This research aims to develop an AI-powered digital twin for bioelectrochemical systems (BES) to optimize energy production, wastewater treatment, and bioremediation. By integrating microbial, electrochemical, and operational data, the digital twin will simulate and predict BES behavior using deep learning, hybrid modeling, and convolutional neural networks. Real-time updates and reinforcement learning will enable adaptive control and performance optimization, while explainable AI will clarify decision-making. The system will be scaled to pilot applications through industrial collaboration. (Miguel)
Publication(s):
- Gomez, M.E.P., Park, E., Zheng, Y., Bassi, A., and Liu, T. Exploring the Application of Artificial Intelligence for Bioelectrochemical Systems: A Review of Recent Research. Green Energy and Resources. , 2025, 3(3): 100141. DOI: 10.1016/j.gerr.2025.100141