Stochastic optimization for process intensification

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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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Gutiérrez-Antonio, C., & Bonilla-Petriciolet, A. (2016). Stochastic optimization for process intensification. In Process Intensification in Chemical Engineering: Design Optimization and Control (pp. 261–277). Springer International Publishing. https://doi.org/10.1007/978-3-319-28392-0_9

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