Stand-alone green twin for plant microbial fuel cell (PMFC) monitoring and optimization

Abstract

In the current context of energy transition and sustainability, the need for natural systems capable of generating and managing their own energy is increasingly relevant. This work presents the development of a Green Twin, a digital replica integrated with real-time monitoring systems, to optimize the potential energy generation in a Plant Microbial Fuel Cell (PMFC). The system correlates environmental parameters such as soil moisture, temperature and light intensity, with the bioelectric output generated by a plant and the microbial activity in its rhizosphere. By leveraging environmental sensors and temporal data analysis, the system can identify the optimal conditions in which the PMFC achieves stable and sustainable energy production. A predictive model is established through the analysis of parameter correlations, temporal trends and behavioral rules, enabling the system to anticipate environmental variations and proactively adjust conditions to enhance bioelectrical conversion efficiency. This study proposes a stand-alone implementation of a Green Twin using a Spider Plant (Chlorophytum comosum (Thunb.) Jacques), integrated with an Internet of Everything (IoE) framework to continuously monitor plant status and power generation metrics. The proposed system contributes to the advancement of self-sustaining potential energy generation technologies, with potential applications in agricultural, remote monitoring or ecosystem preservation and conservation, aligning with emerging trends in green technology and ecofriendly electronics.