机械蒸汽再压缩蒸发结晶系统自适应逐时优化研究

姜华; 秦华; 宫武旗

doi:10.7652/xjtuxb202403005

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机械蒸汽再压缩蒸发结晶系统自适应逐时优化研究

—————————————————————— | 更新时间：2025-07-09

- 机械蒸汽再压缩蒸发结晶系统自适应逐时优化研究
- A Study on Adaptive Hourly Optimization for the Mechanical Vapor Recompression Evaporative Crystallization System
- 西安交通大学学报 2024年58卷第3期页码：49-57
- 作者机构：
  
  1. 西安科技大学能源学院,西安,710054
  2. 西安交通大学能源与动力工程学院,西安,710049
- 作者简介：
  
  : 2023-05-09。作者简介: 姜华(1973—),女,博士,副教授。基金项目: 国家科技重大专项资助项目(2017-Ⅴ-0012-0064)。
- 基金信息：
  
  : 2023-05-09。作者简介: 姜华(1973—),女,博士,副教授。基金项目: 国家科技重大专项资助项目(2017-Ⅴ-0012-0064)。
- DOI：10.7652/xjtuxb202403005
  中图分类号： TQ051.62;X703
- 网络首发：2024-03-10，
  
  纸质出版：2024
- 稿件说明：
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姜华, 秦华, 宫武旗. 机械蒸汽再压缩蒸发结晶系统自适应逐时优化研究[J]. 西安交通大学学报, 2024,58(3):49-57.

JIANG Hua, QIN Hua, GONG Wuqi. A Study on Adaptive Hourly Optimization for the Mechanical Vapor Recompression Evaporative Crystallization System[J]. 2024, 58(3): 49-57.
姜华, 秦华, 宫武旗. 机械蒸汽再压缩蒸发结晶系统自适应逐时优化研究[J]. 西安交通大学学报, 2024,58(3):49-57. DOI： 10.7652/xjtuxb202403005.

JIANG Hua, QIN Hua, GONG Wuqi. A Study on Adaptive Hourly Optimization for the Mechanical Vapor Recompression Evaporative Crystallization System[J]. 2024, 58(3): 49-57. DOI： 10.7652/xjtuxb202403005.

摘要

为实现工业废水处理过程中进料流量和进料浓度波动状态下的机械蒸汽再压缩系统逐时优化

建立了基于自适应SPAE2算法的逐时优化模型。采用SPEA2算法并结合自适应交叉概率、变异概率以及组合权重法的多目标逐时优化

以系统总功耗和总换热面积为优化目标

得到蒸发温度、压缩温升的最优组合。在原始研究的基础上

改进了SPEA2算法

并且将原始数据与优化结果进行对比

自适应SPEA2算法具有更强的全局寻优能力

优化结果具有更高准确性。利用最小二乘法对优化结果进行数据拟合

优化结果与恒定蒸发工况下结果相比:系统总功耗平均降低123.7 kW

换热面积平均减少36.3 m

; 性能系数和效率分别平均提高8.8%和26.6%

损失平均降低102.3 kW。研究结果表明

所建立的系统逐时优化模型可以得到系统进料波动状态下各设备参数逐时变化值

提高了目标系统的能量利用率和热力学完善度。

Abstract

To achieve the hourly optimization of the mechanical vapor recompression(MVR)system under the condition of fluctuating feed flow and concentration in industrial wastewater treatment processes

an hourly optimization model based on the adaptive SPAE2 algorithm is proposed in this study. The SPEA2 algorithm is employed along with adaptive crossover probability

mutation probability

and a combination weighting method for multi-objective hourly optimization. The total power consumption and total heat transfer area of the system are considered as optimization object

ives to obtain the optimal combination of evaporation temperature and compression temperature rise. Building on the original research

the SPEA2 algorithm is improved

and the original data is compared with the optimization results. It is concluded that the adaptive SPEA2 algorithm exhibits stronger global optimization ability

leading to more accurate optimization results. The least squares method is used to fit the optimization results. When compared with the results under constant evaporation conditions

the optimization results show the following improvements on average: the total power consumption decreases by 123.7 kW

the heat transfer area decreases by 36.3 m

the coefficient of performance(COP)and energy efficiency increase by 8.8% and 26.6%

and the energy loss decreases by 102.3 kW. These results indicate that the hourly optimization model established for the system can obtain the hourly variations of equipment parameters under fluctuating feed conditions. This improves the system's energy utilization efficiency and thermodynamic performance.

关键词

Keywords

references

GEP Research. Report on the development of industrial waste water treatment industry [EB/OL]. [2023-04-25]. http://www.gepresearch.com/77/view-764255-1.html.

李强平. 机械蒸汽再压缩系统在高浓度含盐废水处理中的应用研究 [D]. 杭州: 浙江工业大学, 2019.

傅伟良, 张城镇, 陈蒙, 等. 机械蒸汽再压缩(MVR)热泵干燥的研究进展 [J]. 环境工程, 2021, 39(5): 131-134.

FU Weiliang, ZHANG Chengzhen, CHEN Meng, et al. Research progress on mechanical vapor recompression heat pump drying [J]. Environmental Engineering, 2021, 39(5): 131-134.

