Arabian Journal for Science and Engineering最新文献

Excess production of water from oil and gas reservoirs is one of the major concerns in the hydrocarbon industry because of the handling of unwanted produced water uneconomically. Polymer gel treatment can be considered as one of the remedies to control excess water production. This review presents a detailed discussion on advancements in research for crosslinked polymer gel systems, covering their mechanisms, types, and field applications. Mainly in situ crosslinked gels, preformed particle gels, foamed gels, and nanoparticle-enhanced gels were examined with a focus on their stability and efficiency. The importance of these systems in profile modification and water shutoff was highlighted. Different factors affecting polymer gel systems like high temperature and high-salinity environments, types of accelerators, pH, polymer concentration, gel degradation, injectivity control, and types of nanoparticles were discussed in detail. New technologies, such as resin-based gels and hybrid crosslinked systems, were introduced. The kinetics of the polymer gel formation and its gelation time control mechanisms were also deliberated. The main focus of the review is on how feasible the modification of polymer gels can be in water shutoff over field application, permeability modification, and conformance control. A comparative analysis of various global projects utilizing polymer gel systems was conducted to assess their real-world applications.

This manuscript explores the dynamic field of retinal fundus image classification, harnessing diverse machine and deep learning (DL) techniques. It emphasizes the transformative potential of transformer-based architectures, originally designed for natural language processing, in reshaping image classification tasks. These architectures excel in capturing long-range dependencies within images, enhancing the comprehension of complex patterns. The research addresses the persistent challenge of limited training data by introducing innovative data augmentation strategies. A pioneering stacked augmentation approach, incorporating DL-based techniques, refines images at the pixel level, producing nuanced augmented counterparts. Notably, this approach systematically stacks augmented images along the third dimension, enhancing model accuracy while significantly reducing the sample size, expediting the training process. Additionally, the manuscript introduces the Meerkat optimizer, a cooperative multi-agent optimization technique, to enhance the classification accuracy of the squeeze-and-excitation network (SENet). Inspired by Meerkat social behavior, this optimization strategy navigates the solution space efficiently, leading to robust model configurations. Comparative evaluations with traditional optimization techniques validate the superior performance of Meerkat-optimized SENet. In a broader context, the study sheds light on the nuanced behaviors of various transformer networks in retinal fundus image classification, including pyramid vision transformer, bottleneck transformer, convolutional vision transformer, swin transformer, ViT, spatial transformer network (STNet), and SENet. Furthermore, an in-depth analysis of augmentation insights highlights consistent performance improvement across transformer networks when coupled with DL-based augmentation. SENet emerges as a standout performer, showcasing exceptional learning and generalization in diverse augmentation scenarios and datasets. The investigation into decision variables optimization for SENet through the Meerkat optimizer provides detailed insights into the network's behavior, including the selection of squeeze type ((S)), excitation operator ((E)), and reduction ratio ((r)), showcasing the adaptability and efficiency of the Meerkat optimization strategy.

This study examined, for the first time, the impact of excess magnesia (0–8 wt%) on phase formation and transformation in non-stoichiometric cordierite ceramics prepared through conventional reaction sintering of nano-powders of Al₂O₃, SiO₂, and MgO. Diffraction and thermal analysis methods were used to characterize the formed phases and their subsequent transformations. Activation energy (E_a) values for the formation of enstatite and cordierite were determined through non-isothermal analysis using the Kissinger equation. The density, coefficient of thermal expansion (CTE), and hardness of sintered samples were measured using a densimeter, dilatometer, and hardness tester, respectively. Additionally, the fracture surface of sintered specimens was characterized using a field emission scanning electron microscope (FE-SEM) coupled with energy dispersive spectroscopy (EDS). It was found that the temperatures at which enstatite and cordierite form increase with heating rate and decrease with the increase in excess magnesia. The formation of enstatite in sample (MAS0M) requires an activation energy of 655 kJ mol⁻¹. This energy increased to 748 and 698 kJ mol⁻¹ for samples MAS2M and MAS4M, and then decreased to 644 and 645 kJ mol⁻¹ for samples MAS8M and MAS8M. The formation of α-cordierite in sample MAS0M requires an activation energy of 684 kJ mol⁻¹. This energy increases to 869, 904, 950, and 894 kJ mol⁻¹ for samples MAS2M, MAS4M, MAS6M, and MAS8M. The prepared materials demonstrated similar phase transformations, ultimately resulting in the formation of α-cordierite single phase from the alumina-silica-magnesia powder mixture of stoichiometric composition. Cordierite, sapphirine, and enstatite were formed in the mixtures with an excess of magnesia. The bulk density of samples sintered at 1350 °C for 2 h increased from 2.58 to 2.88 g cm⁻³ as the excess magnesia content increased from 0 to 8 wt%, and the CTE also increased from 1.16 × 10^–6 to 2.53 × 10^–6 K⁻¹. The sample with 4 wt% excess magnesia exhibited the highest hardness of 10 GPa.

