Effects of uncertainties in thermodynamic data and models on process calculations

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.