基于矩阵编码的遗传算法多杂质间歇用水网络优化

doi:10.11949/j.issn.0438-1157.20141496

化工学报 ›› 2015, Vol. 66 ›› Issue (5): 1838-1843.DOI: 10.11949/j.issn.0438-1157.20141496

基于矩阵编码的遗传算法多杂质间歇用水网络优化

叶梦晴, 解新安, 李璐, 李雁

华南农业大学食品学院, 广东广州 510642

收稿日期:2014-10-07 修回日期:2015-02-13 出版日期:2015-05-05 发布日期:2015-05-05
通讯作者: 解新安
基金资助:
广东省科技计划项目(2011B031000010)。

Optimization of a batch water network with multi-contaminants based on matrix encoding genetic algorithm

YE Mengqing, XIE Xin'an, LI Lu, LI Yan

School of Food Science, South China Agricultural University, Guangzhou 510640, Guangdong, China

Received:2014-10-07 Revised:2015-02-13 Online:2015-05-05 Published:2015-05-05
Supported by:
supported by the Science and Technology Program Foundation of Guangdong Province (2011B031000010).

摘要/Abstract

摘要： 针对多杂质间歇用水网络优化设计问题,建立了以新鲜水用量最小为目标的混合整数非线性规划(MINLP)模型,并提出基于矩阵编码的遗传算法和信赖域序列二次规划(trust region sequential quadratic programming,TRSQP)法相结合的求解策略。本文采用矩阵编码方法为时间条件约束提供了方便,从而使用水单元之间水回用关系更加清晰明确;采用基于矩阵编码的遗传算法对整数变量进行优化,采用TRSQP法对连续变量进行优化,集成应用两种方法进行优化求解,从而获得MINLP模型的最优解;并借助MATLAB软件进行编程并实现。利用本文所提出的求解方法和策略对文献中2个典型案例进行求解,求解结果均优于文献数据。实例计算表明,本文所提求解方法是可行的。

关键词: 用水网络, 多杂质, 间歇, 遗传算法

Abstract: For the multi-contaminants batch water network optimization problem, a mixer-integer nonlinear programming model with the objective of minimum fresh water usage was established. Meanwhile, a solution strategy combined with matrix encoding genetic algorithm and trust region sequential quadratic programming(TRSQP)was proposed. In this paper, the matrix encoding method is easy to establish scheduling module, so the water reused relationship between the water units is more clearly defined.The integer variables was optimized by matrix encoding genetic algorithm and the continuous variables was optimized by TRSQP, the optimal strategy integrated the two methods to achieve the solution of MINLP model with the MATLAB software. The proposed method was used to solve two typical examples and the results obtained were superior to those in literature, which demonstrated that the approach introduced by this paper was effective.

Key words: water network, multi-contaminant, batch, genetic algorithm

中图分类号:

TQ021.8

叶梦晴, 解新安, 李璐, 李雁. 基于矩阵编码的遗传算法多杂质间歇用水网络优化[J]. 化工学报, 2015, 66(5): 1838-1843.

YE Mengqing, XIE Xin'an, LI Lu, LI Yan. Optimization of a batch water network with multi-contaminants based on matrix encoding genetic algorithm[J]. CIESC Journal, 2015, 66(5): 1838-1843.

