Applying Pareto Design of GMDH-Type Neural Network for Solid-Liquid Equilibrium of Binary Systems (Isotactic Poly 1-Butene (1)-Organic Solvents (2))
Applying Pareto Design of GMDH-Type Neural Network for Solid-Liquid Equilibrium of Binary Systems (Isotactic Poly 1-Butene (1)-Organic Solvents (2))
عنوان فارسی :
Applying Pareto Design of GMDH-Type Neural Network for Solid-Liquid Equilibrium of Binary Systems (Isotactic Poly 1-Butene (1)-Organic Solvents (2))
عنوان انگلیسی :
Applying Pareto Design of GMDH-Type Neural Network for Solid-Liquid Equilibrium of Binary Systems (Isotactic Poly 1-Butene (1)-Organic Solvents (2))
چکیده انگلیسی:
Isotactic poly (1-butene), ipbu-1, was synthesized by using a metallocene catalyst. The thermodynamic phase behavior of polymer–organic solvents systems is very important in every polymer application. In this paper, the solid–liquid equilibrium of ipbu-1 with different organic solvents (1-heptyne, cyclo octane) was studied by a mathematical model. By considering the experiments temperature-mole fraction results, phase diagram of the polymer solvent systems could be constructed. The temperature and activity coefficient based on mole fraction phase diagrams were predicted by using Pareto genetic design of GMDH-type neural network. The results were very encouraging and congruent with the experimental data.
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