化工学报 ›› 2020, Vol. 71 ›› Issue (3): 1189-1201.doi: 10.11949/0438-1157.20190975

• 过程系统工程 • 上一篇    下一篇

综合考虑经济性与效率的换热网络多目标约束优化方法

王磊1,2(),陈玉婷1,2,徐燕燕1,2,3,叶爽1,2(),黄伟光1,2,3   

  1. 1.中国科学院上海高等研究院,上海 201210
    2.中国科学院大学,北京 100049
    3.上海科技大学物质科学与技术学院,上海 201210
  • 收稿日期:2019-08-30 修回日期:2019-12-05 出版日期:2020-03-05 发布日期:2019-12-24
  • 通讯作者: 叶爽 E-mail:wanglei01@sari.ac.cn;yes@sari.ac.cn
  • 作者简介:王磊(1994—),男,硕士研究生,wanglei01@sari.ac.cn
  • 基金资助:
    国家重点研发计划项目(YS2017YFGH001928);上海市科委科研计划项目(19DZ1205700)

Multi-objective constrained optimization method for heat exchanger network considering comprehensive economy and entransy

Lei WANG1,2(),Yuting CHEN1,2,Yanyan XU1,2,3,Shuang YE1,2(),Weiguang HUANG1,2,3   

  1. 1.Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
    2.University of Chinese Academy of Sciences, Beijing 100049, China
    3.School of Physical Science and Technology, Shanghai Tech University, Shanghai 201210, China
  • Received:2019-08-30 Revised:2019-12-05 Online:2020-03-05 Published:2019-12-24
  • Contact: Shuang YE E-mail:wanglei01@sari.ac.cn;yes@sari.ac.cn

摘要:

换热网络优化中不仅要考虑能量回收的“量”,还要考虑能量回收过程中“质”的耗散问题。在换热网络最大能量回收的前提下,基于不可逆传热过程中的耗散理论,以代表能量回收品质的效率最高为目标,建立综合能量回收数量、品质并同时考虑换热网络经济性的多目标混合整数非线性规划(MOMINLP)模型。根据模型目标有主次之分的特点,基于ε约束法对模型进行分步优化并结合BARON软件进行精确求解,依次求解换热网络的最大能量回收量(MER)与最低年均总成本(TAC),再将所得结果乘以松弛系数εi作为缩小搜索区域的约束条件,获得能量与成本约束下效率的Pareto前沿。通过对经典10SP1案例进行计算求解,最终得到最大能量回收量下,费用松弛系数为1.05时费效比最小的优化方案,而且本文的多目标约束优化方法能够更快求得综合的最优解。最后通过T-Q图中的换热网络组合曲线对比不同优化方案的效率,将换热网络划分为内部换热部分与剩余流股部分,多目标约束优化方法能够降低内部换热不可逆损失,提高剩余流股的温度。

关键词: 换热网络, ε-约束法, 多目标, 优化设计, 集成, 过程控制

Abstract:

For the optimization of the heat exchange network, not only the“quantity”of energy recovery, but also the“quality”dissipation problem in the energy recovery process must be considered. On the premise of the maximum energy recovery(MER) of the heat exchanger network, based on the entransy dissipation theory in the process of irreversible heat transfer, aiming at the highest entransy efficiency which represents the energy recovery quality, we establish a multi-objective mixed integer nonlinear programming (MOMINLP) model, which comprehensively consider the quantity and quality of energy recovery and the economics of the heat exchanger network. According to the characteristics of the model targets, the heat exchanger network is optimized step-by-step based on the ε-constraint algorithm, the maximum energy recovery and minimum total annual cost (TAC) of the heat exchanger network are accurately solved in turn with the BARON software. Then, with the solved results multiplied by the relaxation coefficient εi as the constraint condition for narrowing the search area, the Pareto frontier of the entransy efficiency under the constraints of energy and economy is obtained. Through calculating the classic 10SP1 case, the optimal solution as the minimum ratio of economy to efficiency is obtained when the relaxation coefficient is 1.05, and compared with the results of other literature optimization methods, the multi-objective constrained optimization method in this paper can find the comprehensive optimal solution faster. Finally, the entransy efficiency of different optimization schemes is compared through the heat exchanger networks composite curve in T-Q diagram, which indicates that divide the heat exchanger network into internal part and surplus part firstly, and then optimize the scheme according to the economy and entransy efficiency, this method can reduce the irreversible loss of internal part and increase the temperature of the surplus part.

Key words: heat exchanger network, ε-constrained method, multi-objective, optimal design, integration, process control

中图分类号: 

  • TQ 021.8

图1

换热网络问题示意图"

图2

换热网络总组合曲线"

图3

改造后的换热网络组合曲线"

图4

换热网络超结构模型"

图5

换热网络多目标约束优化方法"

图6

ε约束法缩小搜索区域"

表1

10SP1问题的流股数据"

流体入口温度/K出口温度/K热容流率/(kW?K-1
H14333668.79
H252241110.55
H350033914.77
H454442212.56
H547233917.73
C13334337.62
C23894956.08
C33114948.44
C435545017.28
C536647813.90
HU509509
CU311355

图7

SE与TAC的Pareto前沿"

图8

不同ε2值下的增幅对比"

图9

MER和TAC松弛度1.05条件下以MEE为目标得到的设计方案"

图10

MER条件下最小TAC为目标得到的设计方案"

图11

不同方法得到的组合曲线"

表2

几种方法的结果对比"

文献搜索方法TACAMTD/USDTAC*AMTD /USDSE/(kW?K)η计算时间(CPU time)/s
Floudas弯曲分解方法4140641469.17018130.732
Lewin遗传算法40977.741028.17123640.743
方法1BARON41007.241265.37016240.7311312.61
方法2ε约束法4087041001.96999590.730242.70
本文方法ε约束法42157.943289.27408000.77223.61
1 Jin Z L, Chen X T, Wang Y Q, et al. Heat exchanger network synthesis based on environmental impact minimization[J]. Clean Technologies & Environmental Policy, 2014, 16(1): 183-187.
2 霍兆义, 尹洪超, 赵亮, 等. 国内换热网络综合方法研究进展与展望[J]. 化工进展, 2012, 31(4): 726-731.
Huo Z Y, Yin H C, Zhao L, et al. Process and prospect for the methodology of heat exchanger network synthesis in China[J]. Chemical Industry and Engineering Progress, 2012, 31(4): 726-731.
3 Hwa C S. Mathematical formulation and optimization of heat exchanger networks using separable programming[C]//AIChE-IChemE Symposium Series 4. 1965: 101-106.
4 Furman K C, Sahinidis N V. A critical review and annotated bibliography for heat exchanger network synthesis in the 20th century[J]. Industrial & Engineering Chemistry Research, 2002, 41(10): 2335-2370.
5 Hohmann E C. Optimum networks for heat exchanger[D]. Los Angeles: University of Southern California, 1971.
6 Linnhoff B, Hindmarsh E. The pinch design method for heat exchanger networks[J]. Chemical Engineering Science, 1983, 38(5): 745-763.
7 Asante N D K, Zhu X X. An automated and interactive approach for heat exchanger network retrofit[J]. Chemical Engineering Research & Design, 1997, 75(3): 349-360.
8 Yee T F, Grossmann I E. Simultaneous optimization models for heat integration(II): Heat exchanger network synthesis[J]. Computers & Chemical Engineering, 1990, 14(10): 1165-1184.
9 Yee T F, Grossmann I E, Kravanja Z. Simultaneous optimization models for heat integration(III): Process and heat exchanger network optimization[J]. Computers and Chemical Engineering, 1990, 14(11): 1185-1200.
10 Ke F H, Al-Mutairi E M, Karimi I A. Heat exchanger network synthesis using a stagewise superstructure with non-isothermal mixing[J]. Chemical Engineering Science, 2012, 73(19): 30-43.
11 胡向柏, 崔国民, 涂惟民. 复杂换热网络中的非线性特性分析[J]. 工程热物理学报, 2012, V33(2): 285-287.
Hu X B, Cui G M, Tu W M. The non-linear characteristics analyze of the minlp in the complex heat exchanger networks[J] Journal of Engineering Thermophysics, 2012, V33(2): 285-287.
12 Escobar M, Trierweiler J O, Grossmann I E. Simultaneous synthesis of heat exchanger networks with operability considerations: flexibility and controllability[J]. Computers & Chemical Engineering, 2013, 55(32): 158-180.
13 Pavão L V, Pozo C, Costa C B B, et al. Financial risks management of heat exchanger networks under uncertain utility costs via multi-objective optimization[J]. Energy, 2017, 139: 98-117.
14 Pavão L V, Costa C B B, Ravagnani M a S S, et al. Costs and environmental impacts multi-objective heat exchanger networks synthesis using a meta-heuristic approach[J]. Applied Energy, 2017, 203(1): 304-320.
15 Kang L, Liu Y. Multi-objective optimization on a heat exchanger network retrofit with a heat pump and analysis of CO2 emissions control[J]. Applied Energy, 2015, 154(1): 696-708.
16 李帅龙, 崔国民, 周剑卫. 基于温差均匀性因子协进化的双层算法同步优化换热网络[J]. 热能动力工程, 2017, 32(7): 17-23.
Li S L, Cui G M, Zhou J W, Synchronization algorithm optimization bilayer uniform temperature difference factor of the heat exchanger network based co-evolution[J]. Thermal & Power Engineering, 2017, 32 (7): 17-23.
17 Hamsani M N, Walmsley T G, Liew P Y, et al. Combined pinch and exergy numerical analysis for low temperature heat exchanger network[J]. Energy, 2018, 153: 100-112.
18 Cheng X T, Liang X. Heat transfer entropy resistance for the analyses of two-stream heat exchangers and two-stream heat exchanger networks[J]. Applied Thermal Engineering, 2013, 59(1/2): 87-93.
19 过增元, 梁新刚, 朱宏晔. ——描述物体传递热量能力的物理量[J]. 自然科学进展, 2006, 16(10): 1288-1296.
Guo Z Y, Liang X G, Zhu H Y. Entransy-- a physical quantity describing the ability of an object to transfer heat[J]. Progress in Natural Science, 2006, 16(10): 1288-1296.
20 Chen Q, Liang X G, Guo Z Y. Entransy theory for the optimization of heat transfer — a review and update[J]. International Journal of Heat & Mass Transfer, 2013, 63(15): 65-81.
21 李林, 蒋宁, 盛颂恩, 等. 基于损耗的换热网络优化方法研究进展[J]. 轻工机械, 2011, 1(3): 117-121.
Li L, Jiang N, Sheng S E, et al. Research progress on optimization method of heat exchanger network based on entransy[J]. Light Industry Machinery, 2011, 1(3): 117-121.
22 徐燕燕, 叶爽, 黄伟光. 基于当量热阻的ORC工质选择方法研究[J]. 工程热物理学报, 2018, 39(1): 23-30.
Xu Y Y, Ye S, Huang W G. A method of selecting working fluid and operation conditions at once in an ORC(organic rankine cycle)[J]. Journal of Engineering Thermophysics, 2018, 39(1): 23-30.
23 陈玉婷, 徐燕燕, 王磊, 等. 蒸发器换热过程对ORC系统混合工质选择和运行工况的影响[J]. 化工学报, 2019, 70(5): 1723-1733.
Chen Y T, Xu Y Y, Wang L, et al. Effect of evaporator heat transfer process on selection of mixture and operating condition in ORC system[J]. CIESC Journal, 2019, 70(5): 1723-1733.
24 柳雄斌, 孟继安, 过增元. 换热器参数优化中的熵产极值和耗散极值[J]. 科学通报, 2008, 53(24): 3026-3029.
Liu X B, Meng J A, Guo Z Y. Entropy production extreme value and entransy dissipation extreme value in heat exchanger parameter optimization[J]. Chinese Science Bulletin, 2008, 53(24): 3026-3029.
25 Zhang T, Liu X, Tang H, et al. Exergy and entransy analyses in air-conditioning system (Ι): Similarity and distinction[J]. Energy and Buildings, 2016, 128: 876-885.
26 Lewin D R. A generalized method for HEN synthesis using stochastic optimization(II): The synthesis of cost-optimal networks[J]. Computers & Chemical Engineering, 1998, 22(10): 1387-1405.
27 张亚辉, 胡小锋, 吴传珣. 基于ε-约束法的多目标双边装配线再平衡问题[J]. 计算机集成制造系统, 2016, 22(11): 2551-2562.
Zhang Y H, Hu X F, Wu C X. Multi-objective two-sided assembly line rebalancing problem based on ε-constraint method[J]. Computer Integrated Manufacturing Systems, 2016, 22(11): 2551-2562.
28 Laukkanen T, Tveit T M, Ojalehto V, et al. An interactive multi-objective approach to heat exchanger network synthesis[J]. Computers & Chemical Engineering, 2010, 34(6): 943-952.
29 林露. 基于非支配排序遗传算法的换热网络多目标优化[D]. 杭州: 浙江工业大学, 2013.
Lin L. Multi-objective optimization of heat exchanger networks based on non-dominated sorting genetic algorithm[D]. Hangzhou: Zhejiang University of Technology, 2013.
30 Powell W B. A unified framework for stochastic optimization[J]. European Journal of Operational Research, 2019, 275(3): 795-821.
31 Chen Q, Xu Y C, Guo Z Y. The property diagram in heat transfer and its applications[J]. Science Bulletin, 2012, 57(35): 4646-4652.
32 杨世铭, 陶文铨. 传热学[M]. 4版. 北京: 高等教育出版社, 2006.
Yang S M, Tao W Q. Heat Transfer[M]. 4th ed. Beijing: Higher Education Press, 2006.
33 Chen J J J. Comments on improvements on a replacement for the logarithmic mean[J]. Chemical Engineering Science, 1987, 42(10): 2488-2489.
34 彭富裕, 崔国民, 陈家星. 基于模拟退火算法的换热网络双层优化方法[J]. 石油化工, 2014, 43(5): 536-544.
Peng F Y, Cui G M, Chen J X. Bilevel optimization method for heat exchanger network synthesis based on simulated annealing algorithm[J]. Petrochemical Technology, 2014, 43(5): 536-544.
35 刘璞, 崔国民, 肖媛. 具有步长调整策略的强制进化随机游走算法优化换热网络[J]. 化工进展, 2017, 36(2): 442-450.
Liu P, Cui G M, Xiao Y. Optimizing heat exchanger network by random walking algorithm with compulsive evolution combined with step length adjustment strategy[J]. Chemical Industry and Engineering Progress, 2017, 36(2): 442-450.
36 Tawarmalani M, Sahinidis N V. A polyhedral branch-and-cut approach to global optimization[J]. Mathematical Programming, 2005, 103(2): 225-249.
37 Pho T K, Lapidus L. Topics in computer-aided design(Ⅱ): Synthesis of optimal heat exchanger networks by tree searching algorithms [J]. AIChE Journal, 1973, 19(6): 1182-1189.
38 温卿云, 罗行, 杨杉杉, 等. 换热器网络综合优化10SP1问题的研究[C]//传热传质学学术会议, 2007.
Wen Q Y, Luo X, Yang S S, et al. Research on 10SP1 problem of comprehensive optimization of heat exchanger network[C]//Heat and Mass Transfer Conference, 2007.
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