RESUMEN
In this study, oxidative desulfurization (ODS) of modeled and real oil samples was investigated using manganese-dioxide-supported, magnetic-reduced graphene oxide nanocomposite (MnO2/MrGO) as a catalyst in the presence of an H2O2/HCOOH oxidation system. MnO2/MrGO composite was synthesized and characterized by scanning electron microscope (SEM), energy dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) analyses. The optimal conditions for maximum removal of dibenzothiophene (DBT) from modeled oil samples were found to be efficient at 40 °C temperature, 60 min reaction time, 0.08 g catalyst dose/10 mL, and 2 mL of H2O2/formic acid, under which MnO2/MrGO exhibited intense desulfurization activity of up to 80%. Under the same set of conditions, the removal of only 41% DBT was observed in the presence of graphene oxide (GO) as the catalyst, which clearly indicated the advantage of MrGO in the composite catalyst. Under optimized conditions, sulfur removal in real oil samples, including diesel oil, gasoline, and kerosene, was found to be 67.8%, 59.5%, and 51.9%, respectively. The present approach is credited to cost-effectiveness, environmental benignity, and ease of preparation, envisioning great prospects for desulfurization of fuel oils on a commercial level.
RESUMEN
Multivariate data sets are common in various application areas, such as wireless sensor networks (WSNs) and DNA analysis. A robust mechanism is required to compute their similarity indexes regardless of the environment and problem domain. This study describes the usefulness of a non-metric-based approach (i.e., longest common subsequence) in computing similarity indexes. Several non-metric-based algorithms are available in the literature, the most robust and reliable one is the dynamic programming-based technique. However, dynamic programming-based techniques are considered inefficient, particularly in the context of multivariate data sets. Furthermore, the classical approaches are not powerful enough in scenarios with multivariate data sets, sensor data or when the similarity indexes are extremely high or low. To address this issue, we propose an efficient algorithm to measure the similarity indexes of multivariate data sets using a non-metric-based methodology. The proposed algorithm performs exceptionally well on numerous multivariate data sets compared with the classical dynamic programming-based algorithms. The performance of the algorithms is evaluated on the basis of several benchmark data sets and a dynamic multivariate data set, which is obtained from a WSN deployed in the Ghulam Ishaq Khan (GIK) Institute of Engineering Sciences and Technology. Our evaluation suggests that the proposed algorithm can be approximately 39.9% more efficient than its counterparts for various data sets in terms of computational time.
