Predicting the Non-Linear Conveying Behavior in Single-Screw Extrusion: A Comparison of Various Data-Based Modeling Approaches used with CFD Simulations

The traditional approach to modeling the polymer melt flow in single-screw extruders is based on analytical and numerical analyses. Due to increasing computational power, data-driven modeling has grown significantly in popularity in recent years. In this study, we compared and evaluated databased modeling approaches (i. e., gradient-boosted trees, artificial neural networks, and symbolic regression models based on genetic programming) in terms of their ability to predict – within a hybrid modeling framework – the three-dimensional non-linear throughput-pressure relationship of metering channels in single-screw extruders. By applying the theory of similarity to the governing flow equations, we identified the characteristic dimensionless influencing parameters, which we then varied to create a large dataset covering a wide range of possible applications. For each single design point we conducted numerical simulations and obtained the dimensionless flow rate. The large dataset was divided into three independent sets for training, interpolation, and extrapolation, the first being used to generate and the remaining two to evaluate the models. Further, we added two features derived from expert knowledge to the models and analyzed their influence on predictive power. In addition to prediction accuracy and interpolation and extrapolation capabilities, we evaluated model complexity, interpretability, and time required to learn the models. This study provides a rigorous analysis of various data-based modeling approaches applied to simulation data in extrusion.

Using Symbolic Regression Models to Predict the Pressure Loss of Non-Newtonian Polymer-Melt Flows through Melt-Filtration Systems with Woven Screens

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.

In vivo characterization of a porous hydrogel material for use as a tissue bulking agent

Tissue engineered biomaterial constructs are needed for plastic and reconstructive applications. To successfully form a space-filling tissue, the construct should induce a minimal inflammatory response, create minimal or no fibrotic capsule, and establish a vascular bed within the first few days after implantation to ensure survival of the implanted cells. In addition, the biomaterial should support cellular adhesion and induce tissue ingrowth. A macroporous hydrogel bead using sodium alginate covalently coupled with an arginine, glycine, and aspartic acid-containing peptide was created. A 6-month subcutaneous rat model study was performed to determine if the implanted material induced tissue ingrowth throughout the implantation area and maintained a three-dimensional vascular bed. The implanted materials produced a vascular bed, minimal inflammation and capsule formation, and good tissue ingrowth throughout the experiment. The material retained its bulking capacity by demonstration of no significant change of the cross-sectional area as measured from the center of the implants after the 2-week time point. In addition, the granulation tissue formed around the implant was loosely organized, and the surrounding tissue had integrated well with the implant. These results indicate that this material has the desired properties for the development of soft-tissue-engineering constructs.

Radiolocalization of human prostate tumor in a mouse subrenal capsule model by monoclonal antibody TURP-27

The subrenal capsule assay was used to determine if 125I-labeled anti-prostate monoclonal antibody TURP-27 could target human prostate tumor fragments implanted under the renal capsule of normal immunocompetent C57 BL/6 mice. Maximal binding and optimal tumor to non-tumor tissue ratios occurred within 24-48 hours postadministration of 125I-TURP-27. No significant localization was observed in mice bearing TURP-27 antigen-negative human colon tumor tissue implants or with an isotype-matched control monoclonal antibody. These preclinical data suggest that TURP-27 may have clinical application for imaging metastatic prostate tumors and further application in immunoconjugate and/or radiotherapy of prostate cancer.