河流突发污染事故水质预警动态校正方法及应用研究

发布时间：2018-05-30 23:14

本文选题：河流突发污染事故 + 水质预警　；参考：《浙江大学》2017年硕士论文

【摘要】：我国经济快速发展,环境治理压力日益增大,河流突发污染事故时有发生,往往导致严重的环境、经济和社会问题。准确预测事故发生后下游预警点的污染物浓度对有效控制和减少事故的影响具有重要意义。基于此背景,本文着重研究河流突发污染事故水质预警问题,在上游突发污染事故时可根据实测数据动态校正模型参数和预警结果,设计基于不同场地的污染物扩散实验验证算法,并将相应研究成果应用于具体水质预警系统中。论文主要工作和创新点如下:(1)目前对水质预警模型的研究大都以静态模型为主,难以利用不断丰富的实测数据对静态模型进行持续的校正和优化。为了提高预警模型的准确性,论文结合各断面的实测数据,研究了一种河流突发污染事故水质预警模型的动态校正方法。论文以一维水质模型为基础,在预测过程中纳入实测数据,采取分段校正的方式对预警模型进行动态校正,利用预警点上游各断面的实测浓度数据构造"模拟污染源",进行预警模型参数和预警结果的校正和优化。同时,考虑支流对污染物扩散的影响,根据质量守恒原理进行了支流校正。(2)为了提高校正算法的计算效率,避免算法陷入局部最优解,研究并提出了一种基于改进遗传算法的参数动态校正方法。该方法以标准遗传算法为基础,对编码方式、初始种群生成、交叉和变异方式进行改进,并加入移民算子防止算法早熟。(3)为了降低模型结构误差和测量误差的影响,提高预警结果的准确性,研究提出了一种基于卡尔曼滤波算法的污染物预警结果动态校正方法。该方法以水质模型为基础建立状态方程,通过估计系统噪声协方差矩阵Q和测量噪声协方差矩阵R,在模型参数校正的基础上对预测浓度进行动态校正,以进一步提升预警结果的准确性。在理论研究的基础上,进行了风浪流水槽等不同实验场地的污染物模拟扩散和预测预警实验,以验证水质预警动态校正的有效性。进而,结合某河流(实际水源地)的监控预警要求,参与开发了完整的突发污染事故水质预警系统,并把预警模型参数和预警结果的校正功能在系统上进行部署。
[Abstract]:With the rapid development of economy and the increasing pressure of environmental control, sudden pollution accidents of rivers occur from time to time, which often lead to serious environmental, economic and social problems. It is of great significance to predict the concentration of pollutants in the early warning point after the accident to effectively control and reduce the impact of the accident. Based on this background, this paper focuses on the problem of water quality warning for sudden pollution accidents in rivers. The model parameters and early warning results can be dynamically corrected according to the measured data. The experimental verification algorithm of pollutant diffusion based on different sites is designed, and the corresponding research results are applied to the water quality early warning system. The main work and innovation of this paper are as follows: (1) at present, most of the researches on water quality early warning models are based on static models, so it is difficult to use the abundant measured data to continuously correct and optimize the static models. In order to improve the accuracy of the early warning model, a dynamic correction method of water quality early warning model for river pollution accident is studied in this paper, based on the measured data of each section. Based on the one-dimensional water quality model, the paper introduces the measured data into the prediction process, and adopts the method of segmental correction to dynamically correct the early warning model. The "simulated pollution source" is constructed by using the measured concentration data of each section upstream of the early warning point, and the parameters of the early warning model and the warning result are calibrated and optimized. At the same time, considering the influence of tributaries on pollutant diffusion, the tributary correction is carried out according to the mass conservation principle.) in order to improve the calculation efficiency of the correction algorithm and avoid the algorithm falling into the local optimal solution, A parameter dynamic correction method based on improved genetic algorithm is proposed. This method is based on the standard genetic algorithm, improves the coding method, initial population generation, crossover and mutation, and adds an immigration operator to prevent precocity. To improve the accuracy of early warning results, a dynamic correction method based on Kalman filter is proposed. Based on the water quality model, the state equation is established. By estimating the system noise covariance matrix Q and the measurement noise covariance matrix R, the predicted concentration is dynamically corrected on the basis of the calibration of the model parameters. To further improve the accuracy of early warning results. On the basis of theoretical research, experiments of pollutant diffusion simulation and prediction and early warning in different experimental sites such as wind, wave and current flume were carried out to verify the effectiveness of dynamic correction of water quality early warning. Furthermore, according to the monitoring and warning requirements of a river (actual water source), a complete water quality early warning system for sudden pollution accidents is developed, and the parameters of the early warning model and the correction function of the early warning results are deployed in the system.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X522;TP18

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