Sharif Digital Repository

A general investigation of the thermal conductivity of natural gas as a function of temperature, pressure and composition was carried out to develop a generalized correlation. The model obtained was based on 731 data points of 42 binary mixtures in wide ranges of pressures (0.1-300MPa), temperatures (220-425K) and specific gravities (0.626-1.434). Correction terms for non-hydrocarbons of carbon dioxide and nitrogen were up to 87.8 and 82.8 of mole percent, respectively. The arithmetic average of the model's absolute error was found to be 5.69%, which is acceptable in engineering calculations  

In this study, the local composition based models such as the Wilson, the nonrandom two-liquid (NRTL), and the Wilson-NRF have been applied in correlation and estimation of density, viscosity, and surface tension of biodiesels. The thermodynamic models have been used in correlating the thermophysical properties for 215 experimental data points. These models have the interaction energy between each pair that is considered as adjustable parameters. To decrease the number of these adjustable parameters, it is assumed that the biodiesels are composed of two hypothetical components. The average absolute deviation (AADs) of the correlated density of biodiesels for the Wilson, the NRTL, and the... 

In this paper, a new correlation to calculate the thermal conductivity of supercritical carbon dioxide based on 668 data points from the literature is introduced. The proposed correlation is valid in temperature range from 310 to 960 K, and pressures range between 7.4 and 210 MPa. The average absolute error of the model was found to be 2.7 and 2.4% in the comparison with the literature and NIST data respectively, which demonstrates superiority of the model over other methods  

In this paper, the Heidaryan and Jarrahian equation of state (Heidaryan and Jarrahian, 2013) has been adapted as a first worldwide cubic EOS to calculate the density of dry natural gases, wet natural gases, and single-phase gas condensates "sweet and sour mixtures" (up to 73.85, 97.63 and 38.37 mol percent of H2S, CO2, and N2 respectively) even when the gas composition is unknown, through new gas specific gravity correlation equations. Correction terms of water content as high as 10 mol percent of H2O and hythane (natural gas + hydrogen) as high as 74.9 mol percent of H2 were obtained. The equation of state was validated with 8985 experimental compressibility factor data points from 308... 

The bulk of the worldwide annual commercial production of ethylene is based on thermal cracking of petroleum hydrocarbons with steam. In this research, the effect of coil outlet temperature on the reactor yield has been studied. In order to investigate a reliable mathematical correlation, several data points were obtained by adjusting coil outlet temperature in a real plant. In order to investigate best coil outlet temperature, an objective function was represented. Based on the obtained mathematical correlation and the assumed objective function, it was found that best coil outlet temperature for investigating maximum income is 1,128 K  

Semi-supervised learning based on manifolds has been the focus of extensive research in recent years. Convenient neighbourhood graph construction is a key component of a successful semi-supervised classification method. Previous graph construction methods fail when there are pairs of data points that have small Euclidean distance, but are far apart over the manifold. To overcome this problem, we start with an arbitrary neighbourhood graph and iteratively update the edge weights by using the estimates of the geodesic distances between points. Moreover, we provide theoretical bounds on the values of estimated geodesic distances. Experimental results on real-world data show significant... 

This study proposes a novel non-invasive method for estimating the utility harmonic impedance. Since the major concern about non-invasive methods is the dependency of the accuracy of the results on background harmonic fluctuations, proper measured samples are selected using a three-point data selection technique to increase the method accuracy. Then, a new non-invasive method for the evaluation of the utility harmonic impedance at a point of common coupling (PCC) based on fuzzy logic is presented. In the proposed method, fuzzy logic is applied to the constrained recursive least squares algorithm (CRLS) by designing a set of fuzzy if-then rules. Due to the changes in the quantities at a... 

Until now, many algorithms have been introduced for finding arbitrary shaped clusters, but none of these algorithms is able to identify all sorts of cluster shapes and structures that are encountered in practice. Furthermore, the time complexity of the existing algorithms is usually high and applying them on large dataseis is time-consuming. In this paper, a novel fast clustering algorithm is proposed. This algorithm distinguishes clusters of different shapes using a twostage clustering approach. In the first stage, the data points are grouped into a relatively large number of fuzzy ellipsoidal sub-clusters. Then, connections between sub-clusters are established according to the Bhatiacharya... 

Recently, several algorithms have been introduced for enhancing clustering quality by using supervision in the form of constraints. These algorithms typically utilize the pair wise constraints to either modify the clustering objective function or to learn the clustering distance measure. Very few of these algorithms show the ability of discovering clusters of different shapes along with satisfying the provided constraints. In this paper, a novel semi-supervised clustering algorithm is introduced that uses the side information and finds clusters of arbitrary shapes. This algorithm uses a two-stage clustering approach satisfying the pair wise constraints. In the first stage, the data points... 

Reservoir permeability is an important parameter that its reliable prediction is necessary for reservoir performance assessment and management. Although many empirical formulas are derived regarding permeability and porosity in sandstone reservoirs, these correlations cannot be accurately depicted in carbonate reservoir for the wells that are not cored and for which there are no welltest data. Therefore, having a framework for estimation of these parameters in reservoirs with neither coring samples nor welltest data is crucial. Rock properties are characterized by using different well logs. However, there is no specific petrophysical log for estimating rock permeability; thus, new methods... 

