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Separations
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New Strategies for Oil-Water Separation
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New Strategies for Oil-Water Separation

A special issue of Separations (ISSN 2297-8739). This special issue belongs to the section "Environmental Separations".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 4112

Special Issue Editors

Prof. Dr. Guoyong Wang

E-Mail Website
Guest Editor
School of Materials Science and Engineering, Jilin University, Changchun 130025, China
Interests: development and application of material surface/interface on self-cleaning; separation; pigment; energy storage and conversion; ultrastrong metal
Prof. Dr. Yan Liu

E-Mail Website
Guest Editor
School of Biological and Agricultural Engineering, Jilin University, Changchun 130025, China
Interests: design and preparation of bionic functional surface based on the surface function of typical biological structure; bionic multifunctional surface design; bionic superhydrophobic; anti-icing/frost; oil–water separation; structural color and other multifunctional surface preparations; development and application of bionic multifunctional surface technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The dispersal of energy and matter is spontaneous. Most substances in nature are mixtures, and human activities can also cause material mixing. However, the more concentrated energy and matter there is, the higher the application value; therefore, both academia and industry are looking for ways to gather scattered materials. One of the most widely used strategies is separation. Human beings have long known how to use density difference to pan for gold, and this technology is still used today as it is energy-saving, economical, and ecofriendly. Therefore, a simple, economical, energy-saving, eco-friendly, fast, and continuous separation method has always been the goal pursued by academia. As such, we are initiating this special issue to discuss the latest developments. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: desalination, dust extraction, gas cleaning, and oil–water separation, discussed under one, or a combination, of the following headings:

  1. Research status and future development trends;
  2. Theoretical analysis and computational modeling;
  3. Advanced separation equipment;
  4. Novel preparation technology of multifunctional separation materials.

We look forward to receiving your contributions.

Prof. Dr. Guoyong Wang
Prof. Dr. Yan Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Separations is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • desalination
  • dust extraction
  • gas cleaning
  • oil–water separation
  • wettability
  • absorption

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Published Papers (2 papers)

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Research

16 pages, 2897 KiB  
Article
Increasing the Accuracy and Optimizing the Structure of the Scale Thickness Detection System by Extracting the Optimal Characteristics Using Wavelet Transform
by Abdulilah Mohammad Mayet, Tzu-Chia Chen, Seyed Mehdi Alizadeh, Ali Awadh Al-Qahtani, Ramy Mohammed Aiesh Qaisi, Hala H. Alhashim and Ehsan Eftekhari-Zadeh
Separations 2022, 9(10), 288; https://doi.org/10.3390/separations9100288 - 5 Oct 2022
Cited by 1 | Viewed by 1403
Abstract
Loss of energy, decrement of efficiency, and decrement of the effective diameter of the oil pipe are among the consequences of scale inside oil condensate transfer pipes. To prevent these incidents and their consequences and take timely action, it is important to detect [...] Read more.
Loss of energy, decrement of efficiency, and decrement of the effective diameter of the oil pipe are among the consequences of scale inside oil condensate transfer pipes. To prevent these incidents and their consequences and take timely action, it is important to detect the amount of scale. One of the accurate diagnosis methods is the use of non-invasive systems based on gamma-ray attenuation. The detection method proposed in this research consists of a detector that receives the radiation sent by the gamma source with dual energy (radioisotopes 241Am and 133Ba) after passing through the test pipe with inner scale (in different thicknesses). This structure was simulated by Monte Carlo N Particle code. The simulation performed in the test pipe included a three-phase flow consisting of water, gas, and oil in a stratified flow regime in different volume percentages. The signals received by the detector were processed by wavelet transform, which provided sufficient inputs to design the radial basis function (RBF) neural network. The scale thickness value deposited in the pipe can be predicted with an MSE of 0.02. The use of a detector optimizes the structure, and its high accuracy guarantees the usefulness of its use in practical situations. Full article
(This article belongs to the Special Issue New Strategies for Oil-Water Separation)
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14 pages, 4542 KiB  
Article
Accurate Flow Regime Classification and Void Fraction Measurement in Two-Phase Flowmeters Using Frequency-Domain Feature Extraction and Neural Networks
by Siavash Hosseini, Abdullah M. Iliyasu, Thangarajah Akilan, Ahmed S. Salama, Ehsan Eftekhari-Zadeh and Kaoru Hirota
Separations 2022, 9(7), 160; https://doi.org/10.3390/separations9070160 - 24 Jun 2022
Cited by 8 | Viewed by 2133
Abstract
Two-phase flow is very important in many areas of science, engineering, and industry. Two-phase flow comprising gas and liquid phases is a common occurrence in oil and gas related industries. This study considers three flow regimes, including homogeneous, annular, and stratified regimes ranging [...] Read more.
Two-phase flow is very important in many areas of science, engineering, and industry. Two-phase flow comprising gas and liquid phases is a common occurrence in oil and gas related industries. This study considers three flow regimes, including homogeneous, annular, and stratified regimes ranging from 5–90% of void fractions simulated via the Mont Carlo N-Particle (MCNP) Code. In the proposed model, two NaI detectors were used for recording the emitted photons of a cesium 137 source that pass through the pipe. Following that, fast Fourier transform (FFT), which aims to transfer recorded signals to frequency domain, was adopted. By analyzing signals in the frequency domain, it is possible to extract some hidden features that are not visible in the time domain analysis. Four distinctive features of registered signals, including average value, the amplitude of dominant frequency, standard deviation (STD), and skewness were extracted. These features were compared to each other to determine the best feature that can offer the best separation. Furthermore, artificial neural network (ANN) was utilized to increase the efficiency of two-phase flowmeters. Additionally, two multi-layer perceptron (MLP) neural networks were adopted for classifying the considered regimes and estimating the volumetric percentages. Applying the proposed model, the outlined flow regimes were accurately classified, resulting in volumetric percentages with a low root mean square error (RMSE) of 1.1%. Full article
(This article belongs to the Special Issue New Strategies for Oil-Water Separation)
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