Parameters

A parameter is a named value you can use wherever a number is expected. Parameters are what turn a single calculation into a study: a value written once in the input is a constant, but a parameter can be swept, fitted or sampled from a distribution without touching the model.

They are the feature the rest of the manual leans on. Studies of every kind beyond the plain PHREEQC study exist to vary them, and Parameter Fitting exists to determine them.

Creating one

Parameters live in the Parameters branch, either of the project or of a model. Add one, give it a name and a value.

A parameter’s value can be a plain number or an expression over other parameters — porosity * 2, log10(k_calcite). A parameter defined in terms of others is kept consistent with them: change the one and the other follows.

Every parameter needs a value, including those a study is going to generate. The value is what the model uses when the parameter is not being varied, and it is where a fit starts from.

Using one

In most fields a parameter is used by name.

In a PHREEQC input, where the surrounding text is PHREEQC’s own syntax rather than GibbsStudio’s, a parameter is marked so that GibbsStudio knows to substitute it. The marker is @{ … }@:

@{my_param}@

To calculate with it rather than just use it, put dollar signs inside the marker as well. Everything between them is a mathematical expression:

SOLUTION 0
    Cl      @{$ini_conc$}@
    pe      @{$-1 * pe_param$}@
    Ca      @{$10^C_param$}@

The expression is evaluated and the result substituted before the input reaches PHREEQC, so what PHREEQC reads is a number. Expressions lists what can go inside.

This is also the answer to “why is my parameter being ignored?” — a parameter named in a PHREEQC input without the markers is just text, and PHREEQC will read it as text.

Scope

A parameter on the project is visible everywhere in it. A parameter on a model is visible within that model.

Prefer the project for anything a study will vary, since that is where studies look.

Choosing good parameters

A few habits that save time later:

  • Name them for what they are, not for where they appear. k_calcite survives a rearranged input; param_1 does not.

  • Parameterise the quantity, not its transform. Make the rate constant the parameter and take its logarithm in the expression, rather than storing the logarithm — the number in the table is then the one you would quote. Parameter Fitting can still fit it on a log scale.

  • Keep the count down. Every parameter a study varies multiplies its cost, and every parameter a fit adjusts needs data to determine it.

Examples