@article{scholars19984,
            note = {cited By 1},
          volume = {52},
            year = {2024},
             doi = {10.1016/j.ijhydene.2023.08.290},
         journal = {International Journal of Hydrogen Energy},
           title = {Predicting microbial fuel cell biofilm communities and power generation from wastewaters with artificial neural network},
           pages = {1052--1064},
          author = {Lim, C. E. and Chew, C. L. and Pan, G.-T. and Chong, S. and Arumugasamy, S. K. and Lim, J. W. and Al-Kahtani, A. A. and Ng, H.-S. and Abdurrahman, M.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170710612&doi=10.1016\%2fj.ijhydene.2023.08.290&partnerID=40&md5=e35e47fa3f5281ed297728e0c1d91d09},
        keywords = {Bacteria; Bacteriology; Biofilms; Forecasting; Machine learning; Microbial fuel cells; Physicochemical properties; Wastewater treatment, Artificial neural network modeling; Cell-based; Community generation; Conducting Complexes; Geobacter; Literature studies; Machine-learning; Physicochemical property; Power- generations; Sludge inoculums, Neural networks},
        abstract = {Artificial neural network (ANN) was used to predict the biofilm communities present in the microbial fuel cells (MFCs), as well as the power generation from wastewater treatment. The ANN model was able to predict the total abundances of seven exoelectrogenic bacteria-associated genera, viz. Anaeromyxobacter, Bacillus, Clostridium, Comamonas, Desulfuromonas, Geobacter, and Pseudomonas for the MFCs based on the physicochemical properties of the sludge inocula, with accuracies in the range of 62{\^a}?1/492. An additional ANN model was developed to integrate the biofilm results and predict the power generation from wastewater, with an accuracy of 84 when validating with literature studies. The results show that ANN is a useful tool for predicting the biofilm communities and power generation from MFCs, thus avoiding the necessity of conducting complex biofilm metagenome analysis, and greatly aiding future parametric investigation and scale-up studies. {\^A}{\copyright} 2023 Hydrogen Energy Publications LLC}
}