The Experts below are selected from a list of 39219 Experts worldwide ranked by ideXlab platform
Johannes Khinast - One of the best experts on this subject based on the ideXlab platform.
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Mechanistic Modeling of modular co rotating twin screw extruders
International Journal of Pharmaceutics, 2014Co-Authors: Andreas Eitzlmayr, Jonathan Booth, Philip Shering, Gerold Koscher, Zhenyu Huang, Gavin K Reynolds, Johannes KhinastAbstract:Abstract In this study, we present a one-dimensional (1D) model of the metering zone of a modular, co-rotating twin-screw extruder for pharmaceutical hot melt extrusion (HME). The model accounts for filling ratio, pressure, melt temperature in screw channels and gaps, driving power, torque and the residence time distribution (RTD). It requires two empirical parameters for each screw element to be determined experimentally or numerically using computational fluid dynamics (CFD). The required Nusselt correlation for the heat transfer to the barrel was determined from experimental data. We present results for a fluid with a constant viscosity in comparison to literature data obtained from CFD simulations. Moreover, we show how to incorporate the rheology of a typical, non-Newtonian polymer melt, and present results in comparison to measurements. For both cases, we achieved excellent agreement. Furthermore, we present results for the RTD, based on experimental data from the literature, and found good agreement with simulations, in which the entire HME process was approximated with the metering model, assuming a constant viscosity for the polymer melt.
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Mechanistic Modeling of modular co-rotating twin-screw extruders
International Journal of Pharmaceutics, 2014Co-Authors: Andreas Eitzlmayr, Gavin Reynolds, Jonathan Booth, Philip Shering, Gerold Koscher, Zhenyu Huang, Johannes KhinastAbstract:In this study, we present a one-dimensional (1D) model of the metering zone of a modular, co-rotating twin-screw extruder for pharmaceutical hot melt extrusion (HME). The model accounts for filling ratio, pressure, melt temperature in screw channels and gaps, driving power, torque and the residence time distribution (RTD). It requires two empirical parameters for each screw element to be determined experimentally or numerically using computational fluid dynamics (CFD). The required Nusselt correlation for the heat transfer to the barrel was determined from experimental data. We present results for a fluid with a constant viscosity in comparison to literature data obtained from CFD simulations. Moreover, we show how to incorporate the rheology of a typical, non-Newtonian polymer melt, and present results in comparison to measurements. For both cases, we achieved excellent agreement. Furthermore, we present results for the RTD, based on experimental data from the literature, and found good agreement with simulations, in which the entire HME process was approximated with the metering model, assuming a constant viscosity for the polymer melt. © 2014 Published by Elsevier B.V.
Peter C. Young - One of the best experts on this subject based on the ideXlab platform.
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hypothetico inductive data based Mechanistic Modeling of hydrological systems
Water Resources Research, 2013Co-Authors: Peter C. YoungAbstract:[1] The paper introduces a logical extension to data-based Mechanistic (DBM) Modeling, which provides hypothetico-inductive (HI-DBM) bridge between conceptual models, derived in a hypothetico-deductive manner, and the DBM model identified inductively from the same time-series data. The approach is illustrated by a quite detailed example of HI-DBM analysis applied to the well-known Leaf River data set and the associated HyMOD conceptual model. The HI-DBM model significantly improves the explanation of the Leaf River data and enhances the performance of the original DBM model. However, on the basis of various diagnostic tests, including recursive time-variable and state-dependent parameter estimation, it is suggested that the model should be capable of further improvement, particularly as regards the conceptual effective rainfall mechanism, which is based on the probability distributed model hypothesis. In order to verify the efficacy of the HI-DBM analysis in a situation where the actual model generating the data is completely known, the analysis is also applied to a stochastic simulation model based on a modified HyMOD model.
