Computational analysis of hydrodynamics and light distribution in photo-bioreactors for algae biomass production

Microalgae can be directly used in health food or as bio-filters for waste water treatment. They also have numerous commercial applications in cosmetics, aquaculture and chemical industry as a source of highly valuable molecules, e.g., polyunsaturated fatty acids [1]. Moreover, they are increasingly recognized as a promising source for biodiesel production [2]. To realize the full potential of microalgae, optimal operating conditions for their cultivation in photo-bioreactors (PBR) need to be identified in order to maximize productivity, lipid content, and efficiency of photosynthesis. The most important parameters affecting PBR performance are reactor shape, light intensity distribution, algae growth and other metabolic properties.The presented study aims at analyzing sensitivities to these parameters using Computational Fluid Dynamics (CFD) simulations with the COMSOL Multiphysics software. Specifically, flat panel photo-bioreactors with turbulent mixing due to air sparging and one-sided lighting are studied. First, flow profiles of both liquid and gas phases are computed using the Euler-Euler approach for analyzing the air sparging and detecting potential dead zones. Then, light intensity distributions are calculated, based on absorption and light scattering by algae and gas bubbles. Subsequently, the paths of individual algae are traced, and the environmental conditions they are exposed to are recorded over time, in particular aeration and light intensity. Statistical analysis of the particle traces is performed combining the light exposure with an empirical growth model for algae. Results of the above described simulation stages will be presented and discussed.[1] Spolaore et al.: Commercial applications of microalgae, J. Biosci. Bioeng. 101 (2006): 87-96.[2] Bitog et al.: Application of computational fluid dynamics for modeling and designing photobioreactors for microalgae production: A review, Comput. Electron. Agr. 76 (2011): 131-147

Machine learning in bioprocess development: From promise to practice

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges and point out domains that can potentially benefit from technology transfer and further progress in the field of ML

Modeling and CFD Simulation of nutrient Distribution in picoliter bioreactors for bacterial growth studies on single-cell level

Westerwalbesloh C, Grünberger A, Stute B, et al. Modeling and CFD Simulation of nutrient Distribution in picoliter bioreactors for bacterial growth studies on single-cell level. Lab on a chip. 2015;15(21):4177-4186.A microfluidic device for microbial single-cell cultivation of bacteria was modeled and simulated using COMSOL Multiphysics. The liquid velocity field and the mass transfer within the supply channels and cultivation chambers were calculated to gain insight in the distribution of supplied nutrients and metabolic products secreted by the cultivated bacteria. The goal was to identify potential substrate limitations or product accumulations within the cultivation device. The metabolic uptake and production rates, colony size, and growth medium composition were varied covering a wide range of operating conditions. Simulations with glucose as substrate did not show limitations within the typically used concentration range, but for alternative substrates limitations could not be ruled out. This lays the foundation for further studies and the optimization of existing picoliter bioreactor systems

Efficient simulation of chromatographic separation processes

This work presents the development and testing of an efficient, high resolution algorithm developed for the solution of equilibrium and non-equilibrium chromatographic problems as a means of simultaneously producing high fidelity predictions with a minimal increase in computational cost. The method involves the coupling of a high-order WENO scheme, adapted for use on non-uniform grids, with a piecewise adaptive grid (PAG) method to reduce runtime while accurately resolving the sharp gradients observed in the processes under investigation. Application of the method to a series of benchmark chromatographic test cases, within which an increasing number of components are included over short and long spatial domains and containing shocks, shows that the method is able to accurately resolve the discontinuities and that the use of the PAG method results in a reduction in the CPU runtime of up to 90%, without degradation of the solution, relative to an equivalent uniform grid