I'm trying to use the nonlinear modeling tool to solve the following equation.
Parameter(
{Ea = 35, A = 0.25},
Root(
Exp( -(1000 * Ea) / (:R * :"Test Temperture (K)"n) + A ) *
:"Test Time (hrs)"n
)
);
Ea and A are parameters
R is a constant
Time and Temperature are two columns in my table and I have a column that I set as y, that I'm trying to solve for.
I get a solution for Ea and A but when I plot the predicted vs. the experimental, I don't get a great 1:1 fit (i.e. slope of ~1.5). I have found better solutions to this problem using two separate linearization methods to solve for Ea and A but I would like to run it this way to be able to get the upper and lower bounds for the estimate.
It seems like the software is getting stuck at some local minima vs. driving to an optimal solution. Is there anything I can do to improve upon this method?
Thank you,
JMP version 16.0.0
