arrow
Return

Mixed-integer linear programming model for gasoline blending and distribution scheduling

delete2003-01-16
delete105
PRE
AI
Z
Zhenya Jia
M
Marianthi Ierapetritou *
DOI:10.1021/ie0204843delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Oil refineries are increasingly concerned with improving the planning of their operations and optimizing not only single production units but the whole production enterprise. However, the modeling of the overall refinery operation from the crude oil arrival to the distribution of oil products gives rise to intractable mathematical models. Thus, decomposition methodologies have long been recognized as the major avenue to overcome this computational burden. Following the spatial decomposition which distinguishes the receiving, producing, and delivery end of the refinery, the problem of gasoline blending and distribution is addressed in this paper. The problem involves the optimal operation of gasoline blending, the transfer to productstock tanks, and the delivering schedule to satisfy all of the orders. An efficient mixed-integer linear programming formulation is developed based on continuous representation of the time domain. The assumption of constant recipes is used for the blending stage. The formulation is used to address realistic case studies where feasible solutions are obtained in very reasonable computational time.
Keywords:
GLOBAL OPTIMIZATION
OPERATIONS
FORMULATION
TEXACO
OMEGA
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

I
Industrial and Engineering Chemistry Research
IF:
3.9
Papers:
4.0W
Citations:
9.6W

Organization

No organization information available
Cited Papers

Cited Papers

A planning model for refinery diesel production
err1998-03-01
err104
PREAI
errMoro, LFL; Zanin, AC; Pinto, JM
errShare
errSave
errShare
errSave
errShare
errSave
errShare
errSave
err
IF0
err
err0
PREAI
err
errShare
errSave
researcher View more