广东工业大学学报 ›› 2020, Vol. 37 ›› Issue (01): 69-80.doi: 10.12052/gdutxb.190009

• 综合研究 • 上一篇    下一篇

有机朗肯循环系统工质设计与系统参数的同步优化

王羽鹏, 罗向龙, 梁俊伟, 陈健勇, 杨智, 陈颖   

  1. 广东工业大学 材料与能源学院, 广东 广州 510006
  • 收稿日期:2019-01-14 出版日期:2020-01-25 发布日期:2019-12-31
  • 通信作者: 罗向龙(1978-),男,教授,主要研究方向为热力系统集成与优化、换热器强化与优化,E-mail:lxl-dte@gdut.edu.cn E-mail:lxl-dte@gdut.edu.cn
  • 作者简介:王羽鹏(1993-),男,硕士研究生,主要研究方向为工质物性及分子设计
  • 基金资助:
    国家自然科学基金资助项目（51476037）；广东省应用型科技研发专项资金项目（2016B020243010）

A Simultaneous Optimization of Working Fluid Design and System Parameters in Organic Rankine Cycle

Wang Yu-peng, Luo Xiang-long, Liang Jun-wei, Chen Jian-yong, Yang Zhi, Chen Ying   

  1. School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2019-01-14 Online:2020-01-25 Published:2019-12-31

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摘要： 工质是有机朗肯循环（organic Rankine cycle，ORC）中能量转换的载体，其与冷、热源之间的匹配直接影响ORC系统性能。现有工质提升ORC系统性能有限，新型工质的设计对提升ORC性能非常重要。提出了基于计算机辅助分子设计（computer-aided molecular design，CAMD）的工质设计与和ORC系统同步优化的建模和求解方法，对传统CAMD模型进行了改进。建立了以ORC系统输出净功最大为优化目标的混合整数非线性数学规划（mixed integer non-line programming，MINLP）模型，提出了求解策略。基于9个基本元素选择37个基团，应用于建立的同步优化模型，获得了热源范围353.15~463.15 K和冷源范围293.15~298.15 K工况下的最优工质，并与现有工质进行了对比验证。对比结果表明，新型工质的ORC净功比现有工质ORC净功增加12.46%。对在计算ORC循环性能中涉及的工质物性，如临界温度、临界压力、沸点温度、比热容、密度和相对分子质量等进行了敏感性分析。

关键词: 基团贡献法, 计算机辅助分子设计, 有机朗肯循环, 算法, 优化, 工质筛选

Abstract: The working fluid is the carrier of energy conversion in the organic Rankine cycle (ORC), and its matching with the cold and heat sources directly affects the performance of the ORC system. While the existing working fluid provides a limited improvement for the performance of the ORC system, the design of the novel working fluid is very important for improving the performance of the ORC. The modeling and solving method based on computer-aided molecular design (CAMD) for working fluid design and ORC system is simultaneously optimized, and the traditional CAMD model is improved. A mixed integer nonlinear mathematical programming (MINLP) model with the maximum output net power of ORC system as the optimization target is established, and the solving strategy is proposed. Based on 9 basic elements, 37 groups are selected and applied to the established simultaneous optimization model. The optimal working fluids under the conditions of heat source range of 353.15~463.15 K and cold source range of 293.15~298.15 K are obtained and compared with existing working fluids. The comparisons of net power by ORC show that the novel working fluid is 12.46% higher than the existing working fluids. A sensitivity analysis is performed on the thermodynamic properties such as critical temperature, critical pressure, boiling point temperature, specific heat capacity, density and relative molecular mass involved in calculating the ORC cycle performance.

Key words: group contribution method, computer-aided molecular design, organic Rankin cycle, algorithms, optimization, working fluid selection

中图分类号: 

