Desarrollo de un algoritmo de optimización global en colonias de hormigas con selección de región factible para espacios continuos

ResumenEn este estudio se introduce un nuevo algoritmo para la metaheurística de optimización de colonias de hormigas (ACO) que se ha desarrollado para resolver problemas de optimización global con variables de decisión continuas. El algoritmo propuesto, denominado ACO-FRS, comprende una estrategia para la selección de regiones factibles para el proceso de optimización y realiza la exploración del espacio de solución de forma similar al proceso que realizan las hormigas para la búsqueda de alimento. Se han evaluado 4variantes de este algoritmo empleando varios problemas clásicos de optimización global, y los resultados obtenidos se han comparado con los informados en la literatura para otros algoritmos del tipo ACO para espacios continuos. En general, los resultados obtenidos indican que la inclusión de una selección de regiones factibles permite realizar una búsqueda global mediante la eventual exploración de regiones con bajos niveles de feromonas, aumentando así la viabilidad del método para la localización de la solución del problema de optimización.AbstractThis study introduces a new algorithm for the ant colony optimization (ACO) method, which has been proposed to solve global optimization problems with continuous decision variables. This algorithm, namely ACO-FRS, involves a strategy for the selection of feasible regions during optimization search and it performs the exploration of the search space using a similar approach to that used by the ants during the search of food. Four variants of this algorithm have been tested in several benchmark problems and the results of this study have been compared with those reported in literature for other ACO-type methods for continuous spaces. Overall, the results show that the incorporation of the selection of feasible regions allows the performing of a global search to explore those regions with low level of pheromone, thus increasing the feasibility of ACO for finding the global optimal solution

Development of a global optimization algorithm in ant colonies with feasible region selection for continuous search spaces

En este estudio se introduce un nuevo algoritmo para la metaheurística de optimización de colonias de hormigas (ACO) que se ha desarrollado para resolver problemas de optimización global con variables de decisión continuas. El algoritmo propuesto, denominado ACO-FRS, comprende una estrategia para la selección de regiones factibles para el proceso de optimización y realiza la exploración del espacio de solución de forma similar al proceso que realizan las hormigas para la búsqueda de alimento. Se han evaluado 4 variantes de este algoritmo empleando varios problemas clásicos de optimización global, y los resultados obtenidos se han comparado con los informados en la literatura para otros algoritmos del tipo ACO para espacios continuos. En general, los resultados obtenidos indican que la inclusión de una selección de regiones factibles permite realizar una búsqueda global mediante la eventual exploración de regiones con bajos niveles de feromonas, aumentando así la viabilidad del método para la localización de la solución del problema de optimización.This study introduces a new algorithm for the ant colony optimization (ACO) method, which has been proposed to solve global optimization problems with continuous decision variables. This algorithm, namely ACO-FRS, involves a strategy for the selection of feasible regions during optimization search and it performs the exploration of the search space using a similar approach to that used by the ants during the search of food. Four variants of this algorithm have been tested in several benchmark problems and the results of this study have been compared with those reported in literature for other ACO-type methods for continuous spaces. Overall, the results show that the incorporation of the selection of feasible regions allows the performing of a global search to explore those regions with low level of pheromone, thus increasing the feasibility of ACO for finding the global optimal solution.Peer Reviewe

Optimización multi-objetivo del desempeño y consumo energético de procesos de destilación intensificados para sistemas cuaternarios

Este artículo presenta el diseño y optimización multiobjetivo de sistemas de destilación convencionales e intensificados para la separación de mezclas cuaternarias. Tres casos de estudio fueron utilizados para demostrar las ventajas y limitaciones de las configuraciones de destilación convencionales, nonsharp e intensificadas en términos de la optimización simultánea de la composición de un compuesto clave y el consumo energético para su separación. Los resultados indican que el desempeño de estos esquemas de destilación puede variar sustancialmente cuando la pureza y eficiencia energética del proceso son los objetivos en conflicto. La secuencia convencional de destilación directa puede igualar o incluso mejorar el desempeño de algunos procesos intensificados en las mezclas analizadas. En este trabajo se resalta la importancia de realizar un diseño robusto de secuencias de separación multicomponente con la finalidad de evaluar sus ventajas y limitaciones

Bubble and Dew Point Calculations in Multicomponent and Multireactive Mixtures

Bubble and dew point calculations are useful in chemical engineering and play an important role in the study of separation equipments for non-reactive and reactive mixtures. To the best of the authors’s knowledge, few methods have been proposed for these calculations in systems with several chemical reactions. The objective of this paper is to introduce new conditions for performing bubble and dew point calculations in reactive mixtures. We have developed these conditions based on the application of transformed variables of Ung and Doherty (1995). Using these transformed variables, the solution space is restricted to compositions that are already at chemical equilibrium and by consequence the problem dimension is also reduced. The reliability and efficiency of three equation-solving methods are tested and compared using our equilibrium conditions: a) a simultaneous equation-solving approach using Newton method (SESN), b) an equation-decoupling approach using successive substitution method (EDSS) and c) an optimization approach using the stochastic optimization method Simulated Annealing(OSA). Our results indicated that even for simple reactive systems, bubble and dew point calculations are challenging for classical equation-solving methods and require robust strategies. We conclude that OSA and EDSS methods are reliable to locate bubble and dew points in reactive systems. EDSS is more efficient than OSA; however, OSA does not need initial guesses and is more suitable for difficult problems

