Bio-hydrogen production from crude glycerol: Optimisation through response surface methodology and artificial neural network approach

Abstract

Crude glycerol (CG) is the main by-product of biodiesel production from the transesterification of vegetable or animal oils. The CG's high energy content makes it an effective carbon source for bio-hydrogen production. However, the purity of CG derived from the transesterification process may vary due to impurities such as methanol, soap, salt, etc., which influence the hydrogen production yield. The present study investigated the impacts of initial glycerol, methanol, and soap concentration of CG on the hydrogen production by dark fermentation, and the effect of various factors was evaluated and optimised with the response surface methodology (RSM) and artificial neural network (ANN) approach to maximise the yield of hydrogen production. Both the results of these approaches showed good fitting. The correlation coefficient between actual and predicted values from RSM and ANN models is closer to ∼1, and the lower error shows the applicability of these models. A maximum of 17.67 mmol/L of hydrogen production was obtained via RSM with optimum methanol, soap and glycerol concentrations of 2.75 g/L, 6.07 g/L, and 20.45 g/L, respectively. Besides, the 3-D response surface graphs illustrated the suppressive impact of increased initial concentration of methanol and soap, along with higher initial concentrations of glycerol, on hydrogen production. Further, the outcome of the controlled experiment shows that the presence of impurities (methanol and soap) in glycerol reduces the cumulative hydrogen production by 5 %–17 % compared to the control for over five days.