Abstract:
This thesis presents the design, optimization, and experimental validation of a passive evaporative
cooling system for buildings, utilizing natural plant fibers, specifically cotton and sisal as eco-
friendly evaporative pads. The study aims to provide a sustainable, low-energy alternative to
conventional cooling technologies by combining traditional passive cooling principles with
modern analytical tools and artificial intelligence.
Two system configurations were evaluated: a basic prototype and an improved model featuring
enhanced thermal insulation, water recovery, and real-time performance monitoring. Experimental
tests were conducted under varying airflow rates and pad thicknesses to assess thermal
performance, cooling effectiveness, and water consumption. Cotton demonstrated superior water
retention and cooling performance, while sisal offered greater airflow permeability and structural
durability.
Numerical analysis using Python and psychrometric modeling enabled detailed evaluation of heat
and mass transfer within the system. Additionally, preliminary AI models based on Random Forest
regression were developed to predict system behavior under changing conditions, supporting the
feasibility of intelligent, adaptive cooling solutions.
The results confirm that material properties, pad geometry, and airflow significantly affect system
efficiency. The final prototype represents a low-cost, scalable solution for passive building
cooling, with potential for integration into smart building infrastructures. This work contributes to
the advancement of environmentally responsible cooling technologies and lays the groundwork
for future AI-driven passive systems.
