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Volume 46 (2021)
- Issue 6 Pages 318-
- Issue 5 Pages 248-
- Issue 4 Pages 172-
- Issue 3 Pages 118-
- Issue 2 Pages 58-
- Issue 1 Pages 2-
Volume 46, Issue 6
Displaying 1-8 of 8 articles from this issue
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Special Topic : Exploring the point of contact between machine learning and membrane engineering
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Hiromitsu Takaba, Masaya Miyagawa2021 Volume 46 Issue 6 Pages 318-324
Published: 2021
Released on J-STAGE: December 12, 2021
JOURNAL RESTRICTED ACCESSResearch on processes and materials using machine learning has been attracting attention. Machine learning is a methodology for finding correlations among the vast amount of empirically accumulated measurement data, and it is expected to be used in the field of membrane engineering. Machine learning is completely different from the analysis approaches that have been used in the membrane engineering field. Therefore, if properly applied, it is expected to make it possible to model target systems that could not be analyzed in the past. In this paper, we will review the past researches of this new analysis method, machine learning, applied in the field of membrane engineering.View full abstractDownload PDF (1032K) -
Itsuki Miyazato, Keisuke Takahashi2021 Volume 46 Issue 6 Pages 325-330
Published: 2021
Released on J-STAGE: December 12, 2021
JOURNAL RESTRICTED ACCESSThe introduction of materials informatics has changed the way of understanding and designing the materials. The idea is to design materials from materials data utilizing the data science techniques such as data visualization and machine learning. Here, the concept and methods taken in materials informatics are explored along its applications, thus, it helps to understand the how materials informatics are used in materials science.View full abstractDownload PDF (786K) -
Ryo Nagumo2021 Volume 46 Issue 6 Pages 331-337
Published: 2021
Released on J-STAGE: December 12, 2021
JOURNAL RESTRICTED ACCESSAn estimation of macroscopic fundamental properties is important for efficient materials design. In our recent studies, we have discussed the correlation between a macroscopic property (diffusivity) and a microscopic criterion (residence time) by using molecular dynamics simulations for vinylpyrrolidone analogs/water molecules binary mixtures and ethylene glycol–based solutions. The residence time is strongly correlated with the diffusivity in these systems. We believe that our studies lead to the systematic prediction of the molecular diffusivity from the theoretical calculation of the residence time.View full abstractDownload PDF (1125K) -
Hiromasa Kaneko2021 Volume 46 Issue 6 Pages 338-344
Published: 2021
Released on J-STAGE: December 12, 2021
JOURNAL RESTRICTED ACCESSIn researching, developing, and manufacturing highly functional materials such as membrane materials, it becomes common to utilize chemical data and chemical engineering data for machine learning to improve the efficiency of molecular design, material design, process design, and process control. It is important to construct mathematical model y ＝f (x) with high predictive ability between explanatory variables x and objective variables y, and then, y values can be predicted from x values using the constructed model, and x values can be designed to meet target y values. In this article, as examples of research in chemoinformatics, materials informatics, and process informatics, the estimation of prediction errors in new samples, modeling of metal–organic frameworks with machine learning, adaptive design of experiments with direct inverse analysis for designs of molecules, materials, and processes, and prediction of future transmembrane pressure in a drinking water treatment process are introduced.View full abstractDownload PDF (844K) -
Yoshifumi Fukunishi2021 Volume 46 Issue 6 Pages 345-352
Published: 2021
Released on J-STAGE: December 14, 2021
JOURNAL RESTRICTED ACCESSRegression–prediction models based on Fick’s law are popular approaches in prediction of membrane permeability rather than time–consuming molecular dynamics simulations of permeability process, but the prediction accuracies of these models remain insufficient for drug design, especially design of druggable macrocyclic molecules. In this review, we discuss the framework of mechanism–based regression model and modifications of the model based on the experiments and theoretical calculation.View full abstractDownload PDF (1487K) -
Hiroshi Morita, Takashi Honda, Shun Muroga, Hideaki Nakajima, Taiyo Sh ...2021 Volume 46 Issue 6 Pages 353-358
Published: 2021
Released on J-STAGE: December 12, 2021
JOURNAL RESTRICTED ACCESSVirtual experiment of carbon nanotube (CNT) film based on artificial intelligence is examined. As reported in many previous papers, artificial intelligence can create many kinds of images and data and using those technics many researchers have tried to develop the high functional materials using AI. In this study, one of the deep learning technics, such as generative adversarial network, is introduced to derive the images and properties of CNT films, and its application is explained.View full abstractDownload PDF (845K)
Special Contribution
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Ryosuke Takagi2021 Volume 46 Issue 6 Pages 359-368
Published: 2021
Released on J-STAGE: December 12, 2021
JOURNAL RESTRICTED ACCESSI was requested to write an article about the history, current situation, future etc. related to my research field. My research topic is “Characterization of membrane charge, and modeling of membrane”. I have characterized the membrane charge by analyzing the membrane potential, and have investigated the effect of membrane charge on the ion flux through membrane. I don’t think any researcher would argue with that the membrane charge significantly affects the membrane performance. Even now, we spot the term “membrane potential” in some papers, especially, papers related with ion exchange membranes. However, at present, I guess that none of the researchers in the world studies on membrane potential except me. Thus, it is difficult to review the present situation and future in this filed. In this article, the progress of my research and memories during research life is reported reflecting on my research life.View full abstractDownload PDF (935K)
Products Spotlight
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Shunji Nakatsuka, Toshimitsu Hamada, Yoji Inoue2021 Volume 46 Issue 6 Pages 369-372
Published: 2021
Released on J-STAGE: December 12, 2021
JOURNAL RESTRICTED ACCESSWe have newly developed the tubular reverse osmosis (RO) membrane / nanofiltration (NF) membrane module for wastewater treatment. The membrane has the less fouling properties due to the membrane material of hydrophilic cellulose acetate and has the higher mechanical strength properties due to the support layer. The salt rejections of the RO/NF membrane are designed from 70 to 90% for highly turbid and oily wastewater treatment. The membrane modules are applied for various oily wastewater treatments and achieve extremely high reduction of wastewater volume of 1/40 to 1/20 due to high concentration of wastewater. We expect to utilize the tubular RO/NF technology to zero liquid discharge (ZLD) system in near future.View full abstractDownload PDF (858K)
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