Multitask Lasso Model for Investigating Multimodule Design Factors, Operational Factors, and Covariates in Tubular Microbial Fuel Cells

Microbial fuel cells (MFCs) are a promising technology for simultaneous wastewater treatment and direct electricity generation. To build an effective large-scale MFC in field operation, it is important to identify the critical design and operational factors. However, there are limited studies that focus on developing systematic models to predict MFC performance and conduct variable selection. Herein, a multitask Lasso model (MLM) was employed to characterize the relationship among the input variables (design factors, operational factors, and covariates) and the output variables (normalized energy recovery (NER) and organic removal efficiency) of a tubular MFC with five modules. The proposed MLM can not only select the important input variables to predict the MFC performance but also simultaneously and accurately predict multiple output variables. The important design and operational factors were identified on the basis of the variable selection in the MLM. It was found that cathode moisture, especially the catholyte pump frequency, was significant on NER of the tubular MFC but relatively insignificant on organic removal efficiency. The proposed MLM can be an effective tool to advance the knowledge of potential input variables affecting the MFC performance toward future scaling up.