Articles in This Issue
Abstract
The growing demand for energy, coupled with the continued dominance of fossil fuels as the primary energy source, necessitates eco-friendly technologies that simultaneously enhance oil recovery (EOR) and reduce the impact of their emissions. Only one task, which is the CO2-EOR project, can combine these two sustainable development goals. Further, employing green nanotechnology, including nanoparticles and nanofluids, ensures a sustainable approach to controlling and enhancing rock wettability, thereby enhancing hydrocarbon production and carbon storage. However, the performance of nanofluids in subsurface formations is limited by the stability of these nano-dispersions at the harsh conditions of reservoirs. This work thus synthesizes silica nanoparticles from waste bentonite as a green source and modifies the surface properties with a silane group to formulate a stable nanofluid for subsurface applications. The produced nanoparticles were characterized via Fourier Transform Infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), zetasizer, and dynamic light scattering (DLS). Moreover, the efficiency of nanoparticles as wettability-modifying agents was studied using contact angle and spontaneous imbibition tests. FTIR measurements confirmed the presence of silane on the surface of hybrid silica nanoparticles, as indicated within the Wavenumber 2950 cm-1. Moreover, XRD measurements revealed that hybrid nanoparticles showed lower noise than pure ones. Results also showed that silane-treated nanoparticles (hybrid) are more tolerant to high salinity (≥ 0.5wt% brine), and green-synthesized nanoparticles have a drastic ability to invert the wettability of oil-wet surfaces (θ≥123°) to water-wet (θ ≤ 28°) at ambient conditions and also reduce the contact angle from 175° to 68°) at CO2-EOR conditions. The study concludes that these green nanofluids are highly efficient for EOR and carbon geosequestration projects when properly formulated.
Abstract
The objective of the current study is to determine the accuracy of a computational model that has been developed to simulate polyurethane foaming reactions by comparing its results with experimental findings on the system using both physical and chemical blowing agents. There was high concordance between the model outputs and the laboratory results in regard to the temporal development of reaction temperature as well as the resulting foam density, both of which were highly faithful recreations. The discussion provided further information about the optimization of the performance of cyclohexane, particularly when used in synergy with chemically active blowing agents, which speed up foaming. Besides, the polymerization dynamics were contained in the simulation, thus providing rich information on the structural changes that occur during the foaming process. Taken together, the results present a strong basis for the process performance optimization, as well as the predictive modeling of the blowing agent behavior. In the future, it will involve expanding the simulation model to include a wider range of agents, reaction mechanisms, and kinetics.
Abstract
In this study, we deposited the Al2O3 nanostructure on 304 stainless steel using PLD technique. The stainless-steel specimens were successfully coated with Al2O3 nanostructure, and surface morphology was examined using an optical microscope. The findings confirmed the nanostructured nature of the films. Tafel curve analysis was used to determine the polarization of the sample before being subjected to laser shock penning treatment. Additional tests were then carried out on the samples following pulse laser deposition.
Abstract
Federated learning (FL) offers a robust and privacy-preserving approach for developing collaborative intrusion detection systems (IDS). However, statistical variance severely hinders its practical application. Although privacy-preserving federated learning models have been used to develop intrusion detection systems for cyberattacks, problems arise when statistical variance is present. In practice, the performance of the FedAvg algorithm is significantly affected by the heterogeneous distribution of customer data in a real-world network. This distribution causes skewness among customer data, resulting in poor detection accuracy, delayed convergence, and model instability. In this paper, presents conduct a comprehensive comparison of the Scaffold algorithm with the FedAvg baseline using the CICIDS2017 datasets. Because the Scaffold algorithm addresses the client skew problem using control variables, it is considered a state-of-the-art federated optimization technique under the heterogeneous partitioning approach. This paper documents the importance of using the Scaffold algorithm as a reliable and essential tool for building high-performance detection systems in a variety of scientific settings. Therefore, our results demonstrate that Scaffold achieved more stable convergence and outperformed FedAvg, with a 15.1% increase in F1-score and a 13.6% higher overall accuracy under highly skewed data distributions. The present evaluation process operates through simulation testing, but physical testbed implementation remains essential for future work to evaluate real-world deployment challenges.
