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SCEJ 55th Autumn Meeting (Sapporo, 2024)

Last modified: 2024-09-19 02:24:28

Session programs : ST-22 : E317

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ST-22 [Trans-Division Symposium]
Frontiers of Data-driven Research and Development

Organizers: Shimada Iori (Shinshu Univ.), Kim Sanghong (Tokyo Univ. of Agri. and Tech.), Toya Yoshihiro (Osaka Univ.), Yoshida Hideaki (Sumitomo Chemical), Mukaida Shiho (Mitsui Chemicals), Muroga Shun (AIST), Sugawara Yuuki (Tokyo Tech)

Data science has been rapidly developing in recent years as the fourth science following experimental science, theoretical science, and computational science. In the field of chemical engineering, data-driven science, which derives superior materials and processes by making full use of a large amount of accumulated data and information, is becoming increasingly important, and many efforts are being made. This symposium will have speakers who are making pioneering efforts toward a data-driven society from various viewpoints and discuss future research and development.

Hall E, Day 2 | Hall E, Day 3

TimePaper
ID
Title / AuthorsKeywordsTopic codeAck.
number
Hall E(Block B1 1F B11), Day 2(Sep. 12)
(13:00–14:20) (Chair: Kim Sanghong)
13:0013:20E213Calculating Potential of Solar Cell on Building Façades for Japan's Energy System Optimization in 2050
(Tokyo Tech) *(Stu)Wang Shuai, (Stu)Oya Masashi, (Stu)Otoshi Natsuki, (Reg)Hamasaki Hiroshi, (Reg)Kameda Keisuke, (Reg)Manzhos Sergei, (Reg)Ihara Manabu
Solar cell
facade installation
Energy system optimization
ST-22580
13:2013:40E214Prediction model for real-time power generation of solar cells with shadow simulation based on module structure and irradiance fraction
(Tokyo Tech) *(Stu)Otoshi Natsuki, Kasai Yuya, (Reg)Kameda Keisuke, (Reg)Manzhos Sergei, (Reg)Ihara Manabu
Solar cell
Energy system
Prediction model
ST-22865
13:4014:00E215Analysis of anion selectivity in Mg-based Layered Double Hydroxide using a Universal Neural Network Potential
(Shinshu U. RISM) *(Reg)Aspera Susan Menez, Chen Yingjie, Nguyen Tien Quang, (Reg)Koyama Michihisa
machine learning
layered double hydroxide (LDH)
anion selectivity
ST-22732
14:0014:20E216Molecular design using deep learning and vecror annealing
(NEC) (Reg)Ishida Masahiko
Deep learning
Quantum anealing
Generative model
ST-221099
(14:40–16:00) (Chair: Sugawara Yuuki)
14:4015:00E218[The Outstanding Paper Award] Optimization of metal nanoparticle synthesis conditions using automated flow system and machine learning
(AIST) *(Reg)Ono Takumi, (Reg)Takebayashi Yoshihiro, (ADMAT) Kashiwagi Tsuneo, (AIST) (Reg)Sue Kiwamu
Nanoparticle
Microreactor
Machine Learning
ST-22448
15:0015:20E219Statistical consideration for investigating a direct correlation between solvent effect and hydrogenation of nitrobenzene using flow-tubular reactor
(AIST) *(Reg)Wahyudianto Benny, (Reg)Yamaki Takehiro, (Reg)Hara Nobuo, (Reg)Takebayashi Yoshihiro, (Reg)Kataoka Sho
statistical analysis
hydrogenation
flow reactor
ST-22121
15:2015:40E220Development of a product composition prediction model for catalytic cracking reactions and its application to the prediction of reactions with unknown feedstocks
(Shinshu U.) *(Reg)Shimada Iori, Kodama Yuhei, Yasuike Shun
physics-informed machine learning
catalytic cracking
reaction prediction
ST-221128
15:4016:00E221Automated reaction process analysis using data-driven approaches
(U. Tokyo/Auxilart) *(Reg)Kim Junu, (Riken) Sakata Itsushi, (Independent Researcher) Yamatsuta Eitaro, (U. Tokyo) (Reg)Sugiyama Hirokazu
Dynamic mode decomposition
Chemical reaction
Machine learning
ST-22969
Hall E(Block B1 1F B11), Day 3(Sep. 13)
(9:00–10:20) (Chair: Shimada Iori)
9:009:20E301Development of a transfer learning method using O-PLS
(Kyoto U.) *(Stu)Fukuoka Norihiko, (Reg)Sotowa Ken-Ichiro, (TUAT) (Reg·APCE)Kim Sanghong
O-PLS
transfer learning
calibration transfer
ST-22311
9:209:40E302Application of Transfer Learning Using Generative Adversarial Networks to Virtual Sensing
(Kyoto U.) Kasuga Yasuko, Yoshida Ryoya, (Reg)Kato Shota, *(Reg)Kano Manabu
transfer learning
soft-sensor
Generative Adversarial Networks
ST-2266
9:4010:00E303Control of foam on white water and improvement of operational efficiency by modeling a soft sensor
(RENGO) *(Reg)Okura Y., Inui T., Okahara K.
soft sensor
machine learning
factory automation
ST-2295
10:0010:20E30413C-MFA software with a support function for high accuracy flux estimations by proposing additional experiments
(Osaka U.) *(Reg)Imada Tatsumi, (Reg)Shimizu Hiroshi, (Reg)Toya Yoshihiro
13C-metabolic flux analysis
flux analysis
software
ST-22615
(10:40–12:00) (Chair: Muroga Shun)
10:4011:20E306[Invited lecture] Autonomous Materials Search
(NIMS) Iwasaki Yuma
Machine learning
Autonomous
Active learning
ST-22145
11:2012:00E308[Invited lecture] Representation and Generation of Crystal Structures with Deep Learning
(Toyota Motor) Suzuki Yuta
Materials informatics
Deep learning
Crystal structure
ST-22894
(13:00–13:40) (Chair: Mukaida Shiho)
13:0013:40E313[Invited lecture] Introduction to Case Studies on the Development and Utilization of Informatics Technologies
(Mitsubishi Chemical) (Cor)Sugisawa Hiroki
Informatics
Industry
R&D acceleration
ST-22144
(13:40–14:20) (Chair: Yoshida Hideaki)
13:4014:20E315[Invited lecture] Digital infrastructure to reduce time to market / R&D to production
(Microsoft Japan) Yasunami Yutaka
digital
AI
data integration
ST-22616
(14:20–15:00) (Chair: Toya Yoshihiro)
14:2015:00E317[Invited lecture] Understanding the diversity of metabolisms with the advances of computational metabolomics
(TUAT) Tsugawa Hiroshi
metabolomics
mass spectrometry
informatics
ST-22142

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SCEJ 55th Autumn Meeting (Sapporo, 2024)


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