No abstract

Olive cake is an important solid waste of the olive oil production. It still contains a certain quantity of oil that can be recovered by means of solvent extraction. In this study, two‐level full factorial design was performed to evaluate the effects of four variables and their interactions on the oil extraction by the ethanol 96.0% in a batch reactor. The variables included size of particles, temperature, and time of contact and solvent‐to‐solids ratio. The statistical analysis of the experimental data showed that the extracted oil mass depends on all the examined variables. It also depends on the interactions between size of particles and solvent‐to‐solid ratio and size of particles and temperature. The experimental data were in good agreement with those predicted by the model. PRACTICAL APPLICATIONS Olive cake is solid waste of the olive oil industry that is available in large amounts in many Mediterranean countries and at very low cost. It can be treated or valorized, enabling at the same time the solution to environmental problems caused by the olive oil production process. The economic interest that it presents is especially because of the residual oil that it contains and that can be recovered by solvent extraction. However, this solid–liquid extraction depends on several parameters: the ones inherent to the products (structure and properties of the sample, nature of extraction solvent); and the others to the extraction process (time of contact, temperature of extraction, solvent‐to‐solid ratio, stirring velocity). The experimental design method enables to determine the most important variables and their interaction in the extraction process at the same time performing a low number of experiments.

No abstract

No abstract

The main objective of this paper is to develop a predictive model of vertical wind speed profile. Response surface methodology (RSM) is used for this purpose. RSM is a set of statistical and mathematical techniques useful for the development, improvement and optimisation of processes. It is mainly used in industrial processes and is successfully applied in this paper to model the wind speed at the hub height of the wind turbine. An unconventional model is adopted due to the nature of the input parameters which cannot be controlled or modified. The model validation indicators, namely correlation coefficient ([Formula: see text]) and root mean square error (RMSE = 1.02), give excellent results when comparing predicted and measured wind speeds. For the same data, the RSM model gives a better RMSE compared to the conventional power law and the artificial neural network.