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Stochastic Optimization for Process Intensification

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Process Intensification in Chemical Engineering

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Abstract

This chapter describes and discusses stochastic optimization methods for solving problems involved in process intensification, given an emphasis in multiobjective optimization due to its increasing importance in the chemical engineering community. A brief description of the multiobjective optimization strategies such as genetic algorithms, simulated annealing, tabu search, differential evolution, ant colony and particle swarm optimization is provided, including several applications of evolutionary optimization methods in the intensification of separation processes.

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Authors and Affiliations

  1. Facultad de Química, Universidad Autónoma de Querétaro, Av. Cerro de las Campanas s/n, Col. Las Campanas, Santiago de Querétaro, Querétaro, 76010, Mexico

    Claudia Gutiérrez-Antonio

  2. Instituto Tecnológico de Aguascalientes, Aguascalientes, Mexico

    Adrián Bonilla-Petriciolet

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  1. Claudia Gutiérrez-Antonio
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Correspondence to Claudia Gutiérrez-Antonio .

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  1. Universidad de Guanajuato, Guanajuato, Mexico

    Juan Gabriel Segovia-Hernández

  2. Instituto Tecnológico de Aguascalientes, Aguascalientes, Mexico

    Adrián Bonilla-Petriciolet

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Gutiérrez-Antonio, C., Bonilla-Petriciolet, A. (2016). Stochastic Optimization for Process Intensification. In: Segovia-Hernández, J., Bonilla-Petriciolet, A. (eds) Process Intensification in Chemical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-28392-0_9

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