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SCEJ 54th Autumn Meeting (Fukuoka, 2023)

Last modified: 2023-12-10 19:09:26

Session programs : ST-21 : H106

The preprints(abstracts) are now open (Aug. 28). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants and invited persons are required.

ST-21 [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.), Kaneko Shogo (Sumitomo Chemical), Mukaida Shiho (Mitsui Chemicals), Muroga Shun (AIST)

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 H, Day 1 | Hall H, Day 2

TimePaper
ID
Title / AuthorsKeywordsTopic codeAck.
number
Hall H(835), Day 1(Sep. 11)
(9:00–10:40) (Chair: Shimada Iori)
9:009:20H101Development of machine learning model for CO2 absorption performance of blended amine solutions
(AIST) *(Reg)Fujii Tatsuya, (Reg)Kohno Yuki, (Reg)Makino Takashi, (Tokyo Tech) Sako Masami, Ishihama Keisuke, Yasuo Nobuaki, Kawauchi Susumu
CO2 absorption
machine learning
amine
ST-21445
9:209:40H102Predicting Physical Properties of Structurally Unknown Polymers Using Spectroscopy Data
(Resonac) (Cor)Nagai Yuuki
Machine Learning
Predict
Descriptor
ST-21353
9:4010:00H103(withdrawn)

