Abstract

A knowledge of thermophysical properties is necessary for the design of all process units. Reliable property prediction methods are essential because reliable experimental data are often not available due to concerns about measurement difficulty, cost, scarcity, safety, or environment. In particular, there is a lack of prediction methods for solid properties. Predicted property values can also be used to fill holes in property databases to understand more fully compound characteristics. This work is a comprehensive analysis of the prediction methods available for five commonly needed solid properties. Where satisfactory methods are available, recommendations are made; where methods are unsatisfactory in scope or accuracy, improvements have been made or new methods have been developed. In the latter case, the following general scheme has been used to develop correlations: extraction of a training set of experimental data of a specific accuracy from the DIPPR 801 database, selection of a class of equations to use in the correlation, refinement of the form of the equation through least squares regression, selection of the chemical groups and/or molecular descriptors to be used as independent variables, calculation of coefficient values using the training set, addition of groups where refinement is needed, and a final testing of the resultant correlation against an independent test set of experimental data.

Two new methods for predicting crystalline heat capacity were created. The first is a simple power law method (PL) that uses first-order functional groups. The second is derived as a modification of the Einstein-Debye canonical partition function (PF) that uses the same groups as the PL method with other descriptors to account for molecule size and multiple halogens. The PL method is intended for the temperature range of 50 to 250 K; the PF method is intended for temperatures above 250 K. Both the PL and PF methods have been assigned an uncertainty of 13% in their preferred temperature ranges based on comparisons to experimental data.

A method for estimating heat of sublimation at the triple point was created using the same groups as used in the heat capacity PF method (estimated to have an error of 13%). This method can be used in conjunction with the Clausius-Clapeyron equation to predict solid vapor pressure. Errors in predicted solid vapor pressures averaged about 44.9%. As most solid vapor pressures are extremely small, on the order of one Pascal, this error is small on an absolute scale.

An improvement was developed for an existing DIPPR correlation between solid and liquid densities at the triple point. The new correlation improves the prediction of solid density at the triple point and permits calculation of solid densities over a wide range of temperatures with an uncertainty of 6.3%.

Based on the analysis of melting points performed in this study, Marrero and Gani's method is recommended as the primary method of predicting melting points for organic compounds (deviation from experimental values of 12.5%). This method can be unwieldy due to the large number of groups it employs, so the method of Yalkowsky et al. (13.9% deviation) is given a secondary recommendation due to its broad applicability with few input requirements.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering and Technology; Chemical Engineering

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2003-11-11

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd300

Keywords

chemical engineering, thermodynamics, quantatative structure-property relationships, QSPR, group contributions, solid phase, organic compounds, property prediction

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