参考文献 21

[1]	Wang Chuanqiang (王传强). Study on synthesis of water utilization network in batch processes [D]. Dalian:Dalian University of Technology, 2008
[2]	Tokos H, Novak Pintarič Z, YongRong Y. Bi-objective optimization of a water network via benchmarking [J]. Journal of Cleaner Production, 2013, 39: 168-179
[3]	Feng Xiao (冯霄). Water System Integration Optimization: Method of System Comprehensive Water-Saving Emission Reduction (水系统集成优化:节水减排的系统综合方法) [M]. Beijing: Chemical Industry Press, 2012: 280-316
[4]	Majozi T. Wastewater minimization using central reusable storage in batch plants [J]. Computer Chemistry Engineer, 2005, 29: 1631-1646
[5]	Zhou Feng (周枫), Xu Hongyong (徐宏勇), Cai Lankun (蔡兰坤). Review of the mathematical optimization algorithms for water networks with polymictic impurity [J]. Industrial Water Treatment (工业水处理), 2014, 34 (6): 10-13
[6]	Gouws, J F , Majozi T, Foo D C Y, Chen C L, Lee J Y. Water minimization techniques for batch processes [J]. Industrial & Engineering Chemistry Research, 2010, 49 (19): 8877-8893
[7]	Liang Zhaoming (梁肇铭), Li Yan (李雁), Xie Xin'an (解新安). Rapid design method for water using network with multiple contaminants based on mathematical programming and rules of thumb [J]. CIESC Journal (化工学报), 2013,64 (7): 2535-2542
[8]	Luo Yiqing,Yuan Xigang. Global optimization for the synthesis of integrated water systems with particle swarm optimization algorithm [J].Chinese Journal of Chemical Engineering, 2008,16 (1): 11-15
[9]	Wang Meng (王猛),Yin Hongchao (尹洪超). Design of water network with multiple contaminants based on improved particle swam optimization method [J].Energy Conservation Technology (节能技术), 2010, 28 (3): 209-212
[10]	Prakotpol D, Srinophakun T. GAPinch: Genetic algorithm toolbox for water pinch technology [J].Chemical Engineering and Processing: Process Intensification, 2004, 43 (2): 203-217
[11]	Lavric V, Iancu P, Plesu V. Genetic algorithm optimization of water consumption and wastewater network topology [J]. Journal of Cleaner Production, 2005, 13 (15): 1405-1415
[12]	Lin Wamei (林瓦妹),Li Kequan (李科群),Xie Rongjian (谢荣建),Wen Qingyun (温清云),Luo Xing (罗行). Optimal design of water networks by using genetic algorithm based on criterion of maximal water reuse [J]. Journal of Chemical Industry and Engineering (China) (化工学报), 2008, 59 (2): 410-414
[13]	Du Jian (都健), Wang Hongwei (王洪卫), Meng Xiaoqiong (孟小琼), Li Ying (李英), Du Hongbin (杜红彬), Fan Xishan (樊希山), Yao Pingjing (姚平经). Design of water utilization network with multiple contaminants based on adaptive simulated annealing genetic algorithm [J]. Computers and Applied Chemistry (计算机与应用化学), 2004, 21 (1): 79-82
[14]	Li Fengxi (李凤喜), Yan Liexiang (鄢烈祥), Shi Bin (史彬), Yan Na (鄢娜). Integration and optimization of a water network with multi-contaminants based on line-up competition algorithm [J]. Computers and Applied Chemistry (计算机与应用化学), 2011, 28 (8): 1007-1010
[15]	Jian Qian (菅倩). Research and application of genetic algorithm based on matrix coding [D]. Taiyuan: Taiyuan University of Technology, 2008
[16]	Zhan Hong (战红), Yang Jianjun (杨建军). Optimizaton of the job shop scheduling problem based on operation matrix encoding genetic algorithm [J]. Manufacturing Automation (制造业自动化), 2012, 35 (4): 86-88
[17]	Zhu Zhibin, Zhang Kecun. A new SQP method of feasible directions for nonlinear programming [J]. Applied Mathematics and Computation, 2004, 148 (5): 121-134
[18]	Zhao Min (赵敏), Li Shaoyuan (李少远). Nonlinear model predictive control optimization algorithm based on the trust-region quadratic programming [J]. Control Theory&Applications (控制理论与应用), 2009, 26 (6): 434-640
[19]	Yang Xia (杨霞), Cheng Huanong (程华农), Zheng Shiqing (郑世清). Multiple contaminants water network design for batch processes [J]. Computers and Applied Chemistry (计算机与应用化学), 2009, 26 (8): 1079-1083
[20]	Zhou Liang (邹亮). Multiple contaminants water network design for batch processes [D]. Qingdao: Qingdao University of Science & Technology, 2008
[21]	Du Jian (都健), Hong Shuihong (洪水红), Chen Li (陈理). Optimization of water-using network in a batch processes system with multiple contaminants [J]. Chemical Industry and Engineering Progress (化工进展), 2012, 31 (1): 25-34