100364
10:0010:20H104[Featured presentation] Multimodal Deep Learning for Predictions of Various Properties of Composite Materials
(AIST) *(Reg·PCEF)Muroga Shun, Miki Yasuaki, Hata Kenji
multimodal deep learning
materials informatics
generative deep learning
ST-21602
10:2010:40H105Construction of MI platform for functional materials
(Resonac) (Cor)Sekiguchi Kazuhide
Materials informatics
DX
ST-21450
(10:40–11:20) (Chair: Kaneko Shogo)
10:4011:20H106[Invited lecture] Material exploration and process optimization by digital technology
(NAIST) Fujii Mikiya
Materials Informatics
Process Informatics
Quantum Chemistry
ST-21784
(11:20–12:00) (Chair: Mukaida Shiho)
11:2012:00H108[Invited lecture] Data-driven Approaches for Functional Materials Development in SEKISUI CHEMICAL.
(Sekisui Chemical) (Cor)Masuyama Yoshikazu
Data-Driven Development
Functional Materials
Materials Informatics
ST-21976
(13:00–13:40) (Chair: Mukaida Shiho)
13:0013:40H113[Invited lecture] Remote Operation Support and Automatic Plant Operation Technology In Waste-to-Energy Plants
(JFE Eng.) (Cor)Kojima Hiroshi
Remote operation
Automatic operation
AI and Data analysis
ST-21979
(13:40–15:20) (Chair: Toya Yoshihiro)
13:4014:20H115[Invited lecture] Prediction and control of bacterial evolution through high-throughput automated experiments using robots
(RIKEN) *Shibai Atsushi, Furusawa Chikara
Laboratory automation
Laboratory evolution
Escherichia coli
ST-21805
14:2014:40H117Deep learning model for predicting all protein-protein interactions from sequence data
(Kyutech) *(Reg)Kurata Hiroyuki, Tsukiyama Sho
Cross attention
deep learning
prediction
ST-2133
14:4015:00Break
15:0015:20H119Development of mechanistic cell cultivation models in monoclonal antibody production using data-driven insights
(UTokyo) *(Stu)Okamura K., (Int)Badr S., (Stu)Ichida Y., (Reg·SPCE)Yamada A., (Reg)Sugiyama H.
Biopharmaceuticals
Lactate consumption
Glutamine
ST-21728
(15:20–17:00) (Chair: Muroga Shun)
15:2015:40H120Development of microbial production process by model based metabolic design and directed evolution
(Osaka U.) *(Reg)Shimizu Hiroshi, (Reg)Toya Yoshihiro, (RIKEN) Furusawa Chikara, Shibai Atsushi, (AIST) Horinouchi Takaaki, (Chuo U.) Suzuki Hiroaki, (Osaka U.) Tokuyama Kento, (Reg)Niide Teppei
Model based metabolic pathway design
Directed evolution
Metabolic engineering
ST-21225
15:4016:00H121Machine learning guided enzyme’s molecular recognition specificity conversion
(Osaka U.) *(Reg)Niide Teppei, Sugiki Sou, Mori Seiya, (Reg)Toya Yoshihiro, (Reg)Shimizu Hiroshi
enzyme design
machine learning
ST-21235
16:0016:20H122High accuracy prediction of edible oil oxidation stability by multivariate analysis incorporating chemiluminescence information
(Tohoku U.) *(Stu·PCEF)Yoshida Yuta, (Reg)Hiromori Kousuke, (Reg)Shibasaki-Kitakawa Naomi, (Reg)Takahashi Atsushi
multivariate analysis
oxidative stability
edible oil
ST-21686
16:2016:40H123Application of reaction mechanism search method using chemical reaction neural network to glycerol oxidation reaction
(Shinshu U.) *(Stu)Shionoya Tomoki, (Reg)Shimada Iori
physics informed neural network
kinetics model
data-driven
ST-21480
16:4017:00H124Applicational study of symbolic regression to exploring new materials and constructing kinetics models
(Waseda U.) *(Stu)Isoda T., Takahashi S., (WISE/Mitsubishi Chemical Group) Nakano M., (WISE) Nakajima Y., (Waseda U./WISE) Seino J.
Machine learning
Materials Informatics
Reaction Kinetics
ST-21948
Hall H(835), Day 2(Sep. 12)
(9:00–10:20) (Chair: Kaneko Shogo)
9:009:20H201Elucidation of appropriate data acquisition conditions for API concentration prediction by NIR
(Kyoto U.) *(Stu)Fukuoka Norihiko, (Powrex/TUAT) (Reg)Oishi Takuya, (Powrex) (Reg)Nagato Takuya, (TUAT) (Reg·APCE)Kim Sanghong, (Kyoto U.) (Reg)Sotowa Ken-Ichiro
NIR Spectrum
diffuse reflectance measurement
API concentration prediction
ST-21741
9:209:40H202Development of a soft sensor and a controller system of hydrogen concentration in the exhaust gas in fuel cell systems
(TUAT) *(Stu)Izawa Taisei, (Reg·APCE)Kim Sanghong, (Reg)Matsumoto Miyuki, (Kyoto U.) (Reg)Hasegawa Shigeki, (Reg)Kawase Motoaki
PEFC
Hydrogen control
soft sensor
ST-21203
9:4010:00H203Novel encoding method for high dimensional power consumption data in distributed energy system for short-term electricity demand forecasting
(TokyoTech) *(Stu)Lee Hyojae, (Stu)Tsuda Shunsaku, (Stu)Iijima Taiki, (Reg)Kameda Keisuke, (Reg)Manzhos Sergei, (Reg)Ihara Manabu
electricity demand prediction
distributed energy system
big data
ST-21550
10:0010:20H204Calculation of Tokyo's Photovoltaic Potential and Study of the Effects of Reducing Daily Power Fluctuations from Facade Installations
(Tokyo Tech) *(Stu)Wang Shuai, (Stu)Oya Masashi, (Reg)Kameda Keisuke, (Reg)Manzhos Sergei, (Reg)Ihara Manabu
Facade installation
Photovoltaic power potential
Power fluctuation
ST-21646
(10:20–12:00) (Chair: Kim Sanghong)
10:2010:40H205Gaussian Process Regression Approaches for Process Optimization: A Case Study of Interface State Density Prediction between Insulator and Semiconductor
(NAIST) *(Stu)Matsunaga K., (AIST) Uenuma M., (NAIST) Sato A., Uraoka Y., Miyao T.
Gaussian process regression
length-scale
Metal-oxide-semiconductor
ST-21204
10:4011:00H206Design of integrated upstream and downstream monoclonal antibody production processes using surrogate models
(U. Tokyo) *(Stu)Shigeyama Akinori, (Reg)Hayashi Yusuke, (Int)Badr Sara, (Reg)Sugiyama Hirokazu
Surrogate model
Machine learning
Bayesian optimization
ST-21816
11:0011:20H207Utilization of Bayesian optimization in the process development of drug substance
(Astellas Pharma) *(Reg)Morishita Toshiharu, Sumii Yuta, Hanada Shogo, Shimizu Takashi
DX
Bayesian optimization
Simulation
ST-21167
11:2011:40H208Batch Bayesian optimization method for goal-oriented multi-objective functional materials design
(Resonac) (Cor)Hanaoka Kyohei
Bayesian Optimization
ST-21385
11:4012:00H209Bayesian Optimization Framework for Polymer Composites Design Using High Dimensional Past Materials Data
(Resonac) *(Cor)Arai Ryosuke, (Cor)Sekiguchi Kazuhide, (Cor)Hanaoka Kyohei
Bayesian optimization
DX
Materials informatics
ST-21187

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SCEJ 54th Autumn Meeting (Fukuoka, 2023)


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