Polyetherimide-Montmorillonite Nano-Hybrid Composite Membranes: CO2 Permeance Study via Theoretical Models

The incorporation of aminolauric acid modified montmorillonite (f-MMT) in polyetherimide (PEI) has been implemented to develop hollow fibre nano-hybrid composite membranes (NHCMs) with improved gas separation characteristics. The aforementioned characteristics are caused by enhanced f-MMT spatial dispersion and interfacial interactions with PEI matrix. In this study, existing gas permeation models such as, Nielsen, Cussler, Yang–Cussler, Lape–Cussler and Bharadwaj were adopted to estimate the dispersion state of f-MMT and to predict the CO2 permeance in developed NHCMs. It was found out that the average aspect ratio estimated was 53, with 3 numbers of stacks per unit tactoid, which showed that the intercalation f-MMT morphology is the dominating dispersion state of filler in PEI matrix. Moreover, it was observed that Bharadwaj model showed the least average absolute relative error (%AARE) values till 3 wt. % f-MMT loading in the range of ±10 for a pressure range of 2 to 10 bar. Hence, Bharadwaj was the best fit model for the experimental data compared to other models, as it considers the platelets orientation.

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Shut-in Stabilization Time Optimization for Better Reservoir Pressure Monitoring Harnessing Neural Network Modeling

10.2118/204900-ms ◽

2021 ◽

Author(s):

Mohammad Al Kadem ◽

Ali Al Ssafwany ◽

Ahmed Abdulghani ◽

Hussain Al Nasir

Keyword(s):

Neural Network ◽

Neural Network Modeling ◽

Stabilization Time ◽

Pressure Derivative ◽

Absolute Relative Error ◽

Pressure Sensitive ◽

Time Optimization ◽

Average Absolute Relative Error ◽

Study Results ◽

Artificial Neural Network Ann

Abstract Stabilization time is an essential key for pressure measurement accuracy. Obtaining representative pressure points in build-up tests for pressure-sensitive reservoirs is driven by optimizing stabilization time. An artificial intelligence technique was used in the study for testing pressure-sensitive reservoirs using measuring gauges. The stabilization time function of reservoir characteristics is generally calculated using the diffusivity equation where rock and fluid properties are honored. The artificial neural network (ANN) technique will be used to predict the stabilization time and optimize it using readily available and known inputs or parameters. The values obtained from the formula known as the diffusion formula and the ANN technique are then compared against the actual values measured from pressure gauges in the reservoirs. The optimization of the number of datasets required to be fed to the network to allow for coverage over the whole range is essential as opposed to the clustering of the datasets. A total of about 3000 pressure derivative samples from the wells were used in the testing, training, and validation of the ANN. The datasets are optimized by dividing them into three fractional parts, and the number optimized through monitoring the ANN performance. The optimization of the stabilization time is essential and leads to the improvement of the ANN learning process. The sensitivity analysis proves that the use of the formula and ANN technique, compared to actual datasets, is better since, in the formula and ANN technique, the time was optimized with an average absolute relative error of 3.67%. The results are near the same, especially when the ANN technique undergoes testing using known and easily available parameters. Time optimization is essential since discreet points or datasets in the ANN technique and formula would not work, allowing ANN to work in situations of optimization. The study was expected to provide additional data and information, considering that stabilization time is essential in obtaining the pressure map representation. ANN is a superior technique and, through its superiority, allows for proper optimization of time as a parameter. Thus it can predict reservoir log data almost accurately. The method used in the study shows the importance of optimizing pressure stabilization time through reduction. The study results can, therefore, be applied in reservoir testing to achieve optimal results.

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Gas Permeation Characteristics of CO2 and N2 through PEBAX/ZIF-8 and PEBAX/amineZIF-8 Composite Membranes

Membrane Journal ◽

10.14579/membrane_journal.2020.30.6.409 ◽

2020 ◽

Vol 30 (6) ◽

pp. 409-419

Author(s):

Se Ryeong Hong ◽

◽

So Young O ◽

Hyun Kyung Lee

Keyword(s):

Composite Membranes ◽

Gas Permeation

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Characterization of gas permeation in the pores of Zn(II)-based metal organic framework (MOF)/polymer composite membranes

Separation Science and Technology ◽

10.1080/01496395.2019.1646283 ◽

2019 ◽

Vol 55 (14) ◽

pp. 2604-2614

Author(s):

Carlos Landaverde-Alvarado ◽

Amanda J. Morris ◽

Stephen M. Martin

Keyword(s):

Polymer Composite ◽

Metal Organic Framework ◽

Composite Membranes ◽

Gas Permeation ◽

Metal Organic ◽

Organic Framework

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Polyvinylamine Membranes Containing Graphene-Based Nanofillers for Carbon Capture Applications

Membranes ◽

10.3390/membranes9090119 ◽

2019 ◽

Vol 9 (9) ◽

pp. 119 ◽

Cited By ~ 3

Author(s):

Casadei ◽

Venturi ◽

Giacinti Baschetti ◽

Giorgini ◽

Maccaferri ◽

...

