Diagnosing Parameter Estimability Problems in Polymerization Models,Macromolecular Theory and Simulations

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Diagnosing Parameter Estimability Problems in Polymerization Models
Macromolecular Theory and Simulations ( IF 1.8 ) Pub Date : 2021-08-18 , DOI: 10.1002/mats.202100045
Fei F. Liu ₁ , Anh‐Duong D. Vo ₁ , Kimberley B. McAuley ₁

Affiliation

To understand why some parameters are difficult to estimate in polymerization models, a new diagnostic methodology is proposed. This method is then used to investigate whether parameter estimability difficulties arise from the small influence of certain parameters on model predictions or from correlated effects with other parameters. The proposed method builds on a popular orthogonalization-based parameter-ranking algorithm that ranks parameters from the most estimable to the least estimable. A nylon 6/6,6 copolymerization model and a bio-based polyether (PO3G) model are used to illustrate the effectiveness of the proposed methodology. Diagnosis of the nylon 6/6,6 model reveals that correlated behavior among six parameters related to nylon 6 cyclic dimer formation leads to parameter-estimation difficulties. Diagnosis of the PO3G model reveals that difficulties in estimating low-ranked parameters are mainly due to low-sensitivities of some parameters and to lack of information about these parameters in the data. The proposed methodology will help future modelers make decisions about simplification of their model equations and about possible future experiments aimed at obtaining reliable parameter estimates and model predictions.

中文翻译：

诊断聚合模型中的参数估计问题

为了理解聚合模型中某些参数难以估计的原因，提出了一种新的诊断方法。然后使用该方法来研究参数可估计性的困难是由于某些参数对模型预测的影响很小，还是来自与其他参数的相关效应。所提出的方法建立在流行的基于正交化的参数排序算法之上，该算法将参数从最可估计到最不可估计。使用尼龙 6/6,6 共聚模型和生物基聚醚 (PO3G) 模型来说明所提出方法的有效性。尼龙 6/6,6 模型的诊断表明，与尼龙 6 环状二聚体形成相关的六个参数之间的相关行为导致参数估计困难。PO3G 模型的诊断表明，估计低排序参数的困难主要是由于某些参数的敏感性低以及数据中缺乏有关这些参数的信息。所提出的方法将帮助未来的建模者做出关于简化模型方程的决定，以及关于旨在获得可靠的参数估计和模型预测的未来可能的实验。

更新日期：2021-08-18

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