Study on Hydrogen Sensitivity of Ziegler–Natta Catalysts with Novel Cycloalkoxy Silane Compounds as External Electron Donor

Li, Hongming; Wang, Jing; He, Lei; Feng, Nan; Wang, Fan; Yang, Wantai; Zhang, Mingge; Sun, Tianxu; Huang, Qigu; Yi, Jianjun

doi:10.3390/polym8120433

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…Generally, polymers with high molecular weight have stronger intermolecular interaction, which causes low MFR and higher viscosity [34]. Whereas, polymers with high MFRs often feature low molecular weight or broad molecular weight distribution [8]. Changes in MFR values of polymers influence the properties of fibres, such as elongation and bond strength.…”

Section: Resultsmentioning

confidence: 99%

“…Consequently, compatibilization agents are essential for their production. Melt flow rate (MFR), which is the processability of a polymer, is considered important in PP manufacturing; whereas, PP products with high MFR are easy to process and also boost production speed [8]. Li et al also reported that highly desirable iPP with high MFR is schematised for production by 2025 in China.…”

Section: Introductionmentioning

confidence: 99%

“…The current state of the art methods of producing PP products with good flowability is direct polymerization and degradation by peroxide. However, these two methods are hurdled by limitations, for instance, chemical agents used in the degradation process affects the quality of the final PP products and also peroxide compounds have an unbearable smell [8]. To cope with such issues, it is important to apply modern technologies and find alternate procedures for desired polymers with higher MFRs.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Mechanical and Thermal Properties of iPP and LMPP Blend Fibres by Surface Response Methodology

et al. 2018

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Optimization of the mechanical and thermal properties of isotactic polypropylene (iPP) homopolymer blended with relatively new low molecular low modulus polypropylene (LMPP) at different blend ratios was carried out via surface response methodology (RSM). Regression equations for the prediction of optimal conditions were achieved considering eight individual parameters: naming, elongation at break, tensile strength and elastic modulus, crystallization temperature (TC), first melting temperatures (TM1), heat fusion (Hf), crystallinity, and melt flow rate (MFR), which were measured as responses for the design of experiment (DOE). The adjusted and predicted correlation coefficient (R2) shows good agreement between the actual and the predicted values. To confirm the optimal values from the response model, supplementary experiments as a performance evaluation were conducted, posing better operational conditions. It has been confirmed that the RSM model was adequate to reflect the predicted optimization. The results suggest that the addition of LMPP into iPP could effectively enhance the functionality and processability of blend fibres if correctly proportioned.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of Mechanical and Thermal Properties of iPP and LMPP Blend Fibres by Surface Response Methodology

et al. 2018

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“…Highly hydrogen-sensitive Ziegler–Natta catalysts have become among the most important aspects of polyolefin catalyst research. The higher the hydrogen sensitivity of the catalyst, the better the control of molecular weight and its distribution in a larger range . In order to evaluate the effect of novel internal electron donors on the hydrogen regulation sensitivity of the catalyst, propylene polymerization at three different concentrations of H 2 was performed.…”

Section: Resultsmentioning

confidence: 99%

“…The higher the hydrogen sensitivity of the catalyst, the better the control of molecular weight and its distribution in a larger range. 31 In order to evaluate the effect of novel internal electron donors on the hydrogen regulation sensitivity of the catalyst, propylene polymerization at three different concentrations of H 2 was performed. Cat.5 was selected to explore its hydrogen sensitivity under different hydrogen conditions, and the results of corresponding polymerization are summarized in Table 5.…”

Section: The Composition Analysis Of Catalystsmentioning

confidence: 99%

Direct Preparation of Transparent Isotactic Polypropylene with Supported Ziegler–Natta Catalysts Containing Novel Eco-friendly Internal Electron Donors

Zhang

Feng

et al. 2020

Ind. Eng. Chem. Res.

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This study reported novel eco-friendly Ziegler− Natta catalysts containing bioderived maleic rosinate tri-n-butyl and maleic rosinate tri-n-octyl as internal electron donors for propylene polymerization to obtain transparent isotactic polypropylene. Surprisingly, on the one hand, rosin acid esters can function as internal electron donors to improve the stereoselectivity of the catalyst where the isotacticity of polypropylene obtained reaches 97.85%. On the other hand, they act as nucleating agents to greatly improve the transparency of polypropylene, with a light transmittance of 93.8% and haze of 4.4%, due to internal electron donors evenly dispersed on the surface of the catalyst particles. Furthermore, the catalysts were used to carry out propylene polymerization and showed the highest catalytic activity value of 31.9 kgPP•gCat −1 •h −1 .

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Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

Singh

Majumder

Bera

2022

Polymer Engineering & Sci

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The quality of polypropylene is one of the major components in the plastic industry. The quality of the polypropylene depends upon the melt flow index and the xylene solubility under the given condition such as hydrogen flow, donor flow, pressure, temperature, etc. This study investigates the use of artificial neural network (ANN) modeling for the prediction of the quality of polypropylene for petrochemical plants. This study proposes an ANN model to predict the “melt flow index (MFI) and xylene solubility” as the quality of polypropylene depends upon these two factors. Hydrogen (H2) flow, pressure, temperature, and donor flow are the controlling parameters for the MFI and xylene solubility. The study proposes a new approach for the selection of the best topology using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) multicriterion decision‐making (MCDM) process for the ANN technique. Experimental data are trained with three training algorithms each with four combinations of training functions and each with three combinations of numbers of neurons. In this study, the best model for ANN is found by the Levenberg–Marquardt backpropagation training algorithm with logarithmic sigmoid and hyperbolic tangent sigmoid transfer functions. The best topology for the ANN model for prediction is selected by using the TOPSIS method. Some sensitivity analyses are provided graphically to show the physical nature of the problem.

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Study on Hydrogen Sensitivity of Ziegler–Natta Catalysts with Novel Cycloalkoxy Silane Compounds as External Electron Donor

Cited by 8 publications

References 33 publications

Optimization of Mechanical and Thermal Properties of iPP and LMPP Blend Fibres by Surface Response Methodology

Optimization of Mechanical and Thermal Properties of iPP and LMPP Blend Fibres by Surface Response Methodology

Direct Preparation of Transparent Isotactic Polypropylene with Supported Ziegler–Natta Catalysts Containing Novel Eco-friendly Internal Electron Donors

Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

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