基于结构多样性评价的换热网络全局最优化
|
摘要
针对启发式方法在优化换热网络时由于个体团聚而出现搜索能力下降,建立一种换热网络结构多样性评价方法,对种群中个体结构团聚程度进行衡量,并指导算法改进.对种群进行集团划分,将一定数目的具有公共结构的个体归为一个集团,从而得到个体结构分布;提出分散搜索策略,对于各集团中除集团最优个体外的其它个体,从其公共结构中随机选择若干个换热器进行摄动以分散集团中的个体结构;提出集中搜索策略,通过使其它个体获得最优集团对应公共结构以加强对较优结构的集中开发;采用9股流与15股流两个算例,验证分散搜索策略增强了全局搜索能力,集中搜索策略增强了局部搜索能力,优化结果分别较原算法降低了7 008■·a~(-1)与17 973■·a~(-1)且均优于文献结果.
Individual gathering could cause decline of search ability as heuristic methods are applied to heat exchanger network(HEN)optimization.An evaluation methodology for HEN structure diversity was designed to measure degree of individual structure gathering,and guided algorithm improvement.Firstly,group division of population was performed,individuals of a certain scale with a common structure were classified as a group to get individual structure distribution.Then dispersal search strategy was proposed to give perturbation to heat exchangers randomly selected from common structure of individuals in each group except the best one,which aimed at dispersing individual structures in groups.Concentration search strategy was then proposed to enhance exploitation for excellent structure by making other individuals accept a common structure of the optimal group.Finally,two cases involving nine and fifteen streams proved that dispersal search strategy strengthened global search ability and concentration search strategy strengthened local search ability.It obtained results decreased by 7 008■·a~(-1)and 17 973■·a~(-1),respectively,compared to those obtained by original algorithm.They are superior to results in literature.
Individual gathering could cause decline of search ability as heuristic methods are applied to heat exchanger network(HEN)optimization.An evaluation methodology for HEN structure diversity was designed to measure degree of individual structure gathering,and guided algorithm improvement.Firstly,group division of population was performed,individuals of a certain scale with a common structure were classified as a group to get individual structure distribution.Then dispersal search strategy was proposed to give perturbation to heat exchangers randomly selected from common structure of individuals in each group except the best one,which aimed at dispersing individual structures in groups.Concentration search strategy was then proposed to enhance exploitation for excellent structure by making other individuals accept a common structure of the optimal group.Finally,two cases involving nine and fifteen streams proved that dispersal search strategy strengthened global search ability and concentration search strategy strengthened local search ability.It obtained results decreased by 7 008■·a~(-1)and 17 973■·a~(-1),respectively,compared to those obtained by original algorithm.They are superior to results in literature.
引文
[1] AND K C F, SAHINIDIS N V. A critical review and annotated bibliography for heat exchanger network synthesis in the 20th century [J]. Ind Eng Chem Res, 2002, 41(10):2335-2370.
[2] LINNHOFF B, HINDMARSH E. The pinch design method for heat exchanger networks [J]. Chem Eng Sci, 1983, 38(5):745-763.
[3] PAPOULIAS S A, GROSSMANN I E. A structural optimization approach in process synthesis II: Heat recovery networks [J]. Comput Chem Eng, 1983, 7(6): 707-721.
[4] FLOUDAS C A, CIRIC A R, GROSSMANN I E. Automatic synthesis of optimum heat exchanger network configurations [J]. AIChE J, 1986, 32(2): 276-290.
[5] CERDA J, WESTERBURG A W. Synthesizing heat exchanger networks having restricted stream/stream matches using transportation problem formulations [J]. Chem Eng Sci, 1983, 38(10): 1723-1740.
[6] XU Y C, CHN Q, GUO Z Y. Entransy dissipation-based constraint for optimization of heat exchanger networks in thermal systems [J]. Energy, 2015, 86:696-708.
[7] YEE T F, GROSSMANN I E. Simultaneous optimization models for heat integration II:Heat exchanger network synthesis [J]. Comput Chem Eng, 1990, 14(10): 1165-1184.
[8] CASTIER M. Rigorous multiple utility targeting in heat exchanger networks [J]. Energy Convers Mgmt, 2012, 59(3):74-85.
[9] LOTFI R, BOOZARJOMEHRY R B. Superstructure optimization in heat exchanger network (HEN) synthesis using modular simulators and a genetic algorithm framework [J]. Ind Eng Chem Res, 2010, 49(10):4731-4737.
[10] YERRAMSETTY K M, MURTY C V S. Synthesis of cost-optimal heat exchanger networks using differential evolution [J]. Comput Chem Eng, 2008, 32(8): 1861-1876.
[11] SILVA A P, RAVAGNANI M A S S, BISCAIA E C. Particle swarm optimisation applied in retrofit of heat exchanger networks [J]. Comput Aided Chem Eng, 2009, 27: 1035-1040.
[12] DOLAN W,CUMMINGS P, LEVAN M. Process optimization via simulated annealing: Application to network design [J]. AIChE J, 1989, 35(5): 725-736.
