Optimization of Bioelectrochemical Systems with Power of Artificial Intelligence

Bioelectrochemical systems (BESs) are sophisticated and advanced systems that utilize exoelectrogenic microbes to generate bioenergy. The integration of Artificial Intelligence (AI) plays a crucial role in comprehending, establishing connections, modeling, and predicting both microbial diversity and process parameters, ultimately enhancing the performance of BESs. This approach utilizes cutting-edge computational algorithms that are tailored to the specific architecture of BESs, saving time and improving efficiency compared to outdated manual methods. To achieve optimal outcomes, this study aims to examine and compare existing research endeavors while emphasizing the implementation of AI concepts in the field of bioelectrochemical systems. The AI techniques implemented to predict and optimize the behavior of BES are Artificial Neural Network (ANN), Fuzzy Logic (FL), Multi Gene Genetic Programming (MGGP), and Support Vector Regression (SVR).