【摘要】：海上溢油污染是當今全球海洋污染最嚴重的問題之一,造成了巨大的國民財產(chǎn)損失和環(huán)境損害,對海洋環(huán)境中的溢油污染進行及時、準確的探測,可以縮小災害范圍、厘清責任歸屬。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的熒光活性,采用激光誘導熒光光譜技術(shù)可以對溢油種類進行識別。本文發(fā)展了一套基于激光誘導熒光技術(shù)結(jié)合模式識別手段,識別常見的溢油污染物種類的方法。論文首先簡要介紹了背景和選題意義,針對激光熒光雷達設(shè)備的國內(nèi)外發(fā)展情況、激光誘導熒光手段在油種檢測識別的研究進展進行了調(diào)研,并對本文的主要工作內(nèi)容進行了介紹。此外,介紹了激光誘導熒光光譜技術(shù)及論文中用到的算法的基本原理。本文的工作內(nèi)容包括以下三個方面：第一,基于激光誘導發(fā)射光譜的油種識別研究,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三種算法識別,比較分類結(jié)果和模型訓練難度選擇SVM模型為后續(xù)工作的采用的算法。第二,時間分辨熒光光譜數(shù)據(jù)的特征范圍提取,將測得的時間分辨光譜分別從時間和波長兩個方向降維,結(jié)合SVM模型對時間窗口和波長范圍進行優(yōu)化,從數(shù)據(jù)處理的角度凸顯原油時間分辨熒光光譜的特性范圍。第三,基于時間分辨熒光光譜的油種識別研究,其中特征提取方法為PCA和統(tǒng)計參量提取,識別方法為SVM模型。此外,對以上工作中采用的實驗裝置、實驗樣品和光譜采集工作也進行了詳細介紹。
[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.
【學位授予單位】：內(nèi)蒙古大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：X55;X834

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