Effects of Uncertainties in Thermodynamic Data and Models on Process Calculations

Abstract

Thermodynamic models and experimental data exhibit systematic and random errors. The severity of their errors depends on their use, such as for process calculations in a process simulator. Similarly, the value of better thermodynamic models and/or data should be measured with reference to such use. Strategies for quantification of such thermodynamics-induced process uncertainties via Monte Carlo simulation, regression analysis, and analogies to optimization are described, with simple examples. Such approaches can be used for safety-factor/risk analysis, guidelines for process simulator use, experimental design, and model comparisons.

Uncertainty analysis applied to thermodynamic models and process design – 1. Pure components

“…The objective of this work is to expand on ideas put forth by Whiting and co-workers [3][4][5][6][7][8] through the development of a comprehensive database of critical parameters, acentric factors, interaction parameters and estimation techniques for pure component physical properties and binary interaction parameters taking advantage of new uncertainty on fundamental physical property data available now through NIST's Thermo Data Engine (TDE) [9]. A unique feature of this development is related to the uncertainty information encoded in this new database as well as in estimation methods necessary for the modeling of pseudo-components used to model complex hydrocarbon fluids.…”

“…Notably Whiting and co-workers [3][4][5][6][7][8] showed the importance of the effect of physical property inaccuracies on process design. At the time little quantitative information related to uncertainty was available and these earlier studies were performed using average uncertainties estimated for different physical properties such as the evaluations performed by DIPPR (design institute for physical properties) [18].…”

Effects of physical properties estimation on process design: a case study using AspenPlus

“…In the literature, influence of the number of regressed parameters has already been observed. [4,18] It has been shown that number of parameters not only affects the VLE prediction but also, the simulation results (even without apparent discrepancies in VLE prediction). Boiling point NH3 −33.4…”

“…[3 -6] Then, even with suitable thermodynamic models, the uncertainties in the model parameters can be significant. Through a literature review, Whiting [4] showed the consequences of inaccurate input parameters on process simulation results. According to the author, one should consider the entire system of data generation, model choice, parameters regression/estimation and simulation software choice to provide an actual accurate process optimization.…”

Uncertainty analysis of the CPA and a quadrupolar CPA equation of state – With emphasis on CO2

“…The work of Whiting and co-workers [3,[11][12][13][14][15][16] is perhaps one of the most notable contributions to uncertainty estimates of thermodynamic models. Using a Monte Carlo approach the authors analysed the effect of uncertainties in ther-2 modynamic data and their effect on process design.…”