Last modified: 2023年12月13日 19:10:32
Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
---|---|---|---|---|---|
Hall I, Day 1 | |||||
(13:20–14:40) (Chair: Watanabe Ayumi, Kataoka Sho) | |||||
13:20– 13:40 | I114 | Fluid analysis for gas composition tracking of methane and hydrogen mixture in a gas transportation network (Hitachi) *(Reg)Watanabe Ayumi, Yashiki Tatsurou | Hydrogen Natural gas Fluid anaslysis | 6-b | 130 |
13:40– 14:00 | I115 | CFD-based investigation of a novel decontamination process using ultrasound technology (U. Tokyo) *(Int)Gaddem Mohamed Rami, (Reg)Hayashi Yusuke, (Airex) Futamura Haruka, (Cor)Kawasaki Koji, (U. Tokyo) (Reg)Sugiyama Hirokazu | Biopharmaceuticals Decontamination process CFD modeling | 6-b | 407 |
14:00– 14:20 | I116 | Development of in situ rheometer for fluids in a reserve tank (AIST) *(Reg)Yoshida Taiki, (Hokkaido U.) (Stu)Ohie Kohei, (Reg)Tasaka Yuji | In situ measurement Rheometer Ultrasound | 6-c | 290 |
14:20– 14:40 | I117 | Transport analogy model for a ternary system in a packed distillation column by a control volume method (Kansai Chem. Eng.) *(Reg)Kataoka Kunio, (Reg)Noda Hideo, (Reg)Nishimura Goro, (Kobe U.) (Reg)Ohmura Naoto | Analogy analysis Heat and Mass Transfer Packed distillation column | 6-c | 3 |
(15:00–16:40) (Chair: Mimura Kenichi, Daiguji Masaharu) | |||||
15:00– 15:20 | I119 | Quantitative Comparison of Reinforcement Learning and Model-based Optimal Control for Chemical Processes (Kyoto U.) (Int)Oh Tae Hoon | process control machine learning optimal control | 6-d | 52 |
15:20– 15:40 | I120 | Language model for understanding variables appearing in chemical engineering documents (Kyoto U.) *(Reg)Kato Shota, Zhang Chunpu, Yamamoto Makishi, (Reg)Kano Manabu | Artificial intelligence Language model Natural language processing | 6-f | 391 |
15:40– 16:00 | I121 | Development and Application of Process Simulator "minicappy" by Python (Mimura Eng. Consulting Office) (Reg)Mimura Kenichi | simulation python process simulator | 6-b | 1 |
16:00– 16:20 | I122 | Application of stiction compensation method for control valves to an actual process (ENEOS) *(Reg)Daiguji Masaharu, (TUAT) (Reg)Yamashita Yoshiyuki | process control optimization | 6-d | 699 |
16:20– 16:40 | I123 | [Featured presentation] Development of a decision support system for biodegradation using chemical descriptors and 3D structures (Shizuoka U.) *(Reg)Takeda Kazuhiro, (NITE) Takeuchi Kensuke, Sakuratani Yuki, (Shizuoka U.) (Reg)Kimbara Kazuhide | Decision support system biodegradation chemical descriptors | 6-g | 125 |
Hall I, Day 2 | |||||
(9:00–10:00) (Chair: Oishi Takuya, Kim Sanghong) | |||||
9:00– 9:20 | I201 | Modeling cell metabolic shifts in cultivation processes for monoclonal antibody production (UTokyo) *(Stu)Okamura K., (Int)Badr S., (Reg·SPCE)Yamada A., (Reg)Sugiyama H. | Biopharmaceuticals Lactate consumption Hybrid modeling | 6-b | 394 |
9:20– 9:40 | I202 | Solvent selection based on solvent recycling to improve chemical process (AIST) *(Reg)Yamaki Takehiro, (Reg)Nguyen Thuy, (Reg)Hara Nobuo, (Reg)Taniguchi Satoshi, (Reg)Kataoka Sho | Solvent selection Process design Solvent recycling | 6-b | 449 |
9:40– 10:00 | I203 | Model-based characterization and evaluation of batch and flow syntheses in drug substance manufacturing (U. Tokyo) *(Stu)Kim Junu, (Reg)Sugiyama Hirokazu | Design space Kinetic model Hybrid model | 6-b | 123 |
(10:20–11:20) (Chair: Yamaki Takehiro) | |||||
10:20– 10:40 | I205 | Proposal of a method to improve the performance of MSPC by appropriate standardization of input variables and its application to continuous pharmaceutical manufacturing (Powrex/TUAT) *(Reg)Oishi Takuya, (Powrex) (Reg)Nagato Takuya, (TUAT) (Reg)Kim Sanghong | Multivariate Statistical Process Control Continuous Pharmaceutical Manufacturing Wet Granulation | 6-d | 68 |
10:40– 11:00 | I206 | A comprehensive design approach for injectable manufacturing processes considering new technologies and operational aspects (U. Tokyo) *(Stu)Yamada Masahiro, (Reg)Hayashi Yusuke, (Int)Badr Sara, (Shionogi Pharma) (Cor)Zenitani Kenichi, (Cor)Kubota Kokichi, (Reg)Nakanishi Hayao, (U. Tokyo) (Reg)Sugiyama Hirokazu | Process design Multiobjective evaluation Pareto frontier | 6-b | 171 |
11:00– 11:20 | I207 | [Featured presentation] Prediction of Olefin Metathesis Reactivity Using Machine-Learning Model (Zeon) *(Reg)Nagaoka Masahiro, (Cor)Taira Kanako | machine-learning olefin metathesis catalytic reaction | 6-d | 69 |
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SCEJ 88th Annual Meeting (Tokyo, 2023)