@inproceedings{24ebe1da96dd4b519691a696e58ed26c,
title = "An improved approach to scheduling gasoline blending and order delivery operations",
abstract = "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.",
keywords = "scheduling, gasoline, blending, global optimization, nonlinear prediction",
author = "Nur Hussain and Jie Li and Li Sun and Xin Xiao and Cuiwen Cao",
year = "2018",
month = aug,
day = "2",
doi = "10.1016/B978-0-444-64241-7.50264-0",
language = "English",
isbn = "9780444642417",
series = "Computer Aided Chemical Engineering",
publisher = "Elsevier B.V.",
pages = "1615--1620",
editor = "Eden, {Mario R.} and Ierapetritou, {Marianthi G.} and Towler, {Gavin P.}",
booktitle = "13th International Symposium on Process Systems Engineering (PSE 2018)",
address = "Netherlands",
}