Separate Models Research Articles

Comparison of experimental and simulated separation performance in capillary tube-in-manifold devices

A metal tube-in-manifold packed bed capillary column device, designed to overcome common limitations associated with capillary LC separations, is described. Experimental results of initial packing tests with sub-3 μm core-shell particles demonstrated efficiencies greater than 47,000 plates/m for a separation performed using the column device. Computational fluid dynamics (CFD) modeling of the multicomponent separation used for this work was validated against experimental LC results and the optimized model was able to effectively predict component peak retention times. However, the accuracy of predicted efficiencies requires further refinement. The tube-in-manifold design demonstrates that packed capillary columns with cylindrical cross-sectional channel geometry and ultrahigh pressure, low dead volume fluidic connections are achievable.

Intelligence analysis of membrane distillation via machine learning models for pharmaceutical separation

This study investigates simulation of pharmaceutical separation via membrane distillation process by computational simulation and machine learning modeling strategy. The efficacy of three regression models, i.e., Multi-layer Perceptron (MLP), Gamma Regression, and Support Vector Regression (SVR) in predicting the solute concentration, C(mol/m³), was evaluated. The hyper-parameters were optimized by fine-tuning the models using the Red Deer Algorithm (RDA). Computational analyses were carried out for removal of pharmaceuticals from solution by membrane distillation in continuous mode. Mass transfer and machine learning models were implemented focusing on concentration of solute in the feed section of membrane. Results indicate that the Multi-layer Perceptron model achieved great accuracy with an R2 of 0.9955, an MAE of 0.0084, and an RMSE of 0.0148, effectively capturing complex nonlinear relationships in the data. Gamma Regression also performed acceptably, with fitting R2 of 0.9214, showing its suitability for positively skewed data. The Support Vector Regression model, while capturing the general trend, showed the lowest performance with an R2 of 0.8710. These findings suggest that the Multi-layer Perceptron is the most accurate model for this dataset, followed by Gamma Regression and Support Vector Regression. This underscores the importance of careful model selection and optimization in regression analysis in combination with computational simulation of membrane processes.

Experimental Modeling and Optimization of the Tribo-Electrostatic Separation of PET Fibers From End-of-Life Tires

Experimental Modeling and Optimization of the Tribo-Electrostatic Separation of PET Fibers From End-of-Life Tires

Association of polypharmacy with cognitive impairment in older trauma patients: a cross-sectional study

IntroductionFew if any studies have been conducted to date on the association between polypharmacy and cognitive impairment among older trauma patients. Therefore, we investigated whether polypharmacy is associated with cognitive...

Modeling of hydrogen separation through Pd membrane with vacuum pressure using Taguchi and machine learning methods

The performance of hydrogen purification using palladium (Pd) membrane is analyzed by Taguchi and machine learning (ML) methods. Three system factors are considered: the feed gas mixture composition (i.e., H2, CO2, and H2O), retentate-side total pressure, and vacuum pressure. The Taguchi method designs the experimental cases based on the constructed orthogonal array. In addition, the hydrogen flux is investigated by the analyses of variance (ANOVA) and artificial neural networks (ANN) methods. The results show that the effects of the considered factors on the hydrogen permeation performance can be ranked as feed gas mixture composition > retentate-side total pressure > vacuum pressure. Both the retentate-side pressure and vacuum pressure present a positive effect on hydrogen flux. The average relative error of hydrogen flux between the experimental results and predictions by ANN is 2.1%, which proves that the ANN method is an effective technique for predicting the hydrogen flux through the Pd membrane. The ML classification test is then performed for hydrogen purity by three machine learning methods: decision tree (DT), support vector machine (SVM), and ensemble method. Among the various classification models, the Quadratic SVM model exhibits a relatively high average training accuracy but shows overfitting. However, the bagged model of the ensemble method can achieve an impressive training accuracy of 91.4% and a prediction accuracy of 85.7%.

