本文選題：污水處理 + 支持向量回歸機(jī)��； 參考：《湖南工業(yè)大學(xué)》2015年碩士論文

【摘要】：污水處理廠為保護(hù)水資源和防治水污染做出了重大貢獻(xiàn),但由于污水處理系統(tǒng)是一個(gè)高度非線性、強(qiáng)耦合、多變量和大滯后的復(fù)雜系統(tǒng),其機(jī)理研究還不夠成熟,關(guān)鍵指標(biāo)參數(shù)不能實(shí)現(xiàn)實(shí)時(shí)測(cè)量,且污水處理的效果依賴于污水出水水質(zhì)的好壞。因此建立一個(gè)高效、合理的污水出水指標(biāo)模型,將預(yù)測(cè)結(jié)果作為指導(dǎo)污水廠運(yùn)行的依據(jù),動(dòng)態(tài)調(diào)整污水處理過(guò)程中各工序運(yùn)行狀態(tài),具有一定的現(xiàn)實(shí)意義和應(yīng)用價(jià)值。本文以活性污泥法污水的出水水質(zhì)為研究對(duì)象,對(duì)于污水處理過(guò)程水質(zhì)參數(shù)數(shù)據(jù)分布較復(fù)雜,采用單一核函數(shù)支持向量回歸機(jī)模型建模精度不理想的問(wèn)題,在前人研究成果的基礎(chǔ)上,把多核和智能算法相結(jié)合,建立了出水水質(zhì)參數(shù)的多核支持向量回歸機(jī)(MK-SVR)模型,研究?jī)?nèi)容有以下幾方面:首先了解污水處理工藝流程及方法,對(duì)影響污水處理過(guò)程的水質(zhì)參數(shù)及相關(guān)排放標(biāo)準(zhǔn)進(jìn)行了分析,利用主成分分析(PCA)法對(duì)污水處理過(guò)程的影響因素進(jìn)行降維處理,提取新主元作為支持向量回歸機(jī)的輸入,建立了出水COD、BOD、SS和TN的MK-SVR模型。其次,由于模型自身參數(shù)問(wèn)題的影響,在分析智能算法中粒子群算法(PSO)具有編程方便結(jié)構(gòu)簡(jiǎn)單易于實(shí)現(xiàn)、搜索速度快、收斂能力強(qiáng)等特點(diǎn),提出了利用PSO算法對(duì)MK-SVR模型進(jìn)行參數(shù)尋優(yōu),并針對(duì)基本PSO算法的不足對(duì)其進(jìn)行了改進(jìn)。最后為所提出的模型更具說(shuō)服力,對(duì)幾種不同模型——SVR單核模型、SVR多核模型、基于PCA分析的SVR多核模型與單核模型、基于PCA分析的PSO+MK-SVR模型及基于PCA分析的改進(jìn)PSO+MK-SVR模型的預(yù)測(cè)效果進(jìn)行了對(duì)比,從平均相對(duì)誤差、均方誤差及相關(guān)系數(shù)等幾個(gè)性能指標(biāo)進(jìn)行了分析,結(jié)果表明,基于PCA分析的改進(jìn)PSO+MK-SVR模型預(yù)估效果最好,泛化性能最強(qiáng),為污水廠的實(shí)時(shí)高效運(yùn)行提供強(qiáng)勁的理論支撐。
[Abstract]:Sewage treatment plants have made great contributions to the protection of water resources and the prevention of water pollution. However, because the sewage treatment system is a highly nonlinear, strongly coupled, multivariable and lag complex system, the study of its mechanism is not mature enough. The key parameters can not be measured in real time, and the effect of sewage treatment depends on the quality of effluent. Therefore, it is of practical significance and practical value to establish an efficient and reasonable effluent index model, to take the prediction results as the basis for guiding the operation of the wastewater treatment plant, and to dynamically adjust the operation state of each process in the process of sewage treatment. In this paper, the effluent quality of activated sludge wastewater is taken as the research object. For the complex distribution of water quality parameter data in the process of sewage treatment, the modeling accuracy of single kernel function support vector regression model is not ideal. On the basis of previous research results, a multi-core support vector regression model (MK-SVR) for effluent quality parameters is established by combining multi-core and intelligent algorithms. The research contents are as follows: firstly, the process and methods of wastewater treatment are understood. The influence of water quality parameters and relevant discharge standards on wastewater treatment process is analyzed. Principal component analysis (PCA) method is used to reduce the dimension of wastewater treatment process, and a new principal component is extracted as the input of support vector regression machine. The MK-SVR model of effluent CODDSS and TN was established. Secondly, due to the influence of the model's own parameter problem, Particle Swarm Optimization (PSO) has the advantages of simple and easy programming, fast searching speed and strong convergence ability in the analysis of intelligent algorithm. PSO algorithm is used to optimize the parameters of MK-SVR model, and the basic PSO algorithm is improved. Finally, the proposed model is more persuasive. For several different models, SVR multi-core model, SVR multi-core model based on PCA analysis and single core model, The prediction results of PSO MK-SVR model based on PCA analysis and improved PSO MK-SVR model based on PCA analysis are compared. Several performance indexes, such as average relative error, mean square error and correlation coefficient, are analyzed. The improved PSO MK-SVR model based on PCA has the best prediction effect and the strongest generalization performance, which provides a strong theoretical support for the real-time and efficient operation of the wastewater treatment plant.
【學(xué)位授予單位】：湖南工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：X703;TP181

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 魏瑞霞,孫劍輝,陳金龍;SBR法處理廢水的影響因素[J];重慶環(huán)境科學(xué);2003年11期

2 管秋;王萬(wàn)良;徐新黎;陳勝勇;;基于神經(jīng)網(wǎng)絡(luò)的污水處理指標(biāo)軟測(cè)量研究[J];環(huán)境污染與防治;2006年02期

3 汪洪橋;孫富春;蔡艷寧;陳寧;丁林閣;;多核學(xué)習(xí)方法[J];自動(dòng)化學(xué)報(bào);2010年08期

4 黃細(xì)霞;石繁槐;顧偉;陳善本;;加權(quán)支持向量回歸在線學(xué)習(xí)方法[J];上海交通大學(xué)學(xué)報(bào);2009年06期

5 徐緯芳;劉成忠;顧延濤;;基于PCA和支持向量機(jī)的徑流預(yù)測(cè)應(yīng)用研究[J];水資源與水工程學(xué)報(bào);2010年06期

6 陽(yáng)春華;任會(huì)峰;桂衛(wèi)華;鄢鋒;;代價(jià)約束多核最小二乘支持向量機(jī)及其應(yīng)用[J];信息與控制;2012年05期

7 何淵淘;劉超慧;;多核小波支持向量機(jī)在Carrousel氧化溝系統(tǒng)的應(yīng)用[J];計(jì)算機(jī)系統(tǒng)應(yīng)用;2013年10期

8 董金華;;我國(guó)水污染現(xiàn)狀及處理措施研究[J];資源節(jié)約與環(huán)保;2014年05期

相關(guān)博士學(xué)位論文 前1條

1 劉靖旭;支持向量回歸的模型選擇及應(yīng)用研究[D];國(guó)防科學(xué)技術(shù)大學(xué);2006年

相關(guān)碩士學(xué)位論文 前1條

1 王佳;混合核支持向量機(jī)參數(shù)優(yōu)化及其應(yīng)用研究[D];長(zhǎng)沙理工大學(xué);2011年

，

本文編號(hào)：2065987