SS-7 - Session programs - SCEJ 88th Annual Meeting (Tokyo, 2023)

`SCEJ 88th Annual Meeting (Tokyo, 2023)`

Last modified: 2023-12-13 19:10:32

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Session programs : SS-7

The preprints(abstracts) are now open (Mar. 1st). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants in Period I/II and invited persons are required.

SS-7 Efforts to Utilize DX (Digital Transformation) to Create Vibrant and Sustainable On-Site Capabilities (Poster)

Organizer:

Sawada Yuko (SCEJ)

Towards the realization of sustainable society, various research and development is being carried out. Among them, ammonia in particular is being studied as hydrogen carrier and carbon-free fuel. This session will focus on the utilization of ammonia, and introduce the latest trends and research cases.

Hall Q, Day 3

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
Hall Q(Gymnasium), Day 3(Mar. 17)
(16:00–17:30) (Chair: Kawase Susumu)
16:00– 17:30	Q301	[Invited lecture] Smart factory for chemical manufacturers (JMA Consultants) Kamiyama Yosuke	Smart factory Practical theory Case study	SS-7	322
	Q302	[Requested talk] Data Utilization Activity for DX of Production Field and AI Platform (Kaneka) (Cor)Kinaka Shingo, (Cor)Ichiriki Hiroaki	DX Data Utilization AI Platform	SS-7	324
	Q303	[Invited lecture] Salesforce reference case for Cx including Quality Management / Blockchain reference case for refrigerant circulation platform (IBM Japan) Miyabe Naoki, Saegusa Kenichiro	Saleseforce Blockchain DX	SS-7	329
	Q304	[Requested talk] In Oil and Chemical industries, the damage mechanisms prediction of facilities from the chemical environments by user development of AI machine learning (Facility Eng. Laboratory) (Reg)Matsuda Hiroyasu	AI Decision Tree Damage Mechanism Prediction	SS-7	338
	Q305	[Invited lecture] Energy-saving operation by AI autonomous control (Yokogawa Digital) Kobuchi Keiichiro	AI control Reinforcement learning Energy-saving	SS-7	345
	Q306	[Requested talk] Practical use of digital technology at chemical plant (Sumitomo Chemical) (Reg)Hiraishi Yasuaki	chemical plant digital transformation machine learning	SS-7	348
	Q307	Inverse design of functional separation materials using deep generation models. (Kogakuin U.) *(Stu)Matsumoto Takumi, (Reg)Miyagawa Masaya, (Reg)Takaba Hiromitsu	machine learning polymer membrane gas separation	SS-7	357
	Q308	[Requested talk] Introduction of AI support for overall plant operation including optimization and anomaly detection (Chiyoda) *(Cor)Sato Hideki, (Cor)Ogawa Rentaro, (Cor)Furuichi Kazuya	AI anomaly detection operation improvement	SS-7	369
	Q309	[Requested talk] Recommendation of diagnosis of heating medium boilers and equipment using heat medium (Soken Tecnix) *(Cor)Matsuoka Daisuke, (Cor)Sakashita Ryuji	diagnosis maintenance inspection	SS-7	373
	Q310	[Invited lecture] Utilizing Robotics for "Smart Inspection/DX" of Factories, Plants, etc. (Blue innovation) Hirabaru Sho, *Yamaguchi Yudai	drone robotics digital transformation	SS-7	376
	Q311	[Invited lecture] Inventory reduction through color mixing using CCM (Dainichiseika Color & Chemicals) Maeda Satoshi	CCM colormixing inventory	SS-7	382
	Q312	[Requested talk] Online Trainings on Process Safety Triggered by COVID-19 (SCE・Net) *(Reg)Ushiyama Satoshi, (Reg)Takeuchi Ryo	On-line Training Process Safety	SS-7	383
	Q313	[Requested talk] Improvement of on-site capabilities through digitalization of production technology and improvement of productivity through autonomous control of processes (Sumitomo Bakelite) (Cor)Tanaka Go	Production engineering AI IoT Gemba-ryoku kaizen(Field capabilities improvement) Productivity improvement	SS-7	388
	Q314	[Invited lecture] AI use cases in predictive maintenance that have entered the practical stage (Brains Tech.) Hayashi Takuma	AI Machine Learning Predictive Maintenance	SS-7	393
	Q315	[Requested talk] Eco-friendly semiconductor cleaning technology using ozone water (Ecodesign) *Nagakura Masaya, Saito Junichiro, Yamaguchi Daisuke, Sakamoto Yukiko	Semiconductor Cleaning	SS-7	396

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SCEJ 88th Annual Meeting (Tokyo, 2023)

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