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Santiago de Querétaro, Mexico

Miranda-Galindo E.Y.,University of Guanajuato | Segovia-Hernandez J.G.,University of Guanajuato | Hernandez S.,University of Guanajuato | Gutierrez-Antonio C.,CIATEQ | Briones-Ramirez A.,Exxerpro Solutions
Industrial and Engineering Chemistry Research | Year: 2011

Design of reactive distillation sequences is a major computer-aided design challenge. The optimal design of reactive complex distillation systems is a highly nonlinear and multivariable problem, and the objective function used as optimization criterion is generally nonconvex with several local optimums and subject to several constraints. In addition, several attributes for the design of these separation schemes are often conflicting objectives, and the design problem should be represented from a multiple objective perspective. As a result, solving with traditional optimization methods is not reliable because they generally converge to local optimums and often fail to capture the full Pareto optimal front. In this work, we have studied the design of reactive distillation with thermal coupling (using as study case the production of fatty esters), generalizing the use of a multiobjective genetic algorithm with restrictions coupled to Aspen ONE Aspen Plus, previously used in the design and optimization of intensified distillation systems. The results obtained in the Pareto front indicate that the energy consumption of the complex distillation sequence can be reduced significantly by varying operational conditions. Trends in the energy consumption, total annual cost, and greenhouse gas emissions of the thermally coupled reactive distillation sequences can be obtained. © 2010 American Chemical Society. Source


Cortez-Gonzalez J.,University of Guanajuato | Segovia-Hernandez J.G.,University of Guanajuato | Hernandez S.,University of Guanajuato | Gutierrez-Antonio C.,CIATEQ | And 2 more authors.
Chemical Engineering Research and Design | Year: 2012

The optimal design of complex distillation systems is a highly non-linear and multivariable problem, with several local optimums and subject to different constraints. In addition, some attributes for the design of these separation schemes are often conflicting objectives, and the design problem should be represented from a multiple objective perspective. As a result, solving with traditional optimization methods is not reliable because they generally converge to local optimums, and often fail to capture the full Pareto optimal front. In this paper, a method for the multiobjective optimization of distillation systems, conventional and thermally coupled, with less than N- 1 columns is presented. We use a multiobjective genetic algorithm with restrictions coupled to AspenONE Aspen Plus; so, the complete MESH equations and rigorous phase equilibrium calculations are used. Results show some tendencies in the design of intensified sequences, according to the nature of the mixture and feed compositions. © 2012 The Institution of Chemical Engineers. Source


Gomez-Castro F.I.,University of Guanajuato | Rodriguez-Angeles M.A.,University of Guanajuato | Rodriguez-Angeles M.A.,Instituto Tecnologico Sanmiguelense | Segovia-Hernandez J.G.,University of Guanajuato | And 4 more authors.
Industrial and Engineering Chemistry Research | Year: 2013

Dividing wall columns are intensified process equipment with the capacity of reducing both capital and operational costs for a given vapor-liquid separation, when compared with conventional distillation sequences. For some kinds of mixtures, distillation systems with two dividing walls have been theoretically proved to present lower energy requirements and lower total annual costs than systems with a single dividing wall. Nevertheless, the use of an additional wall may lead to operational issues on the column, because of the more complex arrangement of the walls on the trays of the columns, where additional split of the vapor and liquid streams is expected. Thus, in this work the open-loop properties (minimum singular value and condition number) for the double dividing wall column are studied and compared with those of the dividing wall column for a wide range of frequencies, in order to determinate if the use of additional dividing walls may lead to potential control problems. It has been found that both systems show similar dynamic performance, with advantages for the double dividing wall column for mixtures with low composition of the middle-boiling component. © 2013 American Chemical Society. Source


Bravo-Bravo C.,University of Guanajuato | Segovia-Hernandez J.G.,University of Guanajuato | Hernandez S.,University of Guanajuato | Gomez-Castro F.I.,University of Guanajuato | And 2 more authors.
Chemical Engineering and Processing: Process Intensification | Year: 2013

Innovative hybrid processes offer significant cost savings, particularly for azeotropic or close-boiling mixtures. Hybrid separation processes are characterized by the combination of two or more different unit operations, which contribute to the separation task by different physical separation principles. Despite of the inherent advantages of hybrid separation processes, they are not systematically exploited in industrial applications due to the complexity of the design and optimization of these highly integrated processes. In this work we study a hybrid distillation/melt crystallization process, using conventional and thermally coupled distillation sequences. The design and optimization were carried out using, as a design tool, a multi-objective genetic algorithm with restrictions coupled with the process simulator Aspen Plus™, for the evaluation of the objective function. The results show that this hybrid configuration with thermally coupled arrangements is a feasible option in terms of energy savings, capital investment and control properties. © 2012 Elsevier B.V. Source


Gutierrez-Antonio C.,CIATEQ | Briones-Ramirez A.,Exxerpro Solutions | Briones-Ramirez A.,Aguascalientes Institute of Technology
Computer Aided Chemical Engineering | Year: 2010

Evolutionary algorithms have been recognized to be well suited for multiobjective optimization [1]; their principal disadvantage is the large number of evaluations of objective function required [2], which is accentuated when those are computationally expensive. In this work, we propose the use of artificial neuronal networks, ANN, to speed up a multiobjective genetic algorithm with constraints, with base on the work of Gaspar-Cunha [3]. The neuronal network generates an approximated function of the original objective function, which are switched during the optimization; so, we reduce the evaluations of the original objective function and the computational time. The use of approximated functions created by the ANN allows reaching the optimal zone rapidly. Results show a significant reduction in the number of evaluations of the objective function, as well as in computational time, required to reaching the Pareto front. © 2010 Elsevier B.V. Source

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