Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
The problem of estimating model parameters is encountered frequently in practice. There are several packages available for estimating the parameters for linear advection-dispersion equations (ADE) for which there are exact solutions (e.g., CXTFIT, TFMFIT, etc.). For non- linear transport equations, the use of standard numerical solutions (e.g., Crank-Nicolson) to estimate parameters is very time consuming and hence inefficient. On the other hand, mixing-cell solutions are very efficient by comparison. In particular, the solution obtained from the improved mixing cell model has been found to agree very well with the results of a numerical Crank-Nicolson solution while being much more efficient. Thus, an improved mixing cell model has been used hereto estimate model parameters for a variety of transport models. The code, MCMFIT, makes use of nonlinear least-squares fitting to find optimal parameter values by matching improved mixing cell model predictions with measured experimental data. The experimental data can be either in the form of a breakthrough curve or concentrations within a soil profile. The program can handle linear, Freundlich, Langmuir, and S- curve adsorption isotherms in conjunction with the transport equation. Both equilibrium and non- equilibrium (fully kinetic and two site adsorption) cases can be dealt with along with first- and third-type surface boundary conditions. Use of the program is demonstrated with a number of examples. Both synthetic data as well as data from yield and laboratory experiments have been used in the illustrative examples.
Alexandre Caboussat, Dimitrios Gourzoulidis
Edoardo Charbon, Claudio Bruschini, Arin Can Ülkü