Modeling of propylene polymerization with long chain branching

Mogilicharla, Anitha; Mitra, Kishalay; Majumdar, Saptarshi

doi:10.1016/j.cej.2014.02.052

Cited by 13 publications

(5 citation statements)

References 23 publications

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“…Many previous investigations have tried to understand how LCBs are formed in ethylene or propylene homopolymerization . Nevertheless, only few publications dealt with polymerization kinetic studies that included LCB generation in copolymerization of olefins and dienes, and even fewer proposed mathematical models to describe the polymerization kinetics and microstructure of olefin‐diene branched copolymers …”

Section: Introductionmentioning

confidence: 99%

“…The authors claimed that the model fit the experimental data adequately, but their model was complex, with four dimensions (chain length, LCBs, unsaturated double bonds, and active center placed at a specified carbon on the polymer chain), and several kinetic reaction steps. Mogilicharla et al proposed a model to describe propylene polymerization with LCB formation using a twin catalyst system. Their model predicted the experimental evolution of molecular weights and grafting density.…”

Section: Introductionmentioning

confidence: 99%

“…[18] Many previous investigations have tried to understand how LCBs are formed in ethylene or propylene homopolymerization. [7,14,[27][28][29][30][31][32][33][34][35][36] Nevertheless, only few publications dealt with polymerization kinetic studies that included LCB generation in copolymerization of olefins and dienes, [19,21,32,37] and even fewer proposed mathematical models to describe the polymerization kinetics and microstructure of olefin-diene branched copolymers. [10] Kokko et al [37] reported that the copolymerization of ethylene and 1,7-octadiene with metallocene catalysts had higher selectivity for branch formation and could make LCBs.…”

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confidence: 99%

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Copolymerization of Ethylene with 1,9‐Decadiene: Part I – Prediction of Average Molecular Weights and Long‐Chain Branching Frequencies

Brandão

Alberton

Pinto

et al. 2016

Macro Theory & Simulations

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Long chain branches improve the viscoelastic properties of polyolefins and make them easier to process. The frequency of long chain branching in polyethylenes made with coordination catalysts can be substantially increased by copolymerizing ethylene with small amounts of nonconjugated dienes using an adequate catalyst. In this work, the kinetics of copolymerization of ethylene and 1,9‐decadiene is investigated using a constrained geometry catalyst in a solution polymerization semi‐batch reactor, and a novel mathematical model is proposed to describe the resulting molecular weight distributions. A hybrid approach, combining particle swarm optimization, parameter identifiability procedures, and the Gauss–Newton method is applied to estimate the model parameters. The proposed mechanism includes macromonomer reincorporation through pendant double bonds that result from diene incorporation. The predicted long chain branching frequencies are validated by Monte Carlo simulation. Experimental average molecular weights and ethylene feed flow rates are successfully predicted by both methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Copolymerization of Ethylene with 1,9‐Decadiene: Part I – Prediction of Average Molecular Weights and Long‐Chain Branching Frequencies

Brandão

Alberton

Pinto

et al. 2016

Macro Theory & Simulations

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“…The reaction pathways that lead to the formation of LCBs in ethylene/α-olefins/diene copolymers are complex. Various experimental investigations have studied their polymerization kinetics [6][7][8], while others have focused on the development of mechanistic models to explain their microstructures and to predict properties of interest [9][10][11][12]. [13] proposed a mechanistic model for the semi-batch copolymerization of ethylene and 1,9-decadiene with a metallocene catalyst, which was validated using experimental measurements including the ethylene flow rate (F M ), the number-average molecular weight (M n ), and the weight-average molecular weight (M w ).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Estimation of Significant Kinetic Parameters Applied to the Synthesis of Polyolefins

et al. 2019

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A data-driven strategy for the online estimation of important kinetic parameters was assessed for the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst at different diene concentrations and reaction temperatures. An initial global sensitivity analysis selected the significant kinetic parameters of the system. The retrospective cost model refinement (RCMR) algorithm was adapted and implemented to estimate the significant kinetic parameters of the model in real time. After verifying stability and robustness, experimental data validated the algorithm performance. Results demonstrate the estimated kinetic parameters converge close to theoretical values without requiring prior knowledge of the polymerization model and the original kinetic values.

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“…MI is usually evaluated off‐line with an analytical procedure in the laboratory, which is not only costly but also time‐consuming (2–4 h) , thus leading to off‐specification products and profit losses. Unfortunately, an online analyzer constructed with the mechanism of polymerization is often greatly challenged by the engineering activity and the relatively high complexity of the kinetic behavior and operation of the polymer plants. Therefore, the development of MI online statistical estimation model, which directly uses available process variables at the beginning of the production cycle to predict MI of the final product, is more practical and convenient.…”

Section: Introductionmentioning

confidence: 99%

A real‐time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction

Zhang

Liu

2016

Journal of Chemometrics

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The accurate and reliable real‐time prediction of melt index (MI) is indispensable in quality control of the industrial propylene polymerization (PP) processes. This paper presents a real‐time soft sensor based on optimized least squares support vector machine (LSSVM) for MI prediction. First, the hybrid continuous ant colony differential evolution algorithm (HACDE) is proposed to optimize the parameters of LSSVM. Then, considering the complexity and nondeterminacy of PP plant, an online correcting strategy (OCS) is adopted to update the modeling data and to revise the model's parameters adaptively. Thus, the real‐time prediction model, HACDE‐OCS‐LSSVM, is obtained. Based on the data from a real PP plant, the models of HACDE‐LSSVM, DE‐LSSVM and LSSVM are also developed for comparison. The research results show that the proposed real‐time model achieves a good performance in the practical industrial MI prediction process. Copyright © 2016 John Wiley & Sons, Ltd.

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Modeling of propylene polymerization with long chain branching

Cited by 13 publications

References 23 publications

Copolymerization of Ethylene with 1,9‐Decadiene: Part I – Prediction of Average Molecular Weights and Long‐Chain Branching Frequencies

Copolymerization of Ethylene with 1,9‐Decadiene: Part I – Prediction of Average Molecular Weights and Long‐Chain Branching Frequencies

Data-Driven Estimation of Significant Kinetic Parameters Applied to the Synthesis of Polyolefins

A real‐time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction

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