Application of computational fluid dynamics for modeling and designing photobioreactors for microalgae production: A review

Abstract

The past decade has seen a rapid increase of numerical simulation studies on photobioreactors (PBRs). Developments in computational fluid dynamics (CFD) and the availability of more powerful computers have paved the way for the modeling and designing PRBs, a strategy that was abandoned from the late 1970s until the 1980s because of its complexity. This paper reviews the present status of numerical modeling for PBRs as well the application of CFD in the design of PBR for the mass production of microalgae. Emphasis is on the major breakthroughs in PBR design that may lead to scaling-up. Most simulations have been conducted in bubble column PBRs, which offer many advantages. Their geometry is simple in design with no moving parts, and they are easy to construct and operate. A majority of published simulation studies used two-phase models (air and water) and employed the Eulerian–Eulerian mixture model. CFD models have been vigorously validated by experimental and laboratory studies, with most claiming to have achieved good results. However, current PBR scale-up projects still need to resolve hydrodynamic issues within the PBR, in order to optimize factors for microalgal growth. To create ideal conditions inside the PBR, the main factors that influence microalgal growth such as light intensity and distribution, gas injection and mixing, and hydrodynamics/flow pattern which are the key for design and scale up must be thoroughly understood. The present practice of PBR design using CFD can be considered both an art and a science because of some numerical simulation issues which are yet to be resolved and the complexity of fluid mechanics inside the PBRs. Nonetheless, CFD has proven to be an effective tool in predicting the complex inherent phenomena in the PBRs. The CFD technique has shown to be very promising to successfully design and develop PBRs which can be commercially available for scale-up.