首页> 外文学位 >Modeling total reduced sulfur and sulfur dioxide emissions from a kraft recovery boiler using an artificial neural network, and, Investigating volatile organic compounds in an urban intermountain valley using a TD/GC/MS methodology and intrinsic tracer molecules.
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Modeling total reduced sulfur and sulfur dioxide emissions from a kraft recovery boiler using an artificial neural network, and, Investigating volatile organic compounds in an urban intermountain valley using a TD/GC/MS methodology and intrinsic tracer molecules.

机译:使用人工神经网络对牛皮纸回收锅炉的总还原硫和二氧化硫排放量进行建模,并使用TD / GC / MS方法和固有示踪剂分子研究城市山间山谷中的挥发性有机化合物。

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Back-propagation neural networks were trained to predict total reduced sulfur (TRS) and SO2 emissions from kraft recovery boiler operational data. A 0.721 coefficient of correlation was achieved between actual and predicted sulfur emissions on test data withheld from network training. The artificial neural network (ANN) models found an inverse, linear relationship between TRS/SO2 emissions and percent opacity. A number of relationships among operating parameters and sulfur emissions were identified by the ANN models. These relationships were used to formulate strategies for reducing sulfur emissions. Disagreement between ANN model predictions on a subsequent data set revealed an additional scenario for sulfur release not present in the training data. ANN modeling was demonstrated to be an effective tool for analyzing process variables when balancing productivity and environmental concerns.; Five receptor sites distributed in the Missoula Valley, Montana, were employed to investigate possible VOC (benzene, 2,3,4-trimethylpentane, toluene, ethylbenzene, m-/p-xylene, o-xylene, naphthalene, acetone, chloroform, α-pinene, β-pinene, p-cymene and limonene) sources. The most dominant source of VOCs was found to be vehicle emissions. Furthermore, anthropogenic sources of terpenoids overwhelmed biogenic emissions, on a local scale. Difficulties correlating wind direction and pollutant levels could be explained by wind direction variability, low wind speed and seasonally dependent meteorological factors. Significant evidence was compiled to support the use of p-cymene as a tracer molecule for pulp mill VOC emissions.; Apportionment techniques using o-xylene and p-cymene as tracers for automobile and pulp mill emissions, respectively, were employed to estimate each source's VOC contribution. Motor vehicles were estimated to contribute between 56 and 100 percent of the aromatic pollutants in the Missoula Valley airshed, depending upon the sampling location. Pulp mill emissions were estimated to account from 1 to 34 percent of the aromatic chemicals in the airshed. Measured ambient chloroform levels were attributable to the pulp mill (12-70%) and non-point source urban emissions (7.5–30%).
机译:对反向传播神经网络进行了训练,以预测从牛皮纸回收锅炉运行数据中得出的总还原硫(TRS)和SO 2 排放量。在网络培训中保留的测试数据中,实际硫磺排放量与预期硫磺排放量之间的相关系数为0.721。人工神经网络(ANN)模型发现TRS / SO 2 排放与不透明度百分比之间呈反线性关系。 ANN模型确定了运行参数与硫排放之间的许多关系。这些关系被用来制定减少硫排放的策略。关于后续数据集的ANN模型预测之间的分歧揭示了训练数据中不存在的硫释放的其他情况。神经网络建模被证明是在平衡生产率和环境问题时分析过程变量的有效工具。利用分布在蒙大拿州米苏拉山谷的五个受体位点研究可能的挥发性有机化合物(苯,2,3,4-三甲基戊烷,甲苯,乙苯, m -/ p -二甲苯, o -二甲苯,萘,丙酮,氯仿,α-pine烯,β-pine烯, p -cy烯和li烯来源。发现最主要的VOC排放源是车辆排放。此外,在当地范围内,人为来源的萜类化合物压倒了生物排放物。与风向和污染物水平相关的困难可以用风向的可变性,低风速和季节相关的气象因素来解释。收集了大量证据,以支持使用 p -肉桂烯作为制浆厂VOC排放的示踪剂分子。分别使用 o -二甲苯和 p -cymene作为汽车和制浆厂排放的示踪剂的分摊技术来估算每个来源的VOC贡献。据估计,在密苏拉河谷流域中,机动车占芳族污染物的56%至100%,具体取决于采样地点。据估计,纸浆厂的排放量占气道中芳族化学品的1%至34%。测得的环境氯仿水平可归因于纸浆厂(12-70%)和城市面源排放(7.5-30%)。

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