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The world is confronted with the twin crisis of fossil fuel depletion and environmental degradation caused by fossil fuel usage. Biodiesel produced from renewable feedstocks such as Jatropha seed oil or animal fats by transesterification offers a solution. Although biodiesel has been produced from various vegetable oils such as Jatropha seed oil, the reaction kinetics studies are very few in literature, hence the need for this study. Jatropha curcas seed oil was extracted and analyzed to determine its free fatty acid and fatty acid composition. The oil was transesterified with methanol at a molar ratio of methanol to oil 8:1, using 1% sodium hydroxide catalyst, at different temperatures ranging from 32oC to 65oC, at atmospheric pressure. The order of the reactions with respect to the triglyceride's disappearance in the forward reaction at the chosen temperatures was found to be pseudo-first-order and found to be first-order for the reaction at 32oC. The rate constants of the three consecutive reaction steps at 65oC, namely, triglyceride to diglyceride, diglyceride to monoglyceride, and monoglyceride to glycerol, were found to be 0.422 min-1 0.117 min-1, and 0.037min-1, respectively. Their corresponding activation energies in J/mol were 22.165, 3.136, and 19.770, respectively.

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BACKGROUND The advanced oxidation process using photocatalysts has been proven to be an efficient technique used for the degradation of organic pollutants in wastewater. However, there exists a nonlinear relationship between the process parameters of the photodegradation reaction, which needs to be well understood for the design of an efficient photoreactor. This study employed a backpropagation artificial neural network (BPANN) for the modelling of photocatalytic degradation of indole, anthraquinone dye and methyl blue using undoped and Ag+‐doped TiO2 catalysts. RESULTS A Levenberg–Marquardt algorithm was employed to train the BPANN by varying the hidden neurons to obtained an optimized architecture. Optimized architectures with 3‐14‐1, 4‐12‐1 and 3‐16‐1 consist of the input layers, hidden layer and the output layer, were obtained using the datasets from photodegradation of indole, anthraquinone dye and methyl blue, respectively. The optimized BPANN accurately predicts the indole, anthraquinone dye and methyl blue degradation as a function of colour removal from the wastewater. High coefficients of determination (R2) of 0.999, 0.961 and 0.993 were obtained for the prediction of the photodegradation of indole, anthraquinone dye and methyl blue, respectively, with over 95% confidence level. The study revealed that dye concentration, catalyst dosage and reaction time have the highest level of importance for the photodegradation of indole, anthraquinone dye and methyl blue, respectively. CONCLUSION This study has demonstrated the robustness of BPANN for predictive modelling of photodegradation of organic pollutants such as indole, anthraquinone dye and methyl blue. © 2020 Society of Chemical Industry

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Phenol is present as a highly toxic pollutant in wastewater, and it has a dangerous impact on the environment. In the present research, the phenol removal from wastewater has been achieved using four treatment methods in a bubble column reactor (treatment by ozone only, using packed bubble column reactor with ozone, utilizing ozone with TiO2 NPs catalyst in the reactor without packing, and employing ozone with TiO2 NPs in the presence of packing). The effects of phenol concentration, ozone dosage, TiO2 NPs additions, and contact time on the phenol removal efficiency were determined. It was found that at a contact time of 30 min, the phenol removal was 60.4, 74.9, 86.0, and 100% for the first, second, third, and fourth methods, respectively. The results indicated that the phenol degradation method using catalytic ozonation in a packed bubble column with TiO2 NPs is the best treatment method. This study demonstrated the advantages of using packing materials in a bubble column reactor to enhance the mass transfer process in an ozonation reaction and then increase the phenol removal efficiency. Also, the presence of TiO2 NPs as a catalyst improves the ozonation process via the production of hydroxyl routs. Additionally, the reaction kinetics of ozonation reaction manifested that the first order model is more applicable for the reaction. Eventually, the packed bubble column reactor in the presence of TiO2 NPs catalyst provided a highperformance removal of phenol with a high economic feasibility.

This study investigates the applicability of the Leven-Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H 2 production by methane dry reforming over a Co/Pr 2 O 3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH 4 partial pressure, CO 2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H 2 production. A neural network architecture of 3 13 2, 3 15 2, and 3 15 2 representing the input layer, hidden neuron layer, and target (output) layer were employed for the Leven-Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, respectively. The ANN training with each of the algorithms resulted in an accurate prediction of the rate of CO and H 2 production. The best prediction was, however, obtained using the Bayesian regularization algorithm with the lowest standard error of estimates (SEE). The high values of coefficient of determination (R 2 > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H 2 production were strongly correlated with the observed values.To overcome these challenges, several supported metal-based catalysts have been developed and tested. An extensive review by Abdullah et al. [10] revealed that supported nickel (Ni) catalysts have been mostly investigated for methane dry reforming due to its high catalytic performance. Nevertheless, the Ni-based catalysts are very prone to sintering and carbon deposition [11]. On the other hand, cobalt (Co)-based catalysts which have a comparative activity to Ni have been reported to show superior stability compare to Ni under the same process condition [12,13]. In our previous studies, the use of rare-earth metal oxide-supported Co catalysts for CO-rich hydrogen production showed considerable activity and stability [14][15][16]. However, one major challenge is understanding the kinetics of the methane dry reforming in terms of the rate of H 2 and CO production due to variations in the chemical composition of the various catalysts [17]. This challenge can be overcome by employing an artificial intelligence modeling approach for a better understanding of the process parameters [18,19]. Processes with non-linear and complex relationships between the input and the output parameters are often encountered in real life processes. The better understanding of the non-linear relationship between the input and the output parameters of the process can further be utilized to optimize the process operation and create the basis for the theoretical framework, process automation, and upscaling [20].An artificial intelligence modeling approach using an artificial neural network (ANN) has been widely employed for different catalytic processes, such as hydrodesulfurization [20], methanol steam reforming, glycerol steam reforming [2...