Although scientists spend a great deal of time building and revising EoS, and much journal space is dedicated to introducing these valuable tools, the question of their accuracy inevitably arises because these models are, by definition, a simplification of reality. For a new model, the absence of serious validation seems to be the major obstacle to its industrialization. Moreover, the comparison between experimental data and model predictions is often limited to phase equilibria, ignoring other properties like enthalpies and heat capacities. It is believed that such a time lapse is extensive because, among other reasons, developers usually do not confront model predictions to experimental data that cover a wide range of compositions, temperatures, and pressures. As a rule of thumb, it takes 10 years (3) for a new model to be conceived, developed, validated, and accepted by the industry. However, the industry sometimes seems skeptical when weighing the value gained using a more complex model and rarely updates (2) or replaces its thermodynamic models with newer ones unless a clear advantage is evident. In the academic community, it is often assumed that the accuracy of incoming models is increasing over the years, as they become more sophisticated. It can be argued that in 2020, chemical engineering thermodynamics is a field under steady growth since new models are continuously under development. (1) Indeed, combining an EoS and ideal-gas heat capacities enables one to calculate not only phase equilibria but also all of the thermodynamic properties needed for energy and exergy balances (enthalpy, entropy, exergy, heat capacities, etc.). As a result of their great potential, equations of state (EoSs) represent the cornerstone of thermodynamic models. As an illustration, the Peng–Robinson EoS with classical van der Waals mixing rules and a temperature-dependent binary interaction parameter ( k ij) have been used to correlate the numerous data included in the proposed database, and its performance has been assessed following the proposed methodology.Ĭommercially available computer-aided-process-design software requires thermodynamic models to design, develop, analyze, and optimize chemical processes. The methodology for assessing the performance of a given model is then described. A total of 200 nonelectrolytic binary systems have been selected and divided into nine groups according to the associating character of the components, i.e., their ability to be involved in a hydrogen bond (the nature and strength of the association phenomena are indeed considered a measure of the complexity to model the thermodynamic properties of mixtures).
The goal of this paper is thus to present a database, specifically designed to assess the accuracy of a thermodynamic model or cross-compare models, to explain how it was developed and to enlighten how to use it. In this context, the importance of a reliable free-to-access benchmark database is pivotal and becomes absolutely necessary. Chemical engineering thermodynamics is thus a field under steady development, and to assess the accuracy of a thermodynamic model or to cross-compare two models, it is necessary to confront model predictions with experimental data. In the last 20 years, thousands of publications have been devoted to the development of sophisticated models or to the improvement of already existing EoSs. They not only can be applied to pure substances as well as to mixtures but also constitute the heart of commercially available computer-aided-process-design software. In the last two centuries, equations of state (EoSs) have become a key tool for the correlation and prediction of thermodynamic properties of fluids.