The amount of supercritical CO2 dissolved
in polystyrene
(PS), dissolution rate, and solubility under static conditions at
170–190 °C and 7.5–9.5 MPa were calculated by utilizing
volume-changing-method experiments and numerical simulations. By comparison,
the instantaneous error can be guaranteed to be less than 15%. The
two results are in good agreement, and the reliability of the simulation
method is verified. Based on the obtained results, another parameter
was added to the tested model, and the dissolution rate of supercritical
CO2 in PS under different shear conditions was numerically
simulated. The effects of temperature, pressure, and shear rate on
dissolution were analyzed. The results show that when the temperature
and pressure are constant, the dissolution rate of supercritical CO2 in PS with shear increases significantly compared with that
without shear. The conditions that enable the maximum dissolution
rate are 190 °C, 9.5 MPa, and a shear rate of 240/π. With
the abovementioned pressure and shear rate conditions, the maximum
solubility can be obtained under the temperature of 170 °C.
In order to reveal
the dissolution process, the adsorption kinetics
and diffusion theory are combined and used to describe the adsorption-diffusion
mechanism. This can not only predict the solubility of supercritical
CO2 in polymer melts but also describe two important parameters
of supercritical CO2 in the dissolution process: dissolution
amount and dissolution rate, which can provide a good theoretical
basis for microcellular foaming. To verify the feasibility and accuracy
of the theoretical calculation method, an experimental device for
the volume-changing method under static condition was established.
The results showed that the theoretical calculation value was in good
agreement with the experimental value. In addition, the dissolution
amount and dissolution rate of supercritical CO2 in three
polystyrene melts with different molecular weights under different
temperature and pressure conditions were measured. The results showed
that the difference of polystyrene molecular weight can cause the
change of dissolution rate during the dissolution process, that is,
the larger the molecular weight, the slower the dissolution rate.
Tension detection is a key to improve performance of two-motor system under sensorless operation. This paper presents a new identification method for two-motor system based on artificial neural network and the left-inverse theory. Considering that the system parameters are time-variant and the mathematic model of left-inverse identification is complex, BP neural network is used to build the left-inverse model in this method, which is easy to implement. A simulation model of a two-motor system is developed. The simulated results verify the proposed method. By using this control strategy, the tension can be identified quickly and accurately, in which satisfactory robustness is offered.
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