Egyptian Journal of Petroleum最新文献

Iron oxide nanoparticles (IOXn) are unstable in aqueous solutions, which prevents their use in various applications. Because of this, it was proposed to treat the IOXn surface by employing a dispersant like sodium poly (naphthalene formaldehyde) sulfonate (PNS). In a single easy process, the (IPNS) were made by covering the IOXn with PNS. The material's structure, morphology, and magnetic properties were identified using XRD, TEM, Zeta potential, FTIR, HR-SEM, and a VSM. Due to its complex molecular structure and high anionic charge density, the polymeric dispersant (PNS) displays various characteristics. PNS successfully modifies the IOXn surface to create excellent colloidal electrostatic stability. At pH = 8, the zeta potential decreased from −13 to –23 mv. After the coating procedure, the crystalline structure changed into an amorphous one. The average particle size was decreased from 138 to 32 nm. In addition, there was a slight reduction in the magnetization saturation (Ms).

This study investigated the effectiveness of palm kernel leaves extract (PKLE) as green inhibitor using Central Composite Design (CCD). Phytochemical analysis was performed on the extract. Process variables used for the optimization in this study were: concentration of extract (0.5–1.5 g per litre), time (3–5 days), and temperature (30–50 °C) respectively. Surface characterization was done with Scanning Electron Microscope (SEM). Bioactive constituents were observed from the result of the phytochemical analysis. The best process levels were: inhibitor concentration (1.500 g/l), temperature (30 °C) and time (3 days) with inhibition efficiency of 96.74 %; while the optimal process level validated gave 97.20 %. The SEM results revealed that more film was observed on the validated optimal process level. The PKLE extract was an effective inhibitor.

The ongoing research focuses on preparing three compounds, based on oleic acid esters, through two steps to form 2-(3-acetylthioureido)-3-hydroxy-2-(hydroxymethyl) propyl oleate (Compound A), 2-(3-acetyl thioureido)-2-(hydroxymethyl) propane-1,3-diyl dioleate (Compound B) and 2-(3-acetyl thioureido)-2-[(oleoyloxy) methyl] propane-1,3-diyl dioleate (Compound C). The chemical structure of the prepared compounds has been distinguished and ascertained using different spectroscopic techniques as Fourier transform infrared spectroscopy (FTIR), proton and carbon nuclear magnetic resonance (¹H and ¹³C NMR), quantum chemical study “theoretical studies”. The efficacy of the prepared compounds was ultimately scrutinized as lubricating oils’ additives. It gives excellent results as detergent/ dispersant additives. Both the experimental and theoretical quantum chemistry studies of antioxidant compounds yield insufficiently effective findings. The strength of the produced compounds shows that the one with three oleic groups is most effective when compared to one and two groups. Additionally, the dispersancy increases as the percentage of the dispersants rises from 0.25 to 1.5 %. Keep in mind that as time passes, dispersancy increases more. C > B > A, correspondingly, is the order in which the dispersancy percent decreases.

The impact of seismic vibrational amplitude and frequency in dislodging an entrapped diesel globule in a pore model is experimentally studied. The effective change occurring inside a 3D printed pore model estimated from a systematic image analysis methodology is developed to represent the oil recovery. The pore model is subjected to sinusoidal vibrations with amplitudes ranging from 0.1 mm to 2 mm and frequencies ranging from 5 to 100 Hz. Dislodgment of entrapped diesel is observed for frequency values between 40 and 80 Hz with vibration amplitude above 0.5 mm. Even though increasing the vibration frequency increases the vibration-induced acceleration, the dislodgment of the entrapped fluid is not observed at higher frequencies. Smaller vibration amplitudes imparted by the shaker and a possible slip flow at the oscillating walls occurring at higher frequencies are reasoned for this observation.

One of the greatest challenges faced by industries today is corrosion and of which, one of the most vital forms is stress corrosion cracking (SCC). It brings highest forms of risks to the industry. Performing risk assessment of stress corrosion cracking is critical to ensure that industrial equipment failure is avoided by employing proper maintenance techniques. With the advancement of digital technology and the fourth industrial revolution called Industrial Internet of Things (IIOT), coupled with the availability of computing power and data, advanced analytical tools like artificial intelligence and machine learning bring powerful algorithms for performing advanced corrosion risk assessment. A perusal of the literature reveals that a review focused on the use of machine learning in corrosion risk assessment of stress corrosion cracking is scarce. So, a comprehensive and up-to-date review on this subject is timely. In this work review we present an overview on the machine learning application in the risk assessment of stress corrosion cracking. First, the current state of the art is briefly summarized. The fundamentals of machine learning algorithms and stress corrosion cracking were presented. Existing knowledge gaps were identified and discussed while the challenges and the future perspectives on the employ of machine learning in corrosion risks assessment of stress corrosion cracking were outlined.