  • TK212
[1] 许俊俊, 罗向龙, 王永真, 等. ORC工质选择的多级非结构性模糊决策分析[J]. 化工学报, 2015, 66(3):1051-1058
XU J J, LUO X L, WANG Y Z, et al. Optimum selection of ORC working fluid using multi-level fuzzy optimization and non-structural fuzzy decision[J]. CIESC Journal, 2015, 66(3):1051-1058
[2] 黄仁龙, 罗向龙, 梁志辉, 等. 基于分液冷凝的R245fa/pentane混合工质朗肯循环多目标优化[J]. 化工学报, 2018, 69(5):2040-2048
HUANG R L, LUO X L, LIANG Z H, et al. Multi-objective optimization of Rankine cycle using R245fa/pentane based on liquid-vapor separation[J]. CIESC Journal, 2018, 69(5):2040-2048
[3] BAO J, ZHAO L. A review of working fluid and expander selections for organic Rankine cycle[J]. Renewable and Sustainable Energy Reviews, 2013, 24:325-342
[4] HE C, LIU C, ZHOU M, et al. A new selection principle of working fluids for subcritical organic Rankine cycle coupling with different heat sources[J]. Energy, 2014, 68:283-291
[5] YU H, FENG X, WANG Y. Working fluid selection for organic Rankine cycle (ORC) considering the characteristics of waste heat sources[J]. Industrial & Engineering Chemistry Research, 2016, 55(5):1309-1321
[6] GHOLIVAND M B, KHODADADIAN M, AHMADI F. Computer aided-molecular design and synthesis of a high selective molecularly imprinted polymer for solid-phase extraction of furosemide from human plasma[J]. Analytica Chimica Acta, 2010, 658(2):225-232
[7] VALENCIA-MARQUEZ D, FLORES-TLACUAHUAC A, VASQUEZ-MEDRANO R. Simultaneous optimal design of an extractive column and ionic liquid for the separation of bioethanol-water mixtures[J]. INDUSTRIAL &Engineering Chemistry Research, 2012, 51(17):5866-5880
[8] STRUEBING H, GANASE Z, KARAMERTZANIS P G, et al. Computer-aided molecular design of solvents for accelerated reaction kinetics[J]. Nature Chemistry, 2013, 5(11):952-957
[9] PAPADOPOULOS A I, STIJEPOVIC M, LINKE P. On the systematic design and selection of optimal working fluids for Organic Rankine Cycles[J]. Applied Thermal Engineering, 2010, 30(6-7):760-769
[10] PALMA-FLORES O, FLORES-TLACUAHUAC A, CANSECO-MELCHOR G. Optimal molecular design of working fluids for sustainable low-temperature energy recovery[J]. Computers & Chemical Engineering, 2015, 72:334-349
[11] PALMA-FLORES O, FLORES-TLACUAHUAC A, CANSECO-MELCHORB G. Simultaneous molecular and process design for waste heat recovery[J]. Energy, 2016, 99:32-47
[12] LEMMON E W, HUBER M L, MCLINDEN M O. NIST standard reference database 23:reference fluidthermodynamicandtransportproperties-REFPROP9.1[DB]. Gaithersburg:National Institute of Standards and Technology, 2013.(2013-04-18)[2019-01-10]https://www.nist.gov/srd/refprop.
[13] CHURI N, ACHENIE L E K. Novel mathematical programming model for computer aided molecular disign[J]. Ind Eng Chen Res, 1966, 35(10):3788-3794
[14] JOBACK K, REID R. Estimation of pure-component properties from group-contributions[J]. Chemical Engineering Communications, 1987, 57(1-6):233-243
[15] SU W, ZHAO L, DENG S. Developing a performance evaluation model of organic rankine cycle for working fluids based on the group contribution method[J]. Energy Conversion and Management, 2017, 132:307-315
[16] REID R C, PRAUSNITZ J M, SHERWOOD T K. The properties of gases and liquids[M]. New York:McGraw-Hill, 2001.
[17] BENSON R S. Advanced engineering thermodynamics:thermodynamics and fluid mechanics series[M]. Netherlands:Elsevier, 2013.
[18] AMBROSE D, TSONOPOULOS C, NIKITIN E D, et al. Vapor-liquid critical properties of elements and compounds. 12. review of recent data for hydrocarbons and non-hydrocarbons[J]. Journal of Chemical & Engineering Data, 2015, 60(12):3444-3482
[19] AMBROSE D, TSONOPOULOS C, NIKITIN E D. Vapor-liquid critical properties of elements and compounds. 11. organic compounds containing B + O; Halogens + N, + O, + O + S, + S, + Si; N + O; and O + S, + Si[J]. Journal of Chemical &Engineering Data, 2009, 54(3):669-689
[20] MARSH K N, ABRAMSON A, AMBROSE D, et al. Vapor-liquid critical properties of elements and compounds. 10. organic compounds containing halogens[J]. Journal of Chemical & Engineering Data, 2007, 52(5):1509-1538
[21] MARSH K N, YOUNG C L, MORTON D W, et al. Vapor-liquid critical properties of elements and compounds. 9. organic compounds containing nitrogen[J]. Journal of Chemical & Engineering Data, 2006, 51(2):305-314
[22] BROWN J S, BRIGNOLI R, DAUBMAN S. Methodology for estimating thermodynamic parameters and performance of working fluids for organic Rankine cycles[J]. Energy, 2014, 73:818-828
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