Critical temperatures and pressures of 1-alkanols with 13 to 22 carbon atoms

Nikitin, Eugene D.; Pavlov, P. A.; Popov, Alexander P.

doi:10.1016/s0378-3812(98)00265-9

Cited by 63 publications

(35 citation statements)

References 16 publications

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“…The methods for measuring critical parameters of thermally unstable fluids or reactive compounds (flow method; transient pulse-heating thin wire probe) have been proposed by various authors (see, for example, Refs. [74][75][76][77][78][79]). These methods are capable of residence times between 0.01 ms and 30 s. Unfortunately, these methods have not been applied to the measurement of the critical parameters of methanol.…”

Section: Critical Parametersmentioning

confidence: 99%

“…These methods are capable of residence times between 0.01 ms and 30 s. Unfortunately, these methods have not been applied to the measurement of the critical parameters of methanol. An exception exists for higher molar mass alcohols (n > 12) for which Nikitin et al [77] used a transient pulse-heating thin-wireprobe technique to measure the critical parameters.…”

Section: Critical Parametersmentioning

confidence: 99%

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Thermodynamic Properties of Methanol in the Critical and Supercritical Regions

et al. 2005

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The isochoric heat capacity of pure methanol in the temperature range from 482 to 533 K, at near-critical densities between 274.87 and 331.59 kg · m −3 , has been measured by using a high-temperature and high-pressure nearly constant volume adiabatic calorimeter. The measurements were performed in the singleand two-phase regions including along the coexistence curve. Uncertainties of the isochoric heat capacity measurements are estimated to be within 2%. The single-and two-phase isochoric heat capacities, temperatures, and densities at saturation were extracted from experimental data for each measured isochore. The critical temperature (T c = 512.78 ± 0.02 K) and the critical density (ρ c = 277.49 ± 2 kg · m −3 ) for pure methanol were derived from the isochoric heat-capacity measurements by using the well-established method of quasi-static thermograms. The results of the C V V T measurements together with recent new experimental PVT data for pure methanol were used to develop a thermodynamically self-consistent Helmholtz free-energy parametric crossover model, CREOS97-04. The accuracy of the crossover model was confirmed by a comprehensive comparison with available experimental data for pure methanol and values calculated with various multiparameter equations of state and correlations. In the critical and supercritical regions at 0.98T c T 1.5T c and in the density range 0.35ρ c ρ 1.65ρ c , CREOS97-04 represents all available experimental thermodynamic data for pure methanol to within their experimental uncertainties.

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Section: Critical Parametersmentioning

confidence: 99%

Section: Critical Parametersmentioning

confidence: 99%

Thermodynamic Properties of Methanol in the Critical and Supercritical Regions

et al. 2005

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“…We believe this value to be more reliable than the either of the individual values of 0.290 or 0.292 m 3 ·kmol −1 predicted by the Ambrose [8] and Fedors [9] methods, respectively, because our testing for compounds where experimental data for V c are available has shown that this use of Eq. 7 to extrapolate volumetric data for the given compound is quite reliable.…”

Section: Tool 4: Property Predictionmentioning

confidence: 81%

Database Tools for Evaluating Thermophysical Property Data

et al. 2007

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Most thermophysical-property databases (TPD) provide low-level quality control checks. This manuscript focuses on additional, higher-level data evaluations made possible by the breadth of data stored in the database. For example, thermodynamic equations relate the critical point, vapor-pressure curve, enthalpy of vaporization, liquid density, and liquid and vapor heat capacities to each other. Thermodynamic consistency among these properties can be used to guide selection of the best data sets. Even more broadly, molecular structure-based trends in properties can be identified within the database, and the properties of structurally related compounds can be effectively used to discriminate among available datasets. Automated property predictions can be used in conjunction with the TPD to guide the selection of the most accurate data. These and other high-level consistency tools will be illustrated based on evaluation and quality control work associated with the DIPPR 801 TPD project for pure chemicals.

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“…The development of such methods made it possible to perform measurements of the critical constants of a number of unstable compounds; however, they are characterized by certain ranges of validity. For example, at present, the critical properties of the homologous series of n-alkanes C n H 2n+2 have been measured up to hexatriacontane (n = 36) inclusive [13], those of 1-alkanols C n H 2n+1 OH -to docosanol (n = 22) [14], and those of alkanoic acids C n H 2n+1 COOH -to n = 21 [15]. The critical properties of heavier homologs may be only calculated so far.…”

Section: Methods Of Measuring the Critical Properties Of Thermally Unmentioning

confidence: 99%

“…We used these equations to advantage for correlation of the critical constants of different homologous series [13][14][15]29]. Such correlation was performed separately for each homologous series.…”

Section: Methods Of Calculation Of Critical Constantsmentioning

confidence: 99%