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Hypothetico-inductive data-based Mechanistic Modeling of hydrological systems
Water Resources Research, 2013Co-Authors: Peter C. YoungAbstract:The paper introduces a logical extension to data-based Mechanistic (DBM)\nModeling, which provides hypothetico-inductive (HI-DBM) bridge between\nconceptual models, derived in a hypothetico-deductive manner, and the\nDBM model identified inductively from the same time-series data. The\napproach is illustrated by a quite detailed example of HI-DBM analysis\napplied to the well-known Leaf River data set and the associated HyMOD\nconceptual model. The HI-DBM model significantly improves the\nexplanation of the Leaf River data and enhances the performance of the\noriginal DBM model. However, on the basis of various diagnostic tests,\nincluding recursive time-variable and state-dependent parameter\nestimation, it is suggested that the model should be capable of further\nimprovement, particularly as regards the conceptual effective rainfall\nmechanism, which is based on the probability distributed model\nhypothesis. In order to verify the efficacy of the HI-DBM analysis in a\nsituation where the actual model generating the data is completely\nknown, the analysis is also applied to a stochastic simulation model\nbased on a modified HyMOD model. Citation: Young, P. C. (2013),\nHypothetico-inductive data-based Mechanistic Modeling of hydrological\nsystems, Water Resour. Res., 49, doi: 10.1002/wrcr.20068.
Jürgen Hubbuch - One of the best experts on this subject based on the ideXlab platform.
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Water on hydrophobic surfaces: Mechanistic Modeling of polyethylene glycol-induced protein precipitation
Bioprocess and Biosystems Engineering, 2019Co-Authors: Steffen Großhans, Gang Wang, Jürgen HubbuchAbstract:For the purification of biopharmaceutical proteins, liquid chromatography is still the gold standard. Especially with increasing product titers, drawbacks like slow volumetric throughput and high resin costs lead to an intensifying need for alternative technologies. Selective preparative protein precipitation is one promising alternative technique. Although the capability has been proven, there has been no precipitation process realized for large-scale monoclonal antibody (mAb) production yet. One reason might be that the mechanism behind protein phase behavior is not completely understood and the precipitation process development is still empirical. Mechanistic Modeling can be a means for faster, material-saving process development and a better process understanding at the same time. In preparative chromatography, Mechanistic Modeling was successfully shown for a variety of applications. Lately, a new isotherm for hydrophobic interaction chromatography (HIC) under consideration of water molecules as participants was proposed, enabling an accurate description of HIC. In this work, based on similarities between protein precipitation and HIC, a new precipitation model was derived. In the proposed model, the formation of protein–protein interfaces is thought to be driven by hydrophobic effects, involving a reorganization of the well-ordered water structure on the hydrophobic surfaces of the protein–protein complex. To demonstrate model capability, high-throughput precipitation experiments with pure or prior to the experiments purified proteins lysozyme, myoglobin, bovine serum albumin, and one mAb were conducted at various pH values. Polyethylene glycol (PEG) 6000 was used as precipitant. The precipitant concentration as well as the initial protein concentration was varied systematically. For all investigated proteins, the initial protein concentrations were varied between 1.5 mg/mL and 12 mg/mL. The calibrated models were successfully validated with experimental data. This Mechanistic description of protein precipitation process offers mathematical explanation of the precipitation behavior of proteins at PEG concentration, protein concentration, protein size, and pH.
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Water on hydrophobic surfaces: Mechanistic Modeling of hydrophobic interaction chromatography
Journal of Chromatography A, 2016Co-Authors: Gang Wang, Tobias Hahn, Jürgen HubbuchAbstract:Mechanistic models are successfully used for protein purification process development as shown for ion-exchange column chromatography (IEX). Modeling and simulation of hydrophobic interaction chromatography (HIC) in the column mode has been seldom reported. As a combination of these two techniques is often encountered in biopharmaceutical purification steps, accurate Modeling of protein adsorption in HIC is a core issue for applying holistic model-based process development, especially in the light of the Quality by Design (QbD) approach. In this work, a new Mechanistic isotherm model for HIC is derived by consideration of an equilibrium between well-ordered water molecules and bulk-like ordered water molecules on the hydrophobic surfaces of protein and ligand. The model's capability of describing column chromatography experiments is demonstrated with glucose oxidase, bovine serum albumin (BSA), and lysozyme on Capto™ Phenyl (high sub) as model system. After model calibration from chromatograms of bind-and-elute experiments, results were validated with batch isotherms and prediction of further gradient elution chromatograms.
