In this work, 2099 experimental data of binary systems composed of CO2 and ionic liquids are studied to predict solubility using a multilayer perceptron. The dataset includes 33 different types of ionic liquids over a wide range of temperatures, pressures, and solubilities. The main objective of this work is to propose a procedure for the prediction of CO2 solubility in ionic liquids by establishing four stages to determine the model parameters: (1) selection of the learning algorithm, (2) optimization of the first hidden layer, (3) optimization of the second hidden layer, and (4) selection of the input combination. In this study, a bound is set on the number of model parameters: the number of model parameters must be less than the amount of predicted data. Eight different learning algorithms with (4,m,n,1)-type hidden two-layer architectures (m = 2, 4, …, 10 and n = 2, 3, …, 10) are studied, and the artificial neural network is trained with three input combinations with three combinations of thermodynamic variables such as temperature (T), pressure (P), critical temperature (Tc), critical pressure, the critical compressibility factor (Zc), and the acentric factor (ω). The results show that the 4-6-8-1 architecture with the input combination T-P-Tc-Pc and the Levenberg–Marquard learning algorithm is a very acceptable and simple model (95 parameters) with the best prediction and a maximum absolute deviation close to 10%.
ResumenSe presenta un Método de Contribución de Grupos (MCG) para la estimación de propiedades de sustancias y se plantea como un método básico que podría ser incluido en cursos avanzados de termodinámica, fisicoquímica y física de fluidos. La idea es mostrar a los alumnos que las propiedades físicas y fisicoquímicas de las sustancias están relacionadas con su estructura química y características físicas y químicas de los átomos y grupos químicos que forman una molécula. Por lo tanto el método considera que una molécula está formada por grupos definidos a los que se les asigna un determinado valor como contribución al valor de una propiedad. Luego se supone que dicha contribución es la misma en todo compuesto donde dicho grupo esté presente. Este trabajo presenta algunos ejemplos que pueden ser mostrados en un curso formal y propone temas de tareas cortas y semestrales. Las tareas involucran conceptos generales asociados a la termodinámica, conocimiento sobre planillas de cálculo y manejo de bases de datos. Se discute sobre la utilidad del MCG como una herramienta en la metodología de aprendizaje basado en problemas. Se concluye que este tipo de conceptos y métodos se ajustan bien a cursos avanzados de pregrado o en tópicos y cursos de postgrado, en disciplinas como física, química, ingeniería química y otras afines.Palabras clave: contribución de grupos; método Joback-Reid; termodinámica; temperatura de fusión; aprendizaje basado en problemas
Group Contribution Method: A Fundamental Tool in Advanced Courses of Thermodynamics and Physics of Fluids for Estimating Properties of Substances AbstractThe so-called Group Contribution Method for estimating properties of substances is presented and discussion about its use as a basic method that could be included in advanced courses of thermodynamics, physicalchemistry and physics of fluids is done. The idea is to show students that physical and physical-chemical properties of substances are related to their chemical structure and to the physical and chemical nature of the atoms and groups that form a molecule. Therefore, the method considers that a molecule is formed by defined groups to which a contribution to a given property is assigned. After that, it is assumed that such a contribution is the same in any compound in which that group appears. This works presents some examples that can be shown in a formal course and proposes short and long term assignments. The assignments involve general concepts of thermodynamics, knowledge of spreadsheets calculations, and management of data bases. The usefulness of the Group Contribution Method as a tool in learning methodology based on problems is also discussed. It is concluded that that this types of methods are appropriate for advanced undergraduate and graduate courses of physics, chemistry, chemical engineering and related areas.
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