本文關(guān)鍵詞：基于尖峰自組織遞歸RBF神經(jīng)網(wǎng)絡(luò)的SVI軟測量研究　出處：《北京工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 污泥膨脹 SVI軟測量 尖峰自組織遞歸RBF 軟測量平臺(tái)

【摘要】：污泥膨脹是制約我國城市污水處理廠發(fā)展的瓶頸,由于污泥膨脹成因眾多,機(jī)理復(fù)雜,各種誘發(fā)因素之間相互影響,難以建立其精確的數(shù)學(xué)模型。污泥體積指數(shù)(Sludge Volume Index,SVI)是污泥膨脹的關(guān)鍵參數(shù),其值大小表征污泥沉降性能,被廣泛用來描述污泥膨脹的程度。因此,為了實(shí)現(xiàn)SVI的實(shí)時(shí)測量,文中提出了基于尖峰自組織遞歸RBF(self-organizing recurrent RBF,SR-RBF)神經(jīng)網(wǎng)絡(luò)的SVI軟測量模型,獲得了SVI的實(shí)時(shí)預(yù)測值,實(shí)現(xiàn)了污泥膨脹的預(yù)測。該論文主要研究工作包括以下幾點(diǎn):1.基于污泥膨脹機(jī)理分析和運(yùn)行數(shù)據(jù),獲得了一組適用于SVI軟測量模型的輔助變量。輔助變量的選取是SVI軟測量模型的關(guān)鍵步驟,文中通過研究污泥膨脹理論和誘發(fā)因素,分析污泥膨脹機(jī)理模型,總結(jié)出與SVI相關(guān)性較大的12個(gè)變量;同時(shí),采用主元分析法對初步確定的12個(gè)變量分析,最終確定了SVI軟測量模型的輔助變量,由Qin、BOD、COD、DO、pH、TN組成。2.針對遞歸RBF神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)難以在線調(diào)整的問題,設(shè)計(jì)出一種尖峰自組織遞歸RBF神經(jīng)網(wǎng)絡(luò)。通過大腦皮層信息傳遞模式和生物尖峰神經(jīng)元模型,提出一種結(jié)構(gòu)增長修剪機(jī)制,實(shí)現(xiàn)了遞歸RBF神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)調(diào)整;同時(shí),提出一種自適應(yīng)梯度下降法對網(wǎng)絡(luò)參數(shù)進(jìn)行訓(xùn)練,提高了遞歸RBF神經(jīng)網(wǎng)絡(luò)的性能。非線性系統(tǒng)建模的實(shí)驗(yàn)結(jié)果表明:提出的自組織機(jī)制能夠在線優(yōu)化遞歸RBF神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu),神經(jīng)網(wǎng)絡(luò)預(yù)測精度較高。3.建立了一種基于尖峰自組織遞歸RBF神經(jīng)網(wǎng)絡(luò)的SVI軟測量模型,解決了SVI的在線測量問題。為了實(shí)現(xiàn)SVI的在線測量,將尖峰自組織遞歸RBF神經(jīng)網(wǎng)絡(luò)應(yīng)用于設(shè)計(jì)的軟測量模型,由于尖峰自組織遞歸RBF神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)能夠在線調(diào)整,并且采用二階LM算法對神經(jīng)網(wǎng)絡(luò)參數(shù)進(jìn)行調(diào)整,使得提出的SVI軟測量模型精度較高,且收斂速度較快,實(shí)驗(yàn)結(jié)果驗(yàn)證了所建立SVI軟測量模型的有效性。4.開發(fā)了一種SVI軟測量平臺(tái)。該SVI軟測量平臺(tái)主要包括數(shù)據(jù)庫模塊、登錄模塊、數(shù)據(jù)處理模塊、模型訓(xùn)練及仿真模塊及結(jié)果查詢模塊。首先,利用VS2010軟件完成操作界面的設(shè)計(jì),具備網(wǎng)絡(luò)模型、自定義模型初始參數(shù)及查詢建模結(jié)果等功能。其次,基于Matlab軟件和Mysql數(shù)據(jù)庫編寫了后臺(tái)運(yùn)行程序,實(shí)現(xiàn)軟測量模型的計(jì)算和用戶信息的存儲(chǔ)功能。最后,通過用戶信息管理、數(shù)據(jù)處理、神經(jīng)網(wǎng)絡(luò)模型選擇等模塊間信息傳輸,實(shí)現(xiàn)SVI預(yù)測值的輸出并顯示,達(dá)到污泥膨脹識(shí)別可視化的目的。
[Abstract]:Sludge bulking is a bottleneck restricting the development of municipal wastewater treatment plants in China. Due to the numerous causes of sludge bulking and complex mechanism, various induced factors affect each other. Sludge volume index (Sludge Volume index) is the key parameter of sludge bulking, and its value indicates sludge settling performance. It is widely used to describe the extent of sludge bulking. Therefore, in order to achieve real-time measurement of SVI. In this paper, a SVI soft sensor model based on spike self-organizing recurrent RBF(self-organizing recurrent SR-RBF neural network is proposed. The real-time prediction value of SVI is obtained, and the prediction of sludge bulking is realized. The main research work in this paper includes the following points: 1. Based on the analysis of sludge bulking mechanism and operation data. A set of auxiliary variables suitable for SVI soft sensor model is obtained. The selection of auxiliary variables is the key step of SVI soft sensing model. The sludge bulking theory and inducing factors are studied in this paper. The model of sludge bulking mechanism was analyzed, and 12 variables with great correlation with SVI were summarized. At the same time, the auxiliary variables of SVI soft sensor model were determined by using principal component analysis method. TN composition. 2. Aiming at the problem that the recursive RBF neural network structure is difficult to adjust online. A spike self-organizing recursive RBF neural network is designed and a pruning mechanism of structural growth is proposed by means of the cerebral cortex information transfer model and the biological spike neuron model. The structure adjustment of recursive RBF neural network is realized. At the same time, an adaptive gradient descent method is proposed to train the network parameters. The experimental results of nonlinear system modeling show that the proposed self-organizing mechanism can optimize the recursive RBF neural network on line. The prediction accuracy of neural network is high. 3. A SVI soft sensor model based on spike self-organizing recursive RBF neural network is established. In order to realize the on-line measurement of SVI, the peak self-organizing recursive RBF neural network is applied to the designed soft sensor model. Because the peak self-organizing recursive RBF neural network structure can be adjusted online, and the second-order LM algorithm is used to adjust the neural network parameters, the proposed SVI soft sensor model has higher accuracy. The experimental results verify the validity of the established SVI soft sensor model. 4. A kind of SVI soft sensor platform is developed. The SVI soft sensor platform mainly includes database module. Login module, data processing module, model training and simulation module and result query module. First, the use of VS2010 software to complete the design of the operation interface, with a network model. Custom model initial parameters and query modeling results and other functions. Secondly, based on Matlab software and Mysql database to write a background running program. Finally, through the user information management, data processing, neural network model selection and other modules of information transmission, the output and display of SVI prediction value. The purpose of visualizing sludge bulking identification is achieved.
【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】：2015
【分類號(hào)】：X703;TP183

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 韓紅桂;伍小龍;王麗丹;王思;;絲狀菌污泥膨脹簡化機(jī)理模型[J];化工學(xué)報(bào);2013年12期

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本文編號(hào)：1430455