Paper Conference
Proceedings of uSim Conference 2022: 3rd uSim Conference of IBPSA-Scotland
 
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INTEGRAL MODEL WITH MACHINE LEARNING FOR BALANCING HEAT TRANSFER AND ENERGY GENERATION IN ALGAL BIO-REACTIVE FAÇADES
Adham M. Elmalky, Mohamad T. Araji.Abstract: Microalgae bioreactors represent one renewable energy source that has heat generation capacity in addition to dynamic conversion of light to biomass all year round. If integrated into double-skin façades, the assembly can create building synergies by linking different systems for energy and heat distribution in cold climates. For this purpose, a new integral model was developed by coupling MATLAB ® and EnergyPlus™ to predict the biological energy harvest (E) with such a system on an hourly basis. The model examined the impact of design metrics of double-skin façades, including cavity width (W) and bioreactors tilt angle (β), on the received irradiation in different urban forms with variable densities and building heights. The output from shading analysis was fed to a heat transfer sub-model in order to balance convection, radiation, and energy generation. Results showed that bioreactors integrated into double-skin façades collected less irradiation by up to 53.1% compared to single-skin façades. However, such façades managed to cut down the heating requirements for the water in the microalgae medium by 60.4% (from 272.9 W/m to 108.2 W/m). Higher density contexts decreased E/Emax from 53.5% to 27.8%. This was compensated in high-rise buildings by restoring E/Emax to 63.9%. Using artificial neural networks, the mean error associated with shading factor estimation was 3.7%. The study concluded that E was maximum in low-density contexts with high-rise buildings having W=1.2 m and β=45°. Paper:usim2022_p114