Abstract
The conventional open sun drying is not efficient, it is slow and contaminated and there is a necessity to develop highly advanced technologies in solar drying. The review looks critically at solar dryers that are improved with concentrator, optical, thermal energy storage (TES) or phase-change materials (PCM). The incorporation of parabolic trough or compound parabolic concentrators leads to a high temperature of over 100-115 oC and a thermal efficiency of up to 88 %. Reflective walls are also made to enhance optical capturing by up to 37.6 %, and shorten drying time by 15-20 %. TES/PCM systems increase the operation of TES systems beyond the sunset, nano-enhanced PCMs reduce drying time by 40% and enhance thermal efficiency by more than 48%. These systems demonstrate short payback periods (0.43-5.14 years) with regard to economics. They minimise the emission of CO2 by 2-44 tons/ lifetime of systems. These combined technologies have addressed intermittency and low efficiency and enabled solar drying to be a reliable and cost-effective and sustainable solution, as the UN Sustainable Development Goals of clean energy and climate action suggest.
Abstract
In this research paper, it has been studied the influence of the temperature of the cell on the performance and behavior of two types of modules, which are mono-crystalline silicon (mc-Si) and poly-crystalline silicon (pc-Si) solar modules. The experimental work has been achieved under the outdoor conditions, where the range of cell temperature is between 20 and 60 °C. It was applied three different values of solar radiation [500, 750, and 1000W/m2 (standard condition, where cell temperature of 25 °C, solar irradiance of 1000 W/m², and air mass AM 1.5)]. All tests are achieved under the Iraqi weather conditions in the city of Baghdad city. It was computed the temperature coefficients for each module and during any time during the experiment. It was found that the open circuit voltage decreased with -0.0912 V/ºC and -0.07 V/ºC when using the pc-Si module and mc-Si, respectively. While, the short circuit current increased slightly with 4.4 mA/ºC and 0.3 mA/ºC corresponding to the pc-Si and mc-Si, respectively. Finally, the lowest drop in output power was found when using the pc-Si module (-0.0915 W/ ºC), and the highest drop when using the mc-Si module (-0.1353 W/ ºC).
Abstract
ABSTRACT This paper proposes a low CAPEX selective blending strategy to upgrade regular gasoline quality in Diwaniyah Refinery. It tests the hypothesis that segregating heavy naphtha from the gasoline pool and blending light naphtha only with imported high octane gasoline can increase octane number (RON) and reduce sulfur content while decreasing import requirements. Four volumetric cases were evaluated: the refinery’s current practice (72 vol% imported gasoline + 28 vol% mixed naphtha) and three alternatives replacing mixed naphtha with light naphtha at 72/28, 67/33, and 62/38 vol%. Blends were prepared at ambient conditions and characterized using ASTM D2699 (RON) and ASTM D5453 (sulfur content). Replacing mixed naphtha with light naphtha at the same import ratio increased RON from 82.5 to 84.5 and reduced sulfur content from 157 to 70 ppm. Further reductions in imported high octane gasoline to 67 and 62 vol% maintained sulfur content below 100 ppm (77 and 87 ppm), with RON values of 83.5 and 80.5, respectively. These results were confirmed by Aspen Hysys simulation and ANOVA, indicating that heavy naphtha exerts the strongest negative effect on quality of regular gasoline. The proposed segregation requires only modifications to pipeline routes, enabling improved fuel quality and compliance with sulfur standards while reducing the need for imported gasoline in smaller refineries.
Abstract
This paper reviews the developments of modeling hydraulic fracturing in tight gas formations, progressing from elementary analytical models to more advanced and coupled geomechanical-flow simulators. We discuss the significant progress that has been made in understanding fluid flow behavior of ultra-low permeability formations, which has significantly improved methodology for analyzing this complex problem. Findings demonstrate the importance of using Discrete Fracture Network (DFN) and Embedded Discrete Fracture Model (EDFM) for representation of complex fracture geometries and connectivity. However, it remains a great challenge to model the stress-dependent changes in permeability and porosity and the dynamic changes of fracture properties during fracturing, as well as the multi-scale interactions between induced hydraulic fractures and natural ones. This paper provides a novel iterative modeling framework that integrates multi-scale interactions and proposes a roadmap for data-driven modeling coupled with fluid flow to enhance predictive accuracy in TGR stimulation.