How do you regress thermodynamic model parameters from experimental data?

Parameter regression fits model parameters to minimize difference between calculated and experimental values. Objective function: sum of squared deviations in pressure, temperature, or composition, weighted by experimental uncertainty. Data types: isothermal VLE (T, P, x, y), isobaric VLE, infinite dilution activity coefficients, excess enthalpy, LLE tie lines. Procedure: select model, initialize parameters (from similar systems or UNIFAC prediction), minimize objective using nonlinear optimization (Levenberg-Marquardt), check convergence and parameter correlation. Validation: check consistency tests, predict data not used in regression, examine residual patterns. Tools: Aspen Properties regression, DECHEMA DDB, ThermoFit.