A kinetic modeling framework for the peroxide-initiated radical polymerization of styrene in the presence of rubber particles from recycled tires

Meimaroglou, Dimitrios; Florez, Daniela; Hu, Guohua

doi:10.1016/j.ces.2021.117137

Cited by 9 publications

(11 citation statements)

References 58 publications

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“…This work builds on previously reported results of an ongoing study on the styrene-GTR radical graft polymerization system [27][28][29]. More specifically, a part of this ongoing study has already focused on the modeling of the system via the introduction of a generalized kinetic scheme that has been employed to describe the evolution of the polymerization in the presence of different amounts of GTR and/or benzoyl peroxide (BPO) initiator, under different reaction temperatures [29]. The mathematical model proposed in that work, presented an initial framework for the consideration of the chemical reactions that might occur in the system, as a consequence of the presence of GTR (i.e., as opposed to the presence of pure polybutadiene rubber that is considered in most reported studies [30][31][32][33]) as well as a methodology for the quantification and the gradual "masking" of reaction sites on the surface of GTR.…”

Section: Introductionmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 89%

“…In the present work, a complementary approach is adopted as means to circumvent the manque of knowledge on the exact composition of the system and the associated chemical developments. Accordingly, the present study focuses exclusively on the case where GTR is present in the system, contrary to [29] where both the pure styrene homopolymerization and the GTR-grafting cases were investigated.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, this work presents the implementation of a data-driven modeling approach, on the basis of supervised ML techniques, under varying operating conditions. The developed ML models are implemented as standalone regression models, for the prediction of the monomer conversion developments in an attempt to assess their suitability in modeling this highly complex polymerization system, without using any prior knowledge or mechanistic description of the underlying phenomena, as it is the case in previous works [27][28][29]. As a perspective for future work, the findings of this work will be used in the development of a hybrid modeling approach where both phenomenological and ML models of the system will be combined.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Study on the Styrene–GTR Radical Graft Polymerization: Combination of an Experimental Approach, on Different Scales, with Machine Learning Modeling

et al. 2023

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The study of the styrene–Ground Tire Rubber (GTR) graft radical polymerization is particularly challenging due to the complexity of the underlying kinetic mechanisms and nature of GTR. In this work, an experimental study on two scales (∼10 mL and ∼100 mL) and a machine learning (ML) modeling approach are combined to establish a quantitative relationship between operating conditions and styrene conversion. The two-scale experimental approach enables to verify the impact of upscaling on thermal and mixing effects that are particularly important in this heterogeneous system, as also evidenced in previous works. The adopted experimental setups are designed in view of multiple data production, while paying specific attention in data reliability by eliminating the uncertainty related to sampling for analyses. At the same time, all the potential sources of uncertainty, such as the mass loss along the different steps of the process and the precision of the experimental equipment, are also carefully identified and monitored. The experimental results on both scales validate previously observed effects of GTR, benzoyl peroxide initiator and temperature on styrene conversion but, at the same time, reveal the need of an efficient design of the experimental procedure in terms of mixing and of monitoring uncertainties. Subsequently, the most reliable experimental data (i.e., 69 data from the 10 mL system) are used for the screening of a series of diverse supervised-learning regression ML models and the optimization of the hyperparameters of the best-performing ones. These are gradient boosting, multilayer perceptrons and random forest with, respectively, a test R2 of 0.91 ± 0.04, 0.90 ± 0.04 and 0.89 ± 0.05. Finally, the effect of additional parameters, such as the scaling method, the number of folds and the random partitioning of data in the train/test splits, as well as the integration of the experimental uncertainties in the learning procedure, are exploited as means to improve the performance of the developed models.

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Section: Introductionmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Study on the Styrene–GTR Radical Graft Polymerization: Combination of an Experimental Approach, on Different Scales, with Machine Learning Modeling

et al. 2023

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“…3,4 The reutilization of waste tires by means of energy and material recovery seems to be particularly important. 5 The potential of ground tire rubber (GTR) from waste tire has been explored in different fields for its excellent elasticity and aging resistance, 6 such as blending with raw rubber, 7 plastics [8][9][10][11] asphalt, 12,13 and cement, 14,15 among which the addition of GTR into raw rubber is most studied due to their similar structure. However, the covalently crosslinked polymer networks of rubber do not favor interfacial interactions between GTR and rubber matrix, leading to the generation of stress concentration points in composites and decrease their mechanical properties, which limits the application of GTR in rubber products.…”

Section: Introductionmentioning

confidence: 99%

Reinforcing and plasticizing effects of reclaimed rubber on the vulcanization and properties of natural rubber

Ren

Song

Yang³

et al. 2022

J of Applied Polymer Sci

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The production of high-added value reclaimed rubber (RR) is of great significance for the sustainability of rubber industries. To green recycle waste rubbers and broaden the application of RR, a RR material with potential reinforcing and plasticizing effects on nature rubber (NR) composites are prepared by a thermo-oxidative reclamation process. The reclamation degree of RR is controlled by adjusting the content of soybean oil. The plasticizing effect is demonstrated by the decrease of torque during vulcanization and the enhancement of elongation at break of NR/RR composites with the increasing reclamation of RR. The tensile strength of NR/RR composites is enhanced by adding the RR, and the existence of RR also improves the thermal stability and rheological properties of NR/RR composites. The formation of new bound rubber in NR/RR compound indicates the reinforcing ability of RR. In addition, the average particle size of RR reaches nano-scale according to scanning electron microscope photographs and its dispersion in NR/RR compounds is improved with the increasing reclamation degree of RR as shown in Payne effect. This work demonstrates the reinforcing and plasticizing ability of the RR, which is beneficial to improving the added value of reclaimed and broadening its application.

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A two‐phase method of moments model for high‐impact polystyrene phase inversion conversion and molecular properties

Zhu,

Jin,

et al. 2023

AIChE Journal

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A challenge for high‐impact polystyrene (HIPS) production and design is the more accurate and low computational cost prediction of the phase separation and phase inversion conversion, the first conversion determining the onset of the two‐phase system and the second conversion defining the pathway toward a complex, for example, salami‐like morphology. In this work, a two‐phase deterministic method of moments (MoM) model running in several minutes is therefore developed in the intermediate styrene conversion range (up to around 30%), considering for simplicity only diffusional limitations on termination on an average basis. It is showcased that the phase separation conversion can be taken as 2 m%, and the phase inversion conversion should be calculated by algebraic means. Interestingly, the latter conversion varies with the initial reaction conditions either on a time basis (e.g., variation of initial radical concentration) or both a time and conversion basis (e.g., relative contribution of rubber and St partitioning coefficient). A comparison with the commonly used pseudo‐homogeneous MoM model reveals that by accounting for mass transfer in a more representative two‐phase model, the amount of monomer consumed is slightly reduced, the grafting efficiency decreases with increasing conversion instead of increasing, and the styrene composition in the graft copolymer decreases. The current work additionally puts forward two‐phase MoM data to facilitate future benchmarking with other (stochastic) modeling approaches and enables kinetic insights into heterogeneous grafting polymerization, further promoting the production of multiphase industrial polymer products.

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A kinetic modeling framework for the peroxide-initiated radical polymerization of styrene in the presence of rubber particles from recycled tires

Cited by 9 publications

References 58 publications

A Comprehensive Study on the Styrene–GTR Radical Graft Polymerization: Combination of an Experimental Approach, on Different Scales, with Machine Learning Modeling

A Comprehensive Study on the Styrene–GTR Radical Graft Polymerization: Combination of an Experimental Approach, on Different Scales, with Machine Learning Modeling

Reinforcing and plasticizing effects of reclaimed rubber on the vulcanization and properties of natural rubber

A two‐phase method of moments model for high‐impact polystyrene phase inversion conversion and molecular properties

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