The design of chemical processes relies on simulation. However, the results are strongly dependent on thermodynamic models for the basic properties. While proper choice of the thermodynamic model is important, even with the best available model, the uncertainties in the model parameters and the experimental data that are used to regress them can be significant. The uncertainty induced from the different simulators is another factor to be considered in process design. Through a series of case studies on steady-state and dynamic simulation, we show how uncertainties of the thermodynamic data and models of the system can have a profound effect on the process design. In the meantime, the strategies for quantification of such thermodynamic-parameter-induced uncertainties via Monte Carlo simulation, with the Latin hypercube sampling technique, equal probability sampling technique, and regression analyses, are described, The study indicates that the designs developed through use of these models are significantly sensitive to the parameter uncertainties.