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

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. directly dependent on momentum, heat, mass transfer within photobioreactor environmental conditions. Therefore, in this computational study, heat between thin-layer cascade (TLC) reactor its surrounding was reported based static (location geometry) dynamic (air temperature, solar irradiance, wind velocity) parameters. The resulting model validated using experimental data. Nusselt number monthly average water temperature were computed investigate phenomena TLC atmosphere. In addition, novel corelation used estimate evaporative losses from reactor. effect of geometric properties (inclination angle reactor, depth, channel width) evaluated transfer. Results showed rate optimum for algal significantly affected by hydrodynamics, conditions, design. Water decreased with increase width, slope Furthermore, productivity declined amount evaporated water.