Residence time distribution in a corotating twin-screw extruder

Puaux, Jean Pierre; Bozga, Grigore; Ainser, A.

doi:10.1016/s0009-2509(99)00430-3

Cited by 79 publications

(39 citation statements)

References 18 publications

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“…The reduction of t R is in the range of 50 to 60 % when increasing the throughput from 5 to 15 kg/h. This finding is again consistent with the simulated data obtained by Vergnes et al [30] and experimental data published in [31,32].…”

Section: Influence Of Rotation Speed and Throughput On Residence Timesupporting

confidence: 93%

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Influence of screw configuration, residence time, and specific mechanical energy in twin-screw extrusion of polycaprolactone/multi-walled carbon nanotube composites

Villmow¹,

Kretzschmar²,

Pötschke³

2010

Composites Science and Technology

220

154

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Section: Influence Of Rotation Speed and Throughput On Residence Timesupporting

confidence: 93%

“…Therefore, the plotted lines in Figure 5-A are just a guide for the eyes and are not based on a model assumption. Furthermore, the presented and discussed trends are consistent with experimental data published in [31,32].…”

Section: Influence Of Rotation Speed and Throughput On Residence Timesupporting

confidence: 90%

Influence of screw configuration, residence time, and specific mechanical energy in twin-screw extrusion of polycaprolactone/multi-walled carbon nanotube composites

Villmow¹,

Kretzschmar²,

Pötschke³

2010

Composites Science and Technology

220

154

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show abstract

“…In order to represent the tail in the RTD, three reactors with backward flows are included so as to explain material accumulation at the die (see Figure 6). • The work in [50] uses a combination of a plug flow reactor with a cascade of CSTRs with the same volume and a backward flow Q B (see Figure 7). The plug flow reactor represents the feeding zone and is characterized by a delay t d .…”

Section: The Earlier Models: Static Mapsmentioning

confidence: 99%

A Review of Dynamic Models of Hot-Melt Extrusion

2016

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Hot-melt extrusion is commonly applied for forming products, ranging from metals to plastics, rubber and clay composites. It is also increasingly used for the production of pharmaceuticals, such as granules, pellets and tablets. In this context, mathematical modeling plays an important role to determine the best process operating conditions, but also to possibly develop software sensors or controllers. The early models were essentially black-box and relied on the measurement of the residence time distribution. Current models involve mass, energy and momentum balances and consists of (partial) differential equations. This paper presents a literature review of a range of existing models. A common case study is considered to illustrate the predictive capability of the main candidate models, programmed in a simulation environment (e.g., MATLAB). Finally, a comprehensive distributed parameter model capturing the main phenomena is proposed.

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“…( 4 ) There are several models for fitting the residence density function and the cumulative residence time curve. Among the most common types found in the literature are the axial dispersion model and the back flow cell model [2][3][4][5][6][7]. In our case study, the double Weibull distribution [8] was employed.…”

Section: Introductionmentioning

confidence: 99%