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Simulating and Optimizing Preparative Protein Chromatography with ChromX
Journal of Chemical Education, 2015Co-Authors: Tobias Hahn, Thiemo Huuk, Vincent Heuveline, Jürgen HubbuchAbstract:© 2015 The American Chemical Society and Division of Chemical Education, Inc.Industrial purification of biomolecules is commonly based on a sequence of chromatographic processes, which are adapted slightly to new target components, as the time to market is crucial. To improve time and material efficiency, Modeling is increasingly used to determine optimal operating conditions, thus providing new challenges for current and future bioengineers. At the Karlsruhe Institute of Technology (KIT), Mechanistic Modeling of protein chromatography has long been part of the curriculum of the Bioengineering masters degree program, supported by exercises using simulation software. Emphasis lies on nonlinear preparative chromatography, where the result strongly depends on the sample concentration. For undergraduate students to gain hands-on experience in model-based optimization, a three-week, in-depth laboratory course was designed on the purification of a ternary mixture of proteins using ion-exchange chromatography and Mechanistic Modeling. Students apply in-house software ChromX, which is made available for download, together with tutorials on numerics and practical applications. This article presents the working principle of ChromX and results of the laboratory course for undergraduate students.
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optimizing a chromatographic three component separation a comparison of Mechanistic and empiric Modeling approaches
Journal of Chromatography A, 2012Co-Authors: A. Osberghaus, Stefan Hepbildikler, Michal Haindl, S. Nath, E Von Lieres, Jürgen HubbuchAbstract:The search for a favorable and robust operating point of a separation process represents a complex multi-factor optimization problem. This problem is typically tackled by design of experiments (DoE) in the factor space and empiric response surface Modeling (RSM); however, separation optimizations based on Mechanistic Modeling are on the rise. In this paper, a DoE–RSM-approach and a Mechanistic Modeling approach are compared with respect to their performance and predictive power by means of a case study – the optimization of a multicomponent separation of proteins in an ion exchange chromatography step with a nonlinear gradient (ribonuclease A, cytochrome c and lysozyme on SP Sepharose FF). The results revealed that at least for complex problems with low robustness, the performance of the DoE-approach is significantly inferior to the performance of the Mechanistic model. While some influential factors of the system could be detected with the DoE–RSM-approach, predictions concerning the peak resolutions were mostly inaccurate and the optimization failed. The predictions of the Mechanistic model for separation results were very accurate. Influences of the experimental factors could be quantified and the separation was optimized with respect to several objectives. However, the discussion of advantages and disadvantages of empiric and Mechanistic Modeling generates synergies of both methods and leads to a new optimization concept, which is promising with respect to an efficient employment of high throughput screening data.
Mamoru Mitsuishi - One of the best experts on this subject based on the ideXlab platform.
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Mechanistic Modeling of bone drilling process with experimental validation
Journal of Materials Processing Technology, 2014Co-Authors: Naohiko Sugita, Kanako Harada, Kentaro Ishii, Mamoru MitsuishiAbstract:Abstract In this paper, an improved Mechanistic model is developed to predict the thrust force and torque for bone-drilling operation. The cutting action at the drill point is divided into three regions: the cutting lips, outer portion of the chisel edge (the secondary cutting edges), and inner portion of the chisel edge (the indentation zone). Models that account for the unique mechanics of the cutting process for each of the three regions are formulated. The models are calibrated to bovine cortical bone material using specific cutting pressure equations with modification to take advantage of the characteristics of the drill point geometry. The models are validated for the cutting lips, the chisel edge, and entire drill point for a wide range of spindle speed and feed rate. The predicted results agree well with experimental results. Only the predictions for the drilling torque on the chisel edge are lower than the experimental results under some drilling conditions. The model can assist in the selection of favorable drilling conditions and drill-bit geometries for bone-drilling operations.
