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首页> 外文期刊>Journal of Applied Polymer Science >Olefin polymerization using supported metallocene catalysts: Process representation scheme and mathematical model
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Olefin polymerization using supported metallocene catalysts: Process representation scheme and mathematical model

机译:使用负载型茂金属催化剂的烯烃聚合:过程表示方案和数学模型

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摘要

A mathematical model, including the main morphological features of the polymerization process, is developed to study olefin polymerization with supported metallocene catalysts. Because the relatively large amount of methyl alumoxane (MAO) usually needed as a cocatalyst represents a disadvantage, the model introduces a scheme that simulates the results of the efforts being made in a supported catalyst to reduce MAO requirements to commercially acceptable levels. Critical fragmentation steps in the initial support-catalyst particles that render all active sites effectively available to the monomer are specifically considered, on the basis of the support morphological characteristics. With the available reaction data, fragmentation representation alternatives are discussed and a scheme proposed. Then, a mathematical model is developed based on the above representation scheme, to calculate monomer-concentration, temperature, and macroparticle-size evolutions. The main features of the scheme are displayed and discussed. Both for laboratory and high-productivity conditions, the model is used to predict changes in macro- and microparticle size, porosity, and concentration distribution. Predictions are employed to evaluate the impact of the initial support microparticle arrangement and fragmentation processes on the overall catalyst performance. Polymer yield, concentration profiles, and temperature transients predicted by the model are presented, showing the model application after verifying its accordance with the available experimental data. (C) 2001 John Wiley & Sons, Inc. [References: 29]
机译:建立了包括聚合过程主要形态特征在内的数学模型,以研究负载金属茂催化剂的烯烃聚合反应。由于通常需要用作助催化剂的甲基铝氧烷(MAO)相对较大,因此存在缺点,因此该模型引入了一种方案,该方案模拟了在负载型催化剂中将MAO需求降低至商业可接受水平的努力结果。基于载体的形态特征,特别考虑了使所有活性位点有效地可供单体使用的初始载体-催化剂颗粒中的关键裂解步骤。利用可获得的反应数据,讨论了片段表示的替代方案并提出了方案。然后,基于上述表示方案开发数学模型,以计算单体浓度,温度和大颗粒尺寸演变。显示并讨论了该方案的主要特征。无论是在实验室条件下还是在高生产率条件下,该模型都可用于预测宏观和微观尺寸,孔隙率和浓度分布的变化。使用预测来评估初始载体微粒排列和破碎过程对整体催化剂性能的影响。给出了模型预测的聚合物收率,浓度曲线和温度瞬变,显示了模型在根据可用实验数据进行验证后的应用。 (C)2001 John Wiley&Sons,Inc. [参考:29]

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