[13] LUO X, WEN Q Y, FIEG G. A hybrid genetic algorithm for synthesis of heat exchanger networks [J]. Comput Chem Eng, 2009, 33(6):1169-1181.
[14] CHEN S, CUI G M, ZHANG C W, et al. Optimization of heat integration in dynamic multi-agent differential evolution algorithm [J]. Chinese Journal of Computational Physics, 2016, 33(3):349-357.
[15] CHEN J, CUI G, DUAN H. Multipopulation differential evolution algorithm based on the opposition-based learning for heat exchanger network synthesis [J]. Numer Heat Transf A, 2017, 72(2):126-140.
[16] ZHANG C W, CUI G M, CHEN S, et al. 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] 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.
[18] BLACKWELL T M. Particle swarms and population diversity [J]. Soft Comput, 2005, 9(11):793-802.
[19] Lü Q, SHEN G, YU R. A chaotic approach to maintain the population diversity of genetic algorithm in network training [J]. Comput Biol Chem, 2003, 27(3):363-371.
[20] SALEHINEJAD H, RAHNAMAYAN S, TIZHOOSH H R. Micro-differential evolution: Diversity enhancement and a comparative study [J]. Appl Soft Comput, 2016.
[21] 陈家星, 崔国民, 彭富裕, 等. 基于种群多样性的改进差分进化算法应用于换热网络优化[J]. 热能动力工程, 2017, 32(4):29-37.
[22] LI S L, CUI G M, CHEN S, et al. An improved particle swarm optimization based on diversity monitor and real-time updating strategy [J]. Chinese Journal of Computational Physics, 2017, 34(3):344-355.
[23] BJ?RK K M, WESTERLUND T. Global optimization of heat exchanger network synthesis problems with and without the isothermal mixing assumption [J]. Comput Chem Eng, 2002, 26(11):1581-1593.
[24] 肖媛, 崔国民, 彭富裕,等. PSO用于优化换热网络时对全局搜索能力的研究[J]. 热能动力工程, 2016, 31(1):20-26.
[25] 何巧乐, 崔国民, 许海珠. 文化基因粒子群算法在换热网络连续变量全局优化中的应用[J]. 石油化工, 2014, 43(1):37-45.
[26] PAV?O L V, COSTA C B, RAVAGNANI M S. Heat exchanger network synthesis without stream splits using parallelized and simplified simulated annealing and particle swarm optimization [J]. Chem Eng Sci, 2017, 158:96-107.
[27] 苏海军, 杨煜普, 王宇嘉. 微分进化算法的研究综述[J]. 系统工程与电子技术, 2008, 30(9):1793-1797.
[28] HUO Z, ZHAO L, YIN H, et al. A hybrid optimization strategy for simultaneous synthesis of heat exchanger network [J]. Korean J Chem Eng, 2012, 29(10):1298-1309.
[29] 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.
[30] 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.
[31] PENG F, CUI G. Efficient simultaneous synthesis for heat exchanger network with simulated annealing algorithm [J]. Appl Therm Eng, 2015, 78:136-149.
[32] ZHU X X, O′NEILL B K, ROACH J R, et al. A method for automated heat exchanger network synthesis using block decomposition and non-linear optimization [J]. Chemical Engineering Research & Design, 1995, 73(A8): 919-930.
[33] DUAN H H, CUI G M, CHEN J X, et al. A strategy of differential evolution with opposition-based multi-population parallel [J]. Chinese Journal of Computational Physics, 2016, 33(5):561-569.
[34] FIEG G, LUO X, JE monogenetic algorithm for optimal design of large-scale heat exchanger networks [J]. Chem Eng Process, 2009, 48(11):1506-1516.
[35] BJ?RK K M, PETTERSSON F. Optimization of large-scale heat exchanger network synthesis problems[C]∥Iasted International Conference on Modelling and Simulation, DBLP, 2003:313-318.
[36] BJ?RK K M, NORDMAN R. Solving large-scale retrofit heat exchanger network synthesis problems with mathematical optimization methods [J]. Chem Eng Process, 2005, 44(8):869-876.
[2] LINNHOFF B, HINDMARSH E. The pinch design method for heat exchanger networks [J]. Chem Eng Sci, 1983, 38(5):745-763.
[3] PAPOULIAS S A, GROSSMANN I E. A structural optimization approach in process synthesis II: Heat recovery networks [J]. Comput Chem Eng, 1983, 7(6): 707-721.
[4] FLOUDAS C A, CIRIC A R, GROSSMANN I E. Automatic synthesis of optimum heat exchanger network configurations [J]. AIChE J, 1986, 32(2): 276-290.
[5] CERDA J, WESTERBURG A W. Synthesizing heat exchanger networks having restricted stream/stream matches using transportation problem formulations [J]. Chem Eng Sci, 1983, 38(10): 1723-1740.
[6] XU Y C, CHN Q, GUO Z Y. Entransy dissipation-based constraint for optimization of heat exchanger networks in thermal systems [J]. Energy, 2015, 86:696-708.
[7] YEE T F, GROSSMANN I E. Simultaneous optimization models for heat integration II:Heat exchanger network synthesis [J]. Comput Chem Eng, 1990, 14(10): 1165-1184.
