Gupta 9321865 Measurements of fluid-phase equilibrium necessarily involve experimental error. Current models developed from these data (both equations of state and activity coefficient models) typically are significantly less precise than are the data. The quality of process simulation results based on these models is, thereby, limited. The objective of this project is to develop a rational and rigorous method for evaluating the relationships between the uncertainties in thermodynamic experiments, models, and design. The approach is to use Monte Carlo simulation to study the sensitivities and uncertainties of base cases (chosen in collaboration with academic and industrial colleagues) and thereby to formulate and to evaluate heuristics for experimental design, model development, and process design. Preliminary results show that there is a very significant uncertainty in the design and operation of processes when one considers the seemingly small uncertainties in data for vapor-liquid equilibrium. A common approach in the chemical processing industries is to apply safety factors to designs to account for this uncertainty. However, Monte Carlo uncertainty analysis can quantify the uncertainty and lead to rationalization of the safety factor approach. The development of thermodynamic models from experimental data requires the regression of model parameters, and the results of these regressions can serve as input to the Monte Carlo simulations. However, the classical application of the maximum likelihood regression method assumes that the model is inherently more accurate than are the data. For phase-equilibrium data, often the inverse is true. Therefore, an approach will be developed that eliminates this inconsistency and allows one to compare models and their accuracy's on a rational basis. One ultimate goal of this work is to separate the uncertainty of physical-property into two categories: those caused by model uncertainty and those caused by experimental uncertainty. Mon te Carlo simulations will be performed to develop strategies to make this separation. ***
