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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