[8] CASTIER M. Rigorous multiple utility targeting in heat exchanger networks [J]. Energy Convers Mgmt, 2012, 59(3):74-85.
[9] LOTFI R, BOOZARJOMEHRY R B. Superstructure optimization in heat exchanger network (HEN) synthesis using modular simulators and a genetic algorithm framework [J]. Ind Eng Chem Res, 2010, 49(10):4731-4737.
[10] YERRAMSETTY K M, MURTY C V S. Synthesis of cost-optimal heat exchanger networks using differential evolution [J]. Comput Chem Eng, 2008, 32(8): 1861-1876.
[11] SILVA A P, RAVAGNANI M A S S, BISCAIA E C. Particle swarm optimisation applied in retrofit of heat exchanger networks [J]. Comput Aided Chem Eng, 2009, 27: 1035-1040.
[12] DOLAN W,CUMMINGS P, LEVAN M. Process optimization via simulated annealing: Application to network design [J]. AIChE J, 1989, 35(5): 725-736.
[13] LUO X, WEN Q Y, FIEG G. A hybrid genetic algorithm for synthesis of heat exchanger networks [J]. Comput Chem Eng, 2009, 33(6):1169-1181.
[14] CHEN S, CUI G M, ZHANG C W, et al. Optimization of heat integration in dynamic multi-agent differential evolution algorithm [J]. Chinese Journal of Computational Physics, 2016, 33(3):349-357.
[15] CHEN J, CUI G, DUAN H. Multipopulation differential evolution algorithm based on the opposition-based learning for heat exchanger network synthesis [J]. Numer Heat Transf A, 2017, 72(2):126-140.
[16] ZHANG C W, CUI G M, CHEN S, et al. 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] 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.
[18] BLACKWELL T M. Particle swarms and population diversity [J]. Soft Comput, 2005, 9(11):793-802.
[19] Lü Q, SHEN G, YU R. A chaotic approach to maintain the population diversity of genetic algorithm in network training [J]. Comput Biol Chem, 2003, 27(3):363-371.
[20] SALEHINEJAD H, RAHNAMAYAN S, TIZHOOSH H R. Micro-differential evolution: Diversity enhancement and a comparative study [J]. Appl Soft Comput, 2016.
[21] 陈家星, 崔国民, 彭富裕, 等. 基于种群多样性的改进差分进化算法应用于换热网络优化[J]. 热能动力工程, 2017, 32(4):29-37.
[22] LI S L, CUI G M, CHEN S, et al. An improved particle swarm optimization based on diversity monitor and real-time updating strategy [J]. Chinese Journal of Computational Physics, 2017, 34(3):344-355.
[23] BJ?RK K M, WESTERLUND T. Global optimization of heat exchanger network synthesis problems with and without the isothermal mixing assumption [J]. Comput Chem Eng, 2002, 26(11):1581-1593.
[24] 肖媛, 崔国民, 彭富裕,等. PSO用于优化换热网络时对全局搜索能力的研究[J]. 热能动力工程, 2016, 31(1):20-26.
[25] 何巧乐, 崔国民, 许海珠. 文化基因粒子群算法在换热网络连续变量全局优化中的应用[J]. 石油化工, 2014, 43(1):37-45.
[26] PAV?O L V, COSTA C B, RAVAGNANI M S. Heat exchanger network synthesis without stream splits using parallelized and simplified simulated annealing and particle swarm optimization [J]. Chem Eng Sci, 2017, 158:96-107.
[27] 苏海军, 杨煜普, 王宇嘉. 微分进化算法的研究综述[J]. 系统工程与电子技术, 2008, 30(9):1793-1797.
[28] HUO Z, ZHAO L, YIN H, et al. A hybrid optimization strategy for simultaneous synthesis of heat exchanger network [J]. Korean J Chem Eng, 2012, 29(10):1298-1309.
[29] 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.
[30] 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.
[31] PENG F, CUI G. Efficient simultaneous synthesis for heat exchanger network with simulated annealing algorithm [J]. Appl Therm Eng, 2015, 78:136-149.
[32] ZHU X X, O′NEILL B K, ROACH J R, et al. A method for automated heat exchanger network synthesis using block decomposition and non-linear optimization [J]. Chemical Engineering Research & Design, 1995, 73(A8): 919-930.
[33] DUAN H H, CUI G M, CHEN J X, et al. A strategy of differential evolution with opposition-based multi-population parallel [J]. Chinese Journal of Computational Physics, 2016, 33(5):561-569.
[34] FIEG G, LUO X, JE monogenetic algorithm for optimal design of large-scale heat exchanger networks [J]. Chem Eng Process, 2009, 48(11):1506-1516.
[35] BJ?RK K M, PETTERSSON F. Optimization of large-scale heat exchanger network synthesis problems[C]∥Iasted International Conference on Modelling and Simulation, DBLP, 2003:313-318.
[36] BJ?RK K M, NORDMAN R. Solving large-scale retrofit heat exchanger network synthesis problems with mathematical optimization methods [J]. Chem Eng Process, 2005, 44(8):869-876.