Efficiency increment of CFD modeling by using ANFIS artificial intelligence for thermal-based separation modeling

This study presents a comprehensive analysis of predicting the temperature in a vacuum membrane distillation (VMD) process. The simulation was carried out via computational fluid dynamics (CFD) as well as machine learning. CFD was performed for obtaining temperature distribution in the feed solution, and the calculated temperature was used for training several machine learning models. The dataset comprises over 13,000 observations, which provide a rich source for modeling complex relationships. Three sophisticated regression models were employed: Adaptive Neuro-Fuzzy Inference System (ANFIS), Kernel Ridge Regression (KRR), and Multi-Layer Perceptron (MLP). ANFIS was chosen for its hybrid nature, combining neural networks and fuzzy logic, effectively capturing intricate non-linear relationships in data. ANFIS performance in fitting the data was compared with the other models. Hyper-parameter optimization for these models was conducted using the Tabu Search algorithm to ensure optimal performance. The ANFIS model demonstrated superior performance with an R2 score of 0.9964513 on the training set and 0.9964507 on the test set, alongside a MSE of 0.037655 and a MAE of 0.168272. The robustness of ANFIS was further confirmed by a 3-fold cross-validation mean R2 score of 0.9964579 and a standard deviation of 3.3619616e−05.

Coarse-Grained Modeling of Liquid-Liquid Phase Separation in Cells: Challenges and Opportunities.

Liquid-liquid phase separation (LLPS) within cells gives rise to membraneless organelles, which play pivotal roles in numerous cellular functions. A comprehensive understanding of the functional aspects of intrinsically disordered protein (IDP) condensates necessitates elucidating their inherent structures and establishing correlations with biological functions. Coarse-grained (CG) molecular dynamics (MD) simulations present a promising avenue for gaining insights into LLPS mechanisms of biomacromolecules. Essential to this endeavor is the development of tailored CG force fields for MD simulations, incorporating the full spectrum of biomolecules involved in the formation of condensates and accounting for real-time biochemical reactions coupled to the LLPS. Moreover, developing accurate theoretical frameworks and establishing links between condensate structure and its function are imperative for a thorough comprehension of LLPS of biological systems.

Modeling of Separation with Drying Processes for Compressed Air Using an Experimental Setup with Separation–Condensation and Throttling Devices

In modern industrial plants, compressed air is the most commonly used energy source; however, it is a source of condensation, which is not desirable for pneumatic equipment. This article describes a model of compressed air drying based on the principle of a refrigeration dryer. However, instead of gas refrigerants, the method proposed is to use cooled compressed air as a cooling medium with a temperature below 273 K. The main objective is to study the possibility of replacing harmful refrigerant gases with a neutral type of coolant. To carry out this research, a test bench containing a plate heat exchanger and a throttling device was designed and manufactured. This study has yielded the following scientific results. Firstly, the Joule–Thompson effect was used during the experiments, which facilitated a reduction in the temperature of the compressed air to 255 K. Secondly, using the expanded air and a plate heat exchanger, the temperature of the main compressed air stream was reduced to 280 K, which is very close to the temperature provided by standard-refrigeration-type compressed air dryers. This suggests that it is possible to use compressed air energy to cool the main stream of warm compressed air after the compressor. In general, the temperature range ensures the compressed air quality at the level of class 4 in accordance with international standards.

CsCl enrichment during solidification of molten LiCl–KCl–CsCl salt mixture

Metallic alloy fuels from fast reactors will be reprocessed by a non-aqueous electrochemical technique known as electrorefining. Electrorefining of spent metal fuel results in the accumulation of heat generating fission products especially 137Cs in the electrolyte, which is a eutectic salt mixture of 44 wt% LiCl and 56 wt% KCl. The fission products need to be removed from the salt so as to reduce the decay heat load and contamination thereby facilitating reuse of the salt and minimizing waste generation. Melt crystallization technique is a simple separation process being pursued for separation of fission products. This is a single step process which utilizes the difference in solubility of fission products in the solid and liquid phase. Crystallization involves purification of a substance from a liquid mixture by solidification of the desired component. Numerical modeling of separation of CsCl from a LiCl–KCl eutectic mixture by solidification was carried out using ANSYS Fluent (19.2). The enrichment of CsCl during solidification, its distribution in molten salt and segregation time were evaluated. Further, solidification experiments were conducted using the molten salt mixture and the predicted numerical results were validated.

Cahora Bassa vibrations monitoring between 2010-2022. Analysis of frequencies using HST statistical models for effects separation

Cahora Bassa vibrations monitoring between 2010-2022. Analysis of frequencies using HST statistical models for effects separation

Online Identification of Nonlinear Systems With Separable Structure.

