Wastewaters often contain toxic organic pollutants with a possible adverse effect on human health and aquatic life upon exposure. Persistent organic pollutants such as dyes and pesticides, pharmaceuticals, and other chemicals are gaining extensive attention. Water treatment utilizing photocatalysis has recently received a lot of interest. Photocatalysis is cutting-edge, alternative technology. It has various advantages, including functioning at normal temperatures and atmospheric pressure, cheap prices, no secondary waste creation, and being readily available and easily accessible. This review presented a comprehensive overview of the advances in the application of the photocatalytic process in the treatment of highly polluted industrial wastewater. The analysis of various literature revealed that TiO
2
-based photocatalysts are highly effective in degrading organic pollutants from wastewater compared to other forms of wastewater treatment technologies. The electrical structure of a semiconductor plays a vital role in the photocatalyst's mechanism. The morphology of a photocatalyst is determined by the synthesis method, chemical content, and technical characteristics. The scaled-up of the photoreactors will significantly help in curbing the effect of organic pollutants in wastewater.
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.
The operation of reforming catalysts in a fixed bed reactor undergoes a high level of interaction between the operating parameters and the reaction mechanism. Understanding such an interaction reduces the catalyst deactivation rate. In the present work, three kinds of nanocatalysts (i.e., Pt/HY, Pt-Zn/HY, and Pt-Rh/HY) were synthesized. The catalysts’ performances were evaluated for n-heptane reactions in the fixed bed reactor. The operating conditions applied were the following: 1 bar pressure, WHSV of 4, hydrogen/n-heptane ratio of 4, and the reaction temperatures of 425, 450, 475, 500, and 525 °C. The optimal reaction temperature for all three types of nanocatalysts to produce high-quality isomers and aromatic hydrocarbons was 500 °C. Accordingly, the nanocatalyst Pt-Zn/HY provided the highest catalytic selectivity for the desired hydrocarbons. Moreover, the Pt-Zn/HY-nanocatalyst showed more resistance against catalyst deactivation in comparison with the other two types of nanocatalysts (Pt/HY and Pt-Rh/HY). This work offers more understanding for the application of nanocatalysts in the reforming process in petroleum refineries with high performance and economic feasibility.
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