越云凯, 吴小华, 张振涛. MVR海水淡化系统运行特性分析与优化 [J]. 工程热物理学报, 2018, 39(9): 1985-1990.

YUE Yunkai, WU Xiaohua, ZHANG Zhentao. Operation characteristic analysis and optimization of MVR seawater desalination system [J]. Journal of Engineering Thermophysics, 2018, 39(9): 1985-1990.

HAN Dong, CHEN Junjie, ZHOU Tianhao, et al. Experimental investigation of a batched mechanical vapor recompression evaporation system [J]. Applied Thermal Engineering, 2021, 192: 116940.

CHEN Junjie, HAN Dong, HANG Zhifang, et al. Experimental investigation of a novel batch evaporation system coupled mechanical vapor recompression technology and steam heat storage technology [J]. Innovative Food Science Emerging Technologies, 2021, 68: 102616.

姜华, 张子尧, 宫武旗. MVR并联双效蒸发结晶系统设计及研究 [J]. 化工进展, 2019, 38(10): 4461-4469.

JIANG Hua, ZHANG Ziyao, GONG Wuqi. Design and research of MVR parallel double-effect evaporation crystallization system [J]. Chemical Industry and Engineering Progress, 2019, 38(10): 4461-4469.

姜华, 张子惠, 宫武旗, 等. MVR分质提盐蒸发结晶系统设计及性能分析 [J]. 化工进展, 2022, 41(7): 3947-3956.

JIANG Hua, ZHANG Zihui, GONG Wuqi, et al. Design and performance analysis of mechanical vapor recompression salt fractionation evaporation crystallization system [J]. Chemical Industry and Engineering Progress, 2022, 41(7): 3947-3956.

张子尧, 姜华, 宫武旗. 机械蒸汽再压缩蒸发结晶系统优化设计 [J]. 西安交通大学学报, 2020, 54(4): 101-109.

ZHANG Ziyao, JIANG Hua, GONG Wuqi. Optimization design for mechanical vapor recompression evaporation crystallization system [J]. Journal of Xi'an Jiaotong University, 2020, 54(4): 101-109.

张子尧. 机械蒸汽再压缩系统设计及多目标优化研究 [D]. 西安: 西安科技大学, 2020.

赵林, 吴小华, 孙东亮, 等. 乙醇超重力MVR热泵精馏系统仿真及优化研究 [J]. 工程热物理学报, 2022, 43(3): 679-684.

ZHAO Lin, WU Xiaohua, SUN Dongliang, et al. Simulation and optimization study of ethanol-water high gravity MVR heat pump distillation system [J]. Journal of Engineering Thermophysics, 2022, 43(3): 679-684.

徐超, 丁宁, 栗文明. SWRO+MVR工艺处理净水厂浓盐水零排放工程设计 [J]. 工业水处理, 2020, 40(9): 112-115.

XU Chao, DING Ning, LI Wenming. A combined process of SWRO and MVR design for zero-discharge project for concentrated water of a water purification plant [J]. Industrial Water Treatment, 2020, 40(9): 112-115.

赵红涛, 王树民. 燃煤烟气胺法脱碳MVR再生系统关键参数及能耗分析 [J]. 化工进展, 2020, 39(增1): 256-262.

ZHAO Hongtao, WANG Shumin. Key parameters and energy consumption analysis of amine decarburization regeneration system with MVR for coal-fired flue gas [J]. Chemical Industry and Engineering Progress, 2020, 39(S1): 256-262.

张勤灵, 刘晓华, 张涛. 机械蒸气再压缩系统再生高浓度溶液的性能研究 [J]. 制冷学报, 2021, 42(3): 19-27.

ZHANG Qinling, LIU Xiaohua, ZHANG Tao. Regeneration performance of high concentration solution by mechanical vapor recompression system [J]. Journal of Refrigeration, 2021, 42(3): 19-27.

顾承真, 闵兆升, 洪厚胜. 机械蒸汽再压缩蒸发系统的性能分析 [J]. 化工进展, 2014, 33(1): 30-35.

GU Chengzhen, MIN Zhaosheng, HONG Housheng. Performance analysis of mechanical vapor recompression evaporation system [J]. Chemical Industry and Engineering Progress, 2014, 33(1): 30-35.

侯超, 张振涛, 杨鲁伟, 等. 中药提取液蒸发浓缩过程采用机械蒸汽再压缩技术特性研究 [J]. 现代化工, 2016, 36(11): 172-174.

HOU Chao, ZHANG Zhentao, YANG Luwei, et al. Characteristics research of mechanical vapor recompression technology used in evaporation and concentration of Chinese medicine extract [J]. Modern Chemical Industry, 2016, 36(11): 172-174.

JIANG Hua, ZHANG Zihui, ZHANG Ziyao, et al. Multi-objective optimization of parallel-connected double-effect mechanical vapor recompression system based on genetic algorithm [J]. Journal of Energy Resources Technology, 2023, 145(1): 011701.

