Article pubs.acs.org/jced
Effect of Phase-Equilibrium Uncertainties on Ethyl Acetate Purification Paul M. Mathias* and Henry Z. Kister Fluor Corporation, 3 Polaris Way, Aliso Viejo, California 92698, United States ABSTRACT: This paper continues application of the Margules-based phase-equilibrium uncertainty method to systems with weak and strong deviations from Raoult’s law. The method was developed in order to provide practicing engineers with an intuitive and easily applicable method to quantitatively relate process-design uncertainties to uncertainties in correlated physical properties, specifically nonideal phase equilibrium. The methodology has been used in several case studies: (1) a propylene + propane superfractionator for which small changes in correlated relative volatilities have a large effect on the design of the distillation column; (2) a dehexanizer column that separates a mixture containing many close-boiling hydrocarbon components; (3) a distillation train that separates the acetone + chloroform + benzene ternary mixture, which contains one maximum-boiling azeotrope; and (4) separation of the water + 1-butanol binary mixture, which has a heterogeneous azeotrope. The present work applies the methodology to the purification of ethyl acetate from a ternary mixture with ethanol and water. This purification step occurs in the production of ethyl acetate via esterification of acetic acid with ethanol, and the process design is complex because the ethyl acetate + ethanol + water ternary mixture exhibits a liquid−liquid region and four azeotropes.
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INTRODUCTION Quantification of the uncertainties in measurements of physical properties is becoming a requirement in the chemical-sciences literature. Five key journals in the field of thermodynamics (Journal of Chemical and Engineering Data, Journal of Chemical Thermodynamics, Fluid Phase Equilibria, Thermochimica Acta, and International Journal of Thermophysics) have mandated reporting of combined uncertainties together with the experimental data tables.1−6 However, uncertainties in property correlations are largely ignored in chemical process design (see, e.g., Kim et al.7). Mathias8 recognized that liquid-phase nonideality is a major source of phase-equilibrium uncertainty and developed an intuitive and easy-to-apply method based upon treating the mixture, for the purpose of activity-coefficient perturbation, as a set of pseudobinaries described by the Margules equation. Mathias first applied his methodology to two case studies:8 (1) a propylene + propane distillation column for which small changes in correlated relative volatilities have a large effect on the design of the distillation column and (2) a dehexanizer column that separates a mixture containing many close-boiling hydrocarbon components. The results are in broad agreement with the rules of thumb published by Fair,9 since the design sensitivity is large when the relative volatility approaches unity, but in addition showed quantitatively how the design uncertainty increases with product purity. In a subsequent study, Mathias10 applied the methodology to a distillation train that separates the acetone + chloroform + benzene ternary mixture, which contains one © 2017 American Chemical Society
maximum-boiling azeotrope. This study demonstrated the relationship between phase-equilibrium uncertainty and residue-curve maps11−13 and showed that the approach quantifies the effect of phase-equilibrium uncertainties on process heat requirements and also identifies limits on operating variables (specifically minimum recycle flow). The exercise showed that phase-equilibrium uncertainties have less effect on synthesis (e.g., flowsheet structure) than on column performance (e.g., utility consumption) and therefore that phase-equilibrium uncertainties may be approximated or even ignored in process synthesis but must be quantified in process design. In a third study, the methodology was applied to the separation of the water + 1-butanol binary mixture,14 which is a part of processes to purify butanol produced by fermentation.15 The separation is complex because the binary mixture exhibits liquid−liquid equilibrium and forms a heterogeneous azeotrope and the typical separation scheme includes a decanter as well as two distillation columns. Furthermore, the chosen activity-coefficient model (NRTL16) is unable to correlate the data within the measurement uncertainty. Nevertheless, the methodology was demonstrated to provide useful quantitative estimates of the Special Issue: Memorial Issue in Honor of Ken Marsh Received: February 14, 2017 Accepted: June 7, 2017 Published: June 21, 2017 2872
DOI: 10.1021/acs.jced.7b00172 J. Chem. Eng. Data 2017, 62, 2872−2883
Journal of Chemical & Engineering Data
Article
design uncertainty and also showed that the design uncertainties can be reduced to acceptable levels if the property-correlation development is focused on the region of application. The present work applies the Margules uncertainty approach to the purification of ethyl acetate from an ethyl acetate + ethanol + water ternary mixture. This purification step occurs in the production of ethyl acetate via esterification of acetic acid with ethanol, which has been practiced for many decades.17 More recent publications have presented simulation studies of the production of ethyl acetate through the esterification process.18−20 The present work follows the flowsheet and design of Tang, Huang, and Chien20 but simplifies the modeling of the reactive-distillation column in order to focus on the purification of ethyl acetate from the ethyl acetate + ethanol + water ternary mixture The methodology used here follows that of the previous three studies.8,10,14 The first step is comprehensive data regression to obtain the best model fit in the region of application (atmospheric pressure, about 101.3 kPa) and quantitative estimation of the correlation uncertainties. This is a necessary first step. In the second step, activity coefficients are varied within the process model to relate the design uncertainties to the confidence limits of the correlated model.
model in the NRTL-RK property option in Aspen Plus V8.8. The NRTL-RK property option uses the NRTL activity-coefficient model16 and the Redlich−Kwong equation of state21 for the vapor phase. The pure-component properties come from the Aspen Plus V8.8 pure-component database. This study is at a pressure of 1 atm (101.3 kPa), and the temperatures of interest are approximately 350−375 K, at which the correlations for the vapor pressures of these three compounds from the Aspen Plus database are expected to be accurate to better than 1%. The basis for this confidence is that the Aspen Plus pure-component properties come from the DIPPR 801 database,22 and the correlated vapor pressures have been reported to be accurate to
