Vapour-liquid equilibria and heats of mixing in n-alkane-1-alcohol systems. III. Vapour-liquid equilibria in n-alkane-1-dodecanol systems

Schmelzer, Jürgen; Creutziger, Verena; Lieberwirth, I.; Pfestorf, R.

doi:10.1016/0378-3812(83)80025-9

Cited by 15 publications

(1 citation statement)

References 6 publications

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“…Pertinent 1-alcohol + n- alkane literature data (within the range of C 5 –C 14 ) were investigated and implied that in 1-alcohol + n- alkane systems with m < n , where m and n represent the number of carbon atoms in the alkyl chains of the 1-alcohol and n- alkane, respectively, an azeotrope will form in the T – x – y phase-behavior envelope. − Predictive models are not always equipped to account for complexities arising from complex molecular interactions. Therefore, the nonrandom two-liquid (NRTL) composition-dependent model will be considered alongside the PSRK model in this article.…”

Section: Introductionmentioning

confidence: 99%

Low-Pressure VLE Measurements and Thermodynamic Modeling, with PSRK and NRTL, of Binary 1-Alcohol + n-Alkane Systems

Ferreira

Schwarz

2018

J. Chem. Eng. Data

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New subatmospheric vapor−liquid equilibrium (VLE) data for five binary 1-alcohol + n-alkane systems are presented. An all-glass dynamic recirculating still was used to measure the phase behavior of the 1-pentanol + n-nonane, 1-hexanol + n-decane, 1-heptanol + n-undecane, 1-octanol + n-dodecane, and 1-decanol + n-tetradecane systems at 40 kPa. Each of the five systems displayed an azeotrope, indicating complex molecular interactions. Thermodynamic modeling was conducted with (1) the NRTL activity coefficient model, (2) the predictive Soave− Redlich−Kwong (PSRK) group contribution equation of state (EoS) with original UNIFAC model parameters, and (3) PSRK with newly regressed NRTL parameters. The models, in order of decreasing performance, were NRTL > PSRK (with UNIFAC) > PSRK (with NRTL). Therefore, the PSRK with UNIFAC parameters G E -EoS investigated in this article showed that accurate 1-alcohol + n-alkane VLE data can be predicted without the need to first measure experimental data.

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