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    The simulation of microbial enhanced oil recovery by using a two-layer perceptron neural network

    , Article Petroleum Science and Technology ; Vol. 32, Issue. 22 , 2014 , pp. 2700-2707 ; ISSN: 10916466 Morshedi, S ; Torkaman, M ; Sedaghat, M. H ; Ghazanfari M.H ; Sharif University of Technology
    Abstract
    The authors simulated a reservoir by using two-layer perceptron. Indeed a model was developed to simulate the increase in oil recovery caused by bacteria injection into an oil reservoir. This model was affected by reservoir temperature and amount of water injected into the reservoir for enhancing oil recovery. Comparing experimental and simulation results and also the erratic trend of data show that the neural networks have modeled this system properly. Considering the effects of nonlinear factors and their erratic and unknown impacts on recovered oil, the perceptron neural network can develop a proper model for oil recovery factor in various conditions. The neural networks have not been... 

    Modeling and Control of gas turbine combustor with dynamic and Adaptive Neural networks

    , Article International Journal of Engineering, Transactions B: Applications ; Volume 21, Issue 1 , 2008 , Pages 71-84 ; 1728-144X (ISSN) Mozafari, A. A ; Lahroodi, M ; Sharif University of Technology
    Materials and Energy Research Center  2008
    Abstract
    This paper presents an Artificial Neural Network (ANN)-based modeling technique for prediction of outlet temperature, pressure and mass flow rate of gas turbine combustor. Results obtained by present modeling were compared with those obtained by experiment. The results showed the effectiveness and capability of the proposed modeling technique with reasonable accuracies of about 95 percent. This paper describes a nonlinear SVFAC (State Vector Feedback Adaptive Control) controller scheme for gas turbine combustor. In order to achieve the satisfied control performance, we have to consider the effect of nonlinear factors contained in controller. The controller is adaptively trained to force the...