Paper Info
Title
A Deep Learning-Based Robust Optimization Approach For Refinery Planning Under Uncertainty
Abstract
Refinery planning under uncertainty has gained tremendous attention, and this paper bridges deep learn-ing and robust optimization to address this issue. First, we propose a large-scale mixed-integer linear programming model for refinery planning, where the fixed-yield models of the processing units are used. Prices of final products are considered uncertain parameters in the developed model to enhance the solu-tion's applicability. Second, historical data of different products are collected to construct the uncertainty set characterizing all possible realizations of uncertainty. Third, a deep learning method is employed to capture the uncertainties of product prices, which has been proven to be powerful for high-dimensional price data. Based on the constructed uncertainty set, a data-driven robust optimization model is further developed. Finally, an iterative constraint generation algorithm is applied to solve the data-driven robust optimization problem. Case studies from an actual refinery are presented to showcase the effectiveness of the proposed method, which owes particularly to the representation capability of deep learning. (c) 2021 Elsevier Ltd. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.compchemeng.2021.107495
COMPUTERS & CHEMICAL ENGINEERING
Keywords
DocType
Volume
Refinery planning, Robust optimization, Deep learning, Price uncertainty, Data-driven
Journal
155
ISSN
Citations 
PageRank 
0098-1354
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Cong Wang100.34
Xin Peng22510.91
Chao Shang3236.53
Fan Chen411619.33
Liang Zhao538654.50
Weimin Zhong67914.18