A numerical framework to predict the performances of a tubular photobioreactor from operating and sunlight conditions

The Role of Light on the Microalgae Biotechnology: Fundamentals, Technological Approaches, and Sustainability Issues

Light energy directly affects microalgae growth and productivity. Microalgae in natural environments receive light through solar fluxes, and their duration and distribution are highly variable over time. Consequently, microalgae must adjust their photosynthetic processes to avoid photo limitation and photoinhibition and maximize yield. Considering these circumstances, adjusting light capture through artificial lighting in the main culture systems benefits microalgae growth and induces the production of commercially important compounds. In this sense, this review provides a comprehensive study of the role of light in microalgae biotechnology. For this, we present the main fundamentals and reactions of metabolism and metabolic alternatives to regulate photosynthetic conversion in microalgae cells. Light conversions based on natural and artificial systems are compared, mainly demonstrating the impact of solar radiation on natural systems and lighting devices, spectral compositions, periodic modulations, and light fluxes when using artificial lighting systems. The most commonly used photobioreactor design and performance are shown herein, in addition to a more detailed discussion of light-dependent approaches in these photobioreactors. In addition, we present the principal advances in photobioreactor projects, focusing on lighting, through a patent-based analysis to map technological trends. Lastly, sustainability and economic issues in commercializing microalgae products were presented.

Modeling and Simulation of Photobioreactors with Computational Fluid Dynamics—A Comprehensive Review

Computational Fluid Dynamics (CFD) have been frequently applied to model the growth conditions in photobioreactors, which are affected in a complex way by multiple, interacting physical processes. We review common photobioreactor types and discuss the processes occurring therein as well as how these processes have been considered in previous CFD models. The analysis reveals that CFD models of photobioreactors do often not consider state-of-the-art modeling approaches. As a comprehensive photobioreactor model consists of several sub-models, we review the most relevant models for the simulation of fluid flows, light propagation, heat and mass transfer and growth kinetics as well as state-of-the-art models for turbulence and interphase forces, revealing their strength and deficiencies. In addition, we review the population balance equation, breakage and coalescence models and discretization methods since the predicted bubble size distribution critically depends on them. This comprehensive overview of the available models provides a unique toolbox for generating CFD models of photobioreactors. Directions future research should take are also discussed, mainly consisting of an extensive experimental validation of the single models for specific photobioreactor geometries, as well as more complete and sophisticated integrated models by virtue of the constant increase of the computational capacity.

Establishment of a simple method to evaluate mixing times in a plastic bag photobioreactor using image processing based on freeware tools

Objective

Accurate determination of the mixing time in bioreactors is essential for the optimization of the productivity of bioprocesses. The aim of this work was to develop a simple optical method to determine the mixing time in a photobioreactor. The image processing method should be based on freeware tools, should not require programming skills, and thus could be used in education within high schools and in early stages of undergraduate programs.

Results

An optical method has been established to analyze images from recorded videos of mixing experiments. The steps are: 1. Extraction of a sequence of images from the video file; 2. Cropping of the pictures; 3. Background removal; and 4. Image analysis and mixing time evaluation based on quantification of pixel-to-pixel heterogeneity within a given area of interest. The novel method was generally able to track the dependency between aeration rate and mixing time within the investigated photobioreactor. In direct comparison, a pearson correlation coefficient of rho = 0.99 was obtained. Gas flow rates between 10 L h⁻¹, and 300 L h⁻¹ resulted from mixing times of between 48 and 14 s, respectively. This technique is applicable without programming skills and can be used in education with inexperienced user groups.