Prediction of water removal rate in a natural gas dehydration system using radial basis function neural network

Natural gas commonly contains water as a contaminant that can condense to water or form gas hydrates, which causes a range of problems during gas production, transportation, and processing. Therefore, the removal of gas moisture is of great importance. A common and popular method for removing water contamination from natural gas is using solid dehydrators. Calcium chloride is a nonregenerative desiccant to dehydrate natural gas. With continual water adsorption, CaCl₂ changes to consecutively higher states of hydration, finally producing a CaCl₂ brine solution. This method does not require heating or moving parts. In addition, it does not react with H₂S or CO₂. These features make this method a popular one for drying natural gas. Nevertheless, precise and simple methods are needed to predict the water content of natural gas dried by calcium chloride dehydrator units. In this study, an intelligent method, called the radial basis function neural network, was incorporated to predict the gas moisture dehydrated by calcium chloride in dehydration units. Modeling was performed under different conditions of a fresh recharge and before recharging. The overall correlation factor of 0.9999 for both the fresh charge and before charging conditions showed that the outputs of the proposed models were in agreement with the experimental data. In addition, the developed models were compared with the previously proposed intelligent models and classic correlations. The comparison showed that the developed model is superior to the previously proposed models and correlations regarding the accuracy of prediction.     

Histopathological graded liver lesions: what role does the IVIM analysis method have?

Magnetic Resonance Materials in Physics, Biology and Medicine - This study aims to investigate three different image processing methods on quantitative parameters of IVIM sequence, as well as...     

CP2: Cryptographic privacy protection framework for online social networks

In Online Social Networks (OSNs), users interact with each other by sharing their personal information. One of the concerns in OSNs is how user privacy is protected since the OSN providers have full control over users’ data. The OSN providers typically store users’ information permanently; the privacy controls embedded in OSNs offer few options to users for customizing and managing the dissipation of their data over the network. In this paper, we propose an efficient privacy protection framework for OSNs that can be used to protect the privacy of users’ data and their online social relationships from third parties. The recommended framework shifts the control over data sharing back to the users by providing them with flexible and dynamic access policies. We employ a public-key broadcast encryption scheme as the cryptographic tool for managing information sharing with a subset of a user’s friends. The privacy and complexity evaluations show the superiority of our approach over previous.     

Estimation of research reactor core parameters using cascade feed forward artificial neural networks

The pattern of the core reload program is very important for an optimize use of research reactors. Reactor safety issues and economic efficiency should be considered during pattern studies. In order to find the best core pattern for a research reactor, its reloading program should be solved as a multi-objective and constrained optimization problem. If considered objective functions of the optimization problem can be estimated in very short time, the optimal fuel reloading pattern can be used effectively. In this research a very fast estimation system for suggested core parameters has been developed using cascade feed-forward type of artificial neural networks (ANNs). Four main core parameters are suggested to optimize reactor core adequately. And also to get larger thermal fluxes in the desired irradiation box, a new flexible method was selected. A Software package has been developed to prepare and reform required data for ANNs training. The gradient descent method with momentum weight/bias learning rule has been used to train ANNs. To get the best conditions for considered ANNs training a vast study has been performed. It includes the effects of variation of hidden neurons, hidden layers, activation functions, learning and momentum coefficients, and also the number of training data sets on the training and simulation results. Some experimental convergence criteria are used to study them. A comparison selection rule has been used to adjust desirable conditions. Final training and simulation results show that developed ANNs can be trained and estimate suggested core parameters of research reactors very quickly. It improves effectively pattern optimization process of core reload program.     

QSAR Study of Antimicrobial 3-Hydroxypyridine-4-one and 3-Hydroxypyran-4-one Derivatives Using Different Chemometric Tools

A series of 3-hydroxypyridine-4-one and 3-hydroxypyran-4-one derivatives were subjected to quantitative structure-antimicrobial activity relationships (QSAR) analysis. A collection of chemometrics methods, including factor analysis-based multiple linear regression (FA-MLR), principal component regression (PCR) and partial least squares combined with genetic algorithm for variable selection (GA-PLS) were employed to make connections between structural parameters and antimicrobial activity. The results revealed the significant role of topological parameters in the antimicrobial activity of the studied compounds against S. aureus and C. albicans. The most significant QSAR model, obtained by GA-PLS, could explain and predict 96% and 91% of variances in the pIC₅₀ data (compounds tested against S. aureus) and predict 91% and 87% of variances in the pIC₅₀ data (compounds tested against C. albicans), respectively.     

An analysis of carbone monoxide distribution in large tunnel fires

Fire events and the related toxicants such as CO are responsible for many fatalities in the current century. These hazardous events are much more dangerous when they occur in enclosed spaces. In the present study, a theoretical relation is developed for horizontal distribution of CO in a large tunnel fire. Then, the developed criterion is used to study the effect of some rudimentary parameters such as the heat release rate (HRR) of fire and tunnel’s aspect ratio (AR) on CO and temperature stratification. Theoretical results of various heat release rates and aspect ratios for horizontal distribution of CO are compared with numerical results using fire dynamics simulator (FDS5.5). It is found that big fires have higher rates of CO concentration decay in comparison to the smaller ones due to higher air entrainment into the travelling plume. It is indicated that the smoke travelling at higher values of tunnel AR, dilutes faster. Moreover, using FDS5.5, the relevant variations in temperature and CO concentration are discussed for tunnel angles ranging from ?20° to 20°.     

Using the third-order shear deformation theory to examine magnetic-mechanical buckling of a two-dimensional functionally graded cylindrical panel with longitudinal and circumferential stiffeners

In this study, the magnetic-mechanical buckling of a cylindrical panel made of two-dimensional functionally graded materials (2D-FGMs) has been investigated. The panel contains longitudinal and circumferential stiffeners and has been subjected to a uniform magnetic field as well as axial load. Material properties of the cylindrical panel are assumed to vary continuously in radial and thickness directions as a function of the volume fraction of the components. The magnetic field has been exerted radially. Equilibrium and stability equations have been derived using both Hamilton's principle and principle of minimum potential energy based on the third-order shear deformation theory (TSDT). The generalized differential quadrature method (GDQ) has been employed to solve the coupled differential equations. Moreover, the effect of geometry, load, magnitude of the magnetic field, number of stiffeners, and volume fraction coefficient on the critical buckling load has been determined. The results are in good agreement with the previous related works.