Artificial neural networks (ANNs) are becoming more popular because they can solve complex, nonlinear mathematical problems. The complicated domains of biotechnology, fluid dynamics, and cellular computation all find applications for artificial neural networks. This study uses machine learning techniques to analyze heat transfer and entropy production in unsteady ({text{TiO}}_{2}/text{EG}) nanofluid flow, taking into account nonlinear thermal radiation, Darcy–Forchheimer effects, and mass suction on a spinning sphere. This study examines aggregation and non-aggregation phenomena in terms of dynamic viscosity and thermal conductivity. Skin friction coefficient, heat transfer rate, entropy generation, velocity, and temperature-related nonlinear versions of classic mathematical problems may be solved using the bvp4c solver in MATLAB. Data selection, network creation, training, and performance evaluation using the mean square error metric are all part of the model's artificial neural network architecture. Tables and graphics demonstrate the impact of parameters on subjective profiles. The velocity profile in the x-direction increases with nanoparticle volume fraction (phi), unsteadiness (A), porous media permeability (K,) and magnetic parameter (M), but decreases with the Darcy–Forchheimer coefficient (delta) parameter. The z-direction velocity profile declines with increasing (phi , A, K, M) and (delta). As (A) grows, the temperature profile lowers, but bigger values of the radiation parameter, Biot number, Eckert number, and temperature ratio parameter increase. Skin friction in the x-direction increases with higher ((K, M, phi), and (S)) but decreases with (delta). Similarly, skin friction in the z-direction rises with higher values of (K, phi , M, delta .) As (Ec) increases, the local Nusselt number decreases and rises with (Bi, phi , Rd), and ({theta }_{w}.) As Brinkmann numbers and radiation parameters grow, entropy production increases, showing dual behavior for the magnetic parameter.

This study presents a novel integration of self-sufficient LNG regasification (LNG-R) with a cryogenic air separation unit (ASU), aimed at reducing overall energy demands and enhancing efficiency of the integrated plant. Through a rigorous parametric analysis and optimization using the Teaching–Learning Self-study Optimization (TLSO) algorithm, the integrated self-sufficient LNG-R and ASU system demonstrated significant reductions in energy consumption and reliance on external utilities. The proposed integrated system achieves a specific energy consumption of 0.0221 kWh/kg-Air, with a 0.765 Air/LNG ratio, separating 2754 kg/hr of air and yielding gaseous nitrogen and liquid oxygen with a purity of 99.9%. The process exhibits a coefficient of performance (COP) of 1.33 and an overall exergetic efficiency of 91.2%. This novel LNG-R and ASU integration enhances energy, exergy, and economic efficiency, offering a competitive strategy for optimizing LNG cold energy utilization.

The ferronickel industry requires energy for processing and emits large amounts of CO₂. The enormous energy requirement in the ferronickel industry is due to the low nickel content and high impurities in laterite nickel ore. Furthermore, the ferronickel industry’s high CO₂ emissions can be attributed to the need for reducing agents, such as coal, to reduce nickel and iron oxides. This research aims to study the reduction process of lateritic nickel ore, specifically when NH₃ gas is used as the reduction agent. Several process variables were studied, such as the effects of temperature and time. In addition, thermodynamic analysis was also carried out to determine the possibility of reactions that occurred during the reduction process. Many types of tests were also conducted, including X-ray diffractometer (XRD), inductively coupled plasma (ICP), and scanning electron microscope-energy dispersive X-ray (SEM–EDX) observations. Finally, the higher the temperature and the longer the time used, the higher the reduction degree obtained. It was also possible to obtain the ferronickel phase after the reduction process, which was predicted by thermodynamic calculations and confirmed by XRD. Other impurity minerals such as Fe₃O₄, SiO₂, (Fe, Mg)₂SiO₄, and MgSiO₃ were also found in the reduced product. The best reduction degree in this study was 77.8% when using a temperature of 900 °C and a duration of 120 min. Considering the successful reduction of nickel laterite ore using NH₃ gas, it is theoretically estimated that CO₂ can be reduced by about 22%.

