Inverse fitting in CADET MATLAB interface

Hey hi,

I am a new user of CADET, and I want to learn how to estimate parameters of the model by inverse fitting, so i am trying it with singleGRM on the breakthrough curve. I am getting the following error.
I have attached .m file for reference. Can you please suggest the way.
Thank you.

BTfitting.m (4.9 KB)

BTfitting
Error using MexSimulator/setParameters (line 718)
Expected valid parameters, but parameter 1 is invalid or does not exist.

Error in BTfitting (line 12)
sim.setParameters(params, true(4, 1));

Solved the above issue, but not getting the correct values of parameters.
Is there a need to change tolerance. Can you suggest what should be a way to get the correct values of parameters after a fit?
Thank you.

BTfitting

                                     Norm of      First-order 

Iteration Func-count f(x) step optimality
0 1 0.000254756 0

Initial point is a local minimum.

Optimization completed because the size of the gradient at the initial point
is less than the selected value of the optimality tolerance.

optimalParams =

1.0e-05 *

0.0032    0.0000    0.0000    0.1550

Hi Prashant,

I’ve fixed the file you provided.

  • The parameters were not correct (the component index for the axial dispersion should be -1 but needs to be 0 for this model with one component).
  • The parameter fit starts with the true values. The optimizer has nothing to do because the residual is bascially 0. So I’ve perturbed the values slightly to get the optimizer to do something.
  • When analyzing real data, you should ramp up the discretization. Using only 16 axial cells adds a lot of numerical dispersion.

Please open a public thread so that others can also benefit from the discussion.

BTfitting2.m (4.9 KB)

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Thank you so much, Samuel.

Actually, i want to ask is there any option in forum to open this discussion thread in public or i need to ask the same question under CADET troubleshooting topic.

Thank you.

I’ve moved this topic to the CADET Troubleshooting category.

1 Like