This study introduces a Vorticity Index (VI) and an Imminent Vortex Separation Condition (IVSC), which are considered valuable indicators to quantify the vorticity impact on vertical axis wind turbines (VAWTs) operation. The VI and IVSC are specifically applied to a H-Darrieus vertical axis wind turbine (VAWT). Findings show that these two parameters display a direct relationship with the aerodynamic forces that govern the performance of this type of VAWT. This analysis is accomplished via 2D-CFD simulations of a H-Darrieus with a symmetrical NACA 0018, powered by high winds (8 and 20 m/s), by using a Shear Stress Transport SST k-ω model. The 2D model used is validated for Class II winds (8 m/s), for tip speed ratios (λ) ranging from 0.4 to 0.9. Power coefficients (C_p) predictions are close to those obtained with both 3D simulations and with experimental data, reported in the technical literature. It is found with the numerical simulations developed, that despite the significant increase of the average rotor overall torque values, when the wind speed is augmented from 8 m/s to 20 m/s, the energy extracted by the rotor seems to be moderately lessened by the amplified turbulence and vorticity.

Photocatalytic degradation of phenol under both UV radiation and visible light, using TiO ₂ (Degussa P-25) and TiO ₂ loaded with some transition metal ions (Co, Cu, Fe and Mo) was examined. From the series of metal loaded catalysts, Mo/TiO ₂ was the most efficient one. In the presence of Mo, neither TiO ₂ anatase/rutile fraction nor its pore size diameter has been affected. However, Mo made its surface more acidic. The percentage of phenol degradation reached under visible light was significantly lower than that under UV radiation due to the lower degree of light absorption by the catalyst surface. From the series of studied catalysts, 2 wt% Mo/TiO ₂ was the most efficient one. The synergetic effect between S _BET , mean pore size diameter, catalyst agglomerate size, band gap, ZPC and the type of Mo _x O _y species on TiO ₂ surface, depending on Mo loading, created its photocatalytic performance.

Blends of bitumen, clay, and quartz in water are obtained from the surface mining of the Athabasca Oil Sands. To facilitate its transportation through pipelines, this mixture is usually diluted with locally produced naphtha. As a result of this, naphtha has to be recovered later, in a naphtha recovery unit (NRU). The NRU process is a complex one and requires the knowledge of Vapour-Liquid-Liquid Equilibrium (VLLE) thermodynamics. The present study uses experimental data, obtained in a CREC-VL-Cell, and Artificial Intelligence (AI) for vapour-liquid-liquid equilibrium (VLLE) calculations. The proposed Artificial Neural Networks (ANNs) do not require prior knowledge of the number of vapour-liquid phases. These ANNs involve hyperparameters that are used to obtain the best ANN model architecture. To accomplish this, this study considers (a) R² Coefficients of Determination and (b) ANN training requirements to avoid data underfitting and overfitting. Results demonstrate that temperature has a major influence on ANN vapour pressure predictions, while the concentration of octane, the naphtha surrogate having, in contrast, a lesser effect. Furthermore, the ANN data obtained allows the calculation of octane-in-water and water-in-octane maximum solubilities.

The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis using the Gibbs Free Energy of mixing and the NRTL model, provide a good understanding with calculation issues, when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. To clarify these matters, n-octane and water blends, are good surrogates of naphtha/water mixtures. Runs were developed in a CREC vapor–liquid (VL_Cell operated with octane–water mixtures under dynamic conditions and used to establish the two-phase (liquid–vapor) and three phase (liquid–liquid–vapor) domains. Results obtained demonstrate that the two phase region (full solubility in the liquid phase) of n-octane in water at 100 °C is in the 10⁻⁴ mol fraction range, and it is larger than the 10⁻⁵ mol fraction predicted by Aspen Plus and the 10⁻⁷ mol fraction reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a machine learning (ML) technique was implemented and successfully demonstrated, in the present study.

Design guidelines were applied for the production of methyl acetate in a pervaporation membrane reactor. The limits of operation were determined. The shift in equilibrium is evaluated by a simple model involving simultaneously chemical equilibrium and transport across the membrane. This analysis establishes possibilities and limitations of a pervaporation membrane reactor. The performance of a continuous stirred tank reactor with a pervaporation membrane (PV-CSTR) is analyzed. To achieve conversions higher than 90%, conditions must satisfy D _a > 150 and 0.01 < P _e < 100. Increasing temperature has a negative effect on membrane reactor conversion. The effect of sweep is important at high-permeate pressures. Two design charts were created to illustrate dynamics between permeation rates, reaction rates, and selectivity with conversion. The three powerful tools proposed for the analysis of a pervaporation membrane reactor described the system in a systematic way.