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Go to Editorial ManagerThe 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.
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.
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.
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.