Gas-solid phase equilibrium of biosubstances by two biological algorithms



Título del documento: Gas-solid phase equilibrium of biosubstances by two biological algorithms
Revista: Revista mexicana de física
Base de datos: PERIÓDICA
Número de sistema: 000368122
ISSN: 0035-001X
Autors: 1
1
Institucions: 1Universidad de La Serena, Departamento de Física, La Serena, Coquimbo. Chile
Any:
Període: Nov-Dic
Volum: 59
Número: 6
Paginació: 577-583
País: México
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Analítico, teórico
Resumen en inglés Particle swarm optimization (PSO) and genetic algorithm (GA) are applied to the gas-solid phase equilibrium of biosubstances and to estimate their sublimation pressures (Ps). Four binary systems of supercritical carbon dioxide + biosubstances are considered in this study. The Peng-Robinson equation-of-state with the Wong-Sandler mixing rules, are used as a thermodynamic model to evaluate the fugacity coefficients in the classical solubility equation, and the van Laar model was incorporated to evaluate the excess Gibbs free energy included in the mixing rules. Then, the Ps is calculated from regression analysis of solubility data (y). Ps is usually small for most solid biosubstances and in many cases available experimental techniques cannot be used to obtain accurate values. Therefore, estimation methods must be used to obtain these data. PSO and GA are used for minimize the difference between calculated and experimental solubility. Comparing PSO with GA, it is shown that the results of PSO are better than that of GA, and provide a preferable method to estimate y and Ps of any biosubstances with high accuracy
Disciplines Física y astronomía
Paraules clau: Física,
Física de materia condensada,
Sublimación de presión,
Bioacumulación,
Gas-sólido,
Ecuaciones de estado,
Algoritmos genéticos,
Optimización por enjambre de partículas
Keyword: Physics and astronomy,
Condensed matter physics,
Physics,
Sublimation pressure,
Bioaccumulation,
Gas-solid,
Equation of state,
Genetic algorithms,
Particle swarm optimization
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