Integration of crude-oil scheduling and refinery planning by Lagrangean Decomposition

In this work, a Mixed-Integer Nonlinear Programming (MINLP) modeling framework for integrating short-term Crude-oil Scheduling (CS) and mid-term Refinery Planning (RP) has been developed and effectively solved by a proposed Lagrangean Decomposition (LD) algorithm. The principles of this integration...

Full description

Saved in:
Bibliographic Details
Published in:Computers & chemical engineering 2020-07, Vol.138, p.106812, Article 106812
Main Authors: Yang, Haokun, Bernal, David E., Franzoi, Robert E., Engineer, Faramroze G., Kwon, Kysang, Lee, Sechan, Grossmann, Ignacio E.
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this work, a Mixed-Integer Nonlinear Programming (MINLP) modeling framework for integrating short-term Crude-oil Scheduling (CS) and mid-term Refinery Planning (RP) has been developed and effectively solved by a proposed Lagrangean Decomposition (LD) algorithm. The principles of this integration are based on the fact that both Crude-oil Scheduling and Refinery Planning have their economic net values as their objectives, and that they are physically linked by Crude Distillation Units (CDUs). A multi-scale approach is proposed in the framework to aggregate continuous- and discrete-time formulations in CS and RP, respectively. Compared to hierarchically solving the non-integrated CS and RP, computational results show significant improvement regarding the economic objective values. Moreover, the proposed LD approach requires less CPU time converging to a small (1%-5%) optimality gap when compared to the monolithic approach using state-of-the-art MINLP solvers.
ISSN:0098-1354
DOI:10.1016/j.compchemeng.2020.106812