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A data-driven Gaussian Process Regression (GPR) model is proposed to model the nonlinear relationship between operating conditions and output voltage in Microbial Fuel Cells (MFCs). The model is validated using both simulation and experimental datasets from two-chamber MFCs. The results show that the online GPR model provides a promising method for real-time optimization of MFCs.

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What they did

System
MFC

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Abstract

Abstract Rapidly and accurately modeling of microbial fuel cells (MFCs) plays an important role not only in thorough understanding of the effects of operating conditions on system performance, but also in the successful implementation of real‐time maximization of power output. Although the first principle electrochemical model has better generalization performance, it is often time‐consuming for model construction and is hard to real‐time application. In this study, a nonparametric Gaussian process regression (GPR) model is used to capture the nonlinear relationship between operating conditions and output voltage in the MFCs. A simple online learning strategy is proposed to recursively update the hyper‐parameters of the GPR model. The applicability and effectiveness of the proposed method is validated by both the simulation and experimental datasets from the acetate and the glucose and glutamic acid two‐chamber MFCs. The results illustrate that the online GPR model provides a promising method for capturing the complex nonlinearity phenomenon in MFCs, which can be greatly helpful for further real‐time optimization of MFCs.

Key findings

  • The GPR model captures the complex nonlinearity phenomenon in MFCs.
  • The online learning strategy allows for recursive update of hyper-parameters, enabling real-time application.
  • The proposed method is effective in both simulation and experimental datasets.

Keywords

Microbial fuel cellKrigingNonlinear systemGaussian processMaximizationGeneralization

Identifiers

DOI
10.1002/fuce.201500109
Journal
Fuel Cells
Year
2016