New Paper by Miguel and Evan Published in GER

We’re thrilled to share that our group members, Miguel and Evan, have published a review article in Green Energy and Resources. This is a great recognition of their dedication and hard work. Well done!

Read the article here: https://www.sciencedirect.com/science/article/pii/S2949720525000281

Exploring the application of artificial intelligence for bioelectrochemical systems: A review of recent research

Highlights

  • AI optimizes BES for improved energy recovery and stability.
  • ML models enhance BES performance prediction and decision-making.
  • Data standardization and biological complexity remain key challenges in AI for BES.

Abstract

Bioelectrochemical systems (BES) offer promising solutions for sustainable energy production and wastewater treatment. However, their complex biological and electrochemical dynamics pose significant challenges for traditional modeling approaches. This review explores the recent advancements in applying artificial intelligence (AI) techniques to enhance the performance and scalability of BES technologies. We detailed the roles of machine learning (ML) algorithms, such as artificial neural networks (ANNs), support vector regression (SVR), and random forest regression (RFR), in predicting critical BES performance metrics. Additionally, we discussed metaheuristic optimization techniques that have improved system design and operational parameters, yielding significant gains in energy recovery and stability. The integration of real-time monitoring and adaptive control systems, powered by AI, is highlighted for its potential to dynamically adjust BES operations in response to fluctuating environmental conditions. Despite these advancements, challenges remain, particularly in data standardization and modeling biological complexity within BES. We outline current limitations and future directions, emphasizing the need for robust datasets, standardized methodologies, and advanced AI frameworks to further unlock the potential of AI-driven BES systems in achieving sustainable bioenergy solutions.
GER-GraphicalAbstract

    Keywords

    Bioelectrochemical systems
    Artificial intelligence
    Modeling
    Sustainable energy
    Wastewater treatment