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  本文關(guān)鍵詞：基于密度聚類自組織RBF神經(jīng)網(wǎng)絡(luò)的出水氨氮軟測量研究　出處：《北京工業(yè)大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 出水氨氮 軟測量模型 密度聚類自組織RBF 軟測量平臺

【摘要】：近年來,隨著社會的快速發(fā)展,水污染問題愈發(fā)嚴峻,而水體富營養(yǎng)化是導(dǎo)致水污染的重要原因,水體富營養(yǎng)化會破壞生態(tài)環(huán)境,影響人類健康,因此避免水體富營養(yǎng)化是我國城市污水處理廠建設(shè)的主要目標之一。由于水體富營養(yǎng)化的機理過程復(fù)雜,影響因素眾多,難以建立其精確的數(shù)學(xué)預(yù)測模型,致使水體富營養(yǎng)化難以預(yù)防,而水中氨氮的含量是水體富營養(yǎng)化的關(guān)鍵參數(shù),其值大小可以用來評價水質(zhì),預(yù)防污染。因此,為了實現(xiàn)污水處理廠出水氨氮的實時測量,文中提出了一種基于密度聚類自組織RBF神經(jīng)網(wǎng)絡(luò)的出水氨氮軟測量模型,實現(xiàn)了出水氨氮的及時、準確預(yù)測。本文主要的研究工作包括以下幾點:1.基于污水氨氮處理過程的機理分析與數(shù)據(jù)處理,確定了出水氨氮軟測量模型的輔助變量。輔助變量的選取是建立出水氨氮軟測量模型的關(guān)鍵,本文通過對活性污泥法中生物脫氮過程的機理分析及實際污水處理廠可測量變量,總結(jié)出與出水氨氮相關(guān)的7種輔助變量,在對數(shù)據(jù)進行歸一化處理后,利用主元分析法對初步的7種輔助變量進行了去相關(guān)性處理,最終將出水氨氮軟測量模型的輔助變量維數(shù)由7維降為5維。2.針對RBF神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)參數(shù)難以確定的問題,設(shè)計出一種密度聚類自組織RBF神經(jīng)網(wǎng)絡(luò)。該密度聚類算法以某個樣本點密度值大小及樣本間的歐氏距離為條件進行RBF神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)的自組織調(diào)整,從而實現(xiàn)網(wǎng)絡(luò)結(jié)構(gòu)的確定,并利用梯度下降算法對網(wǎng)絡(luò)參數(shù)進行訓(xùn)練,確定最終的RBF神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)及參數(shù)。非線性系統(tǒng)逼近實驗表明:所提出的自組織機制能夠優(yōu)化RBF神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu),提高了網(wǎng)絡(luò)預(yù)測精度。3.建立了一種基于密度聚類自組織RBF神經(jīng)網(wǎng)絡(luò)的出水氨氮軟測量模型,解決了出水氨氮在線測量精度不高的問題。將提出的密度聚類自組織RBF神經(jīng)網(wǎng)絡(luò)應(yīng)用于出水氨氮的軟測量模型中,由于密度聚類自組織RBF神經(jīng)網(wǎng)絡(luò)能夠根據(jù)樣本數(shù)據(jù)的特點進行網(wǎng)絡(luò)結(jié)構(gòu)的自組織調(diào)整,使得建立的軟測量模型更接近實際的污水處理過程,實驗結(jié)果驗證了所建立的出水氨氮軟測量模型的有效性。4.開發(fā)了一種出水氨氮軟測量平臺。該平臺主要包括用戶注冊及登錄模塊、數(shù)據(jù)處理模塊、模型訓(xùn)練及預(yù)測模塊。首先,利用LabVIEW 2012軟件編寫了平臺操作界面,主要集合了模型選擇、參數(shù)設(shè)置等功能;然后利用Access 2013數(shù)據(jù)庫和Matlab R2012a軟件編寫了后臺運行程序,以實現(xiàn)用戶信息存儲及軟測量模型調(diào)用;最后,通過用戶信息、數(shù)據(jù)處理、模型調(diào)用等模塊間的信息傳輸,實現(xiàn)出水氨氮軟測量過程的可視化。
[Abstract]:In recent years, with the rapid development of society, the problem of water pollution is becoming more and more serious, and eutrophication is an important cause of water pollution, water eutrophication will damage the ecological environment and affect human health. Therefore, avoiding eutrophication of water body is one of the main goals of the construction of municipal wastewater treatment plant in China. Because of the complex mechanism of eutrophication and many factors affecting the eutrophication, it is difficult to establish its accurate mathematical prediction model. It is difficult to prevent eutrophication, and the content of ammonia nitrogen in water is the key parameter of eutrophication, and its value can be used to evaluate water quality and prevent pollution. In order to realize the real-time measurement of ammonia nitrogen in effluent of wastewater treatment plant, a soft sensing model of ammonia nitrogen in effluent based on density clustering self-organizing RBF neural network is proposed in this paper. The main research work in this paper includes the following points: 1. Mechanism analysis and data processing based on the process of ammonia nitrogen treatment of sewage. The auxiliary variables of the effluent ammonia nitrogen soft sensing model are determined, and the selection of the auxiliary variables is the key to the establishment of the effluent ammonia nitrogen soft sensor model. Based on the mechanism analysis of biological denitrification process in activated sludge process and the measurable variables in actual wastewater treatment plant, seven auxiliary variables related to ammonia nitrogen in effluent were summarized, and the data were normalized. The primary 7 auxiliary variables were treated by principal component analysis (PCA). Finally, the dimension of the auxiliary variable of the effluent ammonia soft sensor model is reduced from 7 to 5. 2. Aiming at the problem that the parameters of RBF neural network structure are difficult to determine. A density clustering self-organizing RBF neural network is designed. The density clustering algorithm is based on the Euclidean distance between samples and the size of the density of a sample point to adjust the RBF neural network structure. In order to determine the network structure and use gradient descent algorithm to train the network parameters. The final RBF neural network structure and parameters are determined. The nonlinear system approximation experiments show that the proposed self-organization mechanism can optimize the RBF neural network structure. A soft sensing model of effluent ammonia nitrogen based on density clustering self-organizing RBF neural network was established. The problem of low precision of on-line measurement of ammonia nitrogen in effluent was solved. The density clustering self-organizing RBF neural network was applied to the soft sensing model of ammonia nitrogen in effluent. Because the density clustering self-organizing RBF neural network can adjust the network structure according to the characteristics of the sample data, the established soft-sensor model is closer to the actual sewage treatment process. The experimental results verify the effectiveness of the model. 4. A soft sensing platform of effluent ammonia nitrogen is developed. The platform mainly includes user registration and login module, data processing module. Model training and prediction module. Firstly, the platform operating interface is compiled by using LabVIEW 2012 software, which mainly integrates the functions of model selection, parameter setting and so on. Then, the background running program is written by using Access 2013 database and Matlab R2012a software to realize user information storage and soft sensor model call. Finally, through the information transmission between user information, data processing, model transfer and other modules, the visualization of effluent ammonia soft sensing process is realized.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：X832;TP183

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本文編號：1400741