【摘要】：海上溢油污染是當(dāng)今全球海洋污染最嚴(yán)重的問(wèn)題之一,造成了巨大的國(guó)民財(cái)產(chǎn)損失和環(huán)境損害,對(duì)海洋環(huán)境中的溢油污染進(jìn)行及時(shí)、準(zhǔn)確的探測(cè),可以縮小災(zāi)害范圍、厘清責(zé)任歸屬。由于溢油污染物中包含的多環(huán)芳烴及其化合物有著較強(qiáng)的熒光活性,采用激光誘導(dǎo)熒光光譜技術(shù)可以對(duì)溢油種類進(jìn)行識(shí)別。本文發(fā)展了一套基于激光誘導(dǎo)熒光技術(shù)結(jié)合模式識(shí)別手段,識(shí)別常見的溢油污染物種類的方法。論文首先簡(jiǎn)要介紹了背景和選題意義,針對(duì)激光熒光雷達(dá)設(shè)備的國(guó)內(nèi)外發(fā)展情況、激光誘導(dǎo)熒光手段在油種檢測(cè)識(shí)別的研究進(jìn)展進(jìn)行了調(diào)研,并對(duì)本文的主要工作內(nèi)容進(jìn)行了介紹。此外,介紹了激光誘導(dǎo)熒光光譜技術(shù)及論文中用到的算法的基本原理。本文的工作內(nèi)容包括以下三個(gè)方面：第一,基于激光誘導(dǎo)發(fā)射光譜的油種識(shí)別研究,采集了柴油、汽油、重質(zhì)燃料油和五種原油的發(fā)射光譜,分別用PLS-DA、PCA結(jié)合BP-ANN和SVM三種算法識(shí)別,比較分類結(jié)果和模型訓(xùn)練難度選擇SVM模型為后續(xù)工作的采用的算法。第二,時(shí)間分辨熒光光譜數(shù)據(jù)的特征范圍提取,將測(cè)得的時(shí)間分辨光譜分別從時(shí)間和波長(zhǎng)兩個(gè)方向降維,結(jié)合SVM模型對(duì)時(shí)間窗口和波長(zhǎng)范圍進(jìn)行優(yōu)化,從數(shù)據(jù)處理的角度凸顯原油時(shí)間分辨熒光光譜的特性范圍。第三,基于時(shí)間分辨熒光光譜的油種識(shí)別研究,其中特征提取方法為PCA和統(tǒng)計(jì)參量提取,識(shí)別方法為SVM模型。此外,對(duì)以上工作中采用的實(shí)驗(yàn)裝置、實(shí)驗(yàn)樣品和光譜采集工作也進(jìn)行了詳細(xì)介紹。
[Abstract]:Marine oil spill pollution is one of the most serious problems of marine pollution in the world today, which has caused huge national property loss and environmental damage. Timely and accurate detection of oil spill pollution in the marine environment can narrow down the scope of disasters. Clarify the attribution of responsibility. Because the polycyclic aromatic hydrocarbons (PAHs) and their compounds contained in oil spill pollutants have strong fluorescence activity, laser induced fluorescence spectroscopy can be used to identify the oil spills. In this paper, a method based on laser induced fluorescence (LIF) combined with pattern recognition is developed to identify common types of oil spill pollutants. Firstly, the background and the significance of selecting the topic are briefly introduced. According to the development of lidar equipment at home and abroad, the research progress of laser induced fluorescence in oil detection and recognition is investigated. The main work of this paper is introduced. In addition, the laser induced fluorescence spectroscopy and the basic principle of the algorithm used in this paper are introduced. The work of this paper includes the following three aspects: first, the emission spectra of diesel oil, gasoline, heavy fuel oil and five kinds of crude oil are collected based on the laser induced emission spectrum. PLS-DA,PCA combined with BP-ANN and SVM are used to identify the classification results and the difficulty of model training. The SVM model is chosen as the algorithm for the following work. Secondly, the characteristic range of time-resolved fluorescence spectrum data is extracted, the measured time-resolved spectrum is reduced from time and wavelength direction, and the time window and wavelength range are optimized with SVM model. The characteristic range of time resolved fluorescence spectrum of crude oil is highlighted from the point of view of data processing. Thirdly, the oil species recognition based on time-resolved fluorescence spectrum is studied, in which the feature extraction method is PCA and statistical parameter extraction, and the recognition method is SVM model. In addition, the experimental device, experimental sample and spectrum collection are also introduced in detail.
【學(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：X55;X834

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