【摘要】：近年來(lái),隨著我國(guó)經(jīng)濟(jì)飛速發(fā)展和城市化腳步加快,隨之而來(lái)的水污染問(wèn)題日趨嚴(yán)重,此問(wèn)題已經(jīng)成為我國(guó)乃至全世界水資源面臨的最嚴(yán)重問(wèn)題之一�；瘜W(xué)需氧量(COD)作為評(píng)價(jià)水體污染程度的重要指標(biāo),可以表征水體有機(jī)物的濃度。紫外-可見(jiàn)光譜法檢測(cè)COD無(wú)二次污染、周期短、可實(shí)現(xiàn)在線(xiàn)檢測(cè),是一種綠色檢測(cè)技術(shù)。本文針對(duì)紫外-可見(jiàn)光譜法水質(zhì)COD檢測(cè)方法和建模開(kāi)展了如下研究工作:1.光譜系統(tǒng)與光譜采集為了檢測(cè)水質(zhì)COD值,本文開(kāi)發(fā)了一種紫外-可見(jiàn)光譜水質(zhì)COD檢測(cè)系統(tǒng),并進(jìn)行實(shí)驗(yàn)室鄰苯二甲酸氫鉀標(biāo)準(zhǔn)溶液制備與檢測(cè),采集紫外-可見(jiàn)吸光度光譜數(shù)據(jù)。2.光譜數(shù)據(jù)預(yù)處理技術(shù)研究針對(duì)原始光譜受到大量噪聲影響的問(wèn)題,本文需要一種去噪過(guò)程中盡可能少丟失真實(shí)信息的方法進(jìn)行原始光譜去噪,小波分析可以滿(mǎn)足要求。本文采用小波函數(shù)db8,對(duì)原始光譜進(jìn)行5層小波分解,然后利用軟閾值方式進(jìn)行量化處理,重構(gòu)后的水質(zhì)COD光譜曲線(xiàn)十分光滑,去噪效果顯著。小波去噪后依然存在光譜信息冗余和多重共線(xiàn)性問(wèn)題,采用主成分分析法對(duì)光譜數(shù)據(jù)進(jìn)行降維處理,有效去除冗余信息,保留有用特征信息,提高了機(jī)器學(xué)習(xí)效率。3.水質(zhì)COD檢測(cè)預(yù)測(cè)模型研究由于紫外-可見(jiàn)光光譜數(shù)據(jù)與水質(zhì)COD值的關(guān)系具有復(fù)雜的非線(xiàn)性,無(wú)法采用傳統(tǒng)的機(jī)理建模方法。建立基于BP神經(jīng)網(wǎng)絡(luò)的水質(zhì)COD預(yù)測(cè)模型,可以有效的預(yù)測(cè)水質(zhì)COD值。為了提高預(yù)測(cè)精度,采用改進(jìn)的鯨魚(yú)優(yōu)化算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)參數(shù),建立基于鯨魚(yú)優(yōu)化算法BP神經(jīng)網(wǎng)絡(luò)的水質(zhì)COD預(yù)測(cè)模型,預(yù)測(cè)結(jié)果表明,該模型的預(yù)測(cè)精度更高,可以應(yīng)用于水質(zhì)COD檢測(cè)的預(yù)測(cè)。4.優(yōu)化算法改進(jìn)針對(duì)基本鯨魚(yú)優(yōu)化算法收斂速度慢、收斂精度低的缺陷,提出改進(jìn)的鯨魚(yú)優(yōu)化算法(MWOA),MWOA主要研究了種群初始化機(jī)制和非線(xiàn)性自適應(yīng)權(quán)重策略。仿真結(jié)果表明,改進(jìn)的算法能在尋優(yōu)過(guò)程中保持初始種群多樣性,具有更好的收斂速度和收斂精度。
[Abstract]:In recent years, with the rapid development of economy and the acceleration of urbanization in China, the problem of water pollution is becoming more and more serious, which has become one of the most serious problems faced by water resources in China and even in the world. Chemical oxygen demand (COD), as an important index to evaluate the pollution degree of water body, can characterize the concentration of organic matter in water body. UV-vis spectroscopy is a green detection technology because of its no secondary pollution, short period and on-line detection. In this paper, the following research work has been carried out on the COD detection method and modeling of water quality by UV-vis spectroscopy: 1. In order to detect the COD value of water quality, a UV-vis spectral water quality COD detection system was developed in this paper, and the standard solution of potassium hydrogen phthalate was prepared and detected in laboratory. Collection of UV-vis absorbance spectral data. 2. In order to solve the problem that the original spectrum is affected by a lot of noise, this paper needs a method to Denoise the original spectrum with as little real information as possible in the process of denoising, and wavelet analysis can meet the requirements. In this paper, the wavelet function db8, is used to decompose the original spectrum by 5 layers of wavelet, and then the soft threshold method is used to quantify the original spectrum. The reconstructed COD spectral curve of water quality is very smooth and the denoising effect is remarkable. After wavelet denoising, there are still spectral information redundancy and multiple collinearity problems. Principal component analysis (PCA) is used to reduce the dimension of spectral data, effectively remove redundant information, retain useful feature information, and improve the efficiency of machine learning. Study on the prediction model of COD detection and prediction of water quality because of the complex nonlinear relationship between UV-vis spectral data and COD value of water quality, the traditional mechanism modeling method can not be used. The COD prediction model of water quality based on BP neural network can effectively predict the COD value of water quality. In order to improve the prediction accuracy, the improved whale optimization algorithm is used to optimize the parameters of BP neural network, and a water quality COD prediction model based on whale optimization algorithm BP neural network is established. The prediction results show that the prediction accuracy of the model is higher. It can be applied to the prediction of COD detection of water quality. 4. Aiming at the defects of slow convergence speed and low convergence accuracy of the basic whale optimization algorithm, an improved whale optimization algorithm (MWOA), MWOA is proposed, which mainly studies the population initialization mechanism and nonlinear adaptive weight strategy. The simulation results show that the improved algorithm can maintain the initial population diversity in the optimization process, and has better convergence speed and accuracy.
【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：X832

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