A Constrained Multi-objective Online Operation Optimization Method of Collaborative Distillation and Heat Exchanger Network
-
摘要: 针对蒸馏装置与换热网络间缺乏协同优化导致的分馏精度差和能耗高的问题，提出了一种基于代理模型的约束多目标在线协同操作优化方法.为了解决蒸馏装置与换热网络操作参数协同优化时存在的计算耗时和约束的问题，构建Kriging代理模型来近似目标函数和约束条件，提出了基于随机欠采样和Adaboost的分类代理模型（RUSBoost）来解决类别不平衡的收敛判定预测问题.提出了基于多阶段自适应约束处理的代理模型的模型管理方法，该方法采用基于参考向量激活状态的最大化改善期望准则和可行概率准则更新机制来平衡优化初始阶段种群的多样性和可行性，采用支配参考点的置信下限准则更新机制加快收敛速度.通过不断与机理模型交互来在线更新代理模型，实现在线操作优化.通过测试函数和仿真实例验证了本文方法的有效性.Abstract: To solve low separation precision and high energy-consuming caused by lacking of collaborative operation optimization between atmospheric distillation unit and heat exchanger network, this paper presents a constrained multi-objective online collaborative operation optimization method based on surrogates model. To solve the problems of time-consuming and constraints in operating parameters collaborative optimization of distillation unit and heat exchanger network, the Kriging surrogate models are built to approximate each objective function and constraint, a classification surrogate model based on random undersampling and Adaboost is presented to solve the class imbalance of convergence prediction problem. A model management method of surrogate models based on multi-stage adaptive constraint handling is proposed. The method uses the maximization expected improvement and probability of feasibility criterion updating mechanism based on the reference vector activation state to balance the diversity and feasibility of the population at the early stage of evolution. The convergence rate is accelerated by using lower confidence bound criterion update mechanism dominating the reference point. By constantly interacting with the mechanism model to online update the surrogate model, the online operation optimization is realized. The efficiency of the proposed algorithm is validated by the results of benchmark functions and simulation example.1) 本文责任编委 魏庆来
-
图 3 常压蒸馏塔与换热网络协同操作优化框架
Fig. 3 Collaborative operation optimization framework of atmospheric distillation column and heat exchanger network
表 1 IGD指标比较结果
Table 1 The comparison result of IGD
测试函数 RK-CRVEA EI-PoF RVEA CF1 0.0169(0.0016) 0.0231(0.0027) 0.0338(0.0021) CF2 0.0131(0.0032) 0.0305(0.0102) 0.0473(0.0095) CF3 0.1322(0.0128) 0.3909(0.0764) 0.4351(0.0856) CF4 0.0382(0.0290) 0.1445(0.0325) 0.1622(0.0413) CF5 0.1754(0.0457) 0.3517(0.0824) 0.7395(0.0881) 
下载: 导出CSV
表 2 决策变量的取值范围
Table 2 The range limit of decision variables
序号 变量 单位 下限 上限 实际值 1 $F_{prod-1}$, 石脑油流量 bbl/h 430 600 450 2 $F_{prod-2}$, 煤油流量 bbl/h 315.5 500 351.5 3 $F_{prod-3}$, 轻柴油流量 bbl/h 550.1 802.1 789 4 $F_{prod-4}$, 重柴油流量 bbl/h 180.5 387.5 200 5 $F_{stm-1}$, 塔底蒸汽注入量 kg/h 3 000 4 000 3 400 6 $F_{stm-2}$, 第一汽提塔蒸汽注入量 kg/h 1 000 2 000 1 350 7 $F_{stm-3}$, 第二汽提塔蒸汽注入量 kg/h 1 000 2 000 1 150 8 $F_{PA-1}$, 常顶循环回流量 bbl/h 1 883 2 483 2 000 9 $F_{PA-2}$, 常一中循环回流量 bbl/h 950 1 550 1 258 10 $F_{PA-3}$, 常二中循环回流量 bbl/h 950 1 550 1 258 11 $Q_{PA-1}$, 常顶循环回流负荷 MW 10 20 12.5 12 $Q_{PA-2}$, 常一中循环回流负荷 MW 6 16 10.8 13 $Q_{PA-3}$, 常二中循环回流负荷 MW 6 16 10.8 14 $T_{f}$, 常压炉出口温度 ℃ 330 370 350
下载: 导出CSV
表 3 原油、产品及公用工程的价格
Table 3 Prices of crude oil, distillation product and utilities
