Get Started Fitting Parameters

See documentation below for a walk-through or Input File Schema for a condensed outline of available options.

Files to Fit Parameters

In this tutorial we will assume the user has already read and understood our Setting-up a Thermodynamic Calculation tutorial. Setting up an input file for parameter fitting is similar to writing a thermodynamic input script. Again, two files are needed for a basic calculation:

  1. input.json: Contains a dictionary of instructions and settings.

  2. EOSgroup.json: A dictionary of single group parameters (or estimates of parameters) used in the desired equation of state (EOS).

Parameters can be fit for one component at a time, and for as many parameters as desired can be fit. For this example of ethylbenzene modeled with the SAFT-\(\gamma\)-SW EOS can be found in the examples directory.

input_fit_rhol.json:

{

“bead_configuration”: [[[“aCH”, 5],[“aCCH2”,1],[“CH3”,1]]], “eos”: “saft.gamma_sw”, “EOSgroup”: “../../library/SWgroup.json”, “EOScross”: “../../library/SWcross.json”, “optimization_parameters”: {

“fit_bead” : “aCCH2”, “fit_parameter_names”: [“epsilon”], “epsilon_bounds” : [150.0, 400.0], “parameters_guess” : [300.0]

}, “Yaws”: {

“data_class_type”: “liquid_density”, “calculation_type”: “liquid_properties”, “file”: “ethylbenzene_rhol.csv”

}

}

Note that the name of this file doesn’t really matter, but we use the standard prefix input to denote where these instructions are. This file is processed by the process_param_fit_inputs() function.

The order of these items does not matter, but the first three items must be included, plus one of the following dictionaries discussed below. The bead_configuration and EOSgroup are described in our basic thermodynamic calculation tutorial.

Our new entry, optimization_parameters defines this as a parameter fitting job and outlines the basic instructions.

  • fit_bead: One bead type may have parameters fit at a time, and must be present in the bead_configuration definition.

  • fit_parameter_names: At least one parameter must be listed here. Allowed parameter names are EOS specific. Any of the self-interaction parameters may be listed, in this case its epsilon, but another can be added to this vector.

If one wants to fit a cross-interaction parameter, use the parameter name followed by an underscore and another bead type used in bead_configuration. For example, let’s say we were fitting one heavy atom per bead for methanol. Then "bead_configuration": [[["CH3",1],["OH",1]]] and if we wanted to fit self interaction parameter, epsilon, for “CH3” as well as the cross-interaction with “OH”, then "fit_parameter_names": ["epsilon","epsilon_OH"], although it is recommended that one parameter is fit at a time. * parameters_guess: This optional array contains the initial guess for each respective parameter in fit_parameter_names and is the same length. If this vector is not provided, then the initial guess is taken from EOSgroup. * *_bounds: This optional array is always of length two, containing the high and low limits for the parameter that replaces the asterisk in this definition. Notice above that epsilon_bounds defines the bounds of our chosen fit_param in this example.

Experimental data is the last mandatory entry in the input file. In our example we have one set of experimental data, “Yaws”, but another similar dictionary may be added at the same level with a different key to provide additional fitting data of a

different source or calculations type. The dictionary name doesn’t matter. If the entry data_class_type is in this dictionary, then DESPASITO will identify it as experimental data. These arbitrary names provide flexibility and are used in the output to allow you to identify if a particular data set has a high penalty compared to other data sets when fitting these parameters. This dictionary has a few entries.

  • data_class_type: This defines the data_class_type object used in parameter fitting. See Available Data Types for a list of available data types.

  • calculation_type: Optional…ish. This defines the thermodynamic calculation used by the data_class_type chosen. See the data_class_type for the allowed options. In many cases there is only one option and you don’t need to include it,

however, the TLVE data_class_type can fit parameters to Txi data or Tyi data. We added a feature where the object will try to guess which of these matches the provided data.

  • file: The file name may be included if the data is in the same directory, or the path can be included with it. This file is expected to contain comma separated values, although it doesn’t matter whether a .txt or .csv extension is used.

The top line is skipped to allow inclusion of references. Column headers are the second line. Note that column headers should be thermo properties (e.g. T, P, x1, x2, y1, y2) without suffixes. Mole fractions x1, x2, … should be in the same order as in the bead_configuration line of the input file. No mole fractions should be left out. The column headers in the file are dictionary keys used to initiate the data_class_type object.

  • rhodict: Optional, include options for calculating the density vector that is the foundation of all the thermodynamic calculations. See pressure_vs_volume_arrays() for details.

  • weights: This dictionary allows the user to manually weight the influence of experimental data by some factor. This may be accomplished with a single factor multiplied by the entire array, or a vector of the same length as the experimental

data given. The default is that all data has a weight of 1, but one may weight the data individually in the case of vapor density for the purposes of this tutorial. Maybe we know that the instrument used for collecting this data is not as accurate with low values. Now we can account for that.

After this input file, copy the SWgroup.json file from the example despasito/examples/library and go ahead and run the calculation with:

python -m despasito -i input_fit.json -vv,

It’s that easy! The result will be two files. A log file, despasito.log, contains the details of the calculation at the verbosity level INFO. Although the log file contains the calculation results, the output file, despasito_out.txt contains the best parameter set found.

Note

Try the –jit option to speed it up.

DESPASITO uses global optimization methods from scipy.optimize for parameter fitting. The optional dictionary, global_opts may then be included to specify the method and its options

from global_methods.