Typically, on a generic regression application such as y = a + bx + cx² there are no constraints on the optimization. The coefficients a, b, and c could have either positive or negative values. However, in phenomenological models coefficients represent phenomena and their values are constrained. For example, a delay and a time constant must both have non-negative values. Further, in regression of a model to fit an engineering application there are many other variables to consider. One application could be to fit a distillation column tray-to-tray model to data by adjusting coefficients representing tray efficiency...