A strategy based on convex relaxation for solving the oil refinery operations planning problem

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A strategy based on convex relaxation for solving the oil refinery operations planning problem. / Andrade, Tiago; Ribas, Gabriela; Oliveira, Fabricio.

julkaisussa: Industrial and Engineering Chemistry Research, Vuosikerta 55, Nro 1, 13.01.2016, s. 144-155.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Bibtex - Lataa

@article{10088df62a2343f3bce45c7b452366af,
title = "A strategy based on convex relaxation for solving the oil refinery operations planning problem",
abstract = "Oil refining is one of the most complex activities in the chemical industry due to its nonlinear nature, which ruins the convexity properties and prevents any guarantees of the global optimality of solutions obtained by local nonlinear programming (NLP) algorithms. Moreover, using global optimization algorithms is often not feasible because they typically require large computational efforts. This paper proposes the use of convex relaxations based on McCormick envelopes for the Refinery Operations Planning Problem (ROPP) that can be used to generate both initial solutions for the ROPP and to estimate optimality gaps for the solutions obtained. The results obtained using data from a real-world refinery suggest that the proposed approach can provide reasonably good solutions for the ROPP, even for cases in which there was no solution available using traditional local NLP algorithms. Furthermore, compared with a global optimization solver, the proposed heuristic is capable of obtaining better solutions in less computational time.",
author = "Tiago Andrade and Gabriela Ribas and Fabricio Oliveira",
year = "2016",
month = "1",
day = "13",
doi = "10.1021/acs.iecr.5b01132",
language = "English",
volume = "55",
pages = "144--155",
journal = "Industrial and Engineering Chemistry Research",
issn = "0888-5885",
publisher = "AMERICAN CHEMICAL SOCIETY",
number = "1",

}

RIS - Lataa

TY - JOUR

T1 - A strategy based on convex relaxation for solving the oil refinery operations planning problem

AU - Andrade, Tiago

AU - Ribas, Gabriela

AU - Oliveira, Fabricio

PY - 2016/1/13

Y1 - 2016/1/13

N2 - Oil refining is one of the most complex activities in the chemical industry due to its nonlinear nature, which ruins the convexity properties and prevents any guarantees of the global optimality of solutions obtained by local nonlinear programming (NLP) algorithms. Moreover, using global optimization algorithms is often not feasible because they typically require large computational efforts. This paper proposes the use of convex relaxations based on McCormick envelopes for the Refinery Operations Planning Problem (ROPP) that can be used to generate both initial solutions for the ROPP and to estimate optimality gaps for the solutions obtained. The results obtained using data from a real-world refinery suggest that the proposed approach can provide reasonably good solutions for the ROPP, even for cases in which there was no solution available using traditional local NLP algorithms. Furthermore, compared with a global optimization solver, the proposed heuristic is capable of obtaining better solutions in less computational time.

AB - Oil refining is one of the most complex activities in the chemical industry due to its nonlinear nature, which ruins the convexity properties and prevents any guarantees of the global optimality of solutions obtained by local nonlinear programming (NLP) algorithms. Moreover, using global optimization algorithms is often not feasible because they typically require large computational efforts. This paper proposes the use of convex relaxations based on McCormick envelopes for the Refinery Operations Planning Problem (ROPP) that can be used to generate both initial solutions for the ROPP and to estimate optimality gaps for the solutions obtained. The results obtained using data from a real-world refinery suggest that the proposed approach can provide reasonably good solutions for the ROPP, even for cases in which there was no solution available using traditional local NLP algorithms. Furthermore, compared with a global optimization solver, the proposed heuristic is capable of obtaining better solutions in less computational time.

UR - http://www.scopus.com/inward/record.url?scp=84954458452&partnerID=8YFLogxK

U2 - 10.1021/acs.iecr.5b01132

DO - 10.1021/acs.iecr.5b01132

M3 - Article

VL - 55

SP - 144

EP - 155

JO - Industrial and Engineering Chemistry Research

JF - Industrial and Engineering Chemistry Research

SN - 0888-5885

IS - 1

ER -

ID: 16007562