Separable nonlinear models (SNLMs) are of great importance in system modeling, signal processing, and machine learning because of their flexible structure and excellent description of nonlinear behaviors. The online identification of such models is quite challenging, and previous related work usually ignores the special structure where the estimated parameters can be partitioned into a linear and a nonlinear part. In this brief, we propose an efficient first-order recursive algorithm for SNLMs by introducing the variable projection (VP) step. The proposed algorithm utilizes the recursive least-squares method to eliminate the linear parameters, resulting in a reduced function. Then, the stochastic gradient descent (SGD) algorithm is employed to update the parameters of the reduced function. By considering the tight coupling relationship between linear parameters and nonlinear parameters, the proposed first-order VP algorithm is more efficient and robust than the traditional SGD algorithm and alternating optimization algorithm. More importantly, since the proposed algorithm just uses the first-order information, it is easier to apply it to large-scale models. Numerical results on examples of different sizes confirm the effectiveness and efficiency of the proposed algorithm.

Mathematical modeling of rare earth element separation in electrodialysis with adjacent anion exchange membranes and ethylenediaminetetraacetic acid as chelating agent

This research delves into the effective use of electrodialysis for the separation of rare earth elements (REEs), specifically separating dysprosium (Dy) from praseodymium (Pr) and neodymium (Nd). A robust mathematical model based on the extended Nernst-Planck equation is introduced, simulating the process within a configuration that includes two adjacent anion exchange membranes. The model integrates aspects such as feed equilibrium, ion exchange within the membrane, and overall ion flux. Validation of the model's predictability was conducted through Chi-squared tests and root mean square error (RMSE) calculations, affirming its capability to accurately predict ion concentrations across different compartments. The study examines essential parameters such as applied voltage, rinse solution concentration, and feed concentration, assessing their impacts on separation performance and energy efficiency. Results indicate that higher voltages above 8 V, while speeding up separation, detrimentally impact energy use. It also highlights a critical balance in rinse solution concentration; lower concentrations below 0.05 mol/L enhance energy efficiency but may undercut separation efficacy due to early depletion. A linear correlation between the necessary rinse concentration and feed concentration was established, with higher feed concentrations demonstrating reduced specific energy consumption, thus enhancing overall efficiency. However, challenges remain in current efficiency due to the independent migration of SO42− ions in this specific setup. The findings advocate exploring alternative configurations, like alternating cation and anion exchange membranes, to optimize both environmental and economic aspects of REE separation. This study provides valuable insights and recommendations for refining electrodialysis systems in REE processing, contributing to sustainable and cost-effective electrodialysis systems.

Artificial intelligence modeling and simulation of membrane-based separation of water pollutants via ozone Process: Evaluation of separation

The present study offers a comparative examination of regression models that are utilized for the prediction of concentration (C) in a new hybrid ozone-membrane process for removal of water pollutants. The main focus is on the tracking ozone concentration in the feed side of a membrane contactor system. Computational fluid dynamics (CFD) was carried out to obtain data of ozone concentration (C) for developing some machine learning (ML) models. The models are based on input variables r (m) and z (m). The dataset comprises over 10,000 data, and three different models, namely Convolutional Neural Network (CNN), Support Vector Regression (SVR), and Orthogonal Matching Pursuit (OMP), are evaluated. The hyperparameters of these models are optimized using the Glowworm Swarm Optimization (GSO) technique. Prior to model training, preprocessing steps are applied. The findings suggest that SVR exhibited a noteworthy R2 score of 0.99698, surpassing CNN which obtained a R2 score of 0.98073, and OMP which obtained a R2 score of 0.8748. The aforementioned discoveries offer significant perspectives on the utilization of diverse machine learning models in the prognostication of C, demonstrating their efficacy and proficiency in this particular realm.

