摘要
随着人们环保意识与政策要求的不断加强以及对石油需求的不断增加,大力发展煤制油技术对保障中国能源安全、调整优化能源结构具有重要意义。以煤制油炼厂为主体,考虑炼厂建设、采购、生产、运输、销售过程及煤炭产地供应与市场需求等相关约束条件,以煤制油炼厂收益最大为目标函数,建立煤制油供应链的混合整数线性规划优化模型,并采用基于分支定界法的大规模数学规划求解器(Gurobi)进行求解。研究成果成功应用于中国某地区煤制油炼厂,对煤制油供应链的建设具有较强的指导意义,一定程度上降低了资源消耗,提升了供应链收益,实现了整个供应链的高效运作。
With the continuous enhancement of people’s environmental awareness and policy requirements as well as the increasing demand for oil,vigorously developing coal-to-oil technology is of strategic importance to ensure the energy security,adjust and optimize the energy structure of China.Focusing on the coal-to-oil refinery,considering the construction,procurement,production,transportation and sales process of the refinery,along with the related constraints such as the supply from coal production areas and market demand,the optimal mixed integer linear programming model of the coalto-oil supply chain was established with the maximum profit of the coal-to-oil refinery as the objective function,and was solved by a large-scale mathematical programming solver(Gurobi)based on the branch and bound method.The research achievement has been successfully applied to a coal-to-oil refinery in some region of China,demonstrating its great guiding significance to the construction of the site coal-to-oil supply chain.To a certain extent,it can reduce resource consumption,improve supply chain revenue and realize the efficient operation of the entire supply chain.
作者
邱睿
梁永图
段志刚
王凯琪
周星远
冯跃跃
张浩然
QIU Rui;LIANG Yongtu;DUAN Zhigang;WANG Kaiqi;ZHOU Xingyuan;FENG Yueyue;ZHANG Haoran(College of Mechanical and Transportation Engineering,China University of Petroleum(Beijing)//Beijing Key Laboratoryof Urban Oil and Gas Distribution Technology;Sino-Pipeline International Company Limited;Qinhuangdao Oil&Gas Transportation Sub-Company,PetroChina Pipeline Company)
出处
《油气储运》
CAS
北大核心
2020年第4期412-417,共6页
Oil & Gas Storage and Transportation
基金
国家自然科学基金资助项目“成品油供给链物流系统优化及供给侧可靠性研究”,5187432。
关键词
煤制油
供应链
优化
混合整数线性规划模型
分支定界法
coal-to-oil
supply chain
optimization
mixed integer linear programming model
branch and bound method