scholarly journals Estimating mouthing exposure to chemicals in children’s products

2021 ◽  
Author(s):  
Nicolò Aurisano ◽  
Peter Fantke ◽  
Lei Huang ◽  
Olivier Jolliet
Keyword(s):  
Experimental Data ◽  
High Throughput Screening ◽  
Food Packaging ◽  
Exposure Model ◽  
Study Objective ◽  
Migration Rates ◽  
Proposed Model ◽  
Wide Range ◽  
Multiple Substance ◽  
Chemical Material

Abstract Background Existing models for estimating children’s exposure to chemicals through mouthing currently depends on the availability of chemical- and material-specific experimental migration rates, only covering a few dozen chemicals. Objective This study objective is hence to develop a mouthing exposure model to predict migration into saliva, mouthing exposure, and related health risk from a wide range of chemical-material combinations in children’s products. Methods We collected experimental data on chemical migration from different products into saliva for multiple substance groups and materials, identifying chemical concentration and diffusion coefficient as main properties of influence. To predict migration rates into saliva, we adapted a previously developed migration model for chemicals in food packaging materials. We also developed a regression model based on identified chemical and material properties. Results Our migration predictions correlate well with experimental data (R2 = 0.85) and vary widely from 8 × 10−7 to 32.7 µg/10 cm2/min, with plasticizers in PVC showing the highest values. Related mouthing exposure doses vary across chemicals and materials from a median of 0.005 to 253 µg/kgBW/d. Finally, we combined exposure estimates with toxicity information to yield hazard quotients and identify chemicals of concern for average and upper bound mouthing behavior scenarios. Significance The proposed model can be applied for predicting migration rates for hundreds of chemical-material combinations to support high-throughput screening.

VLE Determination of Carbon Dioxide Loaded Aqueous Alkanolamine Mixtures Using Modified Kent Eisenberg Model

2017 ◽  
Vol 231 (11-12) ◽  
Author(s):  
Humbul Suleman ◽  
Abdulhalim Shah Maulud ◽  
Zakaria Man
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Model Parameters ◽  
Thermodynamic Models ◽  
Carbon Dioxide Absorption ◽  
Proposed Model ◽  
Wide Range ◽  
Experimental Values ◽  
Good Agreement

AbstractA computationally simple thermodynamic framework has been presented to correlate the vapour-liquid equilibria of carbon dioxide absorption in five representative types of alkanolamine mixtures. The proposed model is an extension of modified Kent Eisenberg model for the carbon dioxide loaded aqueous alkanolamine mixtures. The model parameters are regressed on a large experimental data pool of carbon dioxide solubility in aqueous alkanolamine mixtures. The model is applicable to a wide range of temperature (298–393 K), pressure (0.1–6000 kPa) and alkanolamine concentration (0.3–5 M). The correlated results are compared to the experimental values and found to be in good agreement with the average deviations ranging between 6% and 20%. The model results are comparable to other thermodynamic models.

A Nonlinear Visco-Hyper Elastic Constitutive Model for Modeling Behavior of Polyurea at Large Deformations

2019 ◽  
Author(s):  
Shank S. Kulkarni ◽  
Alireza Tabarraei
Keyword(s):  
Experimental Data ◽  
Constitutive Model ◽  
Viscoelastic Model ◽  
Viscoelastic Behavior ◽  
Strain Rates ◽  
Strain Behavior ◽  
Strain And Strain Rate ◽  
Proposed Model ◽  
Wide Range ◽  
Hyper Elastic

