Detecting recalcitrant organic chemicals in water with microbial fuel cells and artificial neural networks

Abstract

This study integrates artificial neural network (ANN) processing with microbial fuel cell (MFC)-based biosensing in the detection of three organic pollutants: aldicarb, dimethyl-methylphosphonate (DMMP), and bisphenol-A (BPA). Overall, the use of the ANN proved to be more reliable than direct correlations for the determination of both chemical concentration and type. The ANN output matched the appropriate chemical concentration and type for three different concentrations and throughout a wide range of stepwise tests. Additionally, chemicals dissolved in the acetate-based feed medium (FM) were accurately identified by the ANN even though the acetate masked the pollutants' effects on electrical current. The ANN also accurately revealed the identity of chemical mixtures. This study is the first to incorporate ANN modeling with MFC-based biosensing for the detection and quantification of organic pollutants that are not readily biodegradable. Furthermore, this work provides insight into the flexibility of MFC-based biosensing as it pertains to limits of detection and its applicability to scenarios where mixtures of pollutants and unique solvents are involved. This research effort is expected to serve as a guide for future MFC-based biosensing efforts.