将 13C-NMR 三元组序列数据与联合分子量和组成数据相结合以估计气相聚乙烯反应器模型中的参数,Macromolecular Theory and Simulations

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将 13C-NMR 三元组序列数据与联合分子量和组成数据相结合以估计气相聚乙烯反应器模型中的参数
Macromolecular Theory and Simulations ( IF 1.8 ) Pub Date : 2023-01-12 , DOI: 10.1002/mats.202200073
Jakob I. Straznicky ₁ , Jennifer P. Aiello ₁ , Lauren A. Gibson ₁ , Yan Jiang ₂ , Timothy Boller ₂ , Hsu Chiang ₂ , Kimberley B. McAuley ₁

Affiliation

三位点茂金属催化剂用于气相半间歇式反应器以生产乙烯/己烯共聚物。在每批结束时，收集并分析聚乙烯 (PE) 以确定碳 13 核磁共振 ( ¹³C-NMR) 三联体序列分布。使用带红外检测器的凝胶渗透色谱法 (GPC-IR) 获得联合分子量 (MW) 和组成分布数据。来自十次实验运行的数据用于动力学参数估计。使用均方误差 (MSE) 选择方法，选择 36 个模型参数中的 23 个，使用可用的聚合速率和 PE 表征数据进行估计。其余参数保持初始猜测以避免过度拟合。将三元组数据添加到参数估计问题允许估计一个额外的参数并导致改进的参数估计。由于包含三元组数据，除其中一个估计参数外，所有参数的标准差均有所下降。更新后的参数估计结果非常适合三联数据以及联合 MW 和成分数据。该模型准确地预测了四个未用于参数估计的验证数据集。新模型及其更新的参数估计对于将新聚合物等级从实验室规模扩大到商业规模非常有价值。

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Combining 13C-NMR Triad Sequence Data with Joint Molecular Weight and Composition Data to Estimate Parameters in a Gas-Phase Polyethylene Reactor Model

A three-site metallocene catalyst is used in a gas-phase semi-batch reactor to produce ethylene/hexene copolymers. At the end of each batch, polyethylene (PE) is collected and analyzed to determine the carbon-13 nuclear magnetic resonance (¹³C-NMR) triad sequence distribution. Joint molecular weight (MW) and composition distribution data are obtained using gel permeation chromatography with an infrared detector (GPC-IR). Data from ten experimental runs are used for kinetic parameter estimation. Using a mean-squared error (MSE) selection methodology, 23 of the 36 model parameters are selected for estimation using the available polymerization rate and PE characterization data. The remaining parameters are held at initial guesses to avoid overfitting. Addition of the triad data to the parameter estimation problem allows for one additional parameter to be estimated and results in improved parameter estimates. Standard deviations of all but one of the estimated parameters decreased due to inclusion of triad data. The updated parameter estimates result in good fits for the triad data and for joint MW and composition data. The model accurately predicts four validation data sets not used for parameter estimation. The new model and its updated parameter estimates will be valuable for scaling up new polymer grades from laboratory-scale to commercial-scale.

更新日期：2023-01-12

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