<![CDATA[The interest in sustainable energy applications is driven by the desire to improve hybrid systems that can consume and simultaneously recover energy in a closed-loop situation. This research examines the possibility of an AI-based, self-reproducing fan that can recover and convert some of its own generated airflow, and convert that to usable electrical energy. Electric fans are inherently bound by their architecture to use their entire input energy for ventilation with no feedback for energy. However, the system here proposes a new fully integrated energy regeneration system by utilizing miniaturized axial turbines, or piezoelectrics, placed within the momentum of the airflow to utilize any remaining kinetic energy as usable electrical energy. The proposed research study utilizes deep reinforcement learning (DRL) and multi-objective approaches based on evolutionary algorithms (MOEA). The proposed DRL and MOEA utilize adaptable meta-level optimization and real-time optimization of its geometric arrangement and turbine geometric arrangement and energy routing. The study's computational fluid dynamics (CFD) models will be validated by utilizing AI-supported simulation environments, iterates through the design space for the various configurations that optimize net energy and axial turbine efficiency without sacrificing their airflow efficiency, and use exhaust volumetric flow rates from the CFD. Energy recovery ratio, effect on fan impact and system sustainability index will be the indicators of success to evaluate the study's sustainable and energy-efficient application. This research takes a significant step in the area of micro-scale regenerative energy systems and suggests an intelligent control system that can respond to changing usage conditions. The implications provide significant opportunities that support developing next-generation smart fans, autonomous operation ventilation systems, and low-power AIoT (Artificial Intelligence of Things) devices. This research is a significant first step in trying to re-engineer airflow systems not as passive consumers of energy, but as active participants in energy recycling, that can contribute to drive innovation for green engineering and intelligent systems.]]>