Analysis of the shear-driven flow in a scale model of a phase separator: Validation of a coupled CFD approach using experimental data from a physical model

The effect of shear stress at the interface between a flowing gaseous phase and a liquid-filled cavity, a scenario commonly found in flue gas/slag phase separators, is investigated in this study. This effect is frequently disregarded in computational fluid dynamics (CFD) simulations within metallurgy, primarily due to its limited influence on the motion of liquids (e.g., slag, liquid metal) when compared to other factors such as stirring by natural convection or bubbling. This study was undertaken to investigate shear-driven flow in a 1:4 scale model of a phase separator in which a flue gas stream coming from a furnace is mobilizing the flow of a slag melt. To cover a wide range of operating conditions, silicone oil with varying viscosities (20, 50, and 70 cSt) was considered for mimicking viscous slag. Additionally, different volumetric airflow rates (18, 27, and 36 m3/h) were considered to simulate the flue gas flow. Particle image velocimetry (PIV) measurements were conducted on the model. The experimental results were utilized to validate computationally efficient, coupled steady-state simulations of the system. Notably, the steady-state simulations, requiring approximately one hour of CPU time, demonstrated a computation time decrease of two magnitudes compared to conventional Volume of Fluid (VOF) modeling. A stable, laminar flow pattern of the liquid phase with surface velocities of up to approximately 30 mm/s was observed for the case with the lowest oil viscosity and the highest airflow rate, while a steady, turbulent flow was observed in the gas phase. Conversely, the lowest surface velocities, measuring around 5 mm/s, were recorded and calculated for the scenario with the highest oil viscosity and the lowest airflow rate. It was noted that velocities increased proportionally with the airflow rate but exhibited a disproportionate relationship with oil viscosity. The prediction of velocity magnitudes by the steady-state CFD model was highly accurate, with only minor discrepancies observed between the measured and calculated flow patterns. In summary, valuable insights into the often-overlooked shear stress effects at the phase boundary in gaseous-liquid flow over a cavity are provided by our study.

Longitudinal patterns of adversity from childhood to adolescence: Examining associations with mental health through emerging adulthood using a random-intercept latent transition analysis.

Childhood adversity can have detrimental impacts on life course mental and physical health. Timing, nature, severity, and chronicity of adversity are thought to explain much of the variability in health and developmental outcomes among exposed individuals. The current study seeks to characterize heterogeneity in adverse experiences over time at the individual, family, and neighborhood domains in a cohort of predominantly Black children (85% Black and 15% White, 46.2% girls, 67.2% free/reduced lunch in first grade), and to examine associations with mental health from sixth grade to age 26. Participants were part of a randomized universal preventive interventions trial in first grade with prospective follow-up through early adulthood. Separate models characterized heterogeneity in adversity in elementary, middle, and high schools. Changes in adversity over time and relationships with mental health (anxiety, depression, suicidal behaviors) were investigated using a random-intercept latent transition analysis (RI-LTA). We identified three-class solutions in early childhood, middle school, and high school. Generally, both a higher and a lower poly-adversity class were observed at each time point, with varying nature of adversity characterized by the third class. RI-LTA indicated prevalent within-individual changes in adverse exposure over time and differential associations with mental health and suicidal behaviors. Results suggest that treating adverse exposures as a static construct may limit the ability to characterize salient variation over time. Identifying complexity in adverse experiences and their relation to health and well-being is key for developing and implementing effective prevention and early intervention efforts to mitigate negative effects through the life course. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Property Separation. Responsibility of Heirs as a Case Study

The paper is devoted to the problem of legal separation of the testator’s property from the property of the heirs. The legal separation of property is understood as giving it a specific legal regime, including the division of property into different pools of assets owed to different creditors. In the comparative legal aspect, the author examines the practical advantages and disadvantages of various models of legal separation of inherited property or the absence of separation. The paper analyzes the problems of Russian regulation of the liability of heirs: the legislative certainty of the liability model; the perception of this model by judicial practice and doctrine; the fairness of the current distribution of risks of insolvency and destruction of inherited property between creditors of the heir and the testator; features of bankruptcy of the estate. Using the case of inherited property, general conclusions are formulated concerning the phenomenon of detached property as an independent phenomenon of civil law, different from the institution of limited liability and the phenomenon of a legal entity.

Numerical Simulations of Thermoacoustic Binary Gas Mixture Batch Separation

In this paper, 2D simulations were carried out to prove the potential of thermoacoustic technology in separating a binary gas mixture. A 2D model of a gas mixture separator was developed, including a loudspeaker responsible for producing acoustic waves in the separation pipe. As a result of the imposed sound waves propagating inside the separator, main parameters including pressure, temperature, and density undergo oscillations, which in turn drive the light and heavy gas components in opposite directions. Through time, one end of the separator is enriched with the light component while the other end is enriched with the heavy one. Simulations were all performed using ANSYS Fluent. The aim was to separate an ideal gas mixture of Helium–Argon and study the impact of different parameters on the separation process.

