Photobioreactor Modeling and Radiative Transfer Analysis for Engineering Purposes

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.

Limnospira indica PCC8005 growth in photobioreactor: model and simulation of the ISS and ground experiments

The Arthrospira-B experiment is the first experiment in space ever allowing the online measurements of both oxygen production rate and growth rate of Limnospira indica PCC8005 in batch photobioreactors running on-board ISS. Four bioreactors were integrated in the ISS Biolab facility. Each reactor was composed of two chambers (gas and liquid) separated by a PTFE membrane and was run in batch conditions. Oxygen production was monitored by online measurement of the total pressure increase in the gas chamber. The experiments are composed of several successive batch cultures for each reactor, performed in parallel on ISS and on ground. In this work, a model for the growth of the cyanobacterium Limnospira indica PCC8005 (also known as Arthrospira or spirulina) in these space membrane photobioreactors was proposed and the simulation results obtained are compared to the experimental results gathered in space and on ground. The photobioreactor model was based on a light transfer limitation model, already used to describe and predict the growth and oxygen production in small to large scale ground photobioreactors. It was completed by a model for pH prediction in the liquid phase allowing assessment of the pH increase associated to the bicarbonate consumption for the biomass growth. A membrane gas-liquid transfer model is used to predict the gas pressure increase in the gas chamber. Substrate limitation is considered in the biological model. A quite satisfactory fit was achieved between experimental and simulation results when a suitable mixing of the liquid phase was maintained. The data showed that microgravity has no first order effect on the oxygen production rate of Limnospira indica PCC8005 in a photobioreactor operating in space in zero gravity conditions.

Addressing nonlinearities in Monte Carlo

Monte Carlo is famous for accepting model extensions and model refinements up to infinite dimension. However, this powerful incremental design is based on a premise which has severely limited its application so far: a state-variable can only be recursively defined as a function of underlying state-variables if this function is linear. Here we show that this premise can be alleviated by projecting nonlinearities onto a polynomial basis and increasing the configuration space dimension. Considering phytoplankton growth in light-limited environments, radiative transfer in planetary atmospheres, electromagnetic scattering by particles, and concentrated solar power plant production, we prove the real-world usability of this advance in four test cases which were previously regarded as impracticable using Monte Carlo approaches. We also illustrate an outstanding feature of our method when applied to acute problems with interacting particles: handling rare events is now straightforward. Overall, our extension preserves the features that made the method popular: addressing nonlinearities does not compromise on model refinement or system complexity, and convergence rates remain independent of dimension.