Please wait a minute...
浙江大学学报(工学版)  2017, Vol. 51 Issue (12): 2459-2465    DOI: 10.3785/j.issn.1008-973X.2017.12.020
化学工程、控制工程     
河流突发污染的污染物浓度动态校正方法
刘景明, 黄平捷, 侯迪波, 张光新, 张宏建
浙江大学 工业控制技术国家重点实验室, 控制科学与工程学院, 浙江 杭州 310027
Dynamic pollutant concentration correction method for river sudden pollution
LIU Jing-ming, HUANG Ping-jie, HOU Di-bo, ZHANG Guang-xin, ZHANG Hong-jian
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
 全文: PDF(1146 KB)   HTML
摘要:

为降低河流突发污染事故的影响,提高下游污染物预测精度、提高预测实时性,结合一维水质模型、卡尔曼滤波及改进的网格寻优算法,综合考虑支流的影响,研究河流突发污染事件中污染物扩散情况的动态预测方法.分析一种改进的网格寻优算法并利用历史数据校正模型参数;借助水质模型构造状态方程引入污染物浓度观测值;运用卡尔曼滤波动态校正预测结果,并在预测过程中考虑支流的影响.在理论研究的基础上,设计基于风浪水槽的污染物模拟扩散实验,对比分析采用不同预测方法的污染物峰现时间、峰值浓度及相对误差.实验结果表明,不同的预测方法所求得的峰现时间相对误差总体相当;采用多步动态校正预测和考虑了支流影响的校正预测方法预测峰值浓度得到的相对误差明显降低.

Abstract:

A dynamic pollutant concentration correction forecasting method for sudden pollution accidents in rivers was explored, in order to reduce the influence of sudden pollution accidents and predict pollutant concentrations in the downstream accurately and timely. The one-dimensional water quality model, Kalman filter algorithm and improved grid optimization algorithm were investigated considering the influence of tributaries. An improved grid optimization algorithm using the historical monitoring data was developed to correct the model parameters; the state equation based on one-dimensional water quality model was established; the Kalman filter algorithm was developed to correct the predicted concentration based on the updated observed data considering the influence of tributaries. On the basis of theoretical research, experiments based on the wave flume were set up to simulate the dispersion of pollutants, and to analyze peak time, peak concentrations and the relative errors of different predict methods. Theoretical and experimental results show that the relative errors of predicted peak-time values are at similar levels by different prediction algorithms; the dynamic correction forecasting method contributes to improve the prediction accuracy and promote the prediction capability of algorithms.

收稿日期: 2016-08-09 出版日期: 2017-11-22
CLC:  TP206  
基金资助:

国家自然科学基金资助项目(U1509208,61573313);浙江省科技厅重大科技专项资助项目(2015C03014);中央高校基本科研业务费专项资金项目(2016FZA6004).

通讯作者: 黄平捷,男,副教授.orcid.org/0000-0002-5487-6097.     E-mail: huangpingjie@zju.edu.cn
作者简介: 刘景明(1992-),男,硕士生,从事水环境监测预警研究.orcid.org/0000-0002-8268-7955.E-mail:ljm_zjuer@163.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  

引用本文:

刘景明, 黄平捷, 侯迪波, 张光新, 张宏建. 河流突发污染的污染物浓度动态校正方法[J]. 浙江大学学报(工学版), 2017, 51(12): 2459-2465.

LIU Jing-ming, HUANG Ping-jie, HOU Di-bo, ZHANG Guang-xin, ZHANG Hong-jian. Dynamic pollutant concentration correction method for river sudden pollution. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(12): 2459-2465.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2017.12.020        http://www.zjujournals.com/eng/CN/Y2017/V51/I12/2459