Design and Control Properties of Arrangements for Distillation of Four Component Mixtures Using Less Than N-1 Columns

The design and study of dynamic properties of distillation sequences using side-stream columns with less than N-1 columns for separation of four-component mixtures were studied. Total annual cost and dynamic properties (using singular value decomposition) were used to compare properties of arrangements with less than N-1 columns to base cases with three columns (conventional case). Quaternary feeds containing four hydrocarbons were analyzed. For systems with low concentrations of one component in the feed, side-stream cascades often show significantly lower operating, capital costs and best dynamic properties compared to the base cases. Low purity requirements also favor side-stream cascades. Some rules are presented to predict which sequence will have the lowest energy consumption capital cost and better dynamic properties

Gibbs Energy Minimization Using Simulated Annealing for Two-phase Equilibrium Calculations in Reactive Systems

Phase equilibrium calculations in systems subject to chemical reactions are involved in the design, synthesis and optimization of reactive separation processes. Until now, several methods have been developed to perform simultaneously physical and chemical equilibrium calculations. However, published methods may face numerical difficulties such as variable initialization dependence, divergence and convergence to trivial solutions or unstable equilibrium states. Besides, these methods generally use conventional composition variables and reactions extents as unknowns which directly affect the numerical implementation, reliability and efficiency of solving strategies. The objective of this work is to introduce and test an alternative approach to perform Gibbs energy minimization in phase equilibrium problems for reactive systems. Specifically, we have employed the transformed composition variables of Ung and Doherty and the stochastic optimization method Simulated Annealing for two-phase equilibrium calculations in reacting systems. Performance of this strategy has been tested using several benchmark problems and results show that proposed approach is generally suitable for the global minimization of transformed Gibbs energy in reactive systems with two-phase equilibrium

Gibbs Energy Minimization Using Simulated Annealing for Two-phase Equilibrium Calculations in Reactive Systems

Phase equilibrium calculations in systems subject to chemical reactions are involved in the design, synthesis and optimization of reactive separation processes. Until now, several methods have been developed to perform simultaneously physical and chemical equilibrium calculations. However, published methods may face numerical difficulties such as variable initialization dependence, divergence and convergence to trivial solutions or unstable equilibrium states. Besides, these methods generally use conventional composition variables and reactions extents as unknowns which directly affect the numerical implementation, reliability and efficiency of solving strategies. The objective of this work is to introduce and test an alternative approach to perform Gibbs energy minimization in phase equilibrium problems for reactive systems. Specifically, we have employed the transformed composition variables of Ung and Doherty and the stochastic optimization method Simulated Annealing for two-phase equilibrium calculations in reacting systems. Performance of this strategy has been tested using several benchmark problems and results show that proposed approach is generally suitable for the global minimization of transformed Gibbs energy in reactive systems with two-phase equilibrium

Adsorption of emerging pollutants on lignin-based activated carbon: Analysis of adsorption mechanism via characterization, kinetics and equilibrium studies

Lignin has been employed as a precursor to synthesize activated carbons with the aim of lignin-biomass revalorization. The properties of these activated carbons were compared, and the best adsorbent was employed to remove two emerging pollutants from water, acetaminophen and acetamiprid. The adsorption mechanisms of pharmaceutical and pesticide compounds were analyzed, modeled and interpreted via statistical physics models. In particular, adsorption kinetics and isotherms of acetaminophen and acetamiprid at temperatures between 20 and 60 ◦C were quantified experimentally. Equilibrium data were fitted to different statistical physics-based isotherm models to establish the corresponding adsorption mechanism. A double layer adsorption model with one type of functional group was the best to correlate and explain the removal of these organic molecules. Steric parameters for the adsorption of these organic compounds were also calculated thus determining that their adsorption was multi-molecular. At tested operating conditions, acetaminophen adsorption was endothermic, while acetamiprid removal was exothermic. Physical adsorption forces were expected to be responsible for the removal of both compounds. This study reports new insights on the adsorption mechanisms of relevant emerging pollutants commonly found in water worldwid

Design and Control Properties of Arrangements for Distillation of Four Component Mixtures Using Less Than N-1 Columns

The design and study of dynamic properties of distillation sequences using side-stream columns with less than N-1 columns for separation of four-component mixtures were studied. Total annual cost and dynamic properties (using singular value decomposition) were used to compare properties of arrangements with less than N-1 columns to base cases with three columns (conventional case). Quaternary feeds containing four hydrocarbons were analyzed. For systems with low concentrations of one component in the feed, side-stream cascades often show significantly lower operating, capital costs and best dynamic properties compared to the base cases. Low purity requirements also favor side-stream cascades. Some rules are presented to predict which sequence will have the lowest energy consumption capital cost and better dynamic properties