scholarly journals Implementation of Spiegler–Kedem and Steric Hindrance Pore Models for Analyzing Nanofiltration Membrane Performance for Smart Water Production

Membranes ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 78 ◽  
Author(s):  
Remya Nair ◽  
Evgenia Protasova ◽  
Skule Strand ◽  
Torleiv Bilstad
Keyword(s):  
Mass Transfer ◽  
Reflection Coefficient ◽  
Oil Recovery ◽  
Water Permeability ◽  
Structural Parameters ◽  
Pure Water ◽  
Model Parameters ◽  
Water Production ◽  
Solute Permeability ◽  
Smart Water

A predictive model correlating the parameters in the mass transfer-based model Spiegler–Kedem to the pure water permeability is presented in this research, which helps to select porous polyamide membranes for enhanced oil recovery (EOR) applications. Using the experimentally obtained values of flux and rejection, the reflection coefficient σ and solute permeability Ps have been estimated as the mass transfer-based model parameters for individual ions in seawater. The reflection coefficient and solute permeability determined were correlated with the pure water permeability of a membrane, which is related to the structural parameters of a membrane. The novelty of this research is the development of a model that consolidates the various complex mechanisms in the mass transfer of ions through the membrane to an empirical correlation for a given feed concentration and membrane type. These correlations were later used to predict ion rejections of any polyamide membrane with a known pure water permeability and flux with seawater as a feed that aids in the selection of suitable nanofiltration (NF) for smart water production.

An Analysis of Unicellular Mass Transfer Using a Microfabricated Experimental Technique

2000 ◽  
Author(s):  
Kelvan Howard ◽  
Boris Rubinsky
Keyword(s):  
Mass Transfer ◽  
Standard Deviation ◽  
Reflection Coefficient ◽  
Membrane Permeability ◽  
Experimental Technique ◽  
Water Permeability ◽  
Permeability Coefficient ◽  
Volume Changes ◽  
Flow Paths

Abstract This study introduces a microfabricated chamber which allows individual cells to be maintained between two different flow paths while kinetic volume changes and membrane permeability are measured. For HBL-100 cells, the data yielded a water permeability (Lp) of 1.1 ±0.5 μm/(min-atm) (mean ± 1 standard deviation) (N = 5) during the uncoupled transport of water at 22°C. In the presence of 6M glycerol, the water permeability (Lp), permeability coefficient (Ps), and the reflection coefficient (σs) were determined to be 2.0± 0.63 μm/(min-atm), 2.7×10E-5 ±6.1×10E-6 cm-sec−1, and 0.76±.05 (N = 6). No previous values of these coefficients could be found for HBL-100 cells.

Nanoparticle Mass Transfer From Lung Airways to Systemic Regions—Part II: Multi-Compartmental Modeling

2013 ◽  
Vol 135 (12) ◽  
Author(s):  
Arun V. Kolanjiyil ◽  
Clement Kleinstreuer
Keyword(s):  
Mass Transfer ◽  
Model Parameters ◽  
Computationally Efficient ◽  
Nanoparticle Deposition ◽  
Aerosol Dynamics ◽  
Multicompartmental Model ◽  
Quantitative Results ◽  
Species Extrapolation ◽  
Critical Insight ◽  
Lung Airways

This is the second article of a two-part paper, combining high-resolution computer simulation results of inhaled nanoparticle deposition in a human airway model (Kolanjiyil and Kleinstreuer, 2013, “Nanoparticle Mass Transfer From Lung Airways to Systemic Regions—Part I: Whole-Lung Aerosol Dynamics,” ASME J. Biomech. Eng., 135(12), p. 121003) with a new multicompartmental model for insoluble nanoparticle barrier mass transfer into systemic regions. Specifically, it allows for the prediction of temporal nanoparticle accumulation in the blood and lymphatic systems and in organs. The multicompartmental model parameters were determined from experimental retention and clearance data in rat lungs and then the validated model was applied to humans based on pharmacokinetic cross-species extrapolation. This hybrid simulator is a computationally efficient tool to predict the nanoparticle kinetics in the human body. The study provides critical insight into nanomaterial deposition and distribution from the lungs to systemic regions. The quantitative results are useful in diverse fields such as toxicology for exposure-risk analysis of ubiquitous nanomaterial and pharmacology for nanodrug development and targeting.

