Abstract
Three types of catalysts, i.e. Pt/γ-alumina (PGA), Ni/γ-alumina (NGA) and Ni–Pt/γ-alumina (NPGA), were prepared by incipient wetness impregnation (IWI) and deposited on a micro-reformer for hydrogen production by ethylene glycol steam reforming (EGSR). Multivariate polynomial regression (MPR) and genetic programming (GP) approaches were used to model the EGSR process based on experimental data. In these models, temperature and weight hourly space velocity (WHSV) as independent variables and ethylene glycol (EG) conversion, H2 selectivity, H2 yield and CO selectivity were considered as target functions. Based on the results, the GP model predicts objective functions with the highest prediction power and this model was selected as the optimal model. For example, for the NGA catalyst and the dependent variable of EG conversion, the values of correlation coefficient (R2) and root mean squared error (RMSE) were 0.9980 and 1.3191, respectively based on the GP model while for the best MPR (cubic) model; these parameters were 0.9735 and 4.2476, respectively. The results showed that the EG conversion values for the NPGA bimetallic catalyst were higher than for the PGA or NGA monometallic catalysts. The maximum values of EG conversion, H2 selectivity and H2 yield for all the catalysts were obtained at a temperature of 450 °C and at a WHSV of 80.8 h−1.
Published Version
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