Effect of systematic and random errors in thermodynamic models on chemicalprocess design and simulation: A Monte Carlo approach

A Monte Carlo method is presented to separate and study the effects of syst ematic and random errors present in thermodynamic data on chemical process design and simulation. From analysis of thermodynamic data found in the lit erature, there is clear evidence of the presence of systematic errors, in p articular, for liquid-liquid equilibria data. For systematic errors, the da ta are perturbed systematically with a rectangular probability distribution , and to analyze random errors, the perturbation is carried out randomly wi th normal probability distributions. Thermodynamic parameters are obtained from appropriate regression methods and used to simulate a given unit opera tion and to obtain cumulative frequency distributions, providing a quantita tive risk assessment and a better understanding of the role of uncertainty in process design and simulation. The results show that the proposed method can clearly distinguish when one type of error is more significant. Potent ial applications are safety factors, process modeling, and experimental des ign.