The aggregation behavior and stability of silver nanoparticles (AgNs) are important considerations when exposed to various environments. AgNs have significant interests due to their unique properties. The cationic polymeric surfactants were investigated to coat or cape AgNs with a semi-permeable layer. Using Fourier Transform Infrared Spectroscopy and Gel permission chromatograph, the chemical structures were verified. Through the use of transmission electron microscopy, ultraviolet spectroscopy, particle size distribution, zeta potential, surface activities, and anti-bacterial activity, the produced cationic PUQOS and PUQLS surfactants containing silver nanoparticles were assessed. The results indicate that silver nanoparticles were stabilized in solution for seven days without aggregation using the prepared surfactants by coating their surfaces. The coating processes altered the zeta potential. The prepared polymeric surfactants with silver nanodispersion demonstrated practical activities against various standard G (+ve) and G (−ve) bacteria, yeast, and fungi.

Usage and functionalization of natural fiber in preparing “wooden-plastic composite (WPC)” is aimed, the research investigates the influence of treatment of “wooden flour” using “carbon nanotube (CNT)”, and applying U.V wreathing conditions on wooden flour-polyethylene (WF-PE) WPC. The novelty of this work is the dual functionalization of WF via hydrolysis and reaction with CNT, for better attachment with PE matrix. WF was firstly washed and then treated with CNT to obtain WWF and functionalized WWF-CNT, respectively. Proposed nanocomposites have been prepared using “extrusion” based on polyethylene filled with 50 wt% of wooden fillers (WF, WWF, WF-CNT and WWF-CNT). “Fourier transform infrared (FTIR)” charts and “transmission electron microscope (TEM)” photos indicated the reaction between functionalized CNT and treated fiber through esterification. The findings of “thermal gravimetric analysis (TGA)” indicate more residual ash and higher onset temperature for PE-WWF-CNT and PE-WF, respectively; the thermal stability of all WPCs was also increased after U.V exposure. Furthermore, “dynamic mechanical analysis (DMA)” shows significant improvement in modulus after treatment reaction, and changing in loss factor and thermal transitions. The WWF-CNT-containing WPC achieved higher moduli, even after U.V exposure, due to good interaction between PE matrix and CNT-functionalized fibers, which restricts the molecular mobility. The characterization sentence declares that composite filled with CNT-functionalized fibers is the most stable formula.

Predicting the variability and subsurface distribution of bituminous sand deposits rely significantly on integrating geophysical techniques such as electrical resistivity mapping with borehole information. While several studies have been conducted to characterize the bitumen-rich sequence of the sedimentary terrain of the study area, there are no or few studies documenting the subsurface architecture of bituminous sands in the transition zones in the study area. This study utilized the integration of 2D electrical resistivity mapping and borehole lithology logs to delineate and characterize bituminous sands in the study area to provide insights into the electrical resistivity signature of bituminous sand deposits within the transition zone in Ondo state, SW Nigeria. Two boreholes (BHI and BH2) and 2D electrical resistivity mapping using dipole–dipole array along five traverses were used for the investigation. A systematic and data-based approach was employed for the mapping of the deposits. The 2D electrical resistivity survey acquisition was carried out on traverses oriented north–south in the direction perpendicular to the sedimentary strike to effectively map the bituminous deposits. The results obtained from the approach employed indicated a maximum of three to four geoelectric layers delineated within the study area. This includes the topsoil, the upper X-horizon, mudstone, and lower Y-horizon. The upper/shallow bituminous sands in the X-horizon, as well as the deeper bituminous sands in the Y-horizon sands, are characterized by a very high resistivity range of about 1000 to about 100,000 Ωm. Findings from this study reveal that the resistivity of bituminous sands in the area is generally high in contrast to the low resistivity signature observed in the sedimentary parts of the area. The results obtained from this study would serve as a means to guide future exploration studies within transition zones in Eastern Dahomey Basin, SW Nigeria.

The present study focuses on using the adsorption character of the layered double hydroxide (LDH) for removal of aromatics species because of its simple production, suitable for ecology, cost less and easy to modify by intercalation organic molecules which cause change from hydrophilic to hydrophobic character and help in increase the removal percentage of mono- and di- aromatics. In this work, nanolayered structures (AlZn NLS) were prepared and characterized by different techniques but cannot remove aromatics. After intercalation by organic molecules (4-aminobenzoic acid, Salicylic acid, Adipic acid and n-capric acid in order), series of AlZn nanohybrids were prepared and showed significant removal of mono- and di-aromatics. The prepared nanohybrids based on n-capric acid showed significant results for removal of both mono-aromatics (33.5%) and di-aromatics (28.6%). The current study concluded that the nanolayered structures of AlZn became effective for removal aromatic species because of the expansion of the interlayered spacing of nanolayers and the increase of hydrophobic character.

Population balance equation is widely used in modeling of particulate processes. In this work, the enlargement of asphaltene particles in a synthetic heptane-toluene mixture (Heptol) was experimentally investigated in a static mode and then modeled by using PBE. Aggregation is considered as sole responsible mechanism for size evolution. Three solution methods including two discretization methods, modified Hounslow (MH) method and Kumar cell average technique (KCAT), and one moment method, quadrature method of moments (QMOM), are considered. The simulation results were compared with experimental data to evaluate each method. The results of simulations indicate that moment method offers higher accuracy and less computational expense respect to discretized methods in prediction of asphaltenes aggregation. In the case of considering fractal structure, both methods inaccurately predicted the number and average diameter of asphaltene aggregates.