参数 原油$P_{crude}$ 水蒸气$P_{stm}$ 燃料油$P_{fuel}$ 冷凝水$P_{cw}$ 石脑油$F_{prod-1}$ 煤油$F_{prod-2}$ 轻柴油$F_{prod-3}$ 重柴油$F_{prod-4}$ 价格 79.6 0.0055 0.017 $4.74\times10^{-3}$ 103.5 92.7 99 96.6 单位 ＄/bbl ＄/kg ＄/MJ ＄/MJ ＄/bbl ＄/bbl ＄/bbl ＄/bbl
下载: 导出CSV
表 4 产品TBP 95 %规定范围(℃)
Table 4 The value range of product TBP 95 % (℃)
产品 石脑油$T95_1$ 煤油$T95_2$ 轻柴油$T95_3$ 重柴油$T95_4$ 上限($T95^L$) 95 175 285 345 下限($T95^H$) 120 200 310 370
下载: 导出CSV
表 5 相同机理模型评估次数下优化结果的比较
Table 5 The comparison results under the same mechanism model evaluation times
优化前 机理模型优化 代理模型辅助优化 燃料油消耗量(MW) 49.21 48.20 (-2.0%) 45.12 (-8.3%) 冷凝水消耗量(MW) 41.01 39.23 (-4.4%) 36.52 (-10.9%) 能耗成本(M＄/y) 1.12 0.96 (-14.2%) 0.85 (-24.0%) 产品净收益(M＄/y) 18.51 19.33 (4.3%) 19.92 (7.5%)
下载: 导出CSV
-
[1] Inamdar S V, Gupta K S, Saraf D N. Multi-objective optimization of an industrial crude distillation unit using the elitist non-dominated sorting genetic algorithm. Chemical Engineering Research and Design, 2004, 82(5):611-623 doi: 10.1205/026387604323142667 [2] 丁进良, 杨翠娥, 陈远东, 柴天佑.复杂工业过程智能优化决策系统的现状与展望.自动化学报, 2018, 44(11):1931-1943 http://www.aas.net.cn/CN/abstract/abstract19377.shtmlDing Jin-Liang, Yang Cui-E, Chen Yuan-Dong, Chai Tian-You. Research progress and prospects of intelligent optimization decision making in complex industrial process. Acta Automatica Sinica, 2018, 44(11):1931-1943 http://www.aas.net.cn/CN/abstract/abstract19377.shtml [3] Yang S L, Wang J M, Shi L Y, Tan Y J, Qiao F. Engineering management for high-end equipment intelligent manufacturing. Frontiers of Engineering Management, 2018, 5(4):420-450 doi: 10.15302/J-FEM-2018050 [4] Ochoa-Estopier L M, Jobson M, Smith R. The use of reduced models for design and optimisation of heat-integrated crude oil distillation systems. Energy, 2014, 75(5):5-13 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ad5c0a3e0c9aa507611554b3b641568d [5] Liebmann K, Dhole V R, Jobson M. Integrated design of a conventional crude Oil distillation tower using pinch analysis. Chemical Engineering Research and Design, 1998, 76(3):335-347 doi: 10.1205/026387698524767 [6] Ding J L, Yang C E, Jin Y C, Chai T Y. Generalized multi-tasking for evolutionary optimization of expensive problems. IEEE Transactions on Evolutionary Computation, 2019, 23(1):44-58 doi: 10.1109/TEVC.2017.2785351 [7] 丁进良, 杨翠娥, 陈立鹏, 柴天佑.基于参考点预测的动态多目标优化算法.自动化学报, 2017, 43(2):313-320 http://www.aas.net.cn/CN/abstract/abstract19009.shtmlDing Jin-Liang, Yang Cui-E, Chen Li-Peng, Chai Tian-You. Dynamic multi-objective optimization algorithm based on reference point prediction. Acta Automatica Sinica, 2017, 43(2):313-320 http://www.aas.net.cn/CN/abstract/abstract19009.shtml [8] Ochoa-Estopier L M, Jobson M. Optimization of heat-integrated crude oil distillation systems. Part I:The Distillation Model. Industrial & Engineering Chemistry Research, 2015, 54(18):4988-5000 http://cn.bing.com/academic/profile?id=3fc6aa9e00cdc136b2d7c0587bc51a70&encoded=0&v=paper_preview&mkt=zh-cn [9] Ochoa-Estopier L M, Jobson M, Chen L, Rodr-guez-Forero C A, Smith R. Optimization of heat-integrated crude oil distillation systems. Part Ⅱ:Heat exchanger network retrofit model. Industrial & Engineering Chemistry Research, 2015, 54(18):5001-5017 http://cn.bing.com/academic/profile?id=73b74f4f04c094c1b2ea88d07a388664&encoded=0&v=paper_preview&mkt=zh-cn [10] Ochoa-Estopier L M, Jobson M. Optimization of heat-integrated crude oil distillation systems. Part