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During minimization, the parameter values used in the model are systematically adjusted to find the smallest value of a particular component at a selected point in the model run. However, by choosing the way in which the model component is defined, minimization can also be used to maximize another component value, or make it converge on a desired value. In our model, let's assume that we want to force the amount of pollutant in Lake2 to be 500 kg on day 20 of the model run. To do this, we shall add a new variable to the model. To follow this example load the model "tutor2.mod"
![]() Now the model definition is completed, we can set up the minimization options.
ModelMaker uses sample points to test the values of selected components at selected moments in time. Sample points are configured for minimization and Monte Carlo analysis in the Sample Points view.
The Sample Points view is created and activated. This view is divided into five columns - Name, Component, Time Point, Monte Carlo and Minimize.
A new sample point, SP1, is created.
The model is now ready to minimize.
The Start Minimization dialog box opens. This dialog summarizes the component, time point and parameter selection, and starts the calculation.
The Minimization Configuration dialog box opens.
ModelMaker runs the model many times, adjusting the values of river_rate and estuary_rate. When the process finishes, the new parameter values are displayed in the sub-view Parameter Results - river_rate = 0.1048 and estuary_rate = 0.0471. Use the update command to adopt the minimize values in the model. Re-run the model and check a table of Lake2 values to confirm that it contains 500 kg of pollutant on day 20. The completed model for this step is "tutor10.mod"
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