In this study, fluid–structure interaction (FSI) modeling was applied for predicting the fluid flow in a specific peristaltic pump, composed of one metallic roller and a hyperelastic tube pumping a viscous Newtonian fluid. Hyperelastic material dynamics and turbulence flow dynamics were coupled in order to describe all the physics of the pump. The commercial finite element software ABAQUS 6.14 was used to investigate the performance of the pump with a 3D transient model. By using this model, it was possible to predict the von Mises stresses in the tube and flow fluctuations. The peristaltic pump generated high pressure and flow pulses due to the interaction between the roller and the tube. The squeezing and relaxing of the tube during the operative phase allowed the liquid to have a pulsatile behavior. Numerical simulation data results were compared with one cycle pressure measurement obtained from pump test loop data, and the maximum difference between real and simulated data was less than 5%. The applicability of FSI modeling for geometric optimization of pump housing was also discussed in order to prevent roller and hose parts pressure peaks. The model allowed to investigate the effect of pump design variations such as tube occlusion, tube diameter, and roller speed on the flow rate, flow fluctuations, and stress state in the tube.

In the current era of high consumption and increasing waste, many products that are believed to be unusable can find a new purpose in the market. For example, the grape peel waste resulting from the production of wine contains numerous bioactive compounds. In reality, grape peels are by-products of winemaking that can be conveniently reused in many different ways, including agronomic use and cosmetic industry applications. Moreover, the by-products can also be used in the energy field as biomass for the production of biogas or in food plants for the production of energy. In this article, to extract polyphenols, grape peels were processed via a cyclically pressurized extraction method known as rapid solid-liquid dynamic extraction (RSLDE), which does not require the use of any organic solvent or include heating or cooling processes that can cause the loss of substances of interest. To better understand the cyclically pressurized extraction process, a numerical simulation was performed to evaluate the exchange between the grape piece solid matrix and water during the extraction process. Furthermore, a finite element model was used to numerically determine the time-dependent concentration distribution at specific times.

Stevia rebaudiana Bertoni is a perennial shrub belonging to the Asteraceae family. The leaves contain a mixture of steviol glycosides with extraordinary sweetening properties, among which the most important are stevioside and rebaudioside A. These components have a high sweetening power, which is about 300 times that of sucrose, and a negligible calorie content. However, their extraction and purification are not easy. In this paper, the extraction technique under cyclic pressure, known as rapid solid-liquid dynamic extraction (RSLDE), was compared using a Naviglio extractor (NE) with conventional maceration. The aim was to identify an efficient and economically viable method for obtaining high amounts of steviol glycosides in a short time. Furthermore, a numerical model was set up for the solid-liquid extraction process of value-added compounds from natural sources. Several parameters must be evaluated in relation to the characteristics of the parts of the plant subjected to extraction. Therefore, since diffusion and osmosis are highly dependent on temperature, it is necessary to control the temperature of the extraction system. On the other hand, the final aim of this work was to provide a scientific and quantitative basis for RSLDE. Therefore, the results obtained from stevia extracts using the corresponding mathematical model allowed hypothesizing the application of this model to the extraction processes of other vegetable matrices.

In the present work, the kinetics of the extraction process from female inflorescences of Canapa sativa subsp. sativa var. sativa were studied, on the basis of determination of the content of cannabinoids: cannabidiolic acid (CBDA), Δ9-tetrahydrocannabinolic acid (THCA), cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), before and after decarboxylation in the oven, in order to evaluate the possible use of the hemp extract obtained in the food sector. Therefore, both conventional maceration (CM) and rapid solid-liquid dynamic extraction (RSLDE), also known as cyclically pressurized extraction (CPE), were carried out, using parts of the plant approximately of the same size. The alcoholic extracts thus obtained were analyzed by high-performance liquid chromatography (HPLC) in order to calculate the percentages of cannabinoids present in the inflorescences and thus be able to evaluate the degree of decarboxylation. Furthermore, the extracts were dried to calculate the percentage of solid material present in it, that was made mainly by cannabinoids. The amount of substance extracted from the inflorescences was about 10% (w/w), for both cases considered. Therefore, the extraction yield was the same in the two cases examined and the final qualities were almost identical. However, the extraction times were significantly different. In fact, the maceration of hemp inflorescences in ethyl alcohol was completed in no less than 24 h, while with the RSLDE the extraction was completed in only 4 h. Finally, for a better understanding of the extraction process with cyclically pressurized extraction, a numerical simulation was carried out which allowed to better evaluate the influence of extractive parameters.

Objective/Scope The definition of the locations of new wells in mature fields is a challenging problem especially in contexts of high geological complexity and low data reliability, when running fluid-flow simulations can be extremely difficult. For this reason, we develop an innovative Surrogate Reservoir Model, based on a data-driven process, which combines Machine Learning algorithms with spatial interpolation techniques. We call our approach WIZARD (acronym for: Well Infilling optimiZAtion through Regression and Data analytics). Methods/Procedures/Process WIZARD is a collection of different data-driven methods for the sake of definition of new infilling well locations on the basis of the expected cumulative oil productions (after a fixed target period) of unexploited areas of the reservoir. The first method, named COSMIC, is used to find a correlation between petrophysical well properties and well productivity through a regression algorithm. The second method, that we call WIZARDMAP, uses spatial interpolation methodologies like K-Nearest Neighbours to estimate input petrophysical well data far away the existing wells and the trained COSMIC model applied to these interpolated data to predict the expected cumulative oil productions in unexploited areas of the reservoir. Finally, predictions of WIZARMAP model are compared with the ones given by another method that we call WIZARDROC, that is a predictive model trained by using only the cumulative oil productions of the existing wells and their locations. Results/Observations/Conclusions WIZARD workflow is applied to a highly faulted and layered reservoir with around 460 wells and 40 years production history, for which petrophysical and production data are available. Validation tests have been performed to evaluate WIZARD accuracy to forecast expected cumulative oil productions for new wells. WIZARD geological and physical soundness have been confirmed by experienced reservoir geologists and engineers, clearly showing that the methodology is reliable and therefore can be used for t least qualitative location screening of new infilling wells. This is furtherly confirmed by the satisfying match between the oil cumulative predictions of WIZARDROC and WIZARDMAP that has been obtained, indicating that the model can be reasonably used for predictive purposes. Novel/Additive Information WIZARD is an innovative Reservoir Surrogate Model that, combining Machine Learning techniques with spatial interpolation methodologies, is able to provide key insights for reservoir management and development plan, by suggesting the most promising locations for the new infilling wells.