This paper introduces a novel coding scheme based on the diffusion map framework. The idea is to run a t-step random walk on the data graph to capture the similarity of a data point to the codebook atoms. By doing this we exploit local similarities extracted from the data structure to obtain a global similarity which takes into account the nonlinear structure of the data. Unlike the locality-based and sparse coding methods, the proposed coding varies smoothly with respect to the underlying manifold. We extend the above transductive approach to an inductive variant which is of great interest for large scale datasets. We also present a method for codebook generation by coarse graining the data... 

Quantitative structure-properties relationship (QSPR) has been applied to modelling and predicting the gas to acetone and gas to acetonitrile solvation enthalpies (ΔH Solv) of organic compounds using partial least squares (PLS), artificial neural network (ANN) and support vector machine (SVM) techniques. Two different datasets were assessed. The first one contained a set of gas to acetone enthalpy of solvation data of 68 different organic compounds while the second one included a total of 69 experimental data points for the enthalpy of solvation in acetonitrile. Genetic algorithm (GA) was used to search the descriptor space and select the descriptors responsible for property. After the... 

Ionic liquids (ILs) have been considered as a good candidate to be replaced by the conventional solvent in recent years due to their potential consumptions and unique properties. In the present study, artificial neural network was used to predict the ternary viscosity of mixtures containing ILs. A collection of 729 experimental data points were gathered from the previously public shed literatures. Different topologies of a multilayer feed forward artificial neural network (MFFANN) were examined and optimum architecture was determined. Ternary viscosity data from the literature for 5 ILs with 547 data points have been used to train the network. In addition, to differentiate dissimilar... 

Reservoir permeability is an important parameter that its reliable prediction is necessary for reservoir performance assessment and management. Although many empirical formulas are derived regarding permeability and porosity in sandstone reservoirs, these correlations cannot be accurately depicted in carbonate reservoir for the wells that are not cored and for which there are no welltest data. Therefore, having a framework for estimation of these parameters in reservoirs with neither coring samples nor welltest data is crucial. Rock properties are characterized by using different well logs. However, there is no specific petrophysical log for estimating rock permeability; thus, new methods... 

In this paper, a recurrent neural network model is developed in order to forecast the energy consumption as a complex nonlinear function of gross domestic product (GDP) and population in Iran. This intelligent model is trained by total energy consumption data as output and the population and GDP as inputs during 1976-2001, while 5 annual data points of the following years (2002-2006) are used to validate the model. It can describe time dependencies efficiently and the convergence rate is much faster. This model forecasts the trend of energy consumption annually. Simulation results show that this model can predict energy consumption in Iran with acceptable accuracy. It is expected that this... 

Distance metric has an important role in many machine learning algorithms. Recently, metric learning for semi-supervised algorithms has received much attention. For semi-supervised clustering, usually a set of pairwise similarity and dissimilarity constraints is provided as supervisory information. Until now, various metric learning methods utilizing pairwise constraints have been proposed. The existing methods that can consider both positive (must-link) and negative (cannot-link) constraints find linear transformations or equivalently global Mahalanobis metrics. Additionally, they find metrics only according to the data points appearing in constraints (without considering other data... 

Clustering is the problem of finding relations in a data set in an supervised manner. These relations can be extracted using the density of a data set, where density of a data point is defined as the number of data points around it. To find the number of data points around another point, region queries are adopted. Region queries are the most expensive construct in density based algorithm, so it should be optimized to enhance the performance of density based clustering algorithms specially on large data sets. Finding the optimum set of region queries to cover all the data points has been proven to be NP-complete. This optimum set is called the skeletal points of a data set. In this paper, we... 

In this paper, a novel clustering algorithm using an agent-based architecture along with a force-based clustering algorithm is proposed. To this end, a set of simple mobile agents thai have limited processing power is used. These agents communicate in a pairwise manner to exchange their position information. As opposed to the bio-inspired clustering algorithms that need a set of local rules to specify the agent movements, in this paper the agent motions are driven from attractive and repulsive potential fields that are created by the data points and the other agents respectively. Each agent moves according to the resulted force from applying the potential fields and announces its next... 

One of the most famous approaches for the segmentation of color images is finding clusters in the color space. Shapes of these clusters are often complex and the time complexity of the existing algorithms for finding clusters of different shapes is usually high. In this paper, a novel clustering algorithm is proposed and used for the image segmentation purpose. This algorithm distinguishes clusters of different shapes using a two-stage clustering approach in a reasonable time. In the first stage, the mean-shift clustering algorithm is used and the data points are grouped into some sub-clusters. In the second stage, connections between sub-clusters are established according to a dissimilarity... 

A new approach, based on Adaptive-Network-based Fuzzy Inference System (ANFIS), is presented for the classification of arthrometric data of normal/ACL-ruptured knees, considering the insufficiency of existing criteria. An ANFIS classifier was developed and tested on a total of 4800 arthrometric data points collected from 40 normal and 40 injured subjects. The system consisted of 5 layers and 8 rules, based on the results of subtractive data clustering, and trained using the hybrid algorithm method. The performance of the system was evaluated in four runs, in the framework of a 4-fold cross validation algorithm. The results indicated a definite correct diagnosis for typical injured and normal...