Published June 2004
As discussed in PEP Reviews 2001-2 and 2004-1, process design attempts to maximize the economic impact and define the best possible process. However, the underlying physical properties used to define the process can be a major source of error. Where the previous reviews focused on gas solubility and vapor-liquid equilibria (VLE), this review examines the impact of liquid-liquid equilibria (LLE) on process design.
As with gas solubility and VLE, early LLE assumptions tend to favor a preferred property system unless there is a priori knowledge of the system that alters the choice. An engineer rarely has the luxury of researching the strengths and weaknesses of alternate property models, or to evaluate the depth and breadth of parameters available for the chosen property model.
Existing processes mostly do not pose such a problem, since some effort will typically be expended on such issues during the development and commercialization of the process. However, for new or existing processes, lack of appropriate property information does present a problem. The consequences are illustrated using same ammoximation process from the previous reviews employing ammonia gas and aqueous hydrogen peroxide to convert cyclohexanone to cyclohexanone oxime. Although this review focuses on the extraction step in the refining section rather than the steps of the reaction section.
The impact of NRTL parameter choices is discussed concerning an extraction column. A complication for LLE is that parameters intended for VLE may not be suitable for LLE or both simultaneously. Using the parameters intended for VLE in an LLE problem are highlighted in the first example case, while the next case studies the addition of available LLE parameters and the use of secondary data sets intended specifically for LLE problems. The final case regresses parameters from data and discusses the impact of limited or approximated data.
This review is intended to illustrate physical property problems and limitations, as well as the impact on simulation results and subsequent process design.