基于ELMAN的化工污水生化系统建模及水质预测

论文编号:DQ268   论文字数:24690,页数:49 

摘　　要
 化工污水处理是污水处理的主要内容，是影响与制约水环境改善的关键因素。其水质控制技术也一直是水处理领域研究的重要课题。本文通过对国内外污水的处理工艺、水质监测技术及处理过程自动控制技术现状的了解与研究，针对污水处理过程中关键水质参数无法在线监测等问题，提出人工神经网络预测技术是未来污水处理测控领域的一个重要发展方向。
 鉴于污水处理过程的强非线性、大时变、严重滞后的特点，难以通过机理分析建立精确的数学模型，本文采用基于动态神经网络技术的辨识建模方法。主要内容有以下三方面：
 1、基于化工污水处理工程实际的数据预处理方法研究
 以来自于实际的化工污水处理厂的现场历史数据，结合工艺实践与数据处理技术，研究数据库建立、误差处理、数据插值与修复等数据预处理方法，为神经网络建模提供了良好的基础数据。
 2、研究与建立污水处理出水水质人工神经网络预测模型
 建立基于Elman动态神经网络的污水处理核心系统——生化系统的神经网络预测模型，以此来预测污水出水水质的氨氮和COD浓度。对比基本Elman算法，从算法和网络结构两方面进行研究、改进。提出了改进的Levenberg-Marquardt反向传播算法和增加预测项的历史输出值的Elman结构算法。
 3、污水水质参数预测模型的仿真研究
 利用MATLAB神经网络工具箱进行水质参数预测模型的仿真研究，仿真研究结果表明：通过算法与结构的改进，建立的生化系统神经网络预测模型在精度、训练时效上都有明显提高，可以很好地预测出水的氨氮和COD浓度。
 总之，本文的研究工作将人工神经网络技术与化工污水处理工程实际相结合，从理论与仿真两方面验证了神经网络预测技术应用于污水处理过程出水水质参数预测的可行性，为进一步实现出水水质控制奠定了坚实基础。
 
 
 
Abstract
Chemical sewage treatment are the main contents of the sewage treatment, It is a key factor in the influence and constraints of water environmental improvement. Its water quality control has been an important subject of research in the field of water treatment technology. In this paper, through understanding and research for the sewage treatment process both at home and abroad, water quality monitoring technology and treating processes automatic control technical aspect, artificial neural network forecasting the future of wastewater treatment technology is an important field of measurement and control the direction of development, for key water quality parameters can not be the issue of online monitoring in sewage treatment process.
In view of the characteristics of strongly nonlinear, large time-varying, a serious lag in the sewage treatment process,it is difficult to establish establish accurate mathematical models through the mechanism analysis. This paper uses modeling methods to identify based on dynamic neural network technology. The main contents contain the following three aspects:In this paper, the main contents contains the following two aspects:
1、Chemical sewage treatment works based on the actual method of data preprocessing.
From the on-site historical data in the the actual chemical sewage treatment plant, Combination of technology and data processing technology practice,research databases, error handling, data interpolation and restoration, such as data pre-processing, It provides a good basis for data for neural network modeling.
2、Research and the establishment of sewage treatment effluent quality artificial neural network prediction model.
The core of the sewage treatment system based on Elman Dynamic Neural Network - biochemical system's neural network forecast model, forecasts the sewage water leakage water quality by the ammonia nitrogen and COD density. Contrasting the basic Elman algorithm, and conducting the research and improvement from the algorithm and the network architecture two aspects, It proposes the improving Levenberg-Marquardt antipropagation algorithm and the Elman structure algorithm of the increasing forecast item′s history value of exports.
3、Simulation research for sewage water quality parameter forecast model.
Working on the water quality parameter forecast model using the MATLAB neural network toolbox the simulation research，The simulation results indicates that: Through the algorithm and the structure improvement, the established biochemical system neural network forecast model has the distinct enhancement in the precision and the training effectiveness, which also can forecast the water leakage ammonia nitrogen and the COD density well.
In a word,this research work actually unifies the artificial neural networks technology and the chemical industry sewage treatment project,and it has confirmed the neural network forecasting technology to apply in the sewage treatment process water leakage water quality parameter forecast feasibility from the theory and the simulation, for further realizing the water leakage water quality control to lay the solid foundation.

目　　录

摘　　要 1
Abstract 2
引　　言 1
1 综述 1
1.1 国内外污水处理工艺发展状况 1
1.2 污水水质监测技术的现状及存在的问题 2
1.2.1 国内外污水水质监测技术的现状 2
1.2.2 污水水质监测中现存的问题 4
1.3 国内外污水处理过程自动控制的研究现状[18-26] 5
2 人工神经网络预测技术及方案论证 7
2.1 人工神经网络预测技术及其实施 7
2.2 人工神经网络的原理 7
2.3 人工神经网络模型的方案论证 17
3 污水处理过程及Elman动态人工神经网络预测模型建立的基础 19
3.1 某化工污水厂污水处理工艺流程简介 19
3.2 输入输出变量确定原则 21
3.3 现场数据采集与数据预处理 22
3.3.1 现场数据采集 22
3.3.2 数据预处理 22
4 研究与建立污水处理出水水质人工神经网络预测模型 25
4.1 污水水质数据来源 25
4.2 确定输入输出变量 25
4.3 Elman神经网络结构的设计 26
5污水水质参数预测模型的仿真研究 31
5.1 改进算法的Elman神经网络模型仿真 33
5.2 改进结构的Elman神经网络模型仿真 36
结　　论 40
参 考 文 献 41
致　　谢 44

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