Ensemble engineering and statistical modeling for parameter calibration towards optimal design of microbial fuel cells

Abstract Mathematical modeling is an important tool to investigate the performance of microbial fuel cell (MFC) towards its optimized design. To overcome the shortcoming of traditional MFC models, an ensemble model is developed through integrating both engineering model and statistical analytics for the extrapolation scenarios in this study. Such an ensemble model can reduce laboring effort in parameter calibration and require fewer measurement data to achieve comparable accuracy to traditional statistical model under both the normal and extreme operation regions. Based on different weight between current generation and organic removal efficiency, the ensemble model can give recommended input factor settings to achieve the best current generation and organic removal efficiency. The model predicts a set of optimal design factors for the present tubular MFCs including the anode flow rate of 3.47 mL min −1 , organic concentration of 0.71 g L −1 , and catholyte pumping flow rate of 14.74 mL min −1 to achieve the peak current at 39.2 mA. To maintain 100% organic removal efficiency, the anode flow rate and organic concentration should be controlled lower than 1.04 mL min −1 and 0.22 g L −1 , respectively. The developed ensemble model can be potentially modified to model other types of MFCs or bioelectrochemical systems.