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Standing wave optimization of SMB using a hybrid simulated annealing and genetic algorithm (SAGA)

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Abstract

In this paper we draw on two stochastic optimization techniques, Simulated Annealing and Genetic Algorithm (SAGA), to create a hybrid to determine the optimal design of nonlinear Simulated Moving Bed (SMB) systems. A mathematical programming model based on the Standing Wave Design (SWD) offers a significant advantage in optimizing SMB systems. SAGA builds upon the strength of SA and GA to optimize the 16 variables of the mixed-integer nonlinear programming model for single- and multi-objective optimizations. The SAGA procedure is shown to be robust with computational time in minutes for single-objective optimization and in a few hours for a multi-objective optimization, which is comprised of more than one hundred points.

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Authors and Affiliations

  1. Seaver College, Pepperdine University, Malibu, CA, 90263, USA

    Fattaneh G. Cauley

  2. School of Electrical Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Stephen F. Cauley

  3. School of Chemical Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Nien-Hwa Linda Wang

Authors
  1. Fattaneh G. Cauley
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  2. Stephen F. Cauley
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  3. Nien-Hwa Linda Wang
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Correspondence to Fattaneh G. Cauley.

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Cauley, F.G., Cauley, S.F. & Wang, NH.L. Standing wave optimization of SMB using a hybrid simulated annealing and genetic algorithm (SAGA). Adsorption 14, 665–678 (2008). https://doi.org/10.1007/s10450-008-9119-8

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  • DOI: https://doi.org/10.1007/s10450-008-9119-8

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