Process Window Identification and Physics-Informed Viscosity Modelling for Twin-Screw Extrusion of Graphite Anode Slurries

Continuous twin-screw extrusion of graphite anode slurries operates in a narrow window: too little kneading leaves carbon black agglomerates, and too much fractures the graphite. Existing studies collapse both defects into a single pass/fail criterion, which is ambiguous because the two are distinct phenomena that no single measurand resolves. This study resolves the upstream half of the causal chain, from extrusion parameters to the high-shear slurry viscosity η100; the downstream half, from η100 through electrode tortuosity to fast-charge performance, was established for the same material system and process line in a companion study, so that the two are complementary rather than overlapping. A graphite peak width criterion (wPar,Gr<0.88) combined with grindometer screening reduces 114 trials to 54 in which, within the detection limits of the two methods, no particle-related cause of viscosity variation remains identifiable, and 52 of these with a recorded specific mechanical energy form the modelling dataset. Two logistic regressions show that the two failure modes are predicted from the machine settings with markedly different sharpness: particle damage by kneading concentration and kneading length (AUC = 0.955, 0.914 under cross-validation) and agglomerate removal by backward elements and screw speed (AUC = 0.740), with backward elements entering both models with opposite signs as the central design trade-off. Inside the window the specific mechanical energy is a genuine but coarse descriptor: after compositional normalisation it accounts for 57% of the viscosity variance, yet it places individual operating points to only 9.5% mean relative error, and pairs of runs receiving the same energy differ by up to 49% in viscosity, so it cannot serve as the descriptor of the network state. A greybox model with eight free parameters reproduces the compositionally normalised viscosity to 5.0% mean relative error, gating a kneading term against a low-concentration path through a sigmoid at cK,crit=0.635, and attains a leave-one-out Q2 of 0.796, where a twenty-one-term response surface benchmark fitted by the same protocol collapses out of sample. Its training error is of the order of the pooled replicate scatter of the process itself.

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Journal
Batteries
Published
2026-11-02
DOI
https://doi.org/10.3390/batteries12110449
Primary Topic
Advancements in Battery Materials
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article

Process Window Identification and Physics-Informed Viscosity Modelling for Twin-Screw Extrusion of Graphite Anode Slurries

Andreas Huth, Mareike Partsch, Alexander Michaelis, Paul Guenther
Batteries
Advancements in Battery Materials
article

Process Window Identification and Physics-Informed Viscosity Modelling for Twin-Screw Extrusion of Graphite Anode Slurries

Andreas Huth, Mareike Partsch, Alexander Michaelis, Paul Guenther
article en

Abstract

Continuous twin-screw extrusion of graphite anode slurries operates in a narrow window: too little kneading leaves carbon black agglomerates, and too much fractures the graphite. Existing studies collapse both defects into a single pass/fail criterion, which is ambiguous because the two are distinct phenomena that no single measurand resolves. This study resolves the upstream half of the causal chain, from extrusion parameters to the high-shear slurry viscosity η100; the downstream half, from η100 through electrode tortuosity to fast-charge performance, was established for the same material system and process line in a companion study, so that the two are complementary rather than overlapping. A graphite peak width criterion (wPar,Gr<0.88) combined with grindometer screening reduces 114 trials to 54 in which, within the detection limits of the two methods, no particle-related cause of viscosity variation remains identifiable, and 52 of these with a recorded specific mechanical energy form the modelling dataset. Two logistic regressions show that the two failure modes are predicted from the machine settings with markedly different sharpness: particle damage by kneading concentration and kneading length (AUC = 0.955, 0.914 under cross-validation) and agglomerate removal by backward elements and screw speed (AUC = 0.740), with backward elements entering both models with opposite signs as the central design trade-off. Inside the window the specific mechanical energy is a genuine but coarse descriptor: after compositional normalisation it accounts for 57% of the viscosity variance, yet it places individual operating points to only 9.5% mean relative error, and pairs of runs receiving the same energy differ by up to 49% in viscosity, so it cannot serve as the descriptor of the network state. A greybox model with eight free parameters reproduces the compositionally normalised viscosity to 5.0% mean relative error, gating a kneading term against a low-concentration path through a sigmoid at cK,crit=0.635, and attains a leave-one-out Q2 of 0.796, where a twenty-one-term response surface benchmark fitted by the same protocol collapses out of sample. Its training error is of the order of the pooled replicate scatter of the process itself.

BatteriesVol. 12(11)
Fraunhofer Institute for Ceramic Technologies and Systems (DE), Technische Universität Dresden (DE)
Openalex Percentile: Top 25%
Advancements in Battery Materials
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