Evaluating the predictive potential of RSM and ANN models in treatment of greywater-syrup mixture using Ekowe clay-PEM microbial fuel cell - Livinus A. Obasi; Cornelius O. Nevo (Academia Green Energy)
This study provides a comparative evaluation of the ability of response surface methodology (RSM) and artificial neural network (ANN) to predict the performance of microbial fuel cell (MFC) driven by greywater-syrup substrate system as anolyte with respect to power generation and wastewater treatment. Fourier transform infrared instrumental analysis of the substrate shows the functional groups of compounds present. A 24 central composite design and a three-layered (4:n:1) feedforward ANN architecture trained by a backpropagation algorithm were used to study and predict the MFC process performance criteria. The ANN gave the best prediction with n = 10 neurons. The response variables (power density generation (mW/m2) and chemical oxygen demand (COD) removal efficiency (%)) were measured against four process input variables: mass of the clay component of the proton exchange membrane (PEM) (g), PEM preparation temperature (PPT), anolyte pH, and concentration. Optimal responses with respect to power density and COD removal of 88.3 mW/m2 and 95.2% were recorded at the values of 70 g, 300°C, 8.5, and 66.9 v/v for mass of clay, PPT, pH, and anolyte concentration, respectively. The power density and COD removal predictive abilities of the ANN and RSM models were evaluated in terms of error functions: root mean square error (RMSE) (0.512; 0.0557), chi-square (0.0510; 0.1240), model predictive error (MPE) (0.3326; 0.3526), and coefficient of determination (R2) (0.9954; 0.9051) and RMSE (0.0272; 0.0707), chi-square (0.0280; 0.181), MPE (0.08242; 0.1569), and R2 (0.9932; 0.9245), respectively. These results indicate the superiority of the ANN in predicting the performance of the MFC over the RSM.