RESUMO
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used algorithm for exploratory clustering applications. Despite the DBSCAN algorithm being considered an unsupervised pattern recognition method, it has two parameters that must be tuned prior to the clustering process in order to reduce uncertainties, the minimum number of points in a clustering segmentation MinPts, and the radii around selected points from a specific dataset Eps. This article presents the performance of a clustering hybrid algorithm for automatically grouping datasets into a two-dimensional space using the well-known algorithm DBSCAN. Here, the function nearest neighbor and a genetic algorithm were used for the automation of parameters MinPts and Eps. Furthermore, the Factor Analysis (FA) method was defined for pre-processing through a dimensionality reduction of high-dimensional datasets with dimensions greater than two. Finally, the performance of the clustering algorithm called FA+GA-DBSCAN was evaluated using artificial datasets. In addition, the precision and Entropy of the clustering hybrid algorithm were measured, which showed there was less probability of error in clustering the most condensed datasets.
RESUMO
This work aims to select a photoreactor flow configuration and operational conditions that maximize the Photocatalytic Space-time Yield in a photoelectrocatalytic reactor to degrade Reactive Red 239 textile dye. A numerical study by Computational Fluid Dynamics (CFD) was carried out to model the phenomena of momentum and species transport and surface reaction kinetics. The photoreactor flow configuration was selected between axial (AF) and tangential (TF) inlet and outlet flow, and it was found that the TF configuration generated a higher Space-time Yield (STY) than the AF geometry in both laminar and turbulent regimes due to the formation of a helical movement of the fluid, which generates velocity in the circumferential and axial directions. In contrast, the AF geometry generates a purely axial flow. In addition, to maximize the Photocatalytic Space-time Yield (PSTY), it is necessary to use solar radiation as an external radiation source when the flow is turbulent. In conclusion, the PSTY can be maximized up to a value of 45 g/day-kW at an inlet velocity of 0.2 m/s (inlet Reynolds of 2830), solar radiation for external illumination, and internal illumination by UV-LEDs of 14 W/m2, using a photoreactor based on tangent inlet and outlet flow.
RESUMO
Photoelectrocatalysis has been highlighted as a tertiary wastewater treatment in the textile industry due to its high dye mineralisation capacity. However, design improvements are necessary to overcome photo-reactors limitations. The present work proposes a preliminary configuration of a photoelectrocatalytic reactor to degrade Reactive Red 239 (RR239) textile dye, using computational fluid dynamics (CFD) to analyse the mass transfer rate, radiation intensity loss (RIL), and its effect on kinetics degradation, over a photoelectrode based on a TiO2 nanotube. A study to increase the space-time yield (STY) was carried out through mass transfer rate and kinetic analysis, varying the optical thickness (δ) between the radiation entrance and the photocatalytic surface, photoelectrode geometry, inlet flow rate, and the surface radiation intensity. The RIL was determined using a 1D Beer-Lambert-based model, and an extinction coefficient experimentally determined by UV-Vis spectroscopy. The results show that in RR239 solutions below concentrations of 6 mg/L, a woven mesh photoelectrode and an optimal optical thickness δ of 1 cm is enough to keep the RIL below 15% and maximise the mass transfer and the STY in around 110 g/m3-day.