Predictive model development and simulation of photobioreactors for algal biomass growth estimation

Abstract

Abstract In the current scenario of energy requirement and the commercialization aspect of algal biofuel and biomass, it is important that means of predicting the production be available. In this paper, the mathematical models are developed for the tubular, bubble column and airlift photobioreactors to predict the productivity of the algal biomass. A modified Monod kinetic equation, incorporating the effect of nutrient and CO2 concentrations, light availability and oxygen built-up, is used to the estimate specific growth rate of the biomass. The light availability inside the reactor is defined in terms of the modified Beer–Lambert’s law as a function of distance from the surface where light is incident and the cell mass concentration. This allows a more accurate measurement of the shading effect. The equations are solved for different reactor types and their estimated productivities are successfully validated against values available in published literature. The model predicts comparatively better productivity for the tubular reactor (1.5 g/L day) than the bubble column and airlift reactor (1.42 and 1.35 g/L day respectively) because tubular reactor has shorter light/dark cycles and better light availability. The analysis is also done to identify the effect of nutrient, carbon dioxide, light and hydrodynamics on the overall productivity.