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    An intelligent modeling approach for prediction of thermal conductivity of CO2

    , Article Journal of Natural Gas Science and Engineering ; Volume 27 , November , 2015 , Pages 138-150 ; 18755100 (ISSN) Shams, R ; Esmaili, S ; Rashid, S ; Suleymani, M ; Sharif University of Technology
    Elsevier  2015
    Abstract
    In the design of a carbon dioxide capture and storage (CCS) process, the thermal conductivity of carbon dioxide is of special concern. Hence, it is quite important to search for a quick and accurate determination of thermal conductivity of CO2 for precise modeling and evaluation of such a process. To achieve this aim, a robust computing methodology, entitled least square support vector machine (LSSVM) modeling, which is coupled with an optimization approach, was used to model this transport property. The model was constructed and evaluated employing a comprehensive data bank (more than 550 data series) covering wide ranges of pressures and temperatures. Before constructing the model, outlier... 

    Investigating the effects of using different types of SiO2 nanoparticles on the mechanical properties of binary blended concrete

    , Article Composites Part B: Engineering ; Volume 54, Issue 1 , 2013 , Pages 52-58 ; 13598368 (ISSN) Najigivi, A ; Khaloo, A ; Iraji Zad, A ; Abdul Rashid, S ; Sharif University of Technology
    2013
    Abstract
    The aim of this study was to assess the effects of two different types of SiO2 nanoparticles (N and M series) with different ratios on the workability and compressive strength of developed binary blended concretes cured in water and lime solution as two different curing media. N and M series SiO2 nanoparticles with an average size of 15 nm were used as obtained from the suppliers. Fresh and hardened concretes incorporating 0.5%, 1.0%, 1.5% and 2.0% of N and 2% of M series nanoparticles with constant water to binder ratio and aggregate content were made and tested. Fresh mixtures were tested for workability and hardened concretes were tested for compressive strength at 7, 28 and 90 days of... 

    An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica-Rice Husk Ash Ternary Blended Concrete

    , Article International Journal of Concrete Structures and Materials ; Volume 7, Issue 3 , September , 2013 , Pages 225-238 ; 22341315 (ISSN) Najigivi, A ; Khaloo, A ; Iraji zad, A ; Abdul Rashid, S ; Sharif University of Technology
    Korea Concrete Institute  2013
    Abstract
    In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were:...