Operation space design of microbial fuel cells combined anaerobic-anoxic-oxic process based on support vector regression inverse model

Microbial Fuel Cells (MFCs) can produce power at the same time of wastewater treatment, which is a new technique for environmental protection and new energy. An appropriate space design of operation variables is very important to improve the performance of MFC process. This paper presents a space design method based on data-driven model but not the traditional mechanism model, which is easy to accomplish in a fast and cost-effective mode. The support vector regression (SVR) forward and inverse model are deduced with the quadratic kernel function, in which the quadratic kernel function is suitable for the mathematical formula in the inversion stage. And the space design of operation variables are proposed to calculate directly from the inverse model with the effect of confidence interval when the model prediction uncertainty are considered. The proposed design method is verified in the real MFC-A2/O equipment. It is shown that the designated operation space is a narrow and effective region of the knowledge space which brackets the entire fraction of the MFC experiment space. And in general terms, the possible product quality from the designated operation space is more densely concentrated on the desired value compared to the tradition forward model design method.