基于EGO算法的常压塔能量优化

doi:10.3969/j.issn.0438-1157.2014.12.038

化工学报 ›› 2014, Vol. 65 ›› Issue (12): 4929-4934.DOI: 10.3969/j.issn.0438-1157.2014.12.038

基于EGO算法的常压塔能量优化

叶贞成, 钱智媛, 罗娜

华东理工大学信息科学与工程学院, 上海 200237

收稿日期:2014-09-03 修回日期:2014-09-13 出版日期:2014-12-05 发布日期:2014-12-05
通讯作者: 罗娜
基金资助:
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).

Atmospheric tower energy optimization based on EGO

YE Zhencheng, QIAN Zhiyuan, LUO Na

College of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

Received:2014-09-03 Revised:2014-09-13 Online:2014-12-05 Published:2014-12-05
Supported by:
supported by the National Basic Research Program of China(2012CB720500), the National Natural Science Foundation of China (U1162202, 21206037), the Natural Science Foundation of Shanghai (13ZR1411500), Fundamental Research Funds for the Central Universities, Shanghai"Technology Innovation Action Plan"Development Platform Construction Project (13DZ2295300) and Shanghai Leading Academic Discipline Project (B504).

摘要/Abstract

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.

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

Abstract: 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.

Key words: algorithm, optimization, computer simulation, surrogate model, efficient global optimization, pumparound reflux

中图分类号:

TE08

叶贞成, 钱智媛, 罗娜. 基于EGO算法的常压塔能量优化[J]. 化工学报, 2014, 65(12): 4929-4934.

YE Zhencheng, QIAN Zhiyuan, LUO Na. Atmospheric tower energy optimization based on EGO[J]. CIESC Journal, 2014, 65(12): 4929-4934.

参考文献 20

[1]	Alabdulkarem A, Mortazavi A, Hwang Y, Radermacher R, Rogers P. Optimization of propane pre-cooled mixed refrigerant LNG plant [J]. Applied Thermal Engineering, 2011, 31(6): 1091-1098
[2]	Jang W H, Hahn J, Hall K R. Genetic/quadratic search algorithm for plant economic optimizations using a process simulator [J]. Computers & Chemical Engineering, 2005, 30(2): 285-294
[3]	Li Xiangyang(李向阳), Ye Zhencheng(叶贞成), Qian Feng(钱锋), Qi Rongbin(祁荣宾). The application of improved PSO in parameters optimization of atmospheric column [J]. Computers and Applied Chemistry(计算机与应用化学), 2010, 27(10): 1345-1348
[4]	Wang Junyan(王钧炎), Huang Dexian(黄德先). Process optimization of catalytic reforming based on differential evolution and HYSYS mechanism model [J]. Journal of Chemical Industry and Engineering (China)(化工学报), 2008, 59(7): 1755-1760
[5]	Vázquez-Ojeda M, Segovia-Hernández J G, Hernández S, et al. Design and optimization of an ethanol dehydration process using stochastic methods [J]. Separation and Purification Technology, 2013, 105: 90-97
[6]	Gao Yuehua(高月华). Optimization methods based on Kriging surrogate model and their application in injection molding[D]. Dalian: Dalian University of Technology, 2009
[7]	Zhang N, Smith R, Bulatov I, et al. Sustaining high energy efficiency in existing processes with advanced process integration technology [J]. Applied Energy, 2013, 101: 26-32
[8]	Bebek A, Seda Ay ?. Optimising pumparound reflux for zero-cost energy savings [J]. Petroleum Technology Quarterly, 2012, 17(3): 15
[9]	Yu Xiaodong(于晓栋), Lü Wenxiang(吕文祥), Huang Dexian(黄德先), Jin Yihui(金以慧). Multi-objective optimization of industrial crude distillation unit based on HYSYS and NSGA-Ⅱ [J]. Journal of Chemical Industry and Engineering(China) (化工学报), 2008, 59(7): 1646-1649
[10]	Ochoa-Estopier L M, Jobson M, Smith R. Operational optimization of crude oil distillation systems using artificial neural networks [J]. Computers & Chemical Engineering, 2013, 59: 178-185
[11]	Motlaghi S, Jalali F, Ahmadabadi M N. An expert system design for a crude oil distillation column with the neural networks model and the process optimization using genetic algorithm framework [J]. Expert Systems with Applications, 2008, 35(4): 1540-1545
[12]	Liau L C K, Yang T C K, Tsai M T. Expert system of a crude oil distillation unit for process optimization using neural networks [J]. Expert Systems with Applications, 2004, 26(2): 247-255
[13]	Yao H, Chu J. Operational optimization of a simulated atmospheric distillation column using support vector regression models and information analysis [J]. Chemical Engineering Research and Design, 2012, 90(12): 2247-2261
[14]	Henao C A, Maravelias C T. Surrogate‐based superstructure optimization framework [J]. AIChE Journal, 2011, 57(5): 1216-1232
[15]	Jones D R, Schonlau M, Welch W J. Efficient global optimization of expensive black-box functions [J]. Journal of Global Optimization, 1998, 13(4): 455-492
[16]	Deng Feng(邓枫). EGO global optimization algorithm and its application [D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2011
[17]	Gengembre E, Ladevie B, Fudym O, et al. A kriging constrained efficient global optimization approach applied to low-energy building design problems [J]. Inverse Problems in Science and Engineering, 2012, 20(7): 1101-1114
[18]	Hu Zhen(胡真). Modeling and control of oil distillation process [D]. Hangzhou: Zhejiang University, 2000
[19]	Kennedy J, Eberhart R C. Particle swarm optimization //Proceedings of the IEEE International Conference on Neural Networks[C]. 1995: 1942-1948
[20]	Deb K. An efficient constraint handling method for genetic algorithms [J]. Computer Methods in Applied Mechanics and Engineering, 2000, 186(2): 311-338

基于EGO算法的常压塔能量优化

Atmospheric tower energy optimization based on EGO

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