With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.
The current study aims to use the molecular dynamics (MD) simulation method to discuss the glass transition behaviour and fractional free volume (
FFV
) of the pure polyethylene terephthalate (PET) and the plasticized PET induced by supercritical carbon dioxide (SC-CO
2
) sorption. The adsorption concentration reproduced through sorption relaxation cycles (SRC) was firstly estimated and in an order of magnitude with the known experimental results available in the reported literature. The
FFV
induced by SC-CO
2
in PET polymer changes regularly, which is proportional to the capacity of SC-CO
2
adsorption with the changes in temperature and pressure. The glass transition temperature (
T
g
) was further estimated to be almost identical to the known experimental values and shows a gradually decreasing tendency with the increase of pressure. Meanwhile, the plasticization of PET polymer studied by radial distribution functions showed that CO
2
molecules occupying the sorption sites on the PET backbone promoted plasticization by increasing the fluidity of the PET backbone chain.
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