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Title: An MINLP formulation for the optimization of multicomponent distillation configurations

Abstract

Designing configurations for multicomponent distillation, a ubiquitous process in chemical and petrochemical industries, is often challenging. This is because, as the number of components increases, the number of admissible distillation configurations grows rapidly and these configurations vary substantially in their energy needs. Consequently, if a method could identify a few energy-efficient choices from this large set of alternatives, it would be extremely attractive to process designers. This paper develops here such a method by solving a Mixed Integer Nonlinear Program (MINLP) that is formulated to pick, among the regular-column configurations of Shah and Agrawal (2010b), those configurations that have a low vapor-duty requirement. To compute the minimum vapor-duty requirement for each column within the configuration, we use techniques that rely on the Underwood’s method. The combined difficulty arising from the nonlinearity of Underwood equations and the combinatorial explosion of the choice-set of alternatives poses unmistakable challenges for the branch-and-bound algorithm, the current method of choice to globally solve MINLPs. To address this difficulty, we exploit the structure of Underwood equations and derive valid cuts that expedite the convergence of branch-and-bound by enabling global solvers, such as BARON, infer tighter bounds on Underwood roots. This provides a quick way to identifymore » a few lucrative alternative configurations for separation of a given non-azeotropic mixture. We illustrate the practicality of our approach on a case-study concerning heavy-crude distillation and on various other examples from the literature.« less

Authors:
 [1];  [1];  [2]
  1. Purdue Univ., West Lafayette, IN (United States). Davidson School of Chemical Engineering
  2. Purdue Univ., West Lafayette, IN (United States). Krannert School of Management
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:
1511496
Grant/Contract Number:  
EE0005768
Resource Type:
Accepted Manuscript
Journal Name:
Computers and Chemical Engineering
Additional Journal Information:
Journal Volume: 125; Journal ID: ISSN 0098-1354
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; multicomponent distillation; mixed integer nonlinear program; fractional program; global optimization

Citation Formats

Tumbalam Gooty, Radhakrishna, Agrawal, Rakesh, and Tawarmalani, Mohit. An MINLP formulation for the optimization of multicomponent distillation configurations. United States: N. p., 2019. Web. doi:10.1016/j.compchemeng.2019.02.013.
Tumbalam Gooty, Radhakrishna, Agrawal, Rakesh, & Tawarmalani, Mohit. An MINLP formulation for the optimization of multicomponent distillation configurations. United States. doi:10.1016/j.compchemeng.2019.02.013.
Tumbalam Gooty, Radhakrishna, Agrawal, Rakesh, and Tawarmalani, Mohit. Thu . "An MINLP formulation for the optimization of multicomponent distillation configurations". United States. doi:10.1016/j.compchemeng.2019.02.013.
@article{osti_1511496,
title = {An MINLP formulation for the optimization of multicomponent distillation configurations},
author = {Tumbalam Gooty, Radhakrishna and Agrawal, Rakesh and Tawarmalani, Mohit},
abstractNote = {Designing configurations for multicomponent distillation, a ubiquitous process in chemical and petrochemical industries, is often challenging. This is because, as the number of components increases, the number of admissible distillation configurations grows rapidly and these configurations vary substantially in their energy needs. Consequently, if a method could identify a few energy-efficient choices from this large set of alternatives, it would be extremely attractive to process designers. This paper develops here such a method by solving a Mixed Integer Nonlinear Program (MINLP) that is formulated to pick, among the regular-column configurations of Shah and Agrawal (2010b), those configurations that have a low vapor-duty requirement. To compute the minimum vapor-duty requirement for each column within the configuration, we use techniques that rely on the Underwood’s method. The combined difficulty arising from the nonlinearity of Underwood equations and the combinatorial explosion of the choice-set of alternatives poses unmistakable challenges for the branch-and-bound algorithm, the current method of choice to globally solve MINLPs. To address this difficulty, we exploit the structure of Underwood equations and derive valid cuts that expedite the convergence of branch-and-bound by enabling global solvers, such as BARON, infer tighter bounds on Underwood roots. This provides a quick way to identify a few lucrative alternative configurations for separation of a given non-azeotropic mixture. We illustrate the practicality of our approach on a case-study concerning heavy-crude distillation and on various other examples from the literature.},
doi = {10.1016/j.compchemeng.2019.02.013},
journal = {Computers and Chemical Engineering},
number = ,
volume = 125,
place = {United States},
year = {2019},
month = {3}
}

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This content will become publicly available on March 7, 2020
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