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.

This study reports a novel hybrid model for the prediction of six critical process variables of importance in an industrial-scale FCC (fluid catalytic cracking) riser reactor: vacuum gas oil (VGO) conversion, outlet riser temperature, light cycle oil (LCO), gasoline, light gases, and coke yields. The proposed model is developed via the integration of a computational particle-fluid dynamics (CPFD) methodology with artificial intelligence (AI). The adopted methodology solves the first principle model (FPM) equations numerically using the CPFD Barracuda Virtual Reactor 22.0^® software. Based on 216 of these CPFD simulations, the performance of an industrial-scale FCC riser reactor unit was assessed using VGO catalytic cracking kinetics developed at CREC-UWO. The dataset obtained with CPFD is employed for the training and testing of a machine learning (ML) algorithm. This algorithm is based on a multiple output feedforward neural network (FNN) selected to allow one to establish correlations between the riser reactor feeding conditions and its outcoming parameters, with a 0.83 averaged regression coefficient and an overall RMSE of 1.93 being obtained. This research underscores the value of integrating CPFD simulations with ML to optimize industrial processes and enhance their predictive accuracy, offering significant advancements in FCC riser reactor unit operations.

This study evaluates the impact of the catalyst-to-oil (C/O) ratio in the 1 to 7 range on the catalytic cracking of vacuum gas oil (VGO). Experiments are conducted using fluid catalytic cracking (FCC)-type catalysts, in a mini-fluidized bench-scale Riser Simulator reactor invented at the Chemical Reactor Engineering Centre (CREC), University of Western Ontario. The CREC Riser Simulator replicates FCC industrial operating conditions such as temperature, species partial pressure, and reaction times. The results indicate that increasing the C/O ratio above 5 slightly impacts VGO conversion, increases light gases yield, decreases light cycle oil (LCO) yield, and stabilizes gasoline yield. These findings align with temperature-programmed desorption (TPD) data, showing how the retention of a larger number of acid sites at a C/O of 7 boosts light gas production and reduces LCO selectivity. These elevated C/O ratios also lead to higher coke formation. The results reported together with future studies conducted by our research team on the impact of higher catalyst flows, larger potential catalyst attrition, higher catalyst loading in the cyclones, and excess heat generated in the catalyst regenerator unit, are of critical value for establishing the impact of C/O ratios in the overall FCC refinery operation.

Vortices play a critical role in the operation of VAWTs (Vertical-Axis Wind Turbines). In spite of this, most studies have approached these matters via the qualitative analysis of vortex shedding, and torque-extraction data. These approaches rely only on the visual observation of vortices that can lead to subjective interpretations. In our work, a 3D framework is employed to address this issue. On this basis, the present study establishes a relationship between vorticity, dynamic stall and turbine performance, by examining various locations along the span and the chord of the airfoil. To conduct this analysis, a 3D-CFD (Computational Fluid Dynamics) simulation of an H-Darrieus with a symmetrical NACA 0018, powered by 8 m/s winds, is considered. The CFD simulations are validated based on the agreement of calculated power coefficients, with those obtained from experimental data, reported in the technical literature, with deviations being lower than 4%. The simulation results for various TSRs (Tip Speed Ratios) report new findings concerning the critical stages of VAWT operation. This shows that there is a link between the maximum vorticity, the imminent vortex-separation condition and the dynamic stall, with this being a function of the various axial positions in the VAWT.