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    Prediction of absolute entropy of ideal gas at 298 K of pure chemicals through GAMLR and FFNN

    , Article Energy Conversion and Management ; Volume 52, Issue 1 , 2011 , Pages 630-634 ; 01968904 (ISSN) Fazeli, A ; Bagheri, M ; Ghaniyari-Benis, S ; Aslebagh, R ; Kamaloo, E ; Sharif University of Technology
    2011
    Abstract
    Thermodynamical optimization for energy conversion system can be performed by decreasing entropy generation. For calculation of entropy, we need to know entropy of ideal gases at 298 K as a reference point. Entropy is a thermodynamic quantity which is not easily measured and prediction of entropy by molecular structures for new designed molecules may be a challenge. An easy and accurate equation for prediction of absolute entropy of pure ideal gas at 298 K was introduced for the first time based on the quantitative structure property relationship (QSPR) approach. Thousand seven hundred pure chemical compounds and 3224 molecular descriptors were used for finding this easy equation by genetic... 

    An enhanced neural network model for predictive control of granule quality characteristics

    , Article Scientia Iranica ; Volume 18, Issue 3 E , 2011 , Pages 722-730 ; 10263098 (ISSN) Neshat, N ; Mahloojifl, H ; Kazemi, A ; Sharif University of Technology
    2011
    Abstract
    An integrated approach is presented for predicting granule particle size using Partial Correlation (PC) analysis and Artificial Neural Networks (ANNs). In this approach, the proposed model is an abstract form from the ANN model, which intends to reduce model complexity via reducing the dimension of the input set and consequently improving the generalization capability of the model. This study involves comparing the capability of the proposed model in predicting granule particle size with those obtained from ANN and Multi Linear Regression models, with respect to some indicators. The numerical results confirm the superiority of the proposed model over the others in the prediction of granule...