Can artificial neural network and response surface methodology reliably predict hydrogen production and COD removal in an UASB bioreactor?

Abstract

Artificial Neural Network (ANN) and Response Surface Methodology (RSM) modeling was employed in an Upflow Anaerobic Sludge Blanket (UASB) bioreactor for optimization of hydrogen yield and COD (Chemical Oxygen Demand) removal efficiency. Experimental data were generated by running seventeen fermentation experiments at varying hydraulic retention times, immobilized cell volumes and temperatures. RSM and ANN models predicted similar optimum conditions for these process parameters. Upon validation, the prediction error for ANN and RSM was observed to be 2.22 and 9.64% on hydrogen yield and 1.01 and 6.34% on COD removal. These results suggested a greater accuracy and higher reliability of ANN in modeling and optimizing the bioprocess parameter interactions associated to the fermentation process. In addition, the study demonstrated a higher molar biohydrogen yield (0.90 mol-H2/mol glucose) and COD removal efficiency (84.81%) in the UASB system optimized by ANN modeling.