Maturation and Conformational Switching of a De Novo Designed Phase-Separating Polypeptide.

Cellular compartments formed by biomolecular condensation are widespread features of cell biology. These organelle-like assemblies compartmentalize macromolecules dynamically within the crowded intracellular environment. However, the intermolecular interactions that produce condensed droplets may also create arrested states and potentially pathological assemblies such as fibers, aggregates, and gels through droplet maturation. Protein liquid-liquid phase separation is a metastable process, so maturation may be an intrinsic property of phase-separating proteins, where nucleation of different phases or states arises in supersaturated condensates. Here, we describe the formation of both phase-separated droplets and proteinaceous fibers driven by a de novo designed polypeptide. We characterize the formation of supramolecular fibers in vitro and in bacterial cells. We show that client proteins can be targeted to the fibers in cells using a droplet-forming construct. Finally, we explore the interplay between phase separation and fiber formation of the de novo polypeptide, showing that the droplets mature with a post-translational switch to largely β conformations, analogous to models of pathological phase separation.

Alcohol Consumption and Illicit Drug Use: Associations With Fall, Fracture, and Acute Health Care Utilization Among People With HIV Infection.

Given alcohol and/or other drug (AOD) use occurs among people with HIV (PWH), we examined its association with falls and fall-related outcomes and whether frailty moderates the association. Northeastern US city. We analyzed an observational cohort of PWH with current or past AOD use. Alcohol measures were any past 14-day heavy use, average alcohol/day, and days with heavy use. Drug use measures were past 30-day illicit use of cocaine, opioids, and sedatives. Repeated cross-sectional associations were estimated with separate multivariable generalized estimating equation regression models for each fall-related outcome. Among PWH (n = 251; mean age 52 [SD = 10]), 35% reported heavy alcohol use, 24% cocaine, 16% illicit opioids, 13% illicit sedatives, and 35% any fall; 27% were frail. Heavy alcohol use was associated with a fall (AOR = 1.49, 95% CI: 1.08 to 2.07), multiple falls (AOR = 1.55 95% CI: 1.10 to 2.19), and fall/fracture-related emergency department visit or hospitalization (AOR = 1.81, 95% CI: 1.10 to 2.97). Higher average alcohol/day and more heavy drinking days were associated with multiple falls. Illicit sedative use was associated with a fall, multiple falls, and emergency department visit/hospitalization and opioid use with fracture. Frailty moderated the association of heavy alcohol use and a fall (AOR = 2.26, 95% CI: 1.28 to 4.01 in those frail) but not in those not frail. The effect of AOD use on falls and fall-related outcomes was most pronounced with alcohol, particularly among frail PWH. Heavy alcohol, illicit sedative, and illicit opioid use are high-priority targets for preventing falls and fall-related consequences for PWH.

A robust non-contact heart rate estimation from facial video based on a non-parametric signal extraction model

Remote photoplethysmography (rPPG) is a non-contact method to extract heart rate (HR) pulse signals from facial videos. However, challenges persist due to head motion disturbances and ambient illumination variations. To address these issues, a novel non-parametric signal extraction model is proposed for HR estimation using the webcam camera. We first select the better-quality RGB signal from the divided face regions through the ranking of signal-to-noise ratio (SNR). Then, we use blind source separation and skin reflection modeling to eliminate the effect of ambient illumination. Finally, we employ a non-parametric signal extraction model named circulant singular spectrum analysis (CiSSA), with frequency-based decomposition and energy-driven reconstruction, to extract the HR-related pulse signal effectively. To evaluate the performance of our method, we conducted the validation experiments on three datasets, including two public datasets (UBFC and PURE dataset) and one self-collected dataset (ILVA dataset). Our method outperformed significantly on all three datasets, achieving Mean Absolute Deviation (MAD) of 2.42, 1.36, and 2.84 bpm, Root Mean Square Error (RMSE) of 1.89, 2.24, and 3.58 bpm, and Pearson Correlation Coefficients (PCC) of 0.98, 0.98, and 0.93, respectively. The experimental results demonstrate that our method achieves comparable performance, particularly in scenarios involving severe head motion disturbances and variations in ambient illumination.