https://github.com/bernalde/optimal-superstructures-dsda

This repository includes the files of the manuscript Optimal design of superstructures for placing units and streams with multiple and ordered available locations. Part I: A new mathematical framework
https://github.com/bernalde/optimal-superstructures-dsda

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This repository includes the files of the manuscript Optimal design of superstructures for placing units and streams with multiple and ordered available locations. Part I: A new mathematical framework

• Host: GitHub
• URL: https://github.com/bernalde/optimal-superstructures-dsda
• Owner: bernalde
• License: mit
• Created: 2020-02-19T20:39:22.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-08-10T19:49:32.000Z (over 4 years ago)
• Last Synced: 2024-11-20T19:44:05.645Z (2 months ago)
• Language: GAMS
• Homepage:
• Size: 140 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Optimal design of process superstructures: CSTR Network case

This repository includes the files of the manuscript [Optimal design of superstructures for placing units and streams with multiple and ordered available locations. Part I: A new mathematical framework](https://doi.org/10.1016/j.compchemeng.2020.106794).

The files included in this repository correspond to the Mixed-integer nonlinear optimization problem to optimally design a continuously stirred tank reactor (CSTR) network. The optimization models and proposed algorithm are implemented in GAMS.

Here we propose a new approach for the optimal design of superstructures in chemical engineering. The method exploits the structure of a specific type of problem, i.e., the case where it is necessary to find the optimal location of a processing unit or a stream over a naturally ordered discrete set. The proposed methodology consists of reformulating the binary variables of the original Mixed-Integer Nonlinear Problem (MINLP) with a smaller set of integer variables referred to as external variables. Then, the reformulated optimization problem can be decomposed into a master Integer Program with Linear Constraints (master IPLC) and primal sub-problems in the form of Fixed Nonlinear Programs (FNLPs), i.e., Nonlinear Programs (NLPs) with integer variables fixed. The use of the Discrete-Steepest Descent Algorithm (D-SDA) is considered for the master IPLC, while the primal FNLPs are solved with existing Nonlinear Programming (NLP) solvers.

The new methodology does not guarantee global optimality; however, the results show that it can find a local solution in a short computational time.