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Title: Global optimization of multicomponent distillation configurations: Global minimization of total cost for multicomponent mixture separations

Abstract

We introduce a global optimization framework for determining the minimum cost required to distill any ideal or near-ideal multicomponent mixture into its individual constituents using a sequence of columns. This new framework extends the Global Minimization Algorithm (GMA) previously introduced by Nallasivam et al. (2016); and we refer to the new framework as the Global Minimization Algorithm for Cost (GMAC). GMAC guarantees global optimality by formulating a nonlinear program (NLP) for each and every distillation configuration in the search space and solving it using global optimization solvers. In conclusion, the case study presented in this work not only demonstrates the need for developing such an algorithm, but also shows the flexibility and effectiveness of GMAC, which enables process engineers to design and retrofit energy efficient and cost-effective distillation configurations.

Authors:
 [1];  [2];  [3];  [4];  [5];  [2];  [2]
  1. Purdue Univ., West Lafayette, IN (United States); Corteva Agriscience, Midland, MI (United States)
  2. Purdue Univ., West Lafayette, IN (United States)
  3. Purdue Univ., West Lafayette, IN (United States); Exxon Mobil Asia Pacific PTE Ltd. (Singapore)
  4. Purdue Univ., West Lafayette, IN (United States); Marathon Petroleum Corp., Findlay, OH (United States)
  5. Purdue Univ., West Lafayette, IN (United States); Yokogawa Electric Corp., Bangalore (India)
Publication Date:
Research Org.:
Purdue Univ., West Lafayette, IN (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office (EE-5A)
OSTI Identifier:
1511698
Grant/Contract Number:  
EE0005768
Resource Type:
Accepted Manuscript
Journal Name:
Computers and Chemical Engineering
Additional Journal Information:
Journal Volume: 126; Journal Issue: C; Journal ID: ISSN 0098-1354
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Multicomponent distillation; Distillation configuration; Global optimization; Cost optimization; Process intensification

Citation Formats

Jiang, Zheyu, Mathew, Tony Joseph, Zhang, Haibo, Huff, Joshua, Nallasivam, Ulaganathan, Tawarmalani, Mohit, and Agrawal, Rakesh. Global optimization of multicomponent distillation configurations: Global minimization of total cost for multicomponent mixture separations. United States: N. p., 2019. Web. doi:10.1016/j.compchemeng.2019.04.009.
Jiang, Zheyu, Mathew, Tony Joseph, Zhang, Haibo, Huff, Joshua, Nallasivam, Ulaganathan, Tawarmalani, Mohit, & Agrawal, Rakesh. Global optimization of multicomponent distillation configurations: Global minimization of total cost for multicomponent mixture separations. United States. doi:10.1016/j.compchemeng.2019.04.009.
Jiang, Zheyu, Mathew, Tony Joseph, Zhang, Haibo, Huff, Joshua, Nallasivam, Ulaganathan, Tawarmalani, Mohit, and Agrawal, Rakesh. Thu . "Global optimization of multicomponent distillation configurations: Global minimization of total cost for multicomponent mixture separations". United States. doi:10.1016/j.compchemeng.2019.04.009.
@article{osti_1511698,
title = {Global optimization of multicomponent distillation configurations: Global minimization of total cost for multicomponent mixture separations},
author = {Jiang, Zheyu and Mathew, Tony Joseph and Zhang, Haibo and Huff, Joshua and Nallasivam, Ulaganathan and Tawarmalani, Mohit and Agrawal, Rakesh},
abstractNote = {We introduce a global optimization framework for determining the minimum cost required to distill any ideal or near-ideal multicomponent mixture into its individual constituents using a sequence of columns. This new framework extends the Global Minimization Algorithm (GMA) previously introduced by Nallasivam et al. (2016); and we refer to the new framework as the Global Minimization Algorithm for Cost (GMAC). GMAC guarantees global optimality by formulating a nonlinear program (NLP) for each and every distillation configuration in the search space and solving it using global optimization solvers. In conclusion, the case study presented in this work not only demonstrates the need for developing such an algorithm, but also shows the flexibility and effectiveness of GMAC, which enables process engineers to design and retrofit energy efficient and cost-effective distillation configurations.},
doi = {10.1016/j.compchemeng.2019.04.009},
journal = {Computers and Chemical Engineering},
number = C,
volume = 126,
place = {United States},
year = {2019},
month = {4}
}

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This content will become publicly available on April 11, 2020
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