SCEJ 53rd Autumn Meeting (Nagano, 2022)
Program search result : 嶋田 五百里 : 18 programs
The preprints can be viewed by clicking the Paper IDs.
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Yellow-back sessions on Technical sessions will be On-site/online Hybrid sessions, others are Online sessions.
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Authors and Chairs (J) field exact matches “嶋田 五百里”; 18 programs are found. (“Poster with Flash” presentations are double-counted.)
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| Day 1 | PA101 | Automatic construction of kinetic models from a small number of data using chemical reaction neural network | physics informed machine learning kinetics model data-driven | SY-61 | 216 |
| Day 1 | PA115 | Effect of adding triglyceride in the deoxygenation reaction of bio-oil using catalytic cracking | catalytic cracking bio-oil model compounds triglyceride | SY-61 | 572 |
| Day 1 | PA118 | Elucidation of interactions in co-processing of plastics and polycyclic aromatic hydrocarbons in catalytic cracking reaction | fluid catalytic cracking plastic polycyclic aromatic hydrocarbons | SY-61 | 321 |
| Day 1 | PA101 | Automatic construction of kinetic models from a small number of data using chemical reaction neural network | physics informed machine learning kinetics model data-driven | SY-61 | 216 |
| Day 1 | PA115 | Effect of adding triglyceride in the deoxygenation reaction of bio-oil using catalytic cracking | catalytic cracking bio-oil model compounds triglyceride | SY-61 | 572 |
| Day 1 | PA118 | Elucidation of interactions in co-processing of plastics and polycyclic aromatic hydrocarbons in catalytic cracking reaction | fluid catalytic cracking plastic polycyclic aromatic hydrocarbons | SY-61 | 321 |
| Day 1 | CB117 | Effect of crystallinity and additives on the hydrothermal gelation of cellulose nanofiber dispersion | cellulose nanofiber hydrothermal gelation | SY-73 | 610 |
| Day 1 | CB121 | Prediction of molecular weight distribution of protein decomposition in subcritical water | subcritical water protein Molecular weight distribution | SY-73 | 690 |
| Day 1 | CB122 | Estimation of temperature dependence of organic compounds solubility in water by machine learning | solubility machine learning phase equilibrium | SY-73 | 709 |
| Day 2 | BB202 | Effects of alumina porous hollow fiber membrane modified with acid functional groups on CO2 desorption in membrane flash process | chemical absorption diethanolamine membrane flash process | SY-82 | 684 |
| Day 2 | BB204 | Iron oxide reduction by tar-containing biochar | woody biomass coke substitute reductant | SY-82 | 827 |
| Day 2 | BB206 | Prediction of pellet density of Japanese cedar from pelletizing conditions | pelletizing pressure moisture regression analysis | SY-82 | 794 |
| Day 2 | DH215 | Gelation of curdlan by wet pulverization process | curdlan hydrogel wet pulverization | SY-71 | 703 |
| Day 3 | PA303 | Effect of Carbon Addition to Oxide Electrodes for Polymer Electrolyte Fuel Cell | polymer electrolyte fuel cell conductive carbon additive oxide catalyst | ST-23 | 777 |
| Day 3 | PA304 | Voltage loss breakdown of carbon-free ionomer-free Pt electrode for polymer electrolyte fuel cell | polymer electrolyte fuel cell overpotential analysis Pt black | ST-23 | 792 |
| Day 3 | DC309 | Prediction of phase equilibrium of water-organic compounds system at high-temperature and high-pressure using machine learning | machine learning prediction of phase equilibrium high-temperature and high-pressure | ST-21 | 532 |
| Day 3 | Chair: | ||||
| DC317 | Inverse design of polymer membrane structure for gas separation using Junction Tree VAE machine learning | machine learning polymer membrane gas separation | ST-21 | 712 | |
| DC318 | Design of both membrane-based process and membrane materials with machine learning | Membrane module Materials Informatics Process design | ST-21 | 187 | |
| DC319 | [Featured presentation] Development of digital twin of the bulk single crystal growth of Si by using PINNs (Physics Informed Neural Networks) | Digital twin Machine learning Physics Informed Neural Networks | ST-21 | 396 | |
| DC320 | Growth interface shape optimization and adaptive process control for InGaSb crystal growth under microgravity using machine learning | Machine Learning Reinforcement Learning Crystal Growth | ST-21 | 428 | |
| Day 3 | DC322 | Effect of physics-based feature engineering in predicting product yields of catalytic cracking reactions | catalytic cracking machine learning feature engineering | ST-21 | 101 |
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SCEJ 53rd Autumn Meeting (Nagano, 2022)
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