The ability to predict physical properties and phase behavior accurately is vital for process simulation. The Data Regression feature allows measured component or mixture data to be correlated into a form which can be used with PRO/II.
The regression options are listed below.
You may supply temperature dependent property data at a number of temperatures and regress them onto any of the equation forms for PRO/II property correlations. The coefficients for the equations are calculated.
Multi-component thermodynamic equilibrium data may be regressed to create binary interaction parameters for Liquid Activity Methods or Equations of State. Using these parameters will ensure that the selected thermodynamic method will reproduce the measured data.
You may regress multi-component heat of mixing or volume of mixing data in order to generate Redlich-Kister binary interaction coefficients.
The regression of these data requires sophisticated non-linear mathematical algorithms. The algorithms used include the weighted Orthogonal Distance Regression algorithm developed by the National Institute of Standards and Technology (NIST) as well as a non-linear least squares correlation.
The algorithms minimize an Objective Function which is a correlation which sums the differences between the supplied data values and the calculated values. The function is selected in the Objective Function Window.
As with all such regressions, initial estimates are required for the parameters. Although default values are provided for these, it always is better for you to supply values appropriate to your specific simulation.
Data Regression can also be done using TDM.