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.
By Peter D. Pavlechko
