The effect of process interaction and response surface optimization of hydrogen‐rich syngas production by catalytic carbon dioxide (CO2) reforming of methane (CH4) was evaluated. The Box‐Behnken design was applied to investigate the influence of CH4 partial pressure, CO2 partial pressure, and temperature on the hydrogen yield. The analysis of variance indicated that temperature and CH4 partial pressure had the most significant impact on the hydrogen yield. Under optimum conditions a maximum hydrogen yield of 71.38 % was achieved. Model validation with the ideal conditions confirmed close agreement of the predicted hydrogen yields with experimental values.
Biomass-derived substrates such as bio-oil and glycerol are gaining wide acceptability as feedstocks to produce hydrogen using a steam reforming process. The wide acceptability can be attributed to a huge amount of glycerol and bio-oil obtained as by-products of biodiesel production and pyrolysis processes. Several parameters have been reported to affect the production of hydrogen by biomass steam reforming. This study investigates the effect of non-linear process parameters on the prediction of hydrogen production by biomass (bio-oil and glycerol) steam reforming using artificial neural network (ANN) modeling technique. Twenty different multilayer ANN model architectures were tested using datasets obtained from the bio-oil and glycerol steam reforming. Two algorithms namely Levenberg-Marquardt and Bayesian regularization were employed for the training of the ANNs. An optimized network configuration consisting of 3 input layer 14 hidden neurons, 1 output layer, and 3 input layer, 5 hidden neurons, and 1 output layer were obtained for the Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by bio-oil steam reforming. While an optimized network configuration consisting of 5 input nodes, 9 hidden neurons, 1 output node, and 5 input nodes, 8 hidden neurons, and 1 output node were obtained for Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by glycerol steam reforming. Based on the optimized network, the predicted hydrogen production from the bio-oil and glycerol steam agreed with the actual values with the coefficient of determination (R 2) > 0.9. A low mean square error of 3.024 × 10 −24 and 6.22 × 10 −15 for the optimized for Levenberg-Marquardt and Bayesian regularization-trained ANN, respectively. The neural network analyses of the two processes showed that reaction temperature and glycerol-to-water molar ratio were the most relevant factors that influenced the production of hydrogen by bio-oil and glycerol steam reforming, respectively. This study has demonstrated the robustness of the ANN as a technique for investigating the effect of non-linear process parameters on hydrogen production by bio-oil and glycerol steam reforming.
In this paper, the extract of Citrus aurantium (CA) was used as a green approach for the preparation of Fe3O4 nanoparticles. The green Fe3O4 (Fe3O4/CA) was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy analysis (EDX), Fourier-transform infrared (FTIR) spectroscopy, Brunauer–Emmett–Teller (BET) surface area measurement, and vibrating sample magnetometry (VSM). The synthesized Fe3O4/CA was used to remove methylene blue (MB) dye from an aqueous solution. A four-factor central composite design (CCD), combined with response surface modeling (RSM), was used to maximize the MB dye removal. The four independent variables, which were initial dye concentration (10–50 mg/L), solution pH (3–9), adsorbent dose (ranging from 200–1000 mg/L), and contact time (30–90 min), were used as inputs to the model of the perecentage dye removal. The results yielded by an analysis of variance (ANOVA) confirmed the high significance of the regression model. The predicted values of the MB dye removal were in agreement with the corresponding experimental values. Optimized conditions for the maximum MB dye removal (93.14%) by Fe3O4/CA were the initial dye concentration (10.02 mg/L), pH (8.98), adsorbent mass (997.99 mg/L), and contact time (43.71 min). The validity of the quadratic model was examined, and good agreement was found between the experimental and predicted values. Our findings demonstrated that green Fe3O4NPs is a good adsorbent for MB removal.
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