalgae, since hydrodynamic forces evoked by excessive turbulent mixing in closed TPBRs may limit the application of these systems, as described in the study of Michiel et al . In order to investigate the influence of the built‐in helical rotors on hydrodynamic forces, the max shear stress and the average shear stress were calculated from simulation in this work.…”

Section: Equipment and Methodsmentioning

confidence: 99%

Mixing characteristics, cell trajectories, pressure loss and shear stress of tubular photobioreactor with inserted self‐rotating helical rotors

Yan

Guan

Jia

et al. 2017

J of Chemical Tech & Biotech

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BACKGROUND Tubular photobioreactors (TPBRs) have great potential in culturing microalgae, but their poor mixing properties make it difficult to increase the surface productivity. In this work, a TPBR with inserted self‐rotating helical rotors, which have been under study for a decade, was designed. RESULTS This paper aimed to show hydrodynamic behaviors of gas–liquid in a TPBR with inserted self‐rotating helical rotors by computational fluid dynamics (CFD) and visualization experiments. It can be concluded that by assembling self‐rotating helical rotors into the TPBR, very remarkable mixing and particle cycle frequencies were achieved. Besides, the influences of rotor lead and liquid inlet velocity on the swirl number, the gas and liquid average circumferential velocities, pressure loss, cell trajectories and shear stress were also investigated. The best choice was to select a rotor lead of 150 mm and a liquid inlet velocity of 0.4–0.5 m s‐1 for microalgae with strong shear force tolerance. Microalgae cultivation experiments verified that helical rotors had a positive effect on biomass productivity. CONCLUSION The results indicated that assembling the self‐rotating helical rotors into TPBR was a feasible and effective method to improve the mixing performance and microalgae productivity. © 2017 Society of Chemical Industry

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“…The friction velocity can be expressed as follows (Leupold et al, ; Michels et al, ):

U_{f} goodbreakinfix= {true(\frac{τ_{s}}{ρ_{l}} true)}^{1 / 2}

where

U_{f}

is the friction velocity (cm/s), and

τ_{s}

is the shear stress (Pa). A friction velocity higher than 1 cm/s damages C. reinhardtii cells and decreases their photosynthetic efficiencies (Leupold et al, ; Michels et al, ). The decrease in the photosynthetic efficiency caused by high friction velocity (>1 cm/s) reduces the PBR productivity.…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…However, microalgal cells are sensitive to fluid dynamic stresses. Intensive mixing can cause shear‐induced damage to these cells and consequently reduce their biomass productivity (Leupold, Hindersin, Gust, Kerner, & Hanelt, ; Michels, van der Goot, Vermuë, & Wijffels, ; Thomas & Gibson, ). In addition to the bubble rising and bursting phenomena in the PBR, the bubble formation at the sparger could also damage algal cells (Barbosa, Hadiyanto, & Wijffels, ).…”

Section: Introductionmentioning

confidence: 99%

CFD and kinetic‐based modeling to optimize the sparger design of a large‐scale photobioreactor for scaling up of biofuel production

Ali

Solsvik

Wagner

et al. 2019

Biotech & Bioengineering

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Microalgal biofuels have not yet achieved wide-spread commercialization, partially as a result of the complexities involved with designing and scaling up of their biosystems. The sparger design of a pilot-scale photobioreactor (120 L) was optimized to enable the scale-up of biofuel production. An integrated model coupling computational fluid dynamics and microalgal biofuel synthesis kinetics was used to simulate the biomass growth and novel biofuel production (i.e., bisabolene) in the photobioreactor. Bisabolene production from Chlamydomonas reinhardtii mutant was used as an example to test the proposed model. To select the optimal sparger configuration, a rigorous procedure was followed by examining the effects of sparger design parameters (number and diameter of sparger holes and gas flow rates) on spatially averaged bubble volume fraction, light intensity, friction velocity, power input, biomass concentration, and bisabolene production. The optimized sparger design increases the final biomass concentration by 18%, thereby facilitating the scaling up of biofuel production.

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Cultivation of shear stress sensitive and tolerant microalgal species in a tubular photobioreactor equipped with a centrifugal pump

Cited by 54 publications

References 61 publications

Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

Mixing characteristics, cell trajectories, pressure loss and shear stress of tubular photobioreactor with inserted self‐rotating helical rotors

CFD and kinetic‐based modeling to optimize the sparger design of a large‐scale photobioreactor for scaling up of biofuel production

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