【作者】 陈安；

【导师】 罗亚田；

【作者基本信息】 武汉理工大学 ， 环境工程， 2003， 硕士

【摘要】 对水处理系统水质参数相关性的正确模拟是实现水处理系统的在线实时控制的重要课题。人工神经网络（ANNs）由于其具备受数据驱动、自组织、自适应、容错性、并行性等显著特征在系统模拟与预测方面应用前景广阔。论文作者首先分析了ANNs解决环境科学领域模式识别、函数逼近、联想记忆等问题的有效性与灵活性。然后，以河南省漯河市污水净化中心Carrousel氧化沟系统为研究对象，针对其进水水质复杂，污染物负荷高，起伏大，系统在线控制滞后的难题，以ANNs为数学工具，为该污水净化中心建立了Carrousel氧化沟系统出水SS预报的BP神经网络模型、出水COD预报的BP神经网络模型及出水TN、TP预报的RBF神经网络模型。这三个模型检验均合格，模型的预报能力可靠，特别是对出水水质指标高端浓度段识别能力较强。模型模拟试验表明，模型性能对网络结构与学习样本质量与规模具有强烈的依赖性。建成的三个模型可用于该污水处理中心氧化沟系统出水SS、COD、TN、TP的实时预报以及出水水质指标（SS、COD、TN、TP）与进水水质指标（SS、COD、NH₄⁺-N、TP、TN）、控制参数(水温T、MLSS、MLVSS、SV₃₀)之间定量关系模拟及相关性分析，为氧化沟系统在线优化控制提供了一条简便的途径。经综合评定，Carrousel氧化沟系统出水TN、TP预报的RBF神经网络模型推广能力最佳，出水SS预报的BP神经网络模型的推广能力次之，出水COD预报BP神经网络模型性能较差，反映在高有机负荷进水条件下模拟曲线有效响应段较窄， 针对以往研究中ANNs方法使用时存在的纰漏，作者提出了完整严格的建模思路，从数据收集与水质参数定性分析、条件假定、数据处理与分析、网络结构及算法选择、模型训练与检验等方面对建模细节进行了详细的探讨，强调机理分析对ANNs这一统计模型的约束性，在模型验证阶段采用了活性污泥系统动力学过程机理辅助判据，取得了良好效果。针对具体的研究对象，为提高模型推广能力，作者提出了数据处理三规则、精度与准确度关系协调、模型外延性分析，系统机理的约束性等措施。最后，在模型验证及应用阶段，作者对模型存在的局限性及其原因进行了探讨。 本论文同时为水处理系统智能化反馈控制研究积累了经验，以此为基础仍以ANNs为数学工具可进一步展开氧化沟系统的后续反馈控制研究。希望本论文对环境科学领域相关研究的深入有所裨益。更多还原

【Abstract】 Stimulating the correlation of water quality parameters correctly is an important topic in the real-time control on-line of water treatment system. Artificial neural networks(ANNs) have ieen widely used as a promising alternative approach in system stimulation and prediction because of their distinguishing features- data-driven, self-organized, self-adaptive, error-contained, parallel. The paper,at the beginning,summarizes the current application of ANNs in the field of environmental science and analyzes the effectiveness and flexibility of ANNs in pattern recognition,dependent variable approach,association and recollection, etc.Then,the carrousel oxidation ditch system in Luohe Center of wastewater treatment ,as a case being difficult to control on-line because the influent characteristics are complex and vary significantly,is presented which makes it necessary to stimulate the dynamic behavior of the oxidation ditch system.To resolve the problem,two kinds of ANNs,error back-propagation(BP) neural network(NN) and radius basis function(RBF) NN are employed and three models are developed to predict effluent SS,COD,total nitrogen(TN) and total phosphrate(TP).The prediction of effluent SS and COD in the oxidation system are based on BPNN respectively,while the effluent TN and TP are predicted simultaneously based on RBFNN.The three models have good performance in test and are capable of adapting to various situations.Especially in the validation ,they can recognize high concentration of effluent with accuracy. Meanwhile.the test of stimulation shows that the models’ performance strongly depends on ANN’s architecture and the quality and size of training and test sets.Finally,the three models,which can be used to predict effluent SS, COD,TN and TP in the Carrousel oxidation system and stimulate the relationship between input variables including influent parameters(SS,COD,TN,TP,NH4+-N),control parameters(water temperature,MLSS, MLVSS,SV30) and output variables including effluent parameters(SS,COD,TN, TP),provide an easy method to optimize the system real-time control on-line and prevent the system from accidents.In comprehensive comparison,RBFNN model predicting effluent TN and TP is superior to BPNN models predicting effluent SS and COD. BPNN model predicting effluent COD has a relative narrow input range of influent COD.Focusing on negligence and mistakes existing in some researches,the paper states strict and integrated modeling steps, including data collection andtransformation,parameter qualitative analyse,condition assumption,the selection of ANN’s architecture and algorithm,NN training and validation, stimulation test and so on. According to the dynamic mechanism of activated slude system,which acts as an auxiliary criterion to evaluate candidate models’ performance,three satisfactory models are achieved .To improve models’ adaptability and ability to generalize, some measures are put forward,such as three rules of data trans--formation,how to cope with the contradiction between precision and accuracy,the model’s extent analyse and the restriction from dynamic mechanism of activated sludge system and so on.Furthermore,the author discusses the three models’ limitation in application and why it is.The last but not the least is that the paper accumulates modeling experience which is helpful to the study of intelligent control of water treatment system.lt is a stepstone to deepen the research on consequent feed-back control on-line of the oxidation ditch system .The author hope all he has done brings related researches valuable assistance.更多还原