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In this work the UNIQUAC equation has been substituted by the original UNIFAC group-contribution model with concentration independent group interaction para-meters.
The behaviour of the system can be well predicted by a simple equilibrium stage model with concentration dependent distribution coefficients to account for non-linearity and coupling between the isotherms for the two components.
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A solid diffusion model with concentration-dependent diffusivity (SDCDD model) is utilized to fit the experimental uptake curves (UCs) and concentration profiles (CPs).
A combined kinetic mass transfer model with concentration-dependent diffusion (parabolic form) has been developed and successfully applied on the dissolved oxygen adsorption system at high surface coverage.
They emphasized that the model used was an inadequate severe sepsis model with concentrations of aPC that remained above the pathological threshold.
The feed forward backpropagation neural network of ANN was used to build predictive model with concentrations of four media components as an input and yield of PGase production as an output of the model.
We included all eligible pollutants in each first-stage model, with concentrations scaled by their IQR, and controlled for same-day linear average temperature, 1- to 3-day averaged temperature, and dew point temperature, and ozone.
Flux and concentration values measured with these processes have been modeled with concentration polarization model, and mass transfer coefficients (k) and polarization concentrations (cm) have been obtained for different operation conditions.
Table 2 shows 〈z〉 and 〈z1〉 for the mouse, rat and human models with concentrations of 0%, 1% and 2% of stable iodine, 127I, homogeneously distributed within the follicle lumen.
These may be described by using a two shell model with constant concentration in every shell.
Results were analysed using a linear random regression model with DM concentration as fixed effect, cow and cut number x farmer as random intercepts and with a cut number x farmer random slope.
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