Use of artificial neural network for the prediction of bioelectricity production in a membrane less microbial fuel cell

Microbial fuel cells (MFCs) are the most recent bioelectrical devices which convert biodegradable organic matters to bioelectricity in presence of active biocatalyst. This system can generate electrons (e−) and protons (H+), in which electrons transfer from anode compartment to cathode chamber through an external circuit. MFC architect is one of important factor that effects on MFC performance. In this study, new membrane-less MFC was fabricated. Mixed culture of anaerobic microorganisms was collected from dairy wastewater effluents (Gella, Amol) as active biocatalysts in anode chamber. Initial open circuit voltage was less than 500 mV. Maximum open circuit voltage of 750 mV was achieved after 95 h of operation time. Maximum obtained power density was 80.12 mW/m2. Artificial neural network was applied for the prediction of bioelectricity production from glucose as electron donors. Fabricated network was presented by multilayer perceptron and had a good ability for prediction with high correlation coefficient (Raverage-ANN2 = 0.99).