首页> 外文会议>European symposium on computer aided process engineering;ESCAPE 21 >Modeling of a batch emulsion copolymerization reactor in the presence of a chain transfer agent: estimability analysis, parameters identification and experimental validation.
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Modeling of a batch emulsion copolymerization reactor in the presence of a chain transfer agent: estimability analysis, parameters identification and experimental validation.

机译:在链转移剂存在下的间歇乳液共聚反应器建模:可估计性分析,参数识别和实验验证。

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This paper deals with estimability analysis, parameter identification and experimental validation of a model developed for a batch reactor where the emulsion copolymerization of styrene and butyl acrylate in the presence of n-dodecyl mercaptan as a chain transfer agent takes place. Accurate estimation of the model parameters is required to obtain reliable predictions of the products end-use properties. However, due to the mathematical model structure and to possible lack of measurements, the estimation of some parameters may be impossible. The main limitations to the parameters estimability are their weak effect on the measured outputs and the correlation between these effects. The objective of the method developed in this paper is to determine the subset of the most influencing parameters that can be estimated from the available experimental data when the complete set of model parameters cannot be estimated. In the case study, it was shown that only 21 parameters out of the 49 involved in the model were estimable. The values of the remaining 28 non estimable parameters weie taken either from previous studies or from literature. Moreover a new method has been developed to determine the confidence domain for the set of estimable and identified parameters.
机译:本文涉及为间歇式反应器开发的模型的可估计性分析,参数识别和实验验证,其中在正十二烷基硫醇作为链转移剂的情况下发生苯乙烯和丙烯酸丁酯的乳液共聚。需要对模型参数进行准确的估算,以获得对产品最终用途特性的可靠预测。但是,由于数学模型的结构以及可能缺少测量值,可能无法估计某些参数。参数可估计性的主要限制是它们对测量输出的弱影响以及这些影响之间的相关性。本文开发的方法的目的是确定当无法估计完整的模型参数集时,可以从可用的实验数据中估计出最具影响力的参数的子集。在案例研究中,表明该模型涉及的49个参数中只有21个参数是可估计的。其余28个不可估计参数的值可从以前的研究或文献中获得。此外,已经开发了一种新方法来确定可估计和已识别参数集的置信域。

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