Abstract The fantastic properties of polyurea such as flexibility, durability, and chemical resistance have brought it a wide range of application in various industries. Effective prediction of the response of polyurea under different loading and environmental conditions necessitates the development of an accurate constitutive model. Similar to most polymers, the behavior of polyurea depends on both the strain and strain rate. Therefore, the constitutive model should be able to capture both these effects on the response of polyurea. To achieve this objective, in this paper, a nonlinear visco-hyper elastic constitutive model is developed by the superposition of a hyperelastic and a viscoelastic model. The proposed constitutive model can capture the behavior of polyurea under compressive as well as tensile loading conditions at various strain rates. Four parameter Ogden model is used to model the hyperelastic behavior of polyurea. The viscoelastic behavior is modeled using a three-parameter standard linear solid (SLS) model. The material parameters of the model are found by curve fitting of the proposed model to the experimental data. Comparison of the proposed model and the experimental data shows that the proposed model can closely reproduce the stress-strain behavior of polyurea under a wide range of strain rates (−6500 to 294 /s).

LIQUIDUS THERMO-BAROMETER FOR THE MODELING OF MAGNETITE — MELT EQUILIBRIUM

2018 ◽  
pp. 71-80
Author(s):  
N. S. Aryaeva ◽  
E. V. Koptev-Dvornikov ◽  
D. A. Bychkov
Keyword(s):  
Experimental Data ◽  
Liquidus Temperature ◽  
Vertical Structure ◽  
Maximum Error ◽  
Silicate Melt ◽  
System Of Equations ◽  
Wide Range ◽  
Basaltic Melts ◽  
Multidimensional Statistics

A system of equations of thermobarometer for magnetite-silicate melt equilibrium was obtained by method of multidimensional statistics of 93 experimental data of a magnetite solubility in basaltic melts. Equations reproduce experimental data in a wide range of basalt compositions, temperatures and pressures with small errors. Verification of thermobarometers showed the maximum error in liquidus temperature reproducing does not exceed ±7 °C. The level of cumulative magnetite appearance in the vertical structure of Tsypringa, Kivakka, Burakovsky intrusions predicted with errors from ±10 to ±50 m.

Dual Release Model to Evaluate Dissolution Profiles from Swellable Drug Polyelectrolyte Matrices

2020 ◽  
Vol 17 (6) ◽  
pp. 511-522 ◽  
Author(s):  
Alicia Graciela Cid ◽  
María Verónica Ramírez-Rigo ◽  
María Celeste Palena ◽  
Elio Emilio Gonzo ◽  
Alvaro Federico Jimenez-Kairuz ◽  
...  
Keyword(s):  
Experimental Data ◽  
Drug Release ◽  
Inflection Point ◽  
Release Profile ◽  
Experimental Point ◽  
Extended Model ◽  
Release Process ◽  
Proposed Model ◽  
Dual Release ◽  
Release Model

Background: Mathematical modeling in modified drug release is an important tool that allows predicting the release rate of drugs in their surrounding environment and elucidates the transport mechanisms involved in the process. Objective: The aim of this work was to develop a mathematical model that allows evaluating the release profile of drugs from polymeric carriers in which the swelling phenomenon is present. Methods: Swellable matrices based on ionic complexes of alginic acid or carboxymethylcellulose with ciprofloxacin were prepared and the effect of adding the polymer sodium salt on the swelling process and the drug release was evaluated. Experimental data from the ciprofloxacin release profiles were mathematically adjusted, considering the mechanisms involved in each stage of the release process. Results: A proposed model, named “Dual Release” model, was able to properly fit the experimental data of matrices presenting the swelling phenomenon, characterized by an inflection point in their release profile. This entails applying the extended model of Korsmeyer-Peppas to estimate the percentage of drug released from the first experimental point up to the inflection point and then a model called Lumped until the final time, allowing to adequately represent the complete range of the drug release profile. Different parameters of pharmaceutical relevance were calculated using the proposed model to compare the profiles of the studied matrices. Conclusion: The “Dual Release” model proposed in this article can be used to predict the behavior of complex systems in which different mechanisms are involved in the release process.

scholarly journals Experimental analysis and ANN prediction on performances of finned oval-tube heat exchanger under different air inlet angles with limited experimental data