[1] 周林岩.我国饮用水安全保障问题研究[D].吉林:吉林大学, 2011. ZHOU Lin-yan. Research on legal system of drinking water safety in China[D]. Jilin:Jilin University, 2011.
[2] HOU D B, SONG X X, ZHANG G X, et al. An early warning and control system for urban, drinking water quality protection:China's experience[J]. Environmental Science and Pollution Research. 2013, 20(7):4496-4508.
[3] STREETER H W,PHELPS E B. A study of the pollution and natural purification of the Ohio River[R]. US Department of Health, Education, and Welfare, 1958.
[4] CAMARA A S,RANDALL C W. The QUAL Ⅱ model[J]. Journal of Environmental Engineering, 1984,110(5):993-996.
[5] 唐大元.WASP水质模型国内外应用研究进展[J].安徽农业科学,2011,39(34):21265-21267. TANG Da-yuan. Research progress of the application of WASP water quality model in domestic and abroad[J]. Journal of Anhui Agricultural Science, 2011, 39(34):21265-21267.
[6] LI R Z, SHIGEKI M. Fuzzy model for Two-Dimensional river water quality simulation under sudden pollutants discharged[J]. Journal of Hydrodynamics. 2007,19(4):434-441.
[7] ZHANG Q Q, XU Y P, XU X, et al. Application of Bayesian network in water quality risk analysis and pollution reduction decision making from small data[C]//2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). Nanjing:IEEE, 2011:84-88.
[8] NEELAKANTAN T R,PUNDARIKANTHAN N V. Neural network-based simulation-optimization model for reservoir operation[J]. Journal of Water Resources Planning and Management, 2000, 126(2):57-64.
[9] 李沫,徐鸣,冒莹.主成分分析与灰色模型在水磨河污染成因分析及污染预测中的应用[J].干旱环境监测,2012,26(4):216-221. LI Mo, XU Ming, MAO Ying. Application of principal components analysis and grey model to pollution origin analysis and forecast in Shuimo River[J]. Arid Environmental Monitoring, 2012, 26(4):216-221.
[10] ZHANG B,QIN Y,HUANG M X, et al. SD-GIS-based temporal-spatial simulation of water quality in sudden water pollution accidents[J]. Computers and Geosciences, 2011, 37(7):874-882.
[11] HOU D B, GE X F, HUANG P J, et al. A real-time, dynamic early-warning model based on uncertaintyanalysis and risk assessment for sudden water pollution accidents[J]. Environmental Science and Pollution Research. 2014, 21(14):8878-8892.
[12] 宋筱轩.动态数据驱动的河流突发性水污染事故预警系统关键技术研究[D].杭州:浙江大学,2014. SONG Xiao-xuan. Research on the key techniques of dynamic data driven early warning system for sudden river pollution accidents[D]. Zhejiang:Zhejiang University, 2014.
[13] 赖瑞勋,方红卫,徐兴亚,等.水沙实时预测数学模型研究[J].水利学报,2014(8):930-937. LAI Rui-xun, FANG Hong-wei, XU Xing-ya,et al. Dynamic numerical model for the prediction of water and sediment transport[J]. Journal of Hydraulic Engineering, 2014(8):930-937.
[14] WU X L, WANG C H, CHEN X, et al. Kalman filtering correction in real-time forecasting with hydrodynamic model[J]. Journal of Hydrodynamics, 2008,20(3):391-397.
[15] 高山红,吴增茂,谢红琴.Kalman滤波在气象数据同化中的发展与应用[J].地球科学进展, 2000(5):571-575. GAO Shan-hong, WU Zeng-mao, XIE Hong-qing. The developments and application of Kalman filters in meteorological data assimilation[J]. Advance in Earth Sciences,2000(5):571-575.
[16] RAUCH W, HENZE M, KONCSOS L, et al. River water quality modeling:I. state of the art[J]. Water Science and Technology, 1998, 38(11):237-244
[17] 宰松梅,郭树龙,王洪胜.几种计算河流纵向弥散系数方法的比较[J].人民黄河,2008, 30(5):20-21. ZAI Song-hai, GUO Shu-long, WANG Hong-sheng. Comparison of several kinds of methods which calculating longitudinal dispersion coefficient[J]. Yellow River, 2008, 30(5):20-21.
[18] 中国环境规划院.全国水环境容量核定技术指南[R].北京:中国环境规划院,2003.

[1] 刘冬旭, 董红召. 共享自行车系统调度区域的分形树自平衡划分算法[J]. 浙江大学学报(工学版), 2018, 52(7): 1275-1283.
[2] 汤雪萍, 鲁天龙, 黄平捷, 侯迪波, 张光新. 行为规划和浓度梯度法联合的河道污染源追踪定位方法[J]. 浙江大学学报(工学版), 2018, 52(3): 543-551.
[3] 刘世成, 王海清, 李平. 基于秩-1矩阵摄动的递归主元分析算法[J]. J4, 2009, 43(5): 827-831.