New empirical correlation for estimation of vaporization enthalpy of algerian saharan blend petroleum fractions

A Real-coded Genetic Algorithm has been used to develop a new correlation to estimate the enthalpy of vaporization for pure compounds and petroleum fractions as a function of the normal boiling point and specific gravity. In developing the correlation 80% of the data was used and the remaining are used for validation. The results of the proposed correlations are compared to others in literature. The comparison indicates that the proposed model is simple to use and more accurate than the most common correlations for predicting the enthalpy of vaporization of pure compounds and petroleum fractions.     

Flammability characteristics of pure hydrocarbons

A structural group contribution method is presented for predicting the flammability characteristics of pure hydrocarbon fluids. The method was used to probe the structural groups that have significant contribution to the overall flammability characteristics and arrive at the sets of groups that can best represent the auto-ignition temperature (AIT), the flash point, and the upper and lower flammability limits of about 500 different substances. The structural groups listed were derived from the Joback definition of group contributions and modified to account for the location of the structural groups in the molecule. The calculated values of the group contributions can predict the said flammability properties of pure components from the knowledge of only the molecular structure. The proposed method is very simple and can predict the flash point and the AIT with average percentage errors of 1.8% and 4.2%, respectively. The upper and lower flammability limits are predicted with average deviations of 1.25 and 0.04 vol%, respectively. The results are further compared to those of other published methods and are shown to be far more accurate.     

Estimating the Kinematic Viscosity of Petroleum Fractions

Kinematic viscosity correlation has been developed for liquid petroleum fractions at 37.78\(\,^{\circ }\hbox {C}\) and \(98.89\,^{\circ }\hbox {C}\) (100 and \(210^{\circ }\hbox {F})\) standard temperatures using a large variety of experimental data. The only required inputs are the specific gravity and the average boiling point temperature. The accuracy of the correlation was compared with several other correlations available in the literature. The proposed correlations proved to be more accurate in predicting the viscosity at 37.78\(\,^{\circ }\hbox {C}\) and \(98.89\,^{\circ }\hbox {C}\) with average absolute deviations of 0.39 and \(0.72\hbox { mm}^{2}/\hbox {s}\), respectively. Another objective was to develop a relation for the variation of viscosity with temperature to predict the viscosity of petroleum fraction at a certain temperature from the knowledge of the viscosity for the same liquid at two other temperatures. The newly developed correlation represents a wide array of temperatures from 20 \(^{\circ }\hbox {C}\) to 150 \(^{\circ }\hbox {C}\) and viscosities from 0.14\(\hbox { mm}^{2}/\hbox {s}\) to 343.64\(\hbox { mm}^{2}/\hbox {s}\). The results have been validated with experimental data consisting of 9558 data points, yielding an overall deviation of \(0.248\hbox { mm}^{2}/\hbox {s}\) and \(\hbox {R}^{2}\) of 0.998. In addition, new formulas were developed to interconvert the viscosity of petroleum fractions from one unit of measure to another based on finding the best fit for a set of experimental data from the literature with \(R^{2}\) as high as 1.0 for many cases. Detailed analysis showed good agreement between the predicted values and the experimental data.     

Conceptual framework for the security of mobile health applications on Android platform

Mobile Health (mHealth) applications are readily accessible to the average user of mobile devices, and despite the potential of mHealth applications to improve the availability, affordability and effectiveness of delivering healthcare services, they handle sensitive medical data, and as such, have also the potential to carry substantial risks to the security and privacy of their users. Developers of applications are usually unknown, and users are unaware of how their data are being managed and used. This is combined with the emergence of new threats due to the deficiency in mobile applications development or the design ambiguities of the current mobile operating systems. A number of mobile operating systems are available in the market, but the Android platform has gained the topmost popularity. However, Android security model is short of completely ensuring the privacy and security of users’ data, including the data of mHealth applications. Despite the security mechanisms provided by Android such as permissions and sandboxing, mHealth applications are still plagued by serious privacy and security issues. These security issues need to be addressed in order to improve the acceptance of mHealth applications among users and the efficacy of mHealth applications in the healthcare systems. Thus, this paper presents a conceptual framework to improve the security of medical data associated with Android mHealth applications, as well as to protect the privacy of their users. Based on the literature review that suggested the need for the intended security framework, three-distinct and successive phases are presented, each of which is described in a separate section. First, discussed the design process of the first phase to develop a security framework for mHealth apps to ensure the security and privacy of sensitive medical data. The second phase is discussed who to achieve the implementation of a prototypic proof-of-concept version of the framework. Finally, the third phase ending discussed the evaluation process in terms of effectiveness and efficiency for the proposed framework.     

Investigating the mechanism of microbiologically influenced corrosion of carbon steel using X-ray micro-computed tomography

Journal of Materials Science - The mechanism of pitting corrosion of carbon steel by an oilfield microbial consortium was investigated using a combination of X-ray micro-computed tomography...     

Comprehensive review and analysis of anti-malware apps for smartphones

Telecommunication Systems - The new and disruptive technologies for ensuring smartphone security are very limited and largely scattered. The available options and gaps in this research area must be...     

Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology

Neural Computing and Applications - This study aimed to develop a new methodology for evaluating and benchmarking a multi-agent learning neural network and Bayesian model for real-time skin...     

Rescuing emergency cases of COVID-19 patients: An intelligent real-time MSC transfusion framework based on multicriteria decision-making methods

Applied Intelligence - Mesenchymal stem cells (MSCs) have shown promising ability to treat critical cases of coronavirus disease 2019 (COVID-19) by regenerating lung cells and reducing immune...     

Grassroots petroleum refinery configuration for heavy oil processing

This paper presents a model-based optimization approach to design a grassroots petroleum refinery for heavy oil processing including residue treating and upgrading. The focus is to determine an optimal refinery configuration to produce light products from an atmospheric residue stream. The proposed linear model can consider a large number of heavy oil processing technologies (more than 70) including multiple operating modes to arrive at a configuration that meets an objective of maximizing profit or a certain fuel type production. Computational results show trends in agreement with real-world practice for residuum refining applications, thus contributing to substantiating practicality of the approach.     

X‐ray micro‐computed tomography analysis of accumulated corrosion products in deep‐water shipwrecks

Accumulated corrosion products from two different shipwrecks which had lain on the seabed (2.5 km depth) for 73 years were systematically analysed by three‐dimensional imaging at high resolution using X‐ray micro‐computed tomography. Complementary surface and chemical characterization experiments were conducted to identify the morphological structure of the corrosion products. Goethite was observed as the main corrosion phase found in both the wreck's corrosion products. However, other corrosion products such as silica, lepidocrocite, maghemite, magnetite, benyacarite, jarosite and amorphous materials were noticed using Fourier transform infrared spectroscopy, Raman spectroscopy and X‐ray diffraction. In addition, mineralized microbial structures were also observed as significant constituents of the corrosion products. However, there were significant differences between samples from the two shipwrecks including porosity, distribution and volume percent of the corrosion products components. The mechanism of different corrosion products formation was proposed and discussed in detail.