Keyword(s):

Graphene Oxide ◽

Carbon Capture ◽

Mechanical Stability ◽

Composite Membranes ◽

Sem Analysis ◽

Gas Permeation ◽

Separation Performance ◽

Low Grade ◽

Atr Spectroscopy ◽

Few Layer Graphene

In the present study, the separation performance of new self-standing polyvinylamine (PVAm) membranes loaded with few-layer graphene (G) and graphene oxide (GO) was evaluated, in view of their use in carbon capture applications. PVAm, provided by BASF as commercial product named LupaminTM, was purified obtaining PVAm films with two degrees of purification: Low Grade (PVAm-LG) and High Grade (PVAm-HG). These two-grade purified PVAm were loaded with 3 wt% of graphene and graphene oxide to improve mechanical stability: indeed, pristine tested materials proved to be brittle when dry, while highly susceptible to swelling in humid conditions. Purification performances were assessed through FTIR-ATR spectroscopy, DSC and TGA analysis, which were carried out to characterize the pristine polymer and its nanocomposites. In addition, the membranes′ fracture surfaces were observed through SEM analysis to evaluate the degree of dispersion. Water sorption and gas permeation tests were performed at 35 °C at different relative humidity (RH), ranging from 50% to 95%. Overall, composite membranes showed improved mechanical stability at high humidity, and higher glass transition temperature (Tg) with respect to neat PVAm. Ideal CO2/N2 selectivity up to 80 was measured, paired with a CO2 permeability of 70 Barrer. The membranes’ increased mechanical stability against swelling, even at high RH, without the need of any crosslinking, represents an interesting result in view of possible further development of new types of facilitated transport composite membranes.

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A highly permeable hollow fibre substrate for Pd/Al2O3 composite membranes in hydrogen permeation

International Journal of Hydrogen Energy ◽

10.1016/j.ijhydene.2015.01.021 ◽

2015 ◽

Vol 40 (8) ◽

pp. 3249-3258 ◽

Cited By ~ 18

Author(s):

Ana Gouveia Gil ◽

Miria Hespanhol M. Reis ◽

David Chadwick ◽

Zhentao Wu ◽

K. Li

Keyword(s):

Hydrogen Permeation ◽

Composite Membranes ◽

Hollow Fibre ◽

Al2o3 Composite

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An Improved CO2-Crude Oil Minimum Miscibility Pressure Correlation

Journal of Chemistry ◽

10.1155/2015/175940 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Hao Zhang ◽

Dali Hou ◽

Kai Li

Keyword(s):

Experimental Data ◽

Crude Oil ◽

Relative Error ◽

Oil And Gas ◽

Empirical Correlation ◽

Volatile Components ◽

Absolute Relative Error ◽

Minimum Miscibility Pressure ◽

New Correlation ◽

Average Absolute Relative Error

Minimum miscibility pressure (MMP), which plays an important role in miscible flooding, is a key parameter in determining whether crude oil and gas are completely miscible. On the basis of 210 groups of CO2-crude oil system minimum miscibility pressure data, an improved CO2-crude oil system minimum miscibility pressure correlation was built by modified conjugate gradient method and global optimizing method. The new correlation is a uniform empirical correlation to calculate the MMP for both thin oil and heavy oil and is expressed as a function of reservoir temperature, C7+molecular weight of crude oil, and mole fractions of volatile components (CH4and N2) and intermediate components (CO2, H2S, and C2~C6) of crude oil. Compared to the eleven most popular and relatively high-accuracy CO2-oil system MMP correlations in the previous literature by other nine groups of CO2-oil MMP experimental data, which have not been used to develop the new correlation, it is found that the new empirical correlation provides the best reproduction of the nine groups of CO2-oil MMP experimental data with a percentage average absolute relative error (%AARE) of 8% and a percentage maximum absolute relative error (%MARE) of 21%, respectively.

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