In the relentless pursuit of sustainable energy solutions, the petroleum industry faces the imperative challenge of mitigating sulfur emissions. This comprehensive review scrutinizes Titanium Dioxide (TiO₂) as an extraordinary catalyst, pushing the boundaries of desulfurization performance in petroleum refining. The abstract begins by underscoring the urgent need for advanced desulfurization technologies, driven by stringent environmental mandates and escalating global energy demands. The spotlight then shifts to the unparalleled physicochemical attributes of TiO₂, showcasing its inherent advantages such as exceptional surface area, stability, and photocatalytic process. A profound exploration of TiO₂’s catalytic mechanisms follows, unraveling its capacity to disintegrate stubborn sulfur–carbon bonds, thereby elevating desulfurization efficiency to unprecedented levels. This review meticulously dissects diverse forms of TiO₂, ranging from nanoparticles to mesoporous structures, and provides a critical analysis of their respective strengths and limitations in catalyzing sulfur removal. Delving into operational nuances, this review examines the impact of temperature, pressure, and catalyst loading on TiO₂ performance, offering crucial insights for optimizing desulfurization processes. The narrative then unfolds to explore cutting-edge developments in TiO₂-based catalysts, encompassing ingenious modifications, composites, and hybrid materials designed to augment catalytic activity and selectivity. Anticipating the road ahead, this review contemplates the challenges and prospects of deploying TiO₂ on an industrial scale, pointing toward avenues for future research and development. This abstract encapsulates a wealth of knowledge, serving as an indispensable resource for researchers, engineers, and policymakers navigating the dynamic landscape of sustainable petroleum refining. TiO₂ emerges as a transformative force, propelling the industry toward cleaner, greener, and more efficient energy production.

Regarding photocatalytic oxidative desulphurization (PODS), titanium oxide (TiO₂) is a promising contender as a catalyst due to its photocatalytic prowess and long-term performance in desulphurization applications. This work demonstrates the effectiveness of double-doping TiO₂ in silver (Ag) and molybdenum (Mo) for use as a novel catalyst in the desulphurization of light-cut hydrocarbons. FESEM, EDS, and AFM were used to characterize the morphology, doping concentration, surface features, grain size, and grain surface area of the Ag/Mo powder. On the other hand, XRD, FTIR spectroscopy, UV-Vis, and PL were used for structure and functional group detection and light absorption analysis based on TiO₂’s illumination properties. The microscopic images revealed nanoparticles with irregular shapes, and a 3D-AFM image was used to determine the catalyst’s physiognomies: 0.612 nm roughness and a surface area of 811.79 m²/g. The average sizes of the grains and particles were calculated to be 32.15 and 344.4 nm, respectively. The XRD analysis revealed an anatase structure for the doped TiO₂, and the FTIR analysis exposed localized functional groups, while the absorption spectra of the catalyst, obtained via UV-Vis, revealed a broad spectrum, including visible and near-infrared regions up to 1053.34 nm. The PL analysis showed luminescence with a lower emission intensity, indicating that the charge carriers were not thoroughly combined. This study’s findings indicate a desulphurization efficiency of 97%. Additionally, the promise of a nano-homogeneous particle distribution bodes well for catalytic reactions. The catalyst retains its efficiency when it is dried and reused, demonstrating its sustainable use while maintaining the desulphurization efficacy. This study highlights the potential of the double doping approach in enhancing the catalytic properties of TiO₂, opening up new possibilities for improving the performance of photo-oxidative processes.

In this paper, the role of nickel-20 chromium and TiO₂ nanoparticles additions for enhancement of corrosion resistance and microhardness of organic zinc-rich coatings were investigated. Three coating samples were prepared by the new additions in different amounts that were experimentally predicted. Particle sizes and coatings micrographs were examined by scanning electron microscope (SEM) and chemical composition was analyzed by energy dispersive spectroscopy (EDS) technique. The corrosion behavior was conducted in 8% concentration HCl by potentiodynamic polarization test and corrosion parameters extracted through Tafel Extrapolation from polarization curves. Microhardness test employed for evaluation the hardness of all obtained coating samples. Results showed that diffusion and dispersion the added nanoparticles through zinc-rich coating (ZRC) improved the hardness of coating significantly. Furthermore, adding nickel-20 chromium nanoparticles to ZRC and incorporation of TiO₂ with it, has been shown an enhancement in corrosion resistance by a percentage of 88.7% and 99.8%, respectively.

The aim of this study is to employ deep learning models for microstructural image classification. The microstructures provide valuable insights into material's history and properties, but manual classification is time consuming, labor-intensive and requires expertise, as these images are particularly challenging and complicated. The research experiment involves evaluating three deep Convolutional Neural Networks (CNNs): AlexNet, GoogLeNet, and SqueezeNet. These models were trained and tested on five colored microscopic image datasets differs in classes number, image sizes and quantities. The datasets were obtained from experimental heat treatments conducted under different conditions on AISI 4140 low alloy steel specimens. The comparison focused on accuracy, elapsed time for training, and the impact of class numbers and characteristics on classification performance. Results demonstrated high accuracies ranging from 86.76% to 98.33%, with SqueezeNet showing superior performance for this task. Faster training intervals were recorded for the dataset with lower classes and quantity of images.

To increase cutting tool life and improve workpiece surface quality, an empirical model was proposed to predict workpiece surface roughness and cutting tool temperature by discovering an empirical equations that depends upon the experimental results of turning carbon steel. These empirical equations predict values; and describe the behaviour of workpiece surface roughness and cutting tool temperature. The experimental work involves turning carbon steel (ASTM Standard A105) by measuring workpiece surface roughness and cutting tool temperature at different cutting speed, feed rate, and depth of cut that consider the major cutting parameters. The results indicated that cutting speed and feed rate has a major effect on workpiece surface roughness and cutting tool temperature. Also, a comparison between experimental and predicted results was made, which show a good agreement, i.e the correlation coefficients reached to 0.9894 for surface roughness and 0.9943% for temperature.