Mathematical models of polymer melt flow in co‐rotating twin‐screw extruders are crucial to screw design and predict processing characteristics, such as pressure distribution, back‐pressure lengths, degree of filling, melt‐temperature increase, and drive power. Twin‐screw modeling focuses predominantly on conveying elements, and kneading blocks are commonly represented with fictitious continuous flights, which significantly simplifies geometry and ignores considerable leakage flow. This work (Part A) presents a comprehensive analysis of the conveying characteristics and power demands of fully intermeshing co‐rotating twin‐screw extruder kneading blocks that considers the complex three‐dimensional geometry without geometrical simplifications. This analysis comprises the following steps: (1) dimensionless description of the geometry, (2) simplification of the governing equations, (3) formulation of novel dimensionless conveying and power parameters, and (4) a parametric design study with the novel approach of using the characteristic angular screw position, which avoids complex numerical algorithms and drastically reduces the computation required. Our comprehensive parametric design study included 1536 independent design points—a vast amount of data that revealed various effects that are highlighted in this work, including new findings on the interactions between geometry and conveying and power parameters. The obtained results serve, for example, as the basis for screw design, optimizations, scale‐up, and soft sensors.

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.

Intermeshing co‐rotating twin‐screw extruders are very versatile because their screw configurations can be tailored both to the application and to the properties of the materials used. Finding the best screw configuration is one of the main purposes of twin‐screw extrusion modeling, and requires models that accurately predict conveying and power consumption. The better the process can be predicted, the better the requirements of the final product can be met. We present novel prediction models of the conveying and power‐consumption behaviors of intermeshing co‐rotating twin‐screw extruder kneading blocks for Newtonian fluids. These are based on numerical simulations and therefore consider the complex three dimensional (3D) geometry of this element type without the need for common simplifications. Our models are thus capable of including all leakage flows and gap influences, which are usually ignored, for example, by the flat‐plate model. Since our models are derived by symbolic regression based on genetic programming, they consist of algebraic functions and are low‐threshold. They can be used to calculate various process parameters for individual kneading blocks or entire screw configurations, as illustrated by a use case.

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