The Experts below are selected from a list of 69 Experts worldwide ranked by ideXlab platform

Farhad Gharagheizi - One of the best experts on this subject based on the ideXlab platform.

  • A simple accurate model for prediction of Flash Point Temperature of pure compounds
    Journal of Thermal Analysis and Calorimetry, 2012
    Co-Authors: Farhad Gharagheizi, Mohammad Hossein Keshavarz, Mehdi Sattari
    Abstract:

    In this study, a simple three-parameter linear model is presented for estimation of Flash Point (FP) of pure compounds. The parameters of the model contain experimental normal boiling Point of the compound and two chemical structure-based parameters. A comprehensive database of FPs containing 1472 pure compounds of various chemical structures was used to develop the model. The squared correlation coefficient and average absolute error of the model calculation results for all of the compounds presented in the database are evaluated to be 0.982 and 7.2 K, respectively.

  • Gene expression programming strategy for estimation of Flash Point Temperature of non-electrolyte organic compounds
    Fluid Phase Equilibria, 2012
    Co-Authors: Farhad Gharagheizi, Poorandokht Ilani-kashkouli, Nasrin Farahani, Amir H Mohammadi
    Abstract:

    The accuracy and predictability of correlations and models to determine the flammability characteristics of chemical compounds are of drastic significance in various chemical industries. In the present study, the main focus is on introducing and applying the gene expression programming (GEP) mathematical strategy to develop a comprehensive empirical method for this purpose. This work deals with presenting an empirical correlation to predict the Flash Point Temperature of 1471 (non-electrolyte) organic compounds from 77 different chemical families. The parameters of the correlation include the molecular weight, critical Temperature, critical pressure, acentric factor, and normal boiling Point of the compounds. The obtained statistical parameters including root mean square of error of the results from DIPPR 801 data (8.8, 8.9, 8.9. K for training, optimization and prediction sets, respectively) demonstrate improved accuracy of the results of the presented correlation with respect to previously-proposed methods available in open literature.

  • Empirical Method for Representing the Flash-Point Temperature of Pure Compounds
    Industrial & Engineering Chemistry Research, 2011
    Co-Authors: Farhad Gharagheizi, Ali Eslamimanesh, Amir H Mohammadi, Dominique Richon
    Abstract:

    Flash Point Temperature is one of the most-widely used physical properties for the evaluation of the flammability hazard of combustible liquids. In this communication, an empirical method involving normal boiling-Point Temperature and number of carbon atoms of the pure compounds is presented for accurate representation of the Flash-Point Temperature of pure substances. A total of 1471 pure compounds belonging to 77 chemical families were used to develop a general correlation. The global absolute average deviation of the model results from experimental values is 2.4%. A successful comparison is finally made with respect to some of the methods proposed in the literature, which apply a similar approach for calculation of the Flash-Point Temperature of pure compounds.

  • a new neural network group contribution method for estimation of upper Flash Point of pure chemicals
    Industrial & Engineering Chemistry Research, 2010
    Co-Authors: Farhad Gharagheizi, Reza Abbasi
    Abstract:

    In this study, a new group contribution-based model is presented for the prediction of the upper Flash Point Temperature of pure compounds based on a large data set containing 1294 pure compounds. The model is a neural network using a number of occurrences of 122 chemical groups in a pure compound to predict its related UFLT (Upper Flash Point Limit). The squared correlation coefficient, average percent error, mean average error, and root-mean-square error of the model over the main data set containing 1294 pure compounds are 0.99, 1.7%, 6, and 8.5, respectively.

  • prediction of Flash Point Temperature of pure components using a quantitative structure property relationship model
    Qsar & Combinatorial Science, 2008
    Co-Authors: Farhad Gharagheizi, Reza Fareghi Alamdari
    Abstract:

    In this work, a general Quantitative Structure–Property Relationship (QSPR) model (, , and ) for the prediction of Flash Points of 1030 pure compounds is developed. Genetic Algorithm-based Multivariate Linear Regression (GA-MLR) technique is used to select four chemical structure-based molecular descriptors from a pool containing 1664 molecular descriptors.

Atilla Bilgin - One of the best experts on this subject based on the ideXlab platform.

  • density Flash Point and heating value variations of corn oil biodiesel diesel fuel blends
    Fuel Processing Technology, 2015
    Co-Authors: Mert Gulum, Atilla Bilgin
    Abstract:

    Abstract In this study, densities of produced corn oil biodiesel and its blends with commercially available petro-diesel fuel have been investigated. The effects of Temperature (T) and biodiesel percentage in blend (X) on the densities of blends were examined. The blends (B5, B10, B15, B20, B50 and B75) were prepared on a volume basis and their densities were measured by following ISO test method at Temperatures of 10, 15, 20, 30 and 40 ° C. New one- and two-dimensional equations were fitted to the measurements for identifying of variations of densities with respect to X and T; and these equations were compared with other equations published in literature. Moreover, the qualities of the corn oil biodiesel and its blends were evaluated by determining the other important properties such as Flash Point Temperature and higher heating value. In order to predict these properties, some equations were also evaluated as a function of biodiesel percentage in blend.

John R Agudelo - One of the best experts on this subject based on the ideXlab platform.

Mert Gulum - One of the best experts on this subject based on the ideXlab platform.

  • density Flash Point and heating value variations of corn oil biodiesel diesel fuel blends
    Fuel Processing Technology, 2015
    Co-Authors: Mert Gulum, Atilla Bilgin
    Abstract:

    Abstract In this study, densities of produced corn oil biodiesel and its blends with commercially available petro-diesel fuel have been investigated. The effects of Temperature (T) and biodiesel percentage in blend (X) on the densities of blends were examined. The blends (B5, B10, B15, B20, B50 and B75) were prepared on a volume basis and their densities were measured by following ISO test method at Temperatures of 10, 15, 20, 30 and 40 ° C. New one- and two-dimensional equations were fitted to the measurements for identifying of variations of densities with respect to X and T; and these equations were compared with other equations published in literature. Moreover, the qualities of the corn oil biodiesel and its blends were evaluated by determining the other important properties such as Flash Point Temperature and higher heating value. In order to predict these properties, some equations were also evaluated as a function of biodiesel percentage in blend.

Mahmood Torabi Angaji - One of the best experts on this subject based on the ideXlab platform.