Ⅲ:Optimization Framework. Industrial & Engineering Chemistry Research, 2015, 54(18):5018-5036 http://cn.bing.com/academic/profile?id=b625acd749d31e770c36c08f14430946&encoded=0&v=paper_preview&mkt=zh-cn [11] Al-Mayyahi M A, Hoadley A F A, Smith N E, Rangaiah G P. Investigating the trade-off between operating revenue and CO2 emissions from crude oil distillation using a blend of two crudes. Fuel, 2011, 90(12):3577-3585 doi: 10.1016/j.fuel.2010.12.043 [12] Ji S, Bagajewicz M. Design of crude distillation plants with vacuum units. I. targeting. Industrial & Engineering Chemistry Research, 2002, 54(24):6094-6099 http://cn.bing.com/academic/profile?id=cd8338396883c39aa0ba3ba3017ae8c6&encoded=0&v=paper_preview&mkt=zh-cn [13] Lopez C D C, Hoyos L J, Mahecha C A, Arellano-Garcia H, Wozny G. Optimization model of crude oil distillation units for optimal crude oil blending and operating conditions. Industrial & Engineering Chemistry Research, 2013, 52(36):12993-13005 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=1d2d8046aaf44c1aaae574764daad9e9 [14] Ibrahim D, Jobson M, Li J, Guillén-Gosálbez G. Optimization-based design of crude oil distillation units using surrogate column models and a support vector machine. Chemical Engineering Research and Design, 2018, 134:212-225 doi: 10.1016/j.cherd.2018.03.006 [15] Yao H H, Chu J Z. Operational optimization of a simulated atmospheric distillation column using support vector regression models and information analysis. Chemical Engineering Research and Design, 2012, 90(12):2247-2261 doi: 10.1016/j.cherd.2012.06.001 [16] Cheng R, Jin Y C, Olhofer M, Sendhoff B. A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 2016, 40(5):773-791 http://cn.bing.com/academic/profile?id=a78f88991f475d68f269707178b2f22e&encoded=0&v=paper_preview&mkt=zh-cn [17] Jones D R, Schonlau M, Welch W J. Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 1998, 13(4):455-492 doi: 10.1023/A:1008306431147 [18] Martínez-Frutos J, Herrero-Pérez D. Kriging based infill sampling criterion for constraint handling in multi-objective optimization. Journal of Global Optimization, 2016, 64(1):1-19 doi: 10.1007/s10898-015-0328-x [19] Guo D, Jin Y C, Ding J L, Chai T Y. Heterogeneous ensemble based infill criterion for evolutionary multi-objective optimization of expensive problems. IEEE Transactions on Cybernetics, 2019, 49(3):1012-1025 doi: 10.1109/TCYB.6221036 [20] Seiffert C, Khoshgoftaar T M, Van Hulse J, Napolitano A. RUSBoost:a hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans, 2010, 40(1):185-197 doi: 10.1109/TSMCA.2009.2029559 [21] Wang J H, Liang G X, Zhang J. Cooperative differential evolution framework for constrained multiobjective optimization. IEEE Transactions on Cybernetics, 2019, 49(6):2060-2072 doi: 10.1109/TCYB.2018.2819208 [22] Zhang Q F, Zhou A M, Jin Y C. RM-MEDA:A regularity model-based multi-objective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation, 2008, 12(1):41-63 doi: 10.1109/TEVC.2007.894202 [23] Zitzler E, Thiele L, Laumanns M, Fonseca C M, Fonseca V G D. Performance assessment of multiobjective optimizers:an analysis and review. IEEE Transactions on Evolutionary Computation, 2003, 7(2):117-132 doi: 10.1109/TEVC.2003.810758 -
点击查看大图
图(7) / 表(5)
计量
- 文章访问数: 1859
- HTML全文浏览量: 326
- PDF下载量: 122
- 被引次数: 0