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Mechanistic Modeling of bone-drilling process with experimental validation
Journal of Materials Processing Technology, 2014Co-Authors: Jianbo Sui, Kanako Harada, Naohiko Sugita, Kentaro Ishii, Mamoru MitsuishiAbstract:In this paper, an improved Mechanistic model is developed to predict the thrust force and torque for bone-drilling operation. The cutting action at the drill point is divided into three regions: the cutting lips, outer portion of the chisel edge (the secondary cutting edges), and inner portion of the chisel edge (the indentation zone). Models that account for the unique mechanics of the cutting process for each of the three regions are formulated. The models are calibrated to bovine cortical bone material using specific cutting pressure equations with modification to take advantage of the characteristics of the drill point geometry. The models are validated for the cutting lips, the chisel edge, and entire drill point for a wide range of spindle speed and feed rate. The predicted results agree well with experimental results. Only the predictions for the drilling torque on the chisel edge are lower than the experimental results under some drilling conditions. The model can assist in the selection of favorable drilling conditions and drill-bit geometries for bone-drilling operations. © 2013 Elsevier B.V. All rights reserved.
Andreas Eitzlmayr - One of the best experts on this subject based on the ideXlab platform.
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Mechanistic Modeling of modular co rotating twin screw extruders
International Journal of Pharmaceutics, 2014Co-Authors: Andreas Eitzlmayr, Jonathan Booth, Philip Shering, Gerold Koscher, Zhenyu Huang, Gavin K Reynolds, Johannes KhinastAbstract:Abstract In this study, we present a one-dimensional (1D) model of the metering zone of a modular, co-rotating twin-screw extruder for pharmaceutical hot melt extrusion (HME). The model accounts for filling ratio, pressure, melt temperature in screw channels and gaps, driving power, torque and the residence time distribution (RTD). It requires two empirical parameters for each screw element to be determined experimentally or numerically using computational fluid dynamics (CFD). The required Nusselt correlation for the heat transfer to the barrel was determined from experimental data. We present results for a fluid with a constant viscosity in comparison to literature data obtained from CFD simulations. Moreover, we show how to incorporate the rheology of a typical, non-Newtonian polymer melt, and present results in comparison to measurements. For both cases, we achieved excellent agreement. Furthermore, we present results for the RTD, based on experimental data from the literature, and found good agreement with simulations, in which the entire HME process was approximated with the metering model, assuming a constant viscosity for the polymer melt.
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Mechanistic Modeling of modular co-rotating twin-screw extruders
International Journal of Pharmaceutics, 2014Co-Authors: Andreas Eitzlmayr, Gavin Reynolds, Jonathan Booth, Philip Shering, Gerold Koscher, Zhenyu Huang, Johannes KhinastAbstract:In this study, we present a one-dimensional (1D) model of the metering zone of a modular, co-rotating twin-screw extruder for pharmaceutical hot melt extrusion (HME). The model accounts for filling ratio, pressure, melt temperature in screw channels and gaps, driving power, torque and the residence time distribution (RTD). It requires two empirical parameters for each screw element to be determined experimentally or numerically using computational fluid dynamics (CFD). The required Nusselt correlation for the heat transfer to the barrel was determined from experimental data. We present results for a fluid with a constant viscosity in comparison to literature data obtained from CFD simulations. Moreover, we show how to incorporate the rheology of a typical, non-Newtonian polymer melt, and present results in comparison to measurements. For both cases, we achieved excellent agreement. Furthermore, we present results for the RTD, based on experimental data from the literature, and found good agreement with simulations, in which the entire HME process was approximated with the metering model, assuming a constant viscosity for the polymer melt. © 2014 Published by Elsevier B.V.