A mixed-integer nonlinear programming algorithm for process systems synthesis

Authors

  • M. A. Duran,

    1. Department of Chemical Engineering, Carnegie-Mellon University, Pittsburgh, PA 15213
    Current affiliation:
    1. Universidad Autonoma Metropolitana-Iztapalapa, Apdo. Postal 55–534, 09340 Mexico City
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  • I. E. Grossmann

    Corresponding author
    1. Department of Chemical Engineering, Carnegie-Mellon University, Pittsburgh, PA 15213
    • Department of Chemical Engineering, Carnegie-Mellon University, Pittsburgh, PA 15213
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Abstract

The problem of synthesizing processing systems via simultaneous structural and parameter optimization is addressed in this paper. Based on a superstructure representation for embedding alternative configurations, a general mixed-integer nonlinear programming (MINLP) framework is presented for the synthesis problem. An efficient outer-approximation algorithm is described for the solution of the underlying optimization problem, which is characterized by linear binary variables and continuous variables that appear in nonlinear functions. The proposed algorithm is based on a bounding sequence that requires the analysis of few system configurations, and the solution of a master problem that identifies new candidate structures. Application of the proposed algorithm is illustrated with the optimal synthesis of gas pipelines.

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