Abstract Extractive distillation processes with N-formylmorpholine (NFM) are used industrially to separate benzene from six carbon non-aromatics. In the process studied in this work, the stream of interest consists of nearly 20 different hydrocarbons. A new set of NRTL parameters was correlated based on literature experimental data. Both vapor-liquid equilibrium as well as infinite dilution activity coefficient data were taken into account; missing parameters were estimated with the UNIFAC group contribution model. The extractive distillation process was simulated using ASPEN Plus®. Very good agreement with plant data was obtained. The influences of the main operational parameters, solvent to feed ratio and solvent temperature, were studied. Theoretical optimum operating values were obtained and can be implemented to improve the industrial process. Extreme static sensitivity with respect to reboiler heat was observed, indicating that this can be the source of instabilities.

Currently, the most successful predictive models for activity coefficients are those based on functional groups such as UNIFAC. In contrast, these models require a large amount of experimental data for the determination of their parameter matrix. A more recent alternative is the models based on COSMO, for which only a small set of universal parameters must be calibrated. In this work, a recalibrated COSMO-SAC model was compared with the UNIFAC (Do) model employing experimental infinite dilution activity coefficient data for 2236 non-hydrogen-bonding binary mixtures at different temperatures. As expected, UNIFAC (Do) presented better overall performance, with a mean absolute error of 0.12 ln-units against 0.22 for our COSMO-SAC implementation. However, in cases involving molecules with several functional groups or when functional groups appear in an unusual way, the deviation for UNIFAC was 0.44 as opposed to 0.20 for COSMO-SAC. These results show that COSMO-SAC provides more reliable predictions for multi-functional or more complex molecules, reaffirming its future prospects.

Cubic equations of state combined with excess Gibbs energy predictive models (like UNIFAC) and equations of state based on applied statistical mechanics are among the main alternatives for phase equilibria prediction involving polar substances in wide temperature and pressure ranges. In this work, the predictive performances of the PC-SAFT with association contribution and Peng-Robinson (PR) combined with UNIFAC (Do) through mixing rules are compared. Binary and multi-component systems involving polar and non-polar substances were analyzed. Results were also compared to experimental data available in the literature. Results show a similar predictive performance for PC-SAFT with association and cubic equations combined with UNIFAC (Do) through mixing rules. Although PC-SAFT with association requires less parameters, it is more complex and requires more computation time.

The modeling, simulation, and dynamic optimization of an industrial reaction system for acetylene hydrogenation are discussed in the present work. The process consists of three adiabatic fixed-bed reactors, in series, with interstage cooling. These reactors are located after the compression and the caustic scrubbing sections of an ethylene plant, characterizing a front-end system; in contrast to the tail-end system where the reactors are placed after the de-ethanizer unit. The acetylene conversion and selectivity profiles for the reactors are optimized, taking into account catalyst deactivation and process constraints. A dynamic optimal temperature profile that maximizes ethylene production and meets product specifications is obtained by controlling the feed and intercoolers temperatures. An industrial acetylene hydrogenation system is used to provide the necessary data to adjust kinetics and transport parameters and to validate the approach.