SCEJ 55th Autumn Meeting
Sep. 11 (Wed) - 13 (Fri), 2024
Sapporo Campus, Hokkaido University
Japanese page
SCEJ 55th Autumn Meeting
Sep. 11 (Wed) - 13 (Fri), 2024
Sapporo Campus, Hokkaido University
Japanese page
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.
Most recent update: 2025-02-14 19:33:01
 The keywords that frequently used in this topics code. | Keywords | Number | |
|---|---|---|---|
| Machine learning | 5 |  | |
| transfer learning | 2 | | |
| Informatics | 2 | | |
| Deep learning | 2 | | |
| Solar cell | 2 | | |
| Generative model | 1 | | |
| ACKN No. | Title/Author(s) | Keywords | Style |
|---|---|---|---|
| 66 | Application of Transfer Learning Using Generative Adversarial Networks to Virtual Sensing | transfer learning soft-sensor Generative Adversarial Networks | O |
| 95 | Control of foam on white water and improvement of operational efficiency by modeling a soft sensor | soft sensor machine learning factory automation | O |
| 121 | Statistical consideration for investigating a direct correlation between solvent effect and hydrogenation of nitrobenzene using flow-tubular reactor | statistical analysis hydrogenation flow reactor | O |
| 142 | [Invited lecture] Understanding the diversity of metabolisms with the advances of computational metabolomics | metabolomics mass spectrometry informatics | O |
| 144 | [Invited lecture] Introduction to Case Studies on the Development and Utilization of Informatics Technologies | Informatics Industry R&D acceleration | O |
| 145 | [Invited lecture] Autonomous Materials Search | Machine learning Autonomous Active learning | O |
| 311 | Development of a transfer learning method using O-PLS | O-PLS transfer learning calibration transfer | O |
| 448 | [The Outstanding Paper Award] Optimization of metal nanoparticle synthesis conditions using automated flow system and machine learning | Nanoparticle Microreactor Machine Learning | O |
| 580 | Calculating Potential of Solar Cell on Building Façades for Japan's Energy System Optimization in 2050 | Solar cell facade installation Energy system optimization | O |
| 615 | 13C-MFA software with a support function for high accuracy flux estimations by proposing additional experiments | 13C-metabolic flux analysis flux analysis software | O |
| 616 | [Invited lecture] Digital infrastructure to reduce time to market / R&D to production | digital AI data integration | O |
| 732 | Analysis of anion selectivity in Mg-based Layered Double Hydroxide using a Universal Neural Network Potential | machine learning layered double hydroxide (LDH) anion selectivity | O |
| 865 | Prediction model for real-time power generation of solar cells with shadow simulation based on module structure and irradiance fraction | Solar cell Energy system Prediction model | O |
| 894 | [Invited lecture] Representation and Generation of Crystal Structures with Deep Learning | Materials informatics Deep learning Crystal structure | O |
| 969 | Automated reaction process analysis using data-driven approaches | Dynamic mode decomposition Chemical reaction Machine learning | O |
| 1099 | Molecular design using deep learning and vecror annealing | Deep learning Quantum anealing Generative model | O |
| 1128 | Development of a product composition prediction model for catalytic cracking reactions and its application to the prediction of reactions with unknown feedstocks | physics-informed machine learning catalytic cracking reaction prediction | O |
Organizing Committee of SCEJ 56th Autumn Meeting (2025)
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