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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 Tc, 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.

Made available in DSpace on 2016-08-17T14:52:56Z (GMT). No. of bitstreams: 1 Juan Moises Mauricio Villanueva.pdf: 944458 bytes, checksum: 0b17f88c59a4149e61f4f3fa0969445e (MD5) Previous issue date: 2005-03-03 / In several applications in electronics, the generation of nonlinear function values using low-cost embedded systems is a problem. The nonlinear functions cannot be directly implemented due to restrictions of fixed-point calculations and limited resolution that are characteristics of the architecture of the processor employed. In this work, a procedure for determining piecewise linear approximation of nonlinear functions for a low-cost embedded system is presented. In order to solve this problem, a hierarchical evolutionary algorithm has been developed for determining the position and the minimal number of breakpoints and the minimal size of the look-up table for storing these breakpoints, for generating the approximated function values. The nonlinear function can be approximated using piecewise linear functions from the obtained breakpoints. The developed algorithm is tested using the case of approximating the first quadrant of a sine function, and the obtained results are presented for different resolutions for the input and output values generation. / Em diversas aplicações em eletrônica existe o problema de gerar valores de funções não lineares utilizando-se sistemas embarcados de baixo custo. Essas funções não lineares não podem ser implementadas diretamente devido às restrições de cálculo em ponto fixo e resolução limitada, características de arquitetura do processador empregado. Nesta dissertação, apresenta-se um procedimento para a determinação de aproximação linear por partes de funções não lineares para sistemas embarcados de baixo custo. Para resolver este problema, desenvolveu-se um algoritmo hierárquico evolutivo que determinará a posição e número mínimo de pontos de quebra e tamanho mínimo da tabela de equivalência para armazenar esses pontos de quebra, para gerar os valores da função aproximada. A função não linear pode então ser aproximada por funções lineares a partir dos valores dos pontos de quebra encontrados. O algoritmo desenvolvido é testado para o caso de aproximação da função seno no primeiro quadrante, e os resultados obtidos são apresentados para diversas resoluções de entrada e de geração dos valores de saída.