A Methodology for Estimating Kinetic Parameters and Reactivity Ratios of Multi‐site Type Catalysts Using Polymerization, Fractionation, and Spectroscopic Techniques

Touloupidis, Vasileios; Albrecht, A. C.; Soares, João B. P.

doi:10.1002/mren.201700056

Cited by 31 publications

(3 citation statements)

References 31 publications

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“…The MWD of the polymer generated by a single-site catalyst conforms to the Schulz–Flory distribution. , Several methodologies for the kinetic parameter estimation and deconvolution have been proposed. Touloupidis introduced an effective methodology capable of estimating all pertinent parameters and reverse engineer experimental results. Feng , carried out parameter estimation centered on a non-standard ethylene/a-olefin gas phase copolymerization reaction mechanism, utilizing MWD and short chain branch distribution (SCBD) as objective functions.…”

Section: Introductionmentioning

confidence: 99%

Kinetic Parameter Estimation of the Polyethylene Process by Bayesian Optimization

Fan,

Zhang,

Hong

et al. 2024

Ind. Eng. Chem. Res.

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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 highthroughput 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.

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Section: Introductionmentioning

confidence: 99%

Kinetic Parameter Estimation of the Polyethylene Process by Bayesian Optimization

Fan,

Zhang,

Hong

et al. 2024

Ind. Eng. Chem. Res.

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“…The first step to solve this problem is to generate more experimental data. For example, Touloupidis et al 14 presented a parameter estimation methodology based on the combination of polymerization, fractionation, and spectroscopic techniques. This method relies on sufficient experimental data obtained in a lab-scale reactor.…”

Section: Introductionmentioning

confidence: 99%

Kinetic Parameter Estimation for Linear Low-Density Polyethylene Gas-Phase Process from Molecular Weight Distribution and Short-Chain Branching Distribution Measurements

Feng

Yang

Wang

et al. 2023

Ind. Eng. Chem. Res.

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Kinetic parameter estimation for a complex copolymerization process has always been a challenge in the modeling procedure. This study aims at the kinetic parameter estimation for a linear low-density polyethylene (LLDPE) gas-phase process from molecular weight distribution (MWD) and short-chain branching distribution (SCBD) measurements. First, experimental MWD and SCBD are simultaneously deconvoluted to obtain intermediate model parameters as output variables. Then, appropriate nominal values of the kinetic parameters are provided by solving an optimization problem. This procedure plays a significant role in narrowing down the range of the nominal values. The determined output variables and nominal parameter values are used to form a sensitivity matrix for parameter estimability analysis. After that, a new parameter ranking strategy is proposed using hierarchical clustering. Based on the determined nominal values, the ranking results obtained using the proposed strategy in a robustness test are more robust than those obtained under random nominal values. Lastly, the hierarchical clustering is combined with Wu's mean squared error-based method to determine an estimable parameter subset, during which the selected kinetic parameters are estimated by matching the intermediate model parameters. The gas-phase copolymerization process model based on the estimated parameter values is further validated with different MWD and SCBD measurements. Model predictions show good agreement with experimental data.

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“…Deconvolution techniques have been used to identify the minimum number of site types on multisite catalysts, as well as their microstructures . Methods that rely on the deconvolution of MWD measured with gel permeation chromatography (GPC), or on the deconvolution of CCD measured with temperature rising elusion fractionation (TREF), crystallization analysis fractionation (CRYSTAF), or crystallization elution fractionation (CEF) have been widely applied to ethylene/1‐olefin copolymers. However, it has been reported that the independent deconvolution of MWD or CCD of the same polymer may lead to conflicting results because some of the different site types may produce chains with similar molecular weight averages but different chemical composition averages, or vice versa …”

Section: Introductionmentioning

confidence: 99%

Simultaneous Deconvolution of the Bivariate Molecular Weight and Chemical Composition Distribution of Ethylene/1‐Hexene Copolymers

Hornchaiya

Anantawaraskul

Soares

et al. 2019

Macro Chemistry & Physics

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The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/macp.201800522. Bivariate Molecular WeightsThe bivariate molecular weight and chemical composition distribution (MWD×CCD) of ethylene/1-hexene copolymers can be measured using TREF×GPC cross fractionation characterization (CFC). In this work, the experimental MWD×CCD of ethylene/1-hexene copolymers made with a Ziegler-Natta catalyst under different polymerization conditions are measured by CFC and deconvoluted to identify the minimum number of site types present in the catalyst. Blends of ethylene/1-hexene copolymers produced with a metallocene catalyst with known MWD×CCD are used to validate the proposed technique. This is a powerful methodology to better understand the nature of active sites on multisite catalysts, and can be beneficial for the development of copolymers with precisely controlled microstructures.

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A Methodology for Estimating Kinetic Parameters and Reactivity Ratios of Multi‐site Type Catalysts Using Polymerization, Fractionation, and Spectroscopic Techniques

Cited by 31 publications

References 31 publications

Kinetic Parameter Estimation of the Polyethylene Process by Bayesian Optimization

Kinetic Parameter Estimation of the Polyethylene Process by Bayesian Optimization

Kinetic Parameter Estimation for Linear Low-Density Polyethylene Gas-Phase Process from Molecular Weight Distribution and Short-Chain Branching Distribution Measurements

Simultaneous Deconvolution of the Bivariate Molecular Weight and Chemical Composition Distribution of Ethylene/1‐Hexene Copolymers

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