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 968-980
Author(s):  
Xueping Du ◽  
Zhijie Chen ◽  
Qi Meng ◽  
Yang Song
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Heat Exchanger ◽  
Correlation Equation ◽  
Tube Heat Exchanger ◽  
Flow Friction ◽  
Air Inlet ◽  
Comparison Results ◽  
Wide Range ◽  
Oval Tube

Abstract A high accuracy of experimental correlations on the heat transfer and flow friction is always expected to calculate the unknown cases according to the limited experimental data from a heat exchanger experiment. However, certain errors will occur during the data processing by the traditional methods to obtain the experimental correlations for the heat transfer and friction. A dimensionless experimental correlation equation including angles is proposed to make the correlation have a wide range of applicability. Then, the artificial neural networks (ANNs) are used to predict the heat transfer and flow friction performances of a finned oval-tube heat exchanger under four different air inlet angles with limited experimental data. The comparison results of ANN prediction with experimental correlations show that the errors from the ANN prediction are smaller than those from the classical correlations. The data of the four air inlet angles fitted separately have higher precisions than those fitted together. It is demonstrated that the ANN approach is more useful than experimental correlations to predict the heat transfer and flow resistance characteristics for unknown cases of heat exchangers. The results can provide theoretical support for the application of the ANN used in the finned oval-tube heat exchanger performance prediction.

scholarly journals Modelling the Inflation and Elastic Instabilities of Rubber-Like Spherical and Cylindrical Shells Using a New Generalised Neo-Hookean Strain Energy Function

2021 ◽  
Author(s):  
Afshin Anssari-Benam ◽  
Andrea Bucchi ◽  
Giuseppe Saccomandi
Keyword(s):  
Experimental Data ◽  
Energy Function ◽  
Limit Point ◽  
Strain Energy ◽  
Cylindrical Shells ◽  
Strain Energy Function ◽  
Model Parameters ◽  
Wide Range ◽  
Cylindrical Tubes ◽  
Gent Model

AbstractThe application of a newly proposed generalised neo-Hookean strain energy function to the inflation of incompressible rubber-like spherical and cylindrical shells is demonstrated in this paper. The pressure ($P$ P ) – inflation ($\lambda $ λ or $v$ v ) relationships are derived and presented for four shells: thin- and thick-walled spherical balloons, and thin- and thick-walled cylindrical tubes. Characteristics of the inflation curves predicted by the model for the four considered shells are analysed and the critical values of the model parameters for exhibiting the limit-point instability are established. The application of the model to extant experimental datasets procured from studies across 19th to 21st century will be demonstrated, showing favourable agreement between the model and the experimental data. The capability of the model to capture the two characteristic instability phenomena in the inflation of rubber-like materials, namely the limit-point and inflation-jump instabilities, will be made evident from both the theoretical analysis and curve-fitting approaches presented in this study. A comparison with the predictions of the Gent model for the considered data is also demonstrated and is shown that our presented model provides improved fits. Given the simplicity of the model, its ability to fit a wide range of experimental data and capture both limit-point and inflation-jump instabilities, we propose the application of our model to the inflation of rubber-like materials.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

scholarly journals Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

2019 ◽  
Vol 11 (6) ◽  
pp. 608 ◽  
Author(s):  
Yun-Jia Sun ◽  
Ting-Zhu Huang ◽  
Tian-Hui Ma ◽  
Yong Chen
Keyword(s):  
Remote Sensing ◽  
Real Data ◽  
Total Variation Regularization ◽  
Joint Sparsity ◽  
Alternating Direction ◽  
Proposed Model ◽  
Comparison Results ◽  
Wide Range ◽  
Image Edges ◽  
Full Consideration

Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

The Petrogenetic Model, an Extension of Bowen's Petrogenetic Grid

1962 ◽  
Vol 99 (6) ◽  
pp. 558-569 ◽  
Author(s):  
Peter J. Wyllie
Keyword(s):  
Experimental Data ◽  
Constant Pressure ◽  
Three Dimensional ◽  
Pore Fluid ◽  
Fluid Composition ◽  
Solid Reaction ◽  
Petrogenetic Model ◽  
Wide Range ◽  
Opposite Face ◽  
Reaction Surfaces

