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Most developed countries have environmental policies that promote the development and application of renewable energies, and among these is biodiesel. However, the optimization of the chemical reaction that is required to produce biodiesel, called transesterification, is a costly and time-consuming process that needs expensive reactants and laboratory equipment. In this context, an artificial neural network (ANN) model has been developed to simulate biodiesel production through the transesterification of used frying olive oil. Afterward, the model was validated with sets of experimental data obtained from the laboratory and that were not used during the training procedure. In this sense, simulated results were similar to those obtained with the help of the classical empirical tests required to perform the transesterification process in a laboratory, thus, indicating the simulated biodiesel yield function has properly reflected the real process. We can conclude that ANNs can be used to predict the biodiesel yield from used olive oil.

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This paper presents an adaptive scheme for gateway discovery in MANETs connected to the Internet. Basically, the proposed scheme reduces the flooding processes related to gateway discovery. To do so, the algorithm dynamically adjusts the interval of emission of the gateway advertisement messages to the need for updated routes to the gateway. The tuning is supported by the analysis of the spatial distribution of nodes in the MANET and by the impact that the relative position of nodes has on the route lifetimes. The simulations show that the proposed adaptive mechanism outperforms the conventional schemes: it decreases network saturation while it achieves lower end-to-end delay and it minimizes the routing overhead.

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