Irreversible reactions: fitting to multiple isothermal data sets

Recommended method: fit k> values at one temperature, fit Ea> values at all other temperatures, then fit k> and Ea> values to all data.

- Set up the
__reactions__ in Excel with initial guesses for the k> rate constant values at Tref. Set Ea> values to be 60 kJ/mol.
__Manually fit__ k> values in Simulator to experiments run at Tref. Update the model and open in Fitting.
- Fit k> values at the reference temperature, Tref. View the Data vs Model plot as a visual check on the fit.
- Fit Ea> values to the other Scenarios (i.e. to experimental data at different temperatures to Tref).
- Fit k> values and Ea> values to all data.
__Update__ the model and run all Scenarios in Simulator.
__Update__ the k> values and Ea> values back to the Excel model.

Reversible reactions: fitting to multiple isothermic data sets

Recommended method: fit k> and Keq values at one temperature, fit Ea> and Ea< values at other temperatures, then fit all parameters to all data.

- Set up the
__reactions__ in Excel with initial guesses for the k> rate constants and Keq equilibrium constants at Tref. Set Ea> and Ea< values to be 60 kJ/mol.
__Manually fit__ k> and Keq values in Simulator to experiments run at Tref. Update the model and open in Fitting.
- Fit k> and Keq values at the reference temperature, Tref. View the Data vs Model plot as a visual check on the fit.
- Fit Ea values to the other Scenarios at different temperatures to Tref.
- Fit k>, Keq and Ea values to all data.
__Update__ the model and run all Scenarios in Simulator.
__Update__ the k>, Keq and Ea values back to the Excel model.

Note: If your reversible reactions are fast (always at equilibrium) then there are fewer parameters to fit. In this case, you do not need to fit the k>, simply give it a large value (exactly how large depends on the model, but you should be able to increase this in Simulator until it has no effect). With k> set to an arbitrarily high value you only need to fit the equilibrium constant Keq and Ea<.

Often a reasonable approximation is to express the reaction heat as the difference between the forward and reverse activation energies. This allows the activation energy of the reverse reaction (Ea<) to be calculated directly from the forward reaction activation energy (Ea>) and the reaction heat (dHr), i.e. Ea< = Ea> - dHr. This relationship can be set up in a __Calculate statement__ and reduces the number of parameters to fit. An example of how to implement this is given in __statements.xls__.

Fitting parameters set in the Scenarios sheet

You only have to update the model **once** at the end of a set of exercises to save the values from all of the Scenarios you have fitted to.

Example workflow:

- Select Scenario1 and its data, data1. Select the parameter to be fitted. Run Fitting. Fitting gives a parameter value = 10.
- Deselect Scenario1 and select Scenario2 and data2. Run Fitting. Fitted parameter has value = 20.
- Deselect Scenario2 and select Scenario3 and data3. Run Fitting. Fitted parameter has value = 30.
- Update the Master model.
- Right-click on the model icon and
__View Log__. You will see that the values 10, 20 and 30 have been logged as modifications to the three Scenarios.
- Update your Excel model with the new Scenario values.