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青岛LNG接收站扩能后IFV与ORV联运模拟与优化

Simulation and optimization of combined transportation of IFV and ORV after capacity expansion of Qingdao LNG terminal
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摘要 以中国石化青岛液化天然气(LNG)接收站扩能项目中气化单元为研究对象,采用Aspen HYSYS模拟软件,建立了开架式海水气化器(ORV)与新增中间介质气化器(IFV)联运的工艺模型,分析了两种气化器的最小海水流量和流量分配,并对海水泵启用方案及能耗进行了优化。结果表明,气化器所需最小海水流量随海水温度呈季节性变化。联运情况下,当海水温度为7~30℃时,优先开启小海水泵可降低能耗约400 kW;当海水温度低于7℃、10 MPa气化系统外输量达到46%~60%时,优先开启小海水泵可降低能耗约400 kW;当外输量达到69%~82%时,优先开启大海水泵可降低能耗约200 kW。 Taking the gasification unit of SINOPEC Qingdao liquefied natural gas(LNG)terminal expansion project as the research object,a process model for the combined transportation of the open rack vaporizer(ORV)and the new intermediate fluid vaporizer(IFV)was established by using Aspen HYSYS simulation software.The minimum seawater flow and flow distribution of two kinds of vaporizers were analyzed,and the application scheme of seawater pump and the optimization of energy consumption were optimized.The results show that the minimum seawater flow required by the vaporizer varies seasonally with seawater temperature.In the case of combined transportation,when the seawater temperature is 7℃ to 30℃,the priority of starting the small seawater pump can reduce the energy consumption by about 400 kW.When the seawater temperature is lower than 7℃ and the output capacity of 10 MPa gasification system reaches 46% to 60%,the priority of starting the small seawater pump can reduce the energy consumption by about 400 kW.When the output capacity reaches 69% to 82%,the first start of the seawater pump can reduce the energy consumption by about 200 kW.
作者 吴斌 于笑 刘景俊 唐建峰 姚宝龙 李童 WU Bin;YU Xiao;LIU Jingjun;TANG Jianfeng;YAO Baolong;LI Tong(SINOPEC Qingdao LNG Co.,Ltd.,Qingdao 266400,Shandong,China;College of Pipeline and Civil Engineering,China University of Petroleum(East China),Qingdao 266580,Shandong,China;SINOPEC Tianjin Liquefied Natural Gas Co.,Ltd.,Tianjin 300457,China)
出处 《天然气化工—C1化学与化工》 CAS 北大核心 2022年第6期134-141,共8页 Natural Gas Chemical Industry
关键词 LNG接收站 IFV ORV 流量分配 模拟优化 LNG terminal IFV ORV flow distribution simulation optimization
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