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GC-MS方法在船舶溢油油源鑒別中的應(yīng)用研究

發(fā)布時(shí)間:2018-01-23 09:53

  本文關(guān)鍵詞: 船舶溢油 油指紋 GC-MS 模糊聚類分析 出處:《集美大學(xué)》2014年碩士論文 論文類型:學(xué)位論文


【摘要】:隨著航運(yùn)業(yè)的快速發(fā)展,船舶溢油事故不斷增加。船舶港區(qū)操作性溢油盡管溢油量小,但溢油事故多,對(duì)港區(qū)環(huán)境影響大。因此,有必要對(duì)此開展溢油油源鑒別,為海事主管部門開展溢油事故調(diào)查提供技術(shù)支撐。 通過運(yùn)用GC-MS方法對(duì)3條船上的12個(gè)可疑油樣及4個(gè)溢油油樣進(jìn)行定性及定量分析,利用原始指紋圖譜、相對(duì)濃度分布特征及特征比值分別對(duì)船舶不同類型的油樣(燃料油、柴油、滑油)和混合油樣(艙底水)進(jìn)行鑒別分析,并通過這些指紋信息對(duì)單一油樣和混合油樣進(jìn)行模糊聚類分析。 研究表明:不同類型的油樣之間,,組分差異較大,正構(gòu)烷烴(含植烷、姥鮫烷)、多環(huán)芳烴、萜、甾類等標(biāo)志性化合物的原始指紋圖譜差異也較明顯,故通過直觀的判斷即可快速有效的鑒定出溢油源;同種類型不同型號(hào)油樣之間,組分差異較小,正構(gòu)烷烴(含植烷、姥鮫烷)、多環(huán)芳烴、萜、甾類等標(biāo)志性化合物的原始指紋圖譜差異也不明顯,故需基于組分的相對(duì)含量及特征比值來進(jìn)行鑒定。本研究亦利用燃料油的相對(duì)濃度及特征比值數(shù)據(jù)分別進(jìn)行了模糊聚類分析,并與燃料油原始指紋圖譜、相對(duì)濃度分布及特征比值分析進(jìn)行了對(duì)比,確定模糊聚類分析適合選取相對(duì)濃度參數(shù);利用模糊聚類分析分別對(duì)單一溢油油樣和混合溢油油樣的正構(gòu)烷烴(含植烷、姥鮫烷)、多環(huán)芳烴、萜、甾類等標(biāo)志性化合物的相對(duì)濃度進(jìn)行分析,使得溢油源與可疑油樣的“親疏遠(yuǎn)近”關(guān)系一目了然,可以更為直觀、客觀地進(jìn)行溢油油源鑒別。
[Abstract]:With the rapid development of the shipping industry, the number of oil spill accidents is increasing. Although the oil spill in the port area is small, there are many oil spill accidents, which have a great impact on the environment of the port area. It is necessary to identify the source of oil spill and provide technical support for the investigation of oil spill accident. The qualitative and quantitative analysis of 12 suspected oil samples and 4 oil spill samples from 3 ships were carried out by using GC-MS method, and the original fingerprint was used. The relative concentration distribution characteristics and characteristic ratios were used to identify different types of oil samples (fuel oil, diesel oil, lubricating oil) and mixed oil samples (bilge water). Through these fingerprint information, the single oil sample and mixed oil sample are analyzed by fuzzy clustering. The results showed that there were significant differences in components among different types of oil samples, and the differences in the original fingerprints of n-alkanes (including phytane, prickly), polycyclic aromatic hydrocarbons (PAHs), terpenes, steroids, and other landmark compounds were also obvious. Therefore, the source of oil spill can be identified quickly and effectively by direct judgment. The difference of components between the same type and different types of oil samples was small, and the difference of the original fingerprint of n-alkanes (including phytane, mackerel, polycyclic aromatic hydrocarbons, terpenes, steroids, etc.) was not obvious. Therefore, it is necessary to identify the components based on the relative content and characteristic ratio. In this study, the relative concentration and characteristic ratio data of fuel oil are also used to carry out fuzzy cluster analysis and the original fingerprint of fuel oil. The relative concentration distribution and characteristic ratio analysis are compared, and the fuzzy cluster analysis is suitable to select the relative concentration parameters. The relative concentrations of n-alkanes (phytane, prickenane, polycyclic aromatic hydrocarbons, terpenes, steroids) of single oil spill and mixed oil spill were analyzed by fuzzy cluster analysis. It makes the relationship between oil spill source and suspected oil sample "close and distant" clear at a glance, and can be used to identify oil spill source more intuitively and objectively.
【學(xué)位授予單位】:集美大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2014
【分類號(hào)】:O657.63;U698.7

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