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作 者:余夕志[1] 董振国[1] 任晓乾[1] 王军[1] 王延儒[1]
机构地区:[1]南京工业大学化学化工学院,江苏南京210009
出 处:《燃料化学学报》2005年第3期372-378,共7页Journal of Fuel Chemistry and Technology
摘 要:当前各国环保法规对柴油中硫的质量分数的限制越来越严格。催化加氢脱硫是实现柴油低硫化的重要途径,动力学和新反应器的研究受到了研究者的广泛关注。本文介绍了柴油馏分中两种典型的难脱除含硫化合物二苯并噻吩和4,6二甲基二苯并噻吩在各类催化剂上加氢脱硫的反应路径,比较了这两种模型含硫化合物的直接脱硫(DDS)和先加氢再脱硫(HYD)路径相对快慢的影响因素。详细综述了假1级、假2级、快慢1级、n级、LH以及抑制剂H2S存在下的动力学模型在描述二苯并噻吩类模型化合物及真实油品的加氢脱硫过程中的研究现状,介绍了神经网络在柴油加氢动力学和脱硫率预测方面的研究进展。还对催化精馏、并流逆流滴流床、两相床反应器等新型加氢脱硫反应器的最新发展作了综述,展望了加氢脱硫动力学及反应器的研究方向和面临的挑战。More and more stringent environmental regulation to lower the sulfur level in diesel fuel leads to extensive studies on the deep hydrodesulfurization (HDS), which is the key to produce the high quality fuel with low sulfur content. Kinetics and novel rector technologies for HDS have attracted wide attention of both researchers and refineries. In this paper, the reaction network for hydrodesulfurization of dibenzothiophen (DBT) and 4,6-dimethyldibenzothiophen (4,6-DMDBT), in which the sulfur is removed most difficultly, is introduced over various catalysts. The effect of component and support of the catalyst on the ratio of the rate of direct desulfurization (DDS) to that of hydrogenation followed desulfurization (HYD) is analyzed. Various kinetics, such as for model reactants related to DBT with or without inhibitors like H2S, and for the real oil feed, are reviewed in detail. The application of artificial neural network to the simulation and prediction of HDS for various diesel oils is presented. New reactor systems for HDS, such as concurrent countercurrent trickle-bed reactor, two-phase reactor, catalytic distillation HDS reactor and so on, are summarized. Further researches and challenges for the kinetics of HDS and reactors are proposed.
关 键 词:加氢脱硫 二苯并噻吩 4 6-二甲基二苯并噻吩 反应动力学 反应器
分 类 号:TE624.431[石油与天然气工程—油气加工工程]
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