Effective Prediction of Monthly Heat Transfer Characteristics in a Thin-Layer Cascade Reactor Subjected to Outdoor Conditions

Abstract

Algal biofuels are intriguingly a renewable energy source that could partially substitute fossil fuels, but further research is required to optimise the growth parameters and establish competitive large-scale cultivation systems. Algal growth is directly dependent on momentum, heat, and mass transfer within the photobioreactor and environmental conditions. Therefore, in this computational study, the heat transfer between the thin-layer cascade (TLC) reactor and its surrounding was reported based on static (location and reactor geometry) and dynamic (air temperature, solar irradiance, and wind velocity) parameters. The resulting model was validated using experimental data. The Nusselt number and the monthly average water temperature were computed to investigate the heat transfer phenomena between the TLC reactor and atmosphere. In addition, a novel corelation was used to estimate the evaporative losses from the TLC reactor. The effect of geometric properties (inclination angle of the reactor, water depth, and channel width) was evaluated on heat transfer. Results showed that heat transfer rate and the optimum water temperature for algal growth were significantly affected by hydrodynamics, environmental conditions, and reactor design. Water temperature decreased with the increase in channel width, water depth, and slope angle of the reactor. Furthermore, algal productivity declined with the increase in the amount of evaporated water.