E317 - ST-22 - Session programs - SCEJ 55th Autumn Meeting (Sapporo, 2024)

`SCEJ 55th Autumn Meeting (Sapporo, 2024)`

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

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Session programs : ST-22 : E317

The preprints are now open. These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants and invited persons are required.

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

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
Hall E(Block B1 1F B11), Day 2(Sep. 12)
(13:00–14:20) (Chair: Kim Sanghong)
13:00– 13:20	E213	Calculating 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-22	580
13:20– 13:40	E214	Prediction 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-22	865
13:40– 14:00	E215	Analysis 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-22	732
14:00– 14:20	E216	Molecular design using deep learning and vecror annealing (NEC) (Reg)Ishida Masahiko	Deep learning Quantum anealing Generative model	ST-22	1099
(14:40–16:00) (Chair: Sugawara Yuuki)
14:40– 15:00	E218	[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-22	448
15:00– 15:20	E219	Statistical 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-22	121
15:20– 15:40	E220	Development 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-22	1128
15:40– 16:00	E221	Automated 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-22	969
Hall E(Block B1 1F B11), Day 3(Sep. 13)
(9:00–10:20) (Chair: Shimada Iori)
9:00– 9:20	E301	Development 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-22	311
9:20– 9:40	E302	Application 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-22	66
9:40– 10:00	E303	Control 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-22	95
10:00– 10:20	E304	¹³C-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-22	615
(10:40–12:00) (Chair: Muroga Shun)
10:40– 11:20	E306	[Invited lecture] Autonomous Materials Search (NIMS) Iwasaki Yuma	Machine learning Autonomous Active learning	ST-22	145
11:20– 12:00	E308	[Invited lecture] Representation and Generation of Crystal Structures with Deep Learning (Toyota Motor) Suzuki Yuta	Materials informatics Deep learning Crystal structure	ST-22	894
(13:00–13:40) (Chair: Mukaida Shiho)
13:00– 13:40	E313	[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-22	144
(13:40–14:20) (Chair: Yoshida Hideaki)
13:40– 14:20	E315	[Invited lecture] Digital infrastructure to reduce time to market / R&D to production (Microsoft Japan) Yasunami Yutaka	digital AI data integration	ST-22	616
(14:20–15:00) (Chair: Toya Yoshihiro)
14:20– 15:00	E317	[Invited lecture] Understanding the diversity of metabolisms with the advances of computational metabolomics (TUAT) Tsugawa Hiroshi	metabolomics mass spectrometry informatics	ST-22	142

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SCEJ 55th Autumn Meeting (Sapporo, 2024)

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SCEJ 55th Autumn Meeting (Sapporo, 2024)

Session programs : ST-22 : E317

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

`SCEJ 55th Autumn Meeting (Sapporo, 2024)`

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