An Overview: Analysis of Heat and Mass Transfer in Fractal Media by Fractal Geometry and Technique

2010 ◽  
Author(s):  
Boming Yu
Keyword(s):  
Heat Transfer ◽  
Mass Transfer ◽  
Porous Media ◽  
Heat And Mass Transfer ◽  
Oil Recovery ◽  
Fractal Geometry ◽  
Boiling Heat Transfer ◽  
Nucleate Boiling ◽  
Fractal Media ◽  
Nucleate Boiling Heat Transfer

In the past three decades, fractal geometry and technique have received considerable attention due to its wide applications in sciences and technologies such as in physics, mathematics, geophysics, oil recovery, material science and engineering, flow and heat and mass transfer in porous media etc. The fractal geometry and technique may become particularly powerful when they are applied to deal with random and disordered media such as porous media, nanofluids, nucleate boiling heat transfer. In this paper, a summary of recent advances is presented in the areas of heat and mass transfer in fractal media by fractal geometry technique. The present overview includes a brief summary of the fractal geometry technique applied in the areas of heat and mass transfer; thermal conductivities of porous media and nanofluids; nucleate boiling heat transfer. A few comments are made with respect to the theoretical studies that should be made in the future.

Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

2021 ◽  
pp. 1-18
Author(s):  
Gisela Vanegas ◽  
John Nejedlik ◽  
Pascale Neff ◽  
Torsten Clemens
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
Numerical Models ◽  
Operating Conditions ◽  
Model Parameters ◽  
Large Set ◽  
Model Parameter ◽  
Production History ◽  
Hydrocarbon Fields ◽  
Parameter Distributions

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

Identification of Structural Parameters in Mistuned Bladed Disks

1997 ◽  
Vol 119 (3) ◽  
pp. 428-438 ◽  
Author(s):  
Marc P. Mignolet ◽  
Chung-Chih Lin
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Structural Model ◽  
Structural Parameters ◽  
Model Parameters ◽  
Disk Model ◽  
Response Data ◽  
State Response ◽  
Bladed Disk ◽  
True Model

The present investigation focused on the estimation of the parameters of a structural model to represent “at best” a set of measurements of the steady state response of a mistuned bladed disk. The applicability of the least squares and maximum likelihood approaches to the identification of the bladed disk model from this data is first investigated. The advantages and drawbacks of these techniques motivate the introduction of a new mixed least squares-maximum likelihood formulation which is shown to recover well the true model parameters from noisy simulated response data.

scholarly journals Modeling of the adsorption kinetics of zinc onto granular activated carbon and natural zeolite

2006 ◽  
Vol 71 (8-9) ◽  
pp. 957-967 ◽  
Author(s):  
Ljiljana Markovska ◽  
Vera Meshko ◽  
Mirko Marinkovski
Keyword(s):  
Mass Transfer ◽  
Activated Carbon ◽  
Simulation Study ◽  
Natural Zeolite ◽  
Granular Activated Carbon ◽  
Model Parameters ◽  
Mass Transfer Resistance ◽  
External Mass Transfer ◽  
Transfer Resistance ◽  
Kinetics Of

The isotherms and kinetics of zinc adsorption from aqueous solution onto granular activated carbon (GAC) and natural zeolite were studied using an agitated batch adsorber. The maximum adsorption capacities of GAC and natural zeolite towards zinc(II) from Langmuir adsorption isotherms were determined using experimental adsorption equilibrium data. The homogeneous solid diffusion model (HSD-model) combined with external mass transfer resistance was applied to fit the experimental kinetic data. The kinetics simulation study was performed using a computer program based on the proposed mathematical model and developed using gPROMS. As the two-mass transfer resistance approach was applied, two model parameters were fitted during the simulation study. External mass transfer and solid phase diffusion coefficients were obtained to predict the kinetic curves for varying initial Zn(II) concentration at constant agitation speed and constant adsorbent mass. For any particular Zn(II) - adsorbent system, k f was constant, except for the lowest initial concentration, while D s was found to increase with increasing initial Zn(II) concentration.

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