巻号一覧
46 巻 (2021)
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46 巻, 6 号
選択された号の論文の8件中1～8を表示しています
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特集：機械学習と膜工学の接点を探る
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高羽 洋充, 宮川 雅矢2021 年 46 巻 6 号 p. 318-324
発行日: 2021年
公開日: 2021/12/12
ジャーナル 認証ありResearch 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.抄録全体を表示PDF形式でダウンロード (1032K) -
宮里 一旗, 高橋 啓介2021 年 46 巻 6 号 p. 325-330
発行日: 2021年
公開日: 2021/12/12
ジャーナル 認証ありThe 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.抄録全体を表示PDF形式でダウンロード (786K) -
南雲 亮2021 年 46 巻 6 号 p. 331-337
発行日: 2021年
公開日: 2021/12/12
ジャーナル 認証ありAn 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.抄録全体を表示PDF形式でダウンロード (1125K) -
金子 弘昌2021 年 46 巻 6 号 p. 338-344
発行日: 2021年
公開日: 2021/12/12
ジャーナル 認証ありIn 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.抄録全体を表示PDF形式でダウンロード (844K) -
福西 快文2021 年 46 巻 6 号 p. 345-352
発行日: 2021年
公開日: 2021/12/14
ジャーナル 認証ありRegression–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.抄録全体を表示PDF形式でダウンロード (1487K) -
森田 裕史, 本田 隆, 室賀 駿, 中島 秀朗, 清水 太陽, 小橋 和文, 岡崎 俊也, 畠 賢治2021 年 46 巻 6 号 p. 353-358
発行日: 2021年
公開日: 2021/12/12
ジャーナル 認証ありVirtual 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.抄録全体を表示PDF形式でダウンロード (845K)
特別寄稿
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髙木 良助2021 年 46 巻 6 号 p. 359-368
発行日: 2021年
公開日: 2021/12/12
ジャーナル 認証ありI 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.抄録全体を表示PDF形式でダウンロード (935K)
製品 & 技術
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中塚 修志, 浜田 敏充, 井上 洋司2021 年 46 巻 6 号 p. 369-372
発行日: 2021年
公開日: 2021/12/12
ジャーナル 認証ありWe 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.抄録全体を表示PDF形式でダウンロード (858K)
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