Accurate prediction of solubility of gases within H2-selective nanocomposite membranes using committee machine intelligent system

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@Article{DASHTI:2018:IJHE,

• author = "Amir Dashti and Hossein Riasat Harami and Mashallah Rezakazemi",
• title = "Accurate prediction of solubility of gases within {H2-selective} nanocomposite membranes using committee machine intelligent system",
• journal = "International Journal of Hydrogen Energy",
• volume = "43",
• number = "13",
• pages = "6614--6624",
• year = "2018",
• keywords = "genetic algorithms, genetic programming, Membranes, Nanocomposite, Modeling, Mass transfer, Diffusion",
• ISSN = "0360-3199",
• DOI = "doi:10.1016/j.ijhydene.2018.02.046",
• URL = "http://www.sciencedirect.com/science/article/pii/S0360319918304245",
• abstract = "In-depth knowledge about the gas sorption within hydrogen (H2) selective nanocomposite membranes at various conditions is crucial, particularly in petrochemical and separation processes. Hence, various artificial intelligence (AI) methods such as multilayer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS), the adaptive neuro-fuzzy inference system optimized by genetic algorithm (GA-ANFIS), Genetic Programming (GP) and Committee Machine Intelligent System (CMIS) were applied to predict the sorption of gases in H2-selective nanocomposite membranes consist of porous nanoparticles as the dispersed phase and polymer matrix as continuous phase. The momentous purpose of this paper was to estimate the sorption of C3H8, H2, CH4 and CO2 within H2-selective nanocomposite membranes considering the effect of nanoparticles loading, critical temperature (gas type characteristics) and upstream pressure. Obtained data were categorized into two parts including training and testing data set. The CMIS method showed more precise results rather than other intelligent models. Having developed different intelligent approaches rely on algorithms, a powerful successor for labor-intensive experimental processes of solubility was revealed. The prediction results and experimental data were significantly consistent in approach with a correlation coefficient (R2) of 0.9999, 0.9987, 0.9998, 0.9995, and 0.9997 for CMIS, GP, GA-ANFIS, ANFIS and ANN models respectively",
• keywords = "genetic algorithms, genetic programming, Membranes, Nanocomposite, Modeling, Mass transfer, Diffusion",

}

Genetic Programming entries for Amir Dashti Hossein Riasat Harami Mashallah Rezakazemi

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