文献类型：期刊文献

中文题名：连续汽油调和调度问题的建模与优化

英文题名：Modelling and Optimization for a Continuous Gasoline Blending Scheduling Problem

作者：伍青青[1];程辉[1]

机构：[1]华东理工大学信息科学与工程学院,上海200030

年份：2020

卷号：49

期号：5

起止页码：615

中文期刊名：信息与控制

外文期刊名：Information and Control

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD2019_2020】；

基金：国家自然科学基金重大资助项目(61590923);国家自然科学基金资助项目(61673277);国家杰出青年科学基金资助项目(61725301);国际(地区)合作与交流项目(61720106008);中央高校基本科研业务费专项资金资助项目(222201917006);上海自然基金资助项目(16ZR1407300)

语种：中文

中文关键词：汽油调和;调度;混合整数非线性规划

外文关键词：gasoline blending;scheduling;mixed integer nonlinear programming(MINLP)

摘要：汽油占炼油厂收益的60%?70%,因此对汽油调和调度问题进行优化引起了工业与学术界的广泛关注.汽油调和调度问题是一个带有复杂约束的混合整数非线性规划问题(mixed integer nonlinear programmin g,MINLP).本文提出了一种新的建模和优化方法用于处理连续汽油调和过程的调度问题.首先,建立一个产量核算非线性规划(nonlinear programming,NLP)模型,目的是验证初始生产计划能否完成.然后,本文建立了配方优化NLP模型,并考虑组分油的属性波动,用于各产品油的调和配方的优化.最后,建立一个生产调度MINLP模型用于调度计划的优化.此外,本文还提出了一种批次滚动优化求解策略来求解所建立的三层模型.在案例分析中,求解结果显示本文提出的建模和优化方法适合于连续汽油调和过程,并能为炼油厂提供合理的生产计划.
Gasoline accounts for 60%-70% of refineries’ revenue. Therefore,optimizing the scheduling problem of the gasoline blending process attracts significant attention from both the industry and the academe. The scheduling problem is a mixed-integer nonlinear programming( MINLP) problem that essentially includes comprehensive constraints. we propose a novel modeling and optimization method for the scheduling problem of a continuous gasoline blending process. First,we propose a nonlinear programming( NLP) model for output verification to validate the feasibility of the initial production plan. Second,we establish a recipe optimization NLP model to obtain the recipes in blending periods,with the property fluctuations taken into consideration. Finally,we formulate an MINLP model for scheduling to obtain the optimal scheduling plan for operation. In addition,we present a batch moving optimization strategy to solve the three-level model. Case analysis results show that the proposed method is suitable for the continuous blending process and can provide reasonable production plans for refineries.