| 人工神经网络在柴油馏分油加氢脱硫中的数学模拟 |
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| 引用本文： | 周轶峰,石玉林,胡志海. 人工神经网络在柴油馏分油加氢脱硫中的数学模拟[J]. 石油学报(石油加工), 2000, 16(1): 81-83 |
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| 作者姓名： | 周轶峰 石玉林 胡志海 |
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| 作者单位： | 石油化工科学研究院,北京,100083 |
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| 摘 要： | 用３层前馈网络根据柴油馏分油性质、工艺条件对产品中硫的质量分数进行了预测，考察了原料油性质和工艺条件对加氢脱硫反应的影响。结果表明，工艺条件对ＨＤＳ反应深度影响的顺序为：反应温度〉空速〉氢对原料油体积比〉氢分压；原料油性质对反应深度的影响顺序为：密度〉５０％馏出点〉氮质量分数〉硫质量分数。
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| 关 键 词： | 柴油 加氢脱硫 神经网络 馏分油 模拟 |
THE MATHEMATIC SIMULATION OF GAS OIL HYDRODESULFURIZATION USING NEURAL NETWORK ALOGRITHM |
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| ZHOU Yi-feng,SHI Yu-lin,HU Zhi-hai. THE MATHEMATIC SIMULATION OF GAS OIL HYDRODESULFURIZATION USING NEURAL NETWORK ALOGRITHM[J]. Acta Petrolei Sinica (Petroleum Processing Section), 2000, 16(1): 81-83 |
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| Authors: | ZHOU Yi-feng SHI Yu-lin HU Zhi-hai |
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| Abstract: | A 3 layer neural network was applied to predict sulfur content of gas oil products in pilot trickle bed reactor Sulfur content is measured as a function of temperature, pressure, hydrogen to oil ratio, space velocity and properties of feed oils The trained network indicated that the sequence of process parameters affecting HDS is: temperature >space velocity > hydrogen to oil ratio > hydrogen pressure;The sequence of feeds properties affecting on HDS is: density > 50%BP > nitrogen content > sulfur content |
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| Keywords: | gas oil hydrodesulfurization neural network |
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