Non-convex chance-constrained optimization for blending recipe design under uncertainties

•Using a nonconvex chance-constrained program to model gasoline blending problem.•Nonlinear mixing rule is used for the calculation of octane rating.•Normal distribution is used to describe the feedstock uncertainties.•The chance-constrained program is solved to a near-global optimal solution.•Two g...

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Vydáno v:Computers & chemical engineering Ročník 139; s. 106868
Hlavní autoři: Yang, Yu, Rosa, Loren dela, Chow, Tsz Yuet Matthew
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 04.08.2020
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ISSN:0098-1354, 1873-4375
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Abstract •Using a nonconvex chance-constrained program to model gasoline blending problem.•Nonlinear mixing rule is used for the calculation of octane rating.•Normal distribution is used to describe the feedstock uncertainties.•The chance-constrained program is solved to a near-global optimal solution.•Two grades of gasoline with nine feedstocks are produced in the case study. A global optimization algorithm is proposed to design blending recipes for gasoline production with nonlinear mixing law and parameter uncertainty. Important fuels, such as gasoline, are produced by mixing several intermediate feedstocks in such a way that all quality specifications are met, and total profit is maximized. Conventional blending design approaches that rely on linear models and deterministic optimization may generate a suboptimal or infeasible solution due to model inaccuracy and failure to account for parameter uncertainty. The proposed work designs the blending recipe subject to chance constraints with normally distributed uncertain parameters and nonlinear mixing rule. The resulting non-convex joint chance-constrained program is solved to a near-global optimum through second-order cone relaxation, branch-and-bound, optimality-based bound tightening, and reformulate-linearization techniques. A case study involving nine feedstocks and two grades of gasoline is presented to demonstrate the effectiveness of the proposed method.
AbstractList •Using a nonconvex chance-constrained program to model gasoline blending problem.•Nonlinear mixing rule is used for the calculation of octane rating.•Normal distribution is used to describe the feedstock uncertainties.•The chance-constrained program is solved to a near-global optimal solution.•Two grades of gasoline with nine feedstocks are produced in the case study. A global optimization algorithm is proposed to design blending recipes for gasoline production with nonlinear mixing law and parameter uncertainty. Important fuels, such as gasoline, are produced by mixing several intermediate feedstocks in such a way that all quality specifications are met, and total profit is maximized. Conventional blending design approaches that rely on linear models and deterministic optimization may generate a suboptimal or infeasible solution due to model inaccuracy and failure to account for parameter uncertainty. The proposed work designs the blending recipe subject to chance constraints with normally distributed uncertain parameters and nonlinear mixing rule. The resulting non-convex joint chance-constrained program is solved to a near-global optimum through second-order cone relaxation, branch-and-bound, optimality-based bound tightening, and reformulate-linearization techniques. A case study involving nine feedstocks and two grades of gasoline is presented to demonstrate the effectiveness of the proposed method.
ArticleNumber 106868
Author Chow, Tsz Yuet Matthew
Rosa, Loren dela
Yang, Yu
Author_xml – sequence: 1
  givenname: Yu
  surname: Yang
  fullname: Yang, Yu
  email: yu.yang@csulb.edu
  organization: Department of Chemical Engineering, California State University Long Beach, Long Beach, CA 90840 USA
– sequence: 2
  givenname: Loren dela
  surname: Rosa
  fullname: Rosa, Loren dela
  organization: Department of Chemical Engineering, California State University Long Beach, Long Beach, CA 90840 USA
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  givenname: Tsz Yuet Matthew
  surname: Chow
  fullname: Chow, Tsz Yuet Matthew
  organization: Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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Sat Nov 29 07:23:54 EST 2025
Fri Feb 23 02:47:14 EST 2024
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Keywords Gasoline blending
Second-order cone program
Chance-constrained program
Global optimization
Language English
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Snippet •Using a nonconvex chance-constrained program to model gasoline blending problem.•Nonlinear mixing rule is used for the calculation of octane rating.•Normal...
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StartPage 106868
SubjectTerms Chance-constrained program
Gasoline blending
Global optimization
Second-order cone program
Title Non-convex chance-constrained optimization for blending recipe design under uncertainties
URI https://dx.doi.org/10.1016/j.compchemeng.2020.106868
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