基于EGO算法的常压塔能量优化  

Atmospheric tower energy optimization based on EGO

作　　者：叶贞成[1] 钱智媛 罗娜[1] 

机构地区：[1]华东理工大学信息科学与工程学院,上海200237

出　　处：《化工学报》2014年第12期4929-4934,共6页CIESC Journal

基　　金：国家重点基础研究发展计划项目(2012CB720500);国家自然科学基金项目(U1162202;21206037);上海市自然科学基金项目(13ZR1411500);中央高校基本科研业务费专项资金;上海市'科技创新行动计划'研发平台建设项目(13DZ2295300);上海市重点学科建设项目(B504)~~

摘　　要：常减压装置能量消耗约占炼厂总用能的25%～30%,在保证产品产量与质量的条件下,优化常减压蒸馏塔操作条件,可有效降低能耗。为了避免随机优化算法对常压塔机理模型进行操作优化时,存在计算资源消耗大、效率低的问题,文中采用基于代理模型的全局优化方法优化常压塔的余热回收过程,在优化迭代过程中用Kriging代理模型来代替耗时的精确模型评估。实验表明模型调用次数相较于粒子群优化算法减少了90%,优化时间减少了85%,实现了能量优化并且保证了侧线产品之间的分离精度。Energy consumption of crude oil distillation unit accounts for about 25% to 30% of refinery total energy. Satisfying the request of product yield and quality, optimizing crude distillation column operating conditions can effectively reduce energy consumption. Using stochastic optimization algorithms directly optimizing model of atmospheric tower is time-consuming and low efficient. In this paper, efficient global optimization algorithm based on surrogate model is applied to optimization of atmospheric tower＇s heat recovery. Kriging surrogate model is used as a replacement to the original model which is time-consuming in iterative optimization process. The result shows that this method decreases 90% number of assessment and decreases 85% optimization time compared with particle swarm optimization and realizes energy savings and meets product separation accuracy requirements.

关 键 词：算法 优化 计算机模拟 代理模型 有效全局优化算法 中段回流 

分 类 号：TE08[石油与天然气工程]