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How a Systematic Approach to Uncerta...

Messerly, Richard Alma.

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How a Systematic Approach to Uncertainty Quantification Renders Molecular Simulation a Quantitative Tool in Predicting the Critical Constants for Large n-Alkanes.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	How a Systematic Approach to Uncertainty Quantification Renders Molecular Simulation a Quantitative Tool in Predicting the Critical Constants for Large n-Alkanes./
作者:	Messerly, Richard Alma.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	243 p.
附註:	Source: Dissertations Abstracts International, Volume: 78-08, Section: B.
Contained By:	Dissertations Abstracts International78-08B.
標題:	Molecular chemistry. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10250881
ISBN:	9781369478945

How a Systematic Approach to Uncertainty Quantification Renders Molecular Simulation a Quantitative Tool in Predicting the Critical Constants for Large n-Alkanes.
Messerly, Richard Alma.

How a Systematic Approach to Uncertainty Quantification Renders Molecular Simulation a Quantitative Tool in Predicting the Critical Constants for Large n-Alkanes. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 243 p.

Source: Dissertations Abstracts International, Volume: 78-08, Section: B.

Thesis (Ph.D.)--Brigham Young University, 2017.

This item is not available from ProQuest Dissertations & Theses.

Accurate thermophysical property data are crucial for designing efficient chemical processes. For this reason, the Design Institute for Physical Properties (DIPPR 801) provides evaluated experimental data and prediction of various thermophysical properties. The critical temperature (Tc), critical density (ρc), critical pressure ( Pc), critical compressibility factor (Zc ), and normal boiling point (Tb) are important constants to check for thermodynamic consistency and to estimate other properties. The n-alkane family is of primary interest because it is generally assumed that other families of compounds behave similarly to the n-alkane family with increasing chain-length. Unfortunately, due to thermal decomposition, experimental measurements of T c, ρc, and Pc for large n-alkanes are scarce and potentially unreliable. For this reason, molecular simulation is an attractive alternative for estimating the critical constants. However, molecular simulation has often been viewed as a tool that is limited to providing qualitative insight. One key reason for this perceived weakness is the difficulty in quantifying the uncertainty of the simulation results. This research focuses on a systematic top-down approach to quantifying the uncertainty in Gibbs Ensemble Monte Carlo (GEMC) simulations for large n-alkanes. We implemented four different methods in order to obtain quantitatively reliable molecular simulation results. First, we followed a rigorous statistical analysis to assign the uncertainty of the critical constants when obtained from GEMC. Second, we developed an improved method for predicting Pc with the standard force field models in the literature. Third, we implemented an experimental design to reduce the uncertainty associated with Tc, ρc, Pc, and Zc. Finally, we quantified the uncertainty associated with the Lennard-Jones 12-6 potential parameters. This research demonstrates how uncertainty quantification renders molecular simulation a quantitative tool for thermophysical property evaluation. Specifically, by quantifying and reducing the uncertainty associated with molecular simulation results, we were able to discern between different experimental data sets and prediction models for the critical constants. In this regard, our results enabled the development of improved prediction models for Tc , ρc, Pc, and Zc for large n-alkanes. In addition, we developed a new Tb prediction model in order to ensure thermodynamic consistency between Tc, Pc, and Tb.

ISBN: 9781369478945Subjects--Topical Terms:

1071612
Molecular chemistry.

How a Systematic Approach to Uncertainty Quantification Renders Molecular Simulation a Quantitative Tool in Predicting the Critical Constants for Large n-Alkanes.
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520 $a Accurate thermophysical property data are crucial for designing efficient chemical processes. For this reason, the Design Institute for Physical Properties (DIPPR 801) provides evaluated experimental data and prediction of various thermophysical properties. The critical temperature (Tc), critical density (ρc), critical pressure ( Pc), critical compressibility factor (Zc ), and normal boiling point (Tb) are important constants to check for thermodynamic consistency and to estimate other properties. The n-alkane family is of primary interest because it is generally assumed that other families of compounds behave similarly to the n-alkane family with increasing chain-length. Unfortunately, due to thermal decomposition, experimental measurements of T c, ρc, and Pc for large n-alkanes are scarce and potentially unreliable. For this reason, molecular simulation is an attractive alternative for estimating the critical constants. However, molecular simulation has often been viewed as a tool that is limited to providing qualitative insight. One key reason for this perceived weakness is the difficulty in quantifying the uncertainty of the simulation results. This research focuses on a systematic top-down approach to quantifying the uncertainty in Gibbs Ensemble Monte Carlo (GEMC) simulations for large n-alkanes. We implemented four different methods in order to obtain quantitatively reliable molecular simulation results. First, we followed a rigorous statistical analysis to assign the uncertainty of the critical constants when obtained from GEMC. Second, we developed an improved method for predicting Pc with the standard force field models in the literature. Third, we implemented an experimental design to reduce the uncertainty associated with Tc, ρc, Pc, and Zc. Finally, we quantified the uncertainty associated with the Lennard-Jones 12-6 potential parameters. This research demonstrates how uncertainty quantification renders molecular simulation a quantitative tool for thermophysical property evaluation. Specifically, by quantifying and reducing the uncertainty associated with molecular simulation results, we were able to discern between different experimental data sets and prediction models for the critical constants. In this regard, our results enabled the development of improved prediction models for Tc , ρc, Pc, and Zc for large n-alkanes. In addition, we developed a new Tb prediction model in order to ensure thermodynamic consistency between Tc, Pc, and Tb.
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