Electrical Submersible Pumps (ESPs) are a critical component of artificial lift systems in the oil and gas industry, valued for their efficiency in high-volume production and adaptability to a wide range of reservoir conditions. However, their performance is often compromised by frequent failures arising from mechanical degradation, electrical faults, operational stresses, and harsh environmental factors. This review provides a comprehensive and systematic evaluation of ESP failure mechanisms and their root causes, with particular emphasis on diagnostic methodologies and predictive maintenance strategies. Recent advancements in machine learning such as XGBoost, Long Short-Term Memory (LSTM) networks, and Principal Component Analysis (PCA) are explored for their applicability in early fault detection and condition-based monitoring. The review also presents best practices in ESP installation, material selection, and real-time surveillance to enhance system reliability. Field-based case studies are included to illustrate the practical implementation of Root Cause Analysis (RCA) and predictive analytics, demonstrating substantial reductions in failure rates, extended pump run lives, and significant cost savings. The findings underscore the necessity of integrating advanced diagnostics and intelligent maintenance frameworks to ensure sustained ESP performance in increasingly complex and demanding production environments.

Supercapacitors can act as an instant energy source to quickly supply electric power to any connected system because they are energy storage devices with high power densities. This feature of the supercapacitor can be used to reduce power fluctuations and access latency in cache memories in the VLSI (very-large-scale integration) design of quickly operating artificial intelligence (AI) processors and memory chips. High-performance computing with the best data throughput is produced by this high-power stability and instantaneous power delivery. This article reviews various thin film micro-supercapacitor integration techniques, fabrication processes, and electrode and electrolyte materials, applicable for on-chip integration of micro-supercapacitors on AI processors and memory chips. This review extends the discussion of the thin film versions of supercapacitors in planar and vertically stacked configurations and their relative advantages in mediating ionic conduction. On-chip fabrication of micro-supercapacitor by laser micropatterning, laser surface roughening, carbonization by pyrolysis, laser-induced reduction, and carbon MEMS to scribe electrode patterns with different materials are reviewed. For the on-chip integration of micro-supercapacitors, this examines various fabrication techniques including photolithography, such as monolithic integration, heterogeneous integration, and 3D stacking. The synthesis and implementation of various carbon-based, transition metal oxide-based, and conducting polymer-based electrode materials in both their pure and composite forms are reviewed.

Several trials have used nanoparticles (NPs) to stabilize foam, improve carbon capture, utilization, and storage (CCUS), and enhanced oil recovery (EOR). Nonetheless, previous research has been unable to differentiate between hydrophilic and hydrophobic/modified NPs to determine their role in foam stabilization. This study explores the regulatory factors and mechanisms of NP-stabilized CO₂ foams and describes techniques for evaluating foam stability. The difficulties encountered, potential directions for future research, and limitations on applicability were all covered while describing how NPs stabilize foam. The efficacy of NP-stabilized foam is contingent upon the types of NPs, modifiers, temperature, salinity, and characteristics of the NPs. Both hydrophilic and hydrophobic NPs stabilize foam networks and enhance detachment energy. The synergistic effects of NPs on surfactants and lamellae are variable. NP foam can emulsify crude oil, enhance reservoir sweep efficiency, and infiltrate low-permeability pores by redirecting fluid. Optimization is necessary to identify the ideal modifier and production technique for modified NPs. Cost-effective, environmentally sustainable NPs may stabilize foam; nevertheless, further work is required to ascertain the control parameters of NP-stabilized foam.

Growing environmental concerns associated with fossil-based jet fuel have spurred intensive research into renewable alternatives for the green aviation industry. In this study, HZSM-5 catalysts impregnated with cobalt (Co) and/or molybdenum (Mo) were successfully synthesized via a simple and eco-friendly spray impregnation method, forming mono- and bimetallic catalysts. For the first time, these catalysts were evaluated in a double-bed reactor system using customized monolayer and bilayer configurations for the atmospheric hydrotreating of crude palm oil into bio-jet fuel. The incorporation of Co and/or Mo modified the physicochemical properties of HZSM-5, reducing its specific surface area and total pore volume while increasing the average pore diameter and total acidity. Among the configurations, the Mo/Z (top)–Co/Z (bottom) bilayer catalyst demonstrated superior performance, achieving a 45.75% liquid product conversion and a 44.97% bio-jet fuel yield with 99.14% selectivity at 350–450 °C. This arrangement effectively promoted hydrodeoxygenation and hydrocracking reactions, producing bio-jet fuel with a freezing point of − 52 °C after vacuum distillation, in compliance with ASTM aviation fuel standards. The catalyst maintained stable performance over five consecutive runs with minimal yield decline, underscoring its durability. Overall, this study presents a promising, cost-effective approach for sustainable aviation fuel production with strong potential for industrial-scale application.