A Data-Driven Rolling-Horizon Online Scheduling Model for Diesel Production of a Real-World Refinery

被引：19

作者：

Cao Cuiwen ^{[1
]}

Gu Xingsheng ^{[1
]}

Xin Zhong ^{[2
]}

机构：

[1] E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

[2] E China Univ Sci & Technol, State Key Lab Chem Engn, Sch Chem Engn, Shanghai 200237, Peoples R China

来源：

AICHE JOURNAL | 2013年 / 59卷 / 04期

基金：

中国国家自然科学基金; 国家高技术研究发展计划(863计划);

关键词：

rolling-horizon optimal control strategy; data-driven; online scheduling; diesel production; uncertainty; ROBUST OPTIMIZATION APPROACH; CONTINUOUS-TIME; UNCERTAINTY; FRAMEWORK;

D O I：

10.1002/aic.13895

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

A rolling-horizon optimal control strategy is developed to solve the online scheduling problem for a real-world refinery diesel production based on a data-driven model. A mixed-integer nonlinear programming (MINLP) scheduling model considering the implementation of nonlinear blending quality relations and quantity conservation principles is developed. The data variations which drive the MINLP model come from different sources of certain and uncertain events. The scheduling time horizon is divided into equivalent discrete time intervals, which describe regular production and continuous time intervals which represent the beginning and ending time of expected and unexpected events that are not restricted to the boundaries of discrete time intervals. This rolling-horizon optimal control strategy ensures the dimension of the diesel online scheduling model can be accepted in industry use. LINGO is selected to be the solution software. Finally, the daily diesel scheduling scheme of one entire month for a real-world refinery is effectively solved. (C) 2012 American Institute of Chemical Engineers AIChE J, 59: 1160-1174, 2013

引用

页码：1160 / 1174

页数：15

共 43 条

[1] A multi-stage stochastic integer programming approach for capacity expansion under uncertainty [J].