Dynamic behavior of a single screw plasticating extruder part II: Dynamic modeling

Chan, D.; Nelson, Richard W.; Lee, L. James

doi:10.1002/pen.760260207

Cited by 23 publications

(14 citation statements)

References 39 publications

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“…They are also promising tools for product development and quality improvement. Dynamic analysis of extrusion data has been widely reported in the plastics extrusion literature (4,(7)(8)(9) and the same basic principles of polymer extrusion can be applied to food extrusion. However, the nature of the food ingredients, interaction between the food components, and physicochemical changes such as protein denaturation and starch gelatinization make modeling of food extrusion more complex.…”

Section: Introductionmentioning

confidence: 99%

“…For the various fluctuations which occur in an extrusion process, steady state models cannot predict the response of the system and the influence of the fluctuations on the extruder performance. Therefore, a dynamic extrusion model is essential for the design of a control system for extrusion (4). Dynamic empirical models offer an understanding of the extrusion process during the transition periods (period between two separate steady states).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Response of a Twin-screw Lab-size Extruder to Changes in Operating Variables

Akdoğan

Rumsey

1996

LWT - Food Science and Technology

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Response of a Twin-screw Lab-size Extruder to Changes in Operating Variables

Akdoğan

Rumsey

1996

LWT - Food Science and Technology

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“…Costin et al 22 critically reviewed dynamic modeling and control of plasticating extruders, and concluded that the gross dynamic input/output behavior may be generally described by simple first-/second-order models. Chan et al 23 studied the pressure responses to screw speed changes as a first-order function times a lead-lag function to effectively model responses with an initial increase or decrease, followed by an exponential decay to the final steady state value. Equation (5a) represents a firstorder process with a dead time in the time domain, while eq.…”

Section: Table IV Steady State Process Gains For Step-input Variationmentioning

confidence: 99%

Analysis of the dynamic behavior of a starch foam extrusion process

Nabar

Narayan

2006

J of Applied Polymer Sci

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ABSTRACT:The starch foam extrusion process was modeled as a multiple input multiple output (MIMO) process, and the dynamics of the process were studied as a response to step changes in the input variables such as starch feed rate, screw speed, moisture content (MC), and poly(hydroxy aminoether) (PHAE) feed rate. The responses were modeled as first-order responses with a time delay. The linearity of the process was determined over a range around the setpoint, and the parameters defining the first-order system such as gain "K," time constant "," and dead time "t d " were determined in the linear range. The transfer function models can then be used in a predictive computer control system for on-line fine-tuning of the operating conditions. This could ensure a consistently high quality product even when low frequency disturbances are present in the system. It was observed that the time constants and the dead times recorded for both the pressure and torque responses did not exhibit significant variation within each manipulated or input variable tested, indicating a dynamic linearity with respect to each manipulated variable. It was also observed that for the same step-input variations in the manipulated variables, the torque loading on the twin-screw extruder exhibited a faster response (lower dead time), and also reached a steady state sooner (lower time constant). The MC and screw speed seem to be the most destabilizing variables, as they induce rapid responses in the process variables. The MC in the extruder was, hence, determined to be the most influential factor in the stability of the process, followed by screw speed and starch feed rate. PHAE feed rate was the least significant variable. Multiple step-input tests were carried out to determine the validity of the principle of superposition. The validity of the principle of superposition implied the linearity of the process in the domain tested.

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“…Chan and Lee [23], Chan et al [24] and Nelson et al [25] proposed Laplace transfer function models (e.g., a first order, a second order and a lead-lag) to describe dynamic responses of an extrusion process. Laplace transfer function models accounted for the basic type of temperature and pressure responses with fitting of a phenomenological kinetic-elastic model to calculate other required parameters.…”

Section: A Control Schemes Based On Laplace Transform or Time Seriesmentioning

confidence: 99%

Single screw extrusion control: A comprehensive review and directions for improvements

Abeykoon

2016

Control Engineering Practice

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Polymer extrusion is usually a complex process, particularly due to the coupled nature of process parameters, and hence highly prone to fluctuations. Although a number of different approaches have been attempted in research/industry over the last few decades for extrusion control, it is still experiencing some problems in achieving consistent product quality. Presently, most of the polymer processing extruders are equipped with PID controllers mainly for the control of the screw speed and barrel temperatures in their set limits. It seems that only both of these controllers are commonly used as the major aids of process control to achieve the required melt quality. Although, the quality of the melt output (i.e., a thermally homogeneous melt output which is constant in quantity and quality over the time) is the key variable in polymer extrusion, only a few control techniques are available which make control decisions by observing the actual melt flow quality. Therefore, the development of new control strategies which consider the actual melt quality, perhaps incorporating industrially popular nonlinear techniques such as artificial intelligence, should be highly valuable. In this work, a critical evaluation is made on the state-of-the-art of the previous control approaches in polymer extrusion in industry and research while identifying their limitations. Then, some of the possible directions for future research and also to develop an advanced process control strategy are presented by eliminating a few of the existing limitations.

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Dynamic behavior of a single screw plasticating extruder part II: Dynamic modeling

Cited by 23 publications

References 39 publications

Dynamic Response of a Twin-screw Lab-size Extruder to Changes in Operating Variables

Dynamic Response of a Twin-screw Lab-size Extruder to Changes in Operating Variables

Analysis of the dynamic behavior of a starch foam extrusion process

Single screw extrusion control: A comprehensive review and directions for improvements

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