Treatment of phenanthrene and benzene using microbial fuel cells operated continuously for possible in situ and ex situ applications

Bioelectrochemical systems could have potential for bioremediation of contaminants either in situ or ex situ. The treatment of a mixture of phenanthrene and benzene using two different tubular microbial fuel cells (MFCs) designed for either in situ and ex situ applications in aqueous systems was investigated over long operational periods (up to 155 days). For in situ deployments, simultaneous removal of the petroleum hydrocarbons (>90% in term of degradation efficiency) and bromate, used as catholyte, (up to 79%) with concomitant biogenic electricity generation (peak power density up to 6.75 mWm−2) were obtained at a hydraulic retention time (HRT) of 10 days. The tubular MFC could be operated successfully at copiotrophic (100 ppm phenanthrene, 2000 ppm benzene at HRT 30 days) and oligotrophic (phenanthrene and benzene, 50 ppb each, HRT 10 days) substrate conditions suggesting its effectiveness and robustness at extreme substrate concentrations in anoxic environments. In the MFC designed for ex situ deployments, optimum MFC performance was obtained at HRT of 30 h giving COD removal and maximum power output of approximately 77% and 6.75 mWm−2 respectively. The MFC exhibited the ability to resist organic shock loadings and could maintain stable MFC performance. Results of this study suggest the potential use of MFC technology for possible in situ/ex situ hydrocarbon-contaminated groundwater treatment or refinery effluents clean-up, even at extreme contaminant level conditions.

A Single-Frame Super-Resolution Innovative Approach

Super-resolution refers to the process of obtaining a high resolution image from one or more low resolution images. In this work, we present a novel method for the super-resolution problem for the limited case, where only one image of low resolution is given as an input. The proposed method is based on statistical learning for inferring the high frequencies regions which helps to distinguish a high resolution image from a low resolution one. These inferences are obtained from the correlation between regions of low and high resolution that come exclusively from the image to be super-resolved, in term of small neighborhoods. The Markov random fields are used as a model to capture the local statistics of high and low resolution data when they are analyzed at different scales and resolutions. Experimental results show the viability of the method.