MCMFIT - EFFICIENT OPTIMAL FITTING OF A GENERALIZED NONLINEAR ADVECTION-DISPERSION MODEL TO EXPERIMENTAL-DATA

The use of standard numerical schemes to solve nonlinear advective-dis persive equations for the estimation of parameters is CPU-time consumi ng and hence not desirable for routine use. An efficient scheme using a novel mixing cell approach has been used to estimate parameter value s by nonlinear least-squares fitting for nonlinear adsorption of a sin gle solute species coupled with one-dimensional transport. A problem w ith gradient methods of nonlinear least-squares fitting is that they a re prone to determine best-fit parameters corresponding to local minim a rather than the global minimum. As is well known, this problem can b e avoided by judicious selection of the starting values. The present c ode, MCMFIT, includes a random search of the parameter space in order to determine a suitable set of initial parameter values. The program a lso includes the option of selecting user-defined initial parameter va lues because of possible physical considerations. These values then ar e passed to the nonlinear least-squares fitting program to obtain the optimal parameter values. Penalty functions have been employed to main tain user-imposed constraints on the parameter values. MCMFIT is capab le of handling linear, Freundlich, Langmuir, and S-curve adsorption is otherms. The use of MCMFIT is demonstrated with the use of synthetic a s well as laboratory and field data.