Pyrolysis of de-oiled seed cake of Jatropha Curcas and catalytic steam reforming of pyrolytic bio-oil to hydrogen

Andrew, Renny; Santhosh, Viswanathan; Somkuwar, Nitin; Gokak, D.T.; Sharma, Pankaj Kumar; Bhargava, Sanjay

doi:10.1016/j.biortech.2016.08.052

Cited by 18 publications

(1 citation statement)

References 34 publications

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“…The analysis of the level of importance for hydrogen production by bio-oil steam reforming shows that the reaction temperature has the highest level of importance of 60% while the glycerol to water ratio has the highest level of importance for hydrogen production by glycerol steam reforming. Renny et al 44 reported the significance of reaction temperature for catalytic bio-oil steam reforming to hydrogen. The hydrogen yield was found to increase with an increase in the reaction temperature from 66.2% at 600 C to 82.5% at 850 C. This observation is also consistent with the study of Chen et al 45 who reported the significant influence of the reaction temperature of hydrogen yield from bio-oil steam reforming.…”

Section: Level Of Importance Of the Parameters On Hydrogen Productionmentioning

confidence: 99%

Modeling the effect of non‐linear process parameters on the prediction of hydrogen production by steam reforming of bio‐oil and glycerol using artificial neural network

2020

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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.

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Section: Level Of Importance Of the Parameters On Hydrogen Productionmentioning

confidence: 99%