the extrusion process is well suited for the processing of highly viscous materials, the high shear can still cause self-heating of the material and possible side reactions. In addition, the short residence time limits the number of possible reactions, and finally, the scale up to industrial pilot and plants comes with many difficulties. The nature of the reactive extrusion process involves a complex flow and a large number of parameters, dependent variables and phenomena that make it difficult to understand and therefore to control and to optimize. In response to this complexity, various strategies have been adopted for the modeling and simulation of reactive extrusion. [5] Two main methodologies can be identified. The first strategy is based on a chemical engineering approach. This method consists in considering the extruder as a succession of ideal chemical reactors, which number and nature depend on the screw profile geometry. A global balance can then lead to an approximation of flow conditions. This method has been used in several studies, and among them, Choulak et al. used it to develop a dynamic 1D model for automatic control of reactive extrusion. [7] This approach is especially well adapted for automatic control because of its fast execution due to the simplifications and its ability to perform in transient regime. However, it requires to adjust parameters to each situation so it cannot be easily used as a predictive tool or to solve scale up issues. The second strategy is a local description of the flow field based on continuum mechanics. It thus reproduces real conditions without ideal representation. The flow is indeed simulated by resolving classical mechanics equations relatively to local geometry, kinematics, and boundary conditions. These models can therefore be totally predictive and can be used for process design, but also for its optimization and predictions in case of a change in the process. Whereas it is a more flexible and accurate way to simulate the extrusion process, it requires a lot of time and computing power and is consequently not adapted for automatic process control. As the flow in the extruder is unsteady, not isothermal, and 3D, it is more accurate to use a 3D local simulation. But in some cases it appears that a 1D local description of the flow and temperature field at steady-state can be sufficient for most engineering issues. [8] In addition, it allows creating a software easily usable for process predictions without needing excessive time or computing power. We used for our simulation the LUDOVIC software developed by Vergnes et al. for twinscrew extrusion that uses this 1D local description. [9] Actually, The purpose of this paper is to combine a classical 1D twin-screw extrusion model with machine learning techniques to obtain accurate predictions of a complex system despite few data. Systems involving reactive polyethylene oligomer dispersed in situ in a polypropylene matrix by reactive twin-screw extrusion are studied for this purpose. The twin-screw extrusion simulati...
Two main problems are studied in this article. The first one is the use of the extrusion process for controlled thermo-mechanical degradation of polyethylene for recycling applications. The second is the data-based modelling of such reactive extrusion processes. Polyethylenes (high density polyethylene (HDPE) and ultra-high molecular weight polyethylene (UHMWPE)) were extruded in a corotating twin-screw extruder under high temperatures (350 °C < T < 420 °C) for various process conditions (flow rate and screw rotation speed). These process conditions involved a decrease in the molecular weight due to degradation reactions. A numerical method based on the Carreau-Yasuda model was developed to predict the rheological behaviour (variation of the viscosity versus shear rate) from the in-line measurement of the die pressure. The results were successfully compared to the viscosity measured from offline measurement assuming the Cox‑Merz law. Weight average molecular weights were estimated from the resulting zero-shear rate viscosity. Furthermore, the linear viscoelastic behaviours (Frequency dependence of the complex shear modulus) were also used to predict the molecular weight distributions of final products by an inverse rheological method. Size exclusion chromatography (SEC) was performed on five samples, and the resulting molecular weight distributions were compared to the values obtained with the two aforementioned techniques. The values of weight average molecular weights were similar for the three techniques. The complete molecular weight distributions obtained by inverse rheology were similar to the SEC ones for extruded HDPE samples, but some inaccuracies were observed for extruded UHMWPE samples. The Ludovic® (SC-Consultants, Saint-Etienne, France) corotating twin-screw extrusion simulation software was used as a classical process simulation. However, as the rheo-kinetic laws of this process were unknown, the software could not predict all the flow characteristics successfully. Finally, machine learning techniques, able to operate in the low-data limit, were tested to build predicting models of the process outputs and material characteristics. Support Vector Machine Regression (SVR) and sparsed Proper Generalized Decomposition (sPGD) techniques were chosen to predict the process outputs successfully. These methods were also applied to material characteristics data, and both were found to be effective in predicting molecular weights. More precisely, the sPGD gave better results than the SVR for the zero-shear viscosity prediction. Stochastic methods were also tested on some of the data and showed promising results.
This work focuses on the extrusion foaming under CO2 of commercial TPV and how the process influences the final morphology of the foam. Moreover, numerical modelling of the cell growth of the extrusion foaming is developed. The results show how a precise control on the saturation pressure, die geometry, temperature and nucleation can provide a homogeneous foam having a low density (<500 kg/m3). This work demonstrates that an optimum of CO2 content must be determined to control the coalescence phenomenon that appears for high levels of CO2. This is explained by longer residence times in the die (time of growth under confinement) and an early nucleation (expansion on the die destabilizes the polymer flow). Finally, this work proposes a model to predict the influence of CO2 on the flow (plasticizing effect) and a global model to simulate the extrusion process and foaming inside and outside the die. For well-chosen nucleation parameters, the model predicts the final mean radius of the cell foam as well as final foam density.
This work aims to evaluate the spinnability of recycled poly(ethylene terephthalate) (rPET) modified by chain extenders. Pyromellitic anhydride (PMDA), JONCRYL ADR 4400, 2,2′-bis(2-oxazoline) (BOZ), and 1,3-phenylene-bis-oxazoline (PBO) were used from 0.1 to 1 wt% on transparent postconsumer PET bottle flakes and opaque rPET flakes containing TiO2 particles. The created molecular architectures were characterized in the first section. Second, their spinnability was assessed via a pilot high-speed spinning process. The corresponding shear rheology, intrinsic viscosity, and elongational rheology were reported. It was observed that PMDA and JONCRYL induce a more drastic increase in melt strength and melt elasticity than BOZ and PBO due to chain branching. In a second step, melt-spun filaments were produced with a low content of chain extenders on standard rPET and opaque rPET matrices. Finally, their degree of crystallinity, molecular orientation, and mechanical properties were determined. PMDA and JONCRYL strongly decrease the spinnability and, consequently, the properties of filaments. However, BOZ improves the opaque rPET filaments’ tenacity while decreasing their degree of crystallinity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.