AbstractBowen's petrogenetic grid is a PT projection containing univariant curves for decarbonation, dehydration, and solid-solid reactions, with vapour pressure (Pf) equal to total pressure (Ps). Analysis of experimental data in the system MgO–CO2–H2O leads to an expansion of this grid. Three of the important variables in metamorphism when Pf = Ps are P, T, and variation of the pore fluid composition between H2O and CO2. These can be illustrated in a three-dimensional petrogenetic model; one face is a PT plane for reactions occurring with pure H2O, and the opposite face is a similar plane for reactions with pure CO2; these are separated by an axis for pore fluid composition varying between H2O and CO2. Superposition of the PT faces of the model provides the petrogenetic grid. The reactions within the model are represented by divariant surfaces, which may meet along univariant lines. For dissociation reactions, the surfaces curve towards lower temperatures as the proportion of non-reacting volatile increases, and solid-solid reaction surfaces are parallel to the vapour composition axis and perpendicular to the PT axes. The relative temperatures of reactions and the lines of intersections of the surfaces can be illustrated in isobaric sections. Isobaric sections are used to illustrate reactions proceeding at constant pressure with (1) pore fluid composition remaining constant during the reaction, with temperature increasing (2) pore fluid composition changing during the reaction, with temperature increasing, and (3) pore fluid changing composition at constant temperature. The petrogenetic model provides a convenient framework for a wide range of experimental data.

Effects of Multiple Impacts and Size Distribution of Particles on Erosion Predictions Using CFD

2007 ◽  
Author(s):  
Yongli Zhang ◽  
Brenton S. McLaury ◽  
Siamack A. Shirzai
Keyword(s):  
Experimental Data ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Target Material ◽  
Average Particle ◽  
Multiple Impacts ◽  
Cfd Simulations ◽  
Erosion Damage ◽  
Wide Range

Erosion equations are usually obtained from experiments by impacting solid particles entrained in a gas or liquid on a target material. The erosion equations are utilized in CFD (Computational Fluid Dynamics) models to predict erosion damage caused by solid particle impingements. Many erosion equations are provided in terms of an erosion ratio. By definition, the erosion ratio is the mass loss of target material divided by the mass of impacting particles. The mass of impacting particles is the summation of (particle mass × number of impacts) of each particle. In erosion experiments conducted to determine erosion equations, some particles may impact the target wall many times and some other particles may not impact the target at all. Therefore, the experimental data may not reflect the actual erosion ratio because the mass of the sand that is used to run the experiments is assumed to be the mass of the impacting particles. CFD and particle trajectory simulations are applied in the present work to study effects of multiple impacts on developing erosion ratio equations. The erosion equation as well as the CFD-based erosion modeling procedure is validated against a variety of experimental data. The results show that the effect of multiple impacts is negligible in air cases. In water cases, however, this effect needs to be accounted for especially for small particles. This makes it impractical to develop erosion ratio equations from experimental data obtained for tests with sand in water or dense gases. Many factors affecting erosion damage are accounted for in various erosion equations. In addition to some well-studied parameters such as particle impacting speed and impacting angle, particle size also plays a significant role in the erosion process. An average particle size is usually used in analyzing experimental data or estimating erosion damage cases of practical interest. In petroleum production applications, however, the size of sand particles that are entrained in produced fluids can vary over a fairly broad range. CFD simulations are also performed to study the effect of particle size distribution. In CFD simulations, particle sizes are normally distributed with the mean equaling the average size of interest and the standard deviation varying over a wide range. Based on CFD simulations, an equation is developed and can be applied to account for the effect of the particle size distribution on erosion prediction for gases and liquids.

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