【摘要】：石油及其產(chǎn)品在中國的能源結(jié)構(gòu)中占有相當(dāng)?shù)谋戎?中國已經(jīng)發(fā)展成為生產(chǎn)和消費(fèi)的石油大國。石油及其產(chǎn)品是多種碳?xì)浠衔锏幕旌衔?其中的輕組分具有較強(qiáng)的揮發(fā)性,給企業(yè)和社會(huì)在安全、環(huán)境、能源等方面產(chǎn)生了嚴(yán)重的影響。揮發(fā)性有機(jī)物是對(duì)大氣對(duì)流層非常重要的痕量組分,在大氣化學(xué)過程中扮演者極其重要的角色,對(duì)二次有機(jī)污染物形成、大氣氧化能力、人體健康等方面都有著重要的影響。石化固定儲(chǔ)罐VOC排放量的估算方法各國有所不同。本文主要采用BP神經(jīng)網(wǎng)絡(luò)估算方法,研究了存儲(chǔ)真實(shí)蒸氣壓、儲(chǔ)罐直徑、周轉(zhuǎn)量,摩爾分子量、儲(chǔ)罐的顏色等參數(shù)對(duì)儲(chǔ)罐排放量的影響,運(yùn)用BP神經(jīng)網(wǎng)絡(luò)可以快速簡單、有效的估算出儲(chǔ)罐的VOCs的排放量,具有重要的指導(dǎo)價(jià)值。(1)通過石化儲(chǔ)罐四種VOCs排放量估算方法的分析,確定影響儲(chǔ)罐排放量的主要影響因素是存儲(chǔ)的日平均溫差、存儲(chǔ)物質(zhì)的真實(shí)蒸氣壓、存儲(chǔ)物質(zhì)的周轉(zhuǎn)量、存儲(chǔ)物資的摩爾分子量等,次要的影響因素有儲(chǔ)罐的容積、存儲(chǔ)物料的高度、儲(chǔ)罐的顏色等。(2)以上海市某石化企業(yè)的汽油儲(chǔ)罐和甲醇罐為案例對(duì)象,計(jì)算儲(chǔ)罐的總損失與財(cái)政部結(jié)果進(jìn)行比較,其中EPA推薦的TANKS模型估算結(jié)果最為接近,汽油罐的靜置損失誤差為39.16%,工作損失誤差為2.14%,甲醇罐的靜置損失誤差為33.24%,工作損失誤差為50.95%;日本的《減少污染物排放和轉(zhuǎn)移登記物質(zhì)排放的成功事例》(以下簡稱事例)的計(jì)算方法與財(cái)政部結(jié)果的差距次之,汽油罐靜置損失誤差為83.14%,工作損失誤差為106.23%,甲醇罐的靜置損失誤差為54.04%,工作損失誤差為142.10%;《石油庫節(jié)能導(dǎo)則》推薦公式計(jì)算結(jié)果誤差最大。(3)運(yùn)用BP神經(jīng)網(wǎng)絡(luò)模擬石化企業(yè)固定儲(chǔ)罐的VOCs排放量,并驗(yàn)證BP模擬結(jié)果的準(zhǔn)確性,其中靜置損失的平均誤差為5.57%,工作損失的平均誤差3.46%。
[Abstract]:Petroleum and its products account for a considerable proportion of China's energy structure, and China has developed into a major oil producer and consumer. Petroleum and its products are mixtures of many hydrocarbons, in which the light components have strong volatility, which has a serious impact on enterprises and society in terms of safety, environment, energy and so on. Volatile organic compounds (VOCs) are very important trace components in the tropospheric layer. Volatile organic compounds (VOCs) play an extremely important role in the process of atmospheric chemistry and have important effects on the formation of secondary organic pollutants, atmospheric oxidation ability, human health and so on. The estimation methods of VOC emission from petrochemical fixed storage tanks are different from country to country. In this paper, the effects of storage real vapor pressure, tank diameter, turnover, molecular weight and color of storage tank on tank emission are studied by using BP neural network estimation method. BP neural network can be used to be fast and simple. It is of great guiding value to estimate the VOCs emission of storage tank effectively. (1) through the analysis of four VOCs emission estimation methods of petrochemical storage tank, it is determined that the main influencing factor of tank emission is the daily average temperature difference of storage. The real vapor pressure of the storage material, the turnover of the storage material, the molar molecular weight of the storage material, etc., the secondary influencing factors are the volume of the storage tank, the height of the storage material, (2) taking the gasoline tank and methanol tank of a petrochemical enterprise in Shanghai as an example, the total loss of the tank is compared with that of the Ministry of Finance, among which the TANKS model recommended by EPA is the closest. The static loss error of gasoline tank is 39.16%, the working loss error is 2.14%, the static loss error of methanol tank is 33.24%, and the working loss error is 50.95%. The difference between the calculation method of Japan's "success stories of reducing Pollutant emissions and transfer of registered substances" (hereinafter referred to as the example) and the results of the Ministry of Finance is the second, with the static loss error of gasoline tanks being 83.14%. The working loss error is 106.23%, the static loss error of methanol tank is 54.04%, and the working loss error is 142.10%. The recommended formula of Oil Depot Energy Saving Guide has the largest error. (3) BP neural network is used to simulate the VOCs emission of fixed storage tanks in petrochemical enterprises, and the accuracy of BP simulation results is verified. The average error of static loss is 5.57% and the average error of work loss is 3.46%.
【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：X74

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