基于矩阵编码的遗传算法多杂质间歇用水网络优化

Optimization of a batch water network with multi-contaminants based on matrix encoding genetic algorithm

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 21

相关文章 15

编辑推荐

Metrics

本文评价

[1]	郑书闽, 郭鹏程, 颜建国, 王帅, 李文博, 周淇. 微小通道内过冷流动沸腾阻力特性实验及预测研究[J]. 化工学报, 2023, 74(4): 1549-1560.
[2]	王雪松, 曾祥宇, 薄翠梅, 汤舒淇, 董超, 李俊, 张泉灵, 金晓明, 叶胜利. PTFE间歇聚合反应过程变周期动态经济优化控制[J]. 化工学报, 2022, 73(9): 3973-3982.
[3]	王雅琳, 潘雨晴, 刘晨亮. 基于GSA-LSTM动态结构特征提取的间歇过程监测方法[J]. 化工学报, 2022, 73(9): 3994-4002.
[4]	周新杰, 王建林, 艾兴聪, 随恩光, 王汝童. 基于IDPC-RVM的多模态间歇过程质量变量在线预测[J]. 化工学报, 2022, 73(7): 3120-3130.
[5]	高学金, 何紫鹤, 高慧慧, 齐咏生. 基于联合典型变量矩阵的多阶段发酵过程质量相关故障监测[J]. 化工学报, 2022, 73(3): 1300-1314.
[6]	林渠成, 廖祖维. 基于分解算法的功热交换网络多目标优化[J]. 化工学报, 2022, 73(11): 5047-5055.
[7]	朱轶林, 张新敬, 徐玉杰, 丁捷, 郭欢, 陈海生. 基于遗传算法-综合计算法的生物质热解气化优化分析[J]. 化工学报, 2021, 72(9): 4910-4920.
[8]	褚菲, 彭闯, 贾润达, 陈韬, 陆宁云. 基于多尺度核JYMKPLS迁移模型的间歇过程产品质量的在线预测方法[J]. 化工学报, 2021, 72(4): 2178-2189.
[9]	鲁秋实, 叶光华, 周兴贵. 半间歇釜式反应器安全高效操作的数值优化策略[J]. 化工学报, 2021, 72(2): 975-983.
[10]	彭肖祎, 董轩, 廖祖维, 杨遥, 孙婧元, 蒋斌波, 王靖岱, 阳永荣. 数学规划与图形方法相结合设计热集成用水网络[J]. 化工学报, 2021, 72(2): 1047-1058.
[11]	石向成, 赵志坚, 巩金龙. 遗传算法在催化体系的全局结构优化中的应用[J]. 化工学报, 2021, 72(1): 27-41.
[12]	王刚, 赵琰. 土壤源热泵供暖间歇运行时间的计算分析[J]. 化工学报, 2020, 71(S1): 430-435.
[13]	郭晓雨, 田喆, 牛纪德, 祝捷. 基于分时电价的区域管网系统储能应用研究[J]. 化工学报, 2020, 71(S1): 293-299.
[14]	李炜, 王晓明, 蒋栋年, 李亚洁, 梁成龙. 基于SHPSO-GA-BP的成品汽油调和中加氢汽油组分辛烷值的预测[J]. 化工学报, 2020, 71(7): 3191-3200.
[15]	耿志强, 毕帅, 王尊, 朱群雄, 韩永明. 基于改进NSGA-Ⅱ算法的乙烯裂解炉操作优化[J]. 化工学报, 2020, 71(3): 1088-1094.