کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7706891 | 1497312 | 2018 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Accurate prediction of solubility of gases within H2-selective nanocomposite membranes using committee machine intelligent system
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
الکتروشیمی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Hydrogen Energy - Volume 43, Issue 13, 29 March 2018, Pages 6614-6624
Journal: International Journal of Hydrogen Energy - Volume 43, Issue 13, 29 March 2018, Pages 6614-6624
نویسندگان
Amir Dashti, Hossein Riasat Harami, Mashallah Rezakazemi,