Thermodynamic Property Prediction for Solid Organic Compounds Based on Molecular Structure

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...

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Main Author: Goodman, Benjamin T.
Format: Others
Published: BYU ScholarsArchive 2003
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Online Access:https://scholarsarchive.byu.edu/etd/106
https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1105&context=etd
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spelling ndltd-BGMYU2-oai-scholarsarchive.byu.edu-etd-11052019-05-16T03:34:46Z Thermodynamic Property Prediction for Solid Organic Compounds Based on Molecular Structure Goodman, Benjamin T. 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. 2003-11-11T08:00:00Z text application/pdf https://scholarsarchive.byu.edu/etd/106 https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1105&context=etd http://lib.byu.edu/about/copyright/ All Theses and Dissertations BYU ScholarsArchive chemical engineering thermodynamics quantatative structure-property relationships QSPR group contributions solid phase organic compounds property prediction Chemical Engineering
collection NDLTD
format Others
sources NDLTD
topic chemical engineering
thermodynamics
quantatative structure-property relationships
QSPR
group contributions
solid phase
organic compounds
property prediction
Chemical Engineering
spellingShingle chemical engineering
thermodynamics
quantatative structure-property relationships
QSPR
group contributions
solid phase
organic compounds
property prediction
Chemical Engineering
Goodman, Benjamin T.
Thermodynamic Property Prediction for Solid Organic Compounds Based on Molecular Structure
description 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.
author Goodman, Benjamin T.
author_facet Goodman, Benjamin T.
author_sort Goodman, Benjamin T.
title Thermodynamic Property Prediction for Solid Organic Compounds Based on Molecular Structure
title_short Thermodynamic Property Prediction for Solid Organic Compounds Based on Molecular Structure
title_full Thermodynamic Property Prediction for Solid Organic Compounds Based on Molecular Structure
title_fullStr Thermodynamic Property Prediction for Solid Organic Compounds Based on Molecular Structure
title_full_unstemmed Thermodynamic Property Prediction for Solid Organic Compounds Based on Molecular Structure
title_sort thermodynamic property prediction for solid organic compounds based on molecular structure
publisher BYU ScholarsArchive
publishDate 2003
url https://scholarsarchive.byu.edu/etd/106
https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1105&context=etd
work_keys_str_mv AT goodmanbenjamint thermodynamicpropertypredictionforsolidorganiccompoundsbasedonmolecularstructure
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