A Novel Genetic Algorithm for Optimizing Microalgae Residence Time in Flat Panel Photobioreactors

Abstract

Microalgae photobioreactors often rely on proper adjustment of residence time, which is a key variable for maximizing thermal and bioenergy generation. Limiting the residence time to a constant value over long periods can reduce the overall system productivity. The aim of this study was to investigate the performance of a new operational strategy for flat panel photobioreactors when integrated into building façades. This was achieved by examining the impact of variable residence time on biomass production and carbon absorption. The study coupled a detailed chemical kinetics model with Genetic Algorithm in the presence of shading assessment. The chemical model validation showed an average error of 3.8%. Different selection techniques were examined for the employed Genetic Algorithm framework to decrease the computational cost. The feasibility of the optimized photobioreactor was further assessed based on reducing the operational carbon footprint when added to building façades. Results showed that the optimal hourly residence times varied between 0.91 days and 1.97 days with an average value of 1.05 days. This increased the daily biomass generation by 28.8%, from 13.9 g/m2 to 17.9 g/m2. Corresponding CO2 extraction was also increased from 31.4 g/m2 to 34.8 g/m2. On annual basis, integrating the photobioreactors into building façades decreased the operational carbon footprint by 10 kgCO2/m2, 50 kgCO2/m2, and 140 kgCO2/m2 for low-rise, mid-rise, and high-rise buildings, respectively.