In this study, an energy consumption model of a decanter centrifuge was proposed, in particular for a technologically evolved machine equipped with an electromechanical recovery system. This model should be suitably coupled with an auto-adaptive controlling technique used to accurately manage the olive oil process. To achieve this goal, a solid physical and theoretical basis that simple to implement is required. To date there have only been limited scientific studies modelling energy consumption applied to the machines used in olive oil extraction processes. Therefore, the model was developed using fluid dynamic analysis and physical constraints to give it a solid basis. It was then simplified sufficiently for future implementation in automatic machine systems. The empirical model was validated through power measurements conducted in two harvesting seasons under varying operating conditions. The model estimates the power absorbed by the bowl and that produced and recovered by the screw, with high accuracy in each harvesting season. When considering the two harvesting seasons as a single season, the prediction accuracy remains considerable, despite a marginal increase in errors (correlation coefficient greater than 0.90). Finally, the model indicates that the screw conveyor speed is the most important parameter to achieve the desired energy recovery level, while the differential speed, which is a process parameter, has only a negligible impact on energy saving.
The anaerobic digestion plant studied in this paper is one of the first full-scale plants using olive oil by-products. This is a two-stage plant with a power of 100 kWe. Two tests were performed: the first on olive pulp and pitted pomace and the second on biomass consisting of 10% crushed cereal. In both cycles, the retention time was 40 days. The production of biogas was between 51 and 52 m3/h, with limited fluctuations. The specific production values of biogas indicate that a volume of biogas greater than 1 m3/kg was produced in both tests. The produced biogas had a methane percentage of about 60% and the specific production (over total volatile solids, TVS) of methane was of the order of 0.70 m3methane/kgTVS. FOS/Alk (ratio between volatile organic acids and alkalinity) was always lower than 1 and tended to decrease in the second digester, indicating a stable methanogenic phase and the proper working of the methanogenic bacteria in the second reactor. The concentration of incoming biomass TPC (total polyphenols content) can vary significantly, due to the seasonality of production or inadequate storage conditions, but all measured values of TPC, between 1840 and 3040 mg gallic acid kg−1, are considered toxic both for acidogenic and methanogenic bacteria. By contrast, during the process the polyphenols decreased to the minimum value at the end of the acidogenic phase, biogas production did not stop, and the methane percentage was high.
Nanofiltration and reverse osmosis are used in the concentration of grape musts in winemaking. Both technologies offer an effective way to concentrate the grape musts, reducing the volume and the solids content to achieve desired characteristics in the final wine. The choice between nanofiltration and reverse osmosis depends on the specific needs of the winemaker and the desired characteristics. It is important to carefully consider the properties of the grape musts and the performance of the selected membranes to optimize the concentration process and ensure the desired outcome. Herein, we present a novel approach that allows us to choose a suitable membrane for an optimal industrial process for the concentration of musts, both in reverse osmosis and nanofiltration. The proposed method consists of combining the fitting equations of laboratory results with the balance equations on the industrial plant. Specifically, a full-scale plant has been designed and assembled with which grape musts of Trebbiano, Verdeca, Black Bombino, and White Bombino varieties have been concentrated through the selected best-performing membranes. Results of the proposed approach show that grape musts with sugar content commercially appreciated when the membranes work at high pressure can be obtained.
In extra virgin olive oil production, it is essential to obtain a well-prepared olive paste which allows not only the extraction of the oil drops from the olives, but also the achievement of a high-quality oil while maintaining high yields. This work addresses the problem of determining the effect of three crushing machines on the viscosity of the olive paste: a hammer crusher, a disk crusher and a de-stoner were tested. The tests were repeated on both the paste leaving each machine and the paste to which water was added; this was done with the main aim of considering the different dilutions of the paste while entering the decanter. A power law and the Zhang and Evans model were used to analyse the rheological behaviour of the paste. The experimental results allow validation of the two models with a high (more than 0.9) coefficient of determination between experimental and numerical data. The results also show that the pastes obtained with the two classic crushing methods (hammers and disks) are almost identical, with a packing factor of about 17.9% and 18.6%, respectively. Conversely, the paste obtained with the de-stoner entails higher viscosity values and a smaller solid packing factor, of about 2.8%. At 30% dilution with water, the volume of the solid concentration dropped to about 11.6% for the hammer and disc crushers, while for the de-stoner it only reached 1.8%. This behaviour is also reflected in the evaluation of yields, which were 6% lower with the de-stoner. No significant differences regarding the legal parameters of oil quality were found using the three different crushing systems. Finally, this paper establishes some fundamental pillars in the research for an optimal model for identifying the rheological behaviour of the paste as a function of the crusher used. Indeed, since there is an increasing need for automation in the oil extraction process, these models can be of great help in optimizing this process.
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