SCHWAER C, HOFMANN J, MÜHLPFORDT M, et al. Modular simulation model for falling film evaporators with novel approach to manage dominant time-varying transport delays [J]. Computers Chemical Engineering, 2020, 132: 106604.

田雨. MVR控制系统设计及控制策略研究 [D]. 北京: 中国科学院大学, 2019.

苗坤宏, 李佳璇, 崔彭帝, 等. 基于流程模拟的中药生产过程溶媒回收工艺动态控制方法研究 [J]. 中草药, 2022, 53(18): 5636-5648.

MIAO Kunhong, LI Jiaxuan, CUI Pengdi, et al. Study on dynamic control method of solvent recovery process in traditional Chinese medicine production based on process simulation [J]. Chinese Traditional and Herbal Drugs, 2022, 53(18): 5636-5648.

庄亮东, 杨悦, 吴桢灏. 遗传算法在Y型偏心支撑组合框架抗震性能优化中的应用研究 [J]. 工程力学, 2023, 40(7): 185-195.

ZHUANG Liangdong, YANG Yue, WU Zhenhao. The application of genetic algorithm in seismic performance optimization of Y-shape eccentrically braced composite frame [J]. Engineering Mechanics, 2023, 40(7): 185-195.

丁耀东, 李平. 脉动流与波纹壁通道耦合强化换热及优化研究 [J]. 西安交通大学学报, 2023, 57(7): 120-129.

DING Yaodong, LI Ping. Research on coupling of pulsating flow and corrugated channels for heat transfer enhancement and genetic algorithm optimization [J]. Journal of Xi'an Jiaotong University,2023, 57(7): 120-129.

COELLO C A C, ZACATENCO C S P. 20 years of evolutionary multi-objective optimization: what has been done and what remains to be done [M]//Computational Intelligence: Principles and Practice. New York, NY, USA: IEEE Computational Intelligence Society, 2006: 73-88.

季顺松, 黄炎焱, 张寒, 等. 基于改进遗传算法的火力分配寻优模型研究 [J]. 南京理工大学学报, 2023, 47(1): 33-40.

JI Shunsong, HUANG Yanyan, ZHANG Han, et al. Research on optimization model of firepower allocation based on improved genetic algorithm [J]. Journal of Nanjing University of Science and Technology, 2023, 47(1): 33-40.

柯俊, 史文库, 钱琛, 等. 采用遗传算法的复合材料板簧多目标优化方法 [J]. 西安交通大学学报, 2015, 49(8): 102-108.

KE Jun, SHI Wenku, QIAN Chen, et al. A multi-objective optimization for composite leaf springs using genetic algorithms [J]. Journal of Xi'an Jiaotong University, 2015, 49(8): 102-108.

杨波, 何宇泽, 许福, 等. 利用改进遗传算法的软件故障定位辅助测试用例生成方法 [J]. 北京航空航天大学学报, 2023, 49(9): 2279-2287.

YANG Bo, HE Yuze, XU Fu, et al. Using improved genetic algorithm for software fault localization aided test case generation [J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(9): 2279-2287.

庄文宇, 张如九, 徐建军, 等. 基于IAGA-BP算法的高拱坝-坝基力学参数反演分析 [J]. 清华大学学报(自然科学版), 2022, 62(8): 1302-1313.

ZHUANG Wenyu, ZHANG Rujiu, XU Jianjun, et al. Inversion analysis to determine the mechanical parameters of a high arch dam and its foundation based on an IAGA-BP algorithm [J]. Journal of Tsinghua University Science and Technology(Natural Science Edition), 2022, 62(8): 1302-1313.

石山, 励庆孚, 王兴华. 基于自适应遗传算法的无刷直流电机的优化设计 [J]. 西安交通大学学报, 2002, 36(12): 1215-1218.

SHI Shan, LI Qingfu, WANG Xinghua. Design optimization of brushless direct current motor based on adaptive genetic algorithm [J]. Journal of Xi'an Jiaotong University, 2002, 36(12): 1215-1218.

SRINIVAS M, PATNAIK L M. Adaptive probabilities of crossover and mutation in genetic algorithms [J]. IEEE Transactions on Systems, Man and Cybernetics, 1994, 24(4): 656-667.

任子武, 伞冶. 自适应遗传算法的改进及在系统辨识中应用研究 [J]. 系统仿真学报, 2006, 18(1): 41-43.

REN Ziwu, SAN Ye. Improved adaptive genetic algorithm and its application research in parameter identification [J]. Journal of System Simulation, 2006, 18(1): 41-43.

邓召学, 郑玲, 李以农, 等. 基于NSGA-II算法的磁流变悬置磁路多目标优化 [J]. 汽车工程, 2015, 37(5): 554-559.

DENG Zhaoxue, ZHENG Ling, LI Yinong, et al. Multi-objective optimization for the magnetic circuit of magneto-rheological mount based on NSGA-II algorithm [J]. Automotive Engineering,2015, 37(5): 554-559.

孙文娟. 自适应遗传算法的改进及其在爆炸冲击响应谱时域合成优化中的应用研究 [D]. 合肥: 中国科学技术大学, 2019.

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