SCEJ 51st Autumn Meeting (2020)
Program search result : 予測 : 14 programs
All sessions can be attended from the On-line (Virtual) Meeting Site.
Preprints(Abstracts) are now open. Click the Paper IDs. (Registered participants and invited persons only)
The ID/PW was sent on Sept. 10 (for earlybird registered participants) or on Sept. 23 (for on-site registered participants).
(Aug. 8) Flash session of SY-69 has been cancelