Using Symbolic Regression Models to Predict the Pressure Loss of Non-Newtonian Polymer-Melt Flows through Melt-Filtration Systems with Woven Screens
Sprache des Titels:
Englisch
Original Kurzfassung:
When selecting a melt-filtration system, the initial pressure
drop is a critical parameter. We used heuristic optimization
algorithms to develop general analytical equations for estimating
the dimensionless pressure loss of square and Dutch
woven screens in polymer processing and recycling. We present
a mathematical description ? without the need for further
numerical methods ? of the dimensionless pressure loss of
non-Newtonian polymer melt-flows through woven screens.
Applying the theory of similarity, we first simplified, and then
transformed into dimensionless form, the governing equations.
By varying the characteristic independent dimensionless
influencing parameters, we created a comprehensive parameter
set. For each design point, the nonlinear governing
equations were solved numerically. We subsequently applied
symbolic regression based on genetic programming to develop
models for the dimensionless pressure drop. Finally, we
validated our models against experiments using both virgin
and slightly contaminated in-house and post-industrial recycling
materials. Our regression models predict the experimental
data accurately, yielding a mean relative error of
MRE = 13.7%. Our modeling approach, the accuracy of
which we have proven, allows fast and stable prediction of
the initial pressure drop of polymer-melt flows through square
woven and Dutch weave screens, rendering further numerical
simulations unnecessary.
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