An improved approach to scheduling gasoline blending and order delivery operations

Nur Hussain, Jie Li, Li Sun, Xin Xiao, Cuiwen Cao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


Scheduling gasoline blending and order delivery operations is an important routine task in an oil refinery since gasoline can account for as much as 60-70% of total revenue. In this work we improve the mixed-integer nonlinear programming (MINLP) model of Li et al. (2016) to ensure optimality and incorporate nonlinear property correlations. To solve such nonconvex MINLP model to 8- global optimality, a global optimization method is proposed. It is shown that our improved model and proposed method can solve industrial examples to 1%- global optimality and generate the same or better solutions with less CPU time than those from Cerda et al. (2016). Using nonlinear property correlations could lead to more accurate prediction than linear correlations.
Original languageEnglish
Title of host publication13th International Symposium on Process Systems Engineering (PSE 2018)
EditorsMario R. Eden, Marianthi G. Ierapetritou, Gavin P. Towler
PublisherElsevier B.V.
Number of pages6
ISBN (Print)9780444642417
Publication statusPublished - 2 Aug 2018
Externally publishedYes

Publication series

NameComputer Aided Chemical Engineering
PublisherElsevier B.V.
ISSN (Print)1570-7946


  • scheduling
  • gasoline
  • blending
  • global optimization
  • nonlinear prediction


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