The design and optimization of polymerization processes rely on accurate kinetic parameters of polymerization reactions since they have a significant impact on polymer properties. We develop a kinetic parameter estimation methodology for non-steady-state olefin polymerization processes. A dynamic model for olefin polymerization reaction kinetics based on the method of moments and instantaneous distribution method is established, which considers reaction temperature fluctuations and concentration variation during polymerization. Bayesian optimization (BO) algorithm is applied in the parameter estimation framework, where the kinetic model is treated as a black box function. The effectiveness of the method developed in this article was demonstrated through three cases, and the parameter estimation strategies used in other literature studies were compared. Additionally, we conduct a comparison of the fitting and extrapolation results obtained from diverse algorithms. Notably, BO emerges as a favorable alternative to deterministic algorithms, as it circumvents the challenges inherent in utilizing gradient-based optimization methods for complex polymerization dynamics models and is more suitable for scenarios with fast prediction in high-throughput experiments. Furthermore, using a dynamic kinetic model is crucial for kinetic parameter estimation in non-steady-state olefin polymerization, as it can reflect the dynamic changes with reaction temperature and concentrations.

中文翻译：

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通过贝叶斯优化估计聚乙烯过程的动力学参数


聚合工艺的设计和优化依赖于聚合反应的准确动力学参数，因为它们对聚合物性能有重大影响。我们开发了非稳态烯烃聚合过程的动力学参数估计方法。建立了基于矩量法和瞬时分布法的烯烃聚合反应动力学动态模型，该模型考虑了聚合过程中反应温度波动和浓度变化。参数估计框架中应用了贝叶斯优化（BO）算法，其中动力学模型被视为黑箱函数。通过三个案例证明了本文开发的方法的有效性，并比较了其他文献研究中使用的参数估计策略。此外，我们还对不同算法获得的拟合和外推结果进行了比较。值得注意的是，BO 成为确定性算法的有利替代方案，因为它规避了在复杂聚合动力学模型中使用基于梯度的优化方法所固有的挑战，并且更适合高通量实验中快速预测的场景。此外，使用动态动力学模型对于非稳态烯烃聚合的动力学参数估计至关重要，因为它可以反映反应温度和浓度的动态变化。