Computational and experimental approaches for investigating membranes diffusion behavior in model diesel fuel

Genetic algorithms trained support vector regression predicting model is conducted to research diffusion behavior of methylnaphthalene and dibenzothiophene in four different membranes of polymethyl methacrylate, polymethyl acrylate, polyvinyl chloride and polyvinyl alcohol in model diesel fuel. It is found that the polyvinyl chloride is optimal membrane material for improving the diffusion selectivity of methylnaphthalene and dibenzothiophene, which demonstrates that the polyvinyl chloride membrane is favorable to the diesel fuel desulfurization. Also, molecular dynamic simulation is applied to validating the performance of genetic algorithm trained support vector regression model. The results of genetic algorithm trained support vector regression model reveal that the simulation values are well agreed with the experimental data and molecular dynamic simulation results. Meanwhile, the performance of the genetic algorithms trained support vector regression predict model is better than that of the genetic algorithms trained neural network model, which indicates that genetic algorithms trained support vector regression method offers a new prospected decision-theoretic approach to the diesel desulfurization.

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