Optimizing Heat and Algal Biomass as Renewable Energy Sources in Building Façade Systems

Abstract

The growing demand for sustainable design strategies in buildings has underscored the potential of microalgae photobioreactors (PBRs) as a renewable source for heat and bioenergy generation. In cold climates, PBR modules integrated in double-skin façades (DSFs) can mitigate snow deposition impact and minimize adverse effects of heat loss to low ambient temperatures. This research presented a two-part study to develop a heat transfer model of DSFs and establish optimal chemical kinetics for PBRs functionality in the system. In the first study, hourly heat transfer analysis was performed to assess PBRs in terms of energy production, panels’ efficiency and tilt angle, DSF cavity width, and heat gain profiles in cellular and multistory typologies. For this purpose, shading analysis was carried out to evaluate the solar radiation received by the PBRs. The system’s productivity in terms of heat and bioenergy generation was maximized using exploratory and multi-objective optimization algorithms, including graphical and Pareto search methods. In the second study, the chemical model was experimentally validated by applying non-invasive biomass prediction methods using RGB image processing and Neural Networks to predict biomass production from solar energy. The chemical model additionally evaluated the impact of variable flow rate of fresh medium and optimized PBR integration into different façade surfaces, ranging from flat to folded and free-form geometries.

In terms of heat transfer in DSFs, PBRs in direct contact with building occupied spaces significantly reduced winter heat loss, achieving an average gain of 35.7 W/m2 compared to a loss of 82.1 W/m2 with conventional façades. It was further noticed that multistory DSFs were advantageous by generating 20.4% more thermal energy and 79.5% more biological energy than cellular DSFs. A developed Trigonometric Model for shading prediction was comparable to Polygon Clipping and Pixel Counting techniques by achieving an average error of 5.2% while significantly reducing simulation time by over 40%. Computationally, Neural Network – Aided algorithms substantially reduced optimization time from 17.2 hours to 6.5 minutes on average. In relation to chemical analysis, variable residence time of microalgae increased biomass generation by 28.8% and CO2 extraction by 10.8% in various façade geometries. Overall, this research established a comprehensive framework on the behavior of DSFs with integrated renewable sources that can alleviate the burden of climate change in the built environment.