Why fixing alpha in the NRTL model might be a bad idea -- Identifiability analysis of a binary Vapor-Liquid equilibrium

TL;DR

This paper analyzes the identifiability of the NRTL model's parameters in vapor-liquid equilibrium data, showing that fixing alpha can impair prediction accuracy and proposing improved regularization techniques.

Contribution

It demonstrates that fixing alpha in the NRTL model can be suboptimal and introduces Generalized Orthogonalization as a better regularization method.

Findings

Fixing alpha may worsen model predictions.

Generalized Orthogonalization outperforms traditional regularization.

Regularization choice significantly impacts parameter estimation.

Abstract

New vapor-liquid equilibrium (VLE) data are continuously being measured and new parameter values, e.g., for the nonrandom two-liquid (NRTL) model are estimated and published. The parameter $\alpha$, the nonrandomness parameter of NRTL, is often heuristically fixed to a value in the range of 0.1 to 0.47. This can be seen as a manual application of a (subset selection) regularization method. In this work, the identifiability of the NRTL model for describing the VLE is analyzed. It is shown that fixing $\alpha$ is not always a good decision and sometimes leads to worse prediction properties of the final parameter estimates. Popular regularization techniques are compared and Generalized Orthogonalization is proposed as an alternative to this heuristic. In addition, the sequential Optimal Experimental Design and Parameter Estimation (sOED-PE) method is applied to study the influence of the regularization methods on the performance of the sOED-PE loop.