A Heat Exchanger Network Optimization Strategy for Internal Utility Evolution

doi:10.19596/j.cnki.1001-246x.8060

Abstract

Abstract: There is an infeasible structure in heat exchanger network optimization in which matching temperatures of cold and hot streams are crossed. Replacing matching by internal utilities is a way to deal with infeasible structure and its solution domain can be expanded. This approach introduces additional fixed investment costs in total annual cost which may be accepted in weaker solutions, which may cause a decrease of optimization efficiency. Therefore, we analyze firstly probable bad influence caused by internal utilities and propose an evolutionary strategy for internal utilities. Total annual cost of the solution which has internal utilities are punished to reduce the accepting probability. If the solution with internal utilities still exist, then heat loads of internal utility exchangers are forced to evolve to optimize the heat load and improve its structure. Effectiveness of the strategy is verified with two examples.

Key words: heat exchanger network, optimization, internal utilities, random walk algorithm with compulsive evolution

CLC Number:

TK124

JIANG Yiwen, CUI Guomin, BAO Zhongkai, LIU Huolin, ZHOU Jinjia. A Heat Exchanger Network Optimization Strategy for Internal Utility Evolution[J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2020, 37(3): 341-351.

References

[1] 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.
[2] LINNHOFF B, HINDMARSH E. The pinch design method for heat exchanger networks[J]. Chemical Engineering Science, 1983, 38(5):745-763.
[3] AZAD A V, GHAEBI H, AMIDPOUR M. Novel graphical approach as fouling pinch for increasing fouling formation period in heat exchanger network (HEN) state of the art[J]. Energy Conversion & Management, 2011, 52(1):117-124.
[4] YEE T F, GROSSMANN I E, KRAVANJA Z. Simultaneous optimization models for heat integration III:Process and heat exchanger network optimization[J]. Computers & Chemical Engineering, 1990, 14(10):1151-1164.
[5] 张勤, 崔国民, 关欣. 换热网络的棋盘模型及其算法[C]. 中国工程热物理学会年会, 2005.
[6] PARIYANI A, GUPTA A, GHOSH P. Design of heat exchanger networks using randomized algorithm[J]. Computers & Chemical Engineering, 2006, 30(6/7):1046-1053.
[7] 彭富裕, 崔国民, 陈上, 等. 基于改进的SWS模型的换热网络优化研究[J]. 工程热物理学报, 2016, 37(9):1973-1983.
[8] FURMAN K C, SAHINIDIS N V. Computational complexity of heat exchanger network synthesis[J]. Computers & Chemical Engineering, 2001, 25(9):1371-1390.
[9] CHOI S H, MANOUSIOUTHAKIS V. Global optimization methods for chemical process design:Deterministic and stochastic approaches[J]. Korean Journal of Chemical Engineering, 2002, 19(2):227-232.
[10] QUESADA I, GROSSMANN I E. An LP/NLP based branch and bound algorithm for convex MINLP optimization problems[J]. Computers & Chemical Engineering, 1992, 16(10-11):937-947.
[11] VISWANATHAN J, GROSSMANN I E. A combined penalty function and outer-approximation method for MINLP optimization[J]. Computers & Chemical Engineering, 1990, 14(7):769-782.
[12] RAVAGNANI M A S S, SILVA A P, ARROYO P A, et al. Heat exchanger network synthesis and optimisation using genetic algorithm[J]. Applied Thermal Engineering, 2005, 25(7):1003-1017.
[13] SILVA A P, RAVAGNANI M A S S, BISCAIA E C, et al. Optimal heat exchanger network synthesis usingparticle swarm optimization[J]. Optimization & Engineering, 2010, 11(3):459-470.
[14] YERRAMSETTY K M, MURTY C V S. Synthesis of cost-optimal heat exchanger networks using differential evolution[J]. Computers and Chemical Engineering, 2008, 32(8):1861-1876.
[15] CHEN S, CUI G, ZHANG C, et al. Optimization of heat integration in dynamic multi-agent differential evolution algorithm[J]. Chinese Journal of Computational Physics, 2016,33(3):349-357.
[16] ZHANG C, CUI G, CHEN S. An improved chaotic ant swarm algorithm for simultaneous synthesis of heat exchanger network[J]. Chinese Journal of Computational Physics, 2017, 34(2):193-204.
[17] ZHU Y, CUI G, XIAO Y, et al. A random walk algorithm with compulsive evolution combined with restrictive-evolution strategy for heat unit in heat exchanger network synthesis[J]. Chinese Journal of Computational Physics, 2017, 34(5):593-602.
[18] 肖媛, 崔国民, 李帅龙. 一种新的用于换热网络全局优化的强制进化随机游走算法[J]. 化工学报, 2016, 67(12):5140-5147.
[19] CHEN S, CUI G M. Uniformity factor of temperature difference in heat exchanger networks[J]. Applied Thermal Engineering, 2016, 102:1366-1373.
[20] 张红亮, 崔国民, 朱玉双, 等. 特殊换热网络算例优化与分析[J]. 化工进展, 2018, 37(5):1692-1701.
[21] KHORASANY R M, FESANGHARY M. A novel approach for synthesis of cost-optimal heat exchanger networks[J]. Computers & Chemical Engineering, 2009, 33(8):1363-1370.
[22] XIAO W, DONG H, LI X, et al. Synthesis of large-scale multistream heat exchanger networks based on stream pseudo temperature[J]. Chinese Journal of Chemical Engineering, 2006, 14(5):574-583.
[23] LAUKKANEN T, FOGELHOLM C J. A bilevel optimization method for simultaneous synthesis of medium-scale heat exchanger networks based on grouping of process streams[J]. Computers & Chemical Engineering, 2011, 35(11):2389-2400.
[24] LUO X, WEN Q Y, FIEG G. A hybrid genetic algorithm for synthesis of heat exchanger networks[J]. Computers & Chemical Engineering, 2009, 33(6):1169-1181.
[25] LAUKKANEN T, TVEIT T M, OJALEHTO V, et al. Bilevel heat exchanger network synthesis with an interactive multi-objective optimization method[J]. Applied Thermal Engineering, 2012, 48(26):301-316.
[26] ZHANG C, CUI G, PENG F. A novel hybrid chaotic ant swarm algorithm for heat exchanger networks synthesis[J]. Applied Thermal Engineering, 2016, 104:707-719.
[27] HUO Z, ZHAO L, YIN H, et al. Simultaneous synthesis of structural-constrained heat exchanger networks with and without stream splits[J]. Canadian Journal of Chemical Engineering, 2013, 91(5):830-842.
[28] PAVÃO L V, COSTA C B B, RAVAGNANI M A S S. Heat exchanger network synthesis without stream splits using parallelized and simplified simulated annealing and particle swarm optimization[J]. Chemical Engineering Science, 2017, 158:96-107.
[29] BAO Z, CUI G, CHEN J, et al. A novel random walk algorithm with compulsive evolution combined with an optimum-protection strategy for heat exchanger network synthesis[J]. Energy, 2018, 152.
[30] CHEN J, CUI G, DUAN H. Multipopulation differential evolution algorithm based on the opposition-based learning for heat exchanger network synthesis[J]. Numerical Heat Transfer Part A:Applications, 2017, 72(2):126-140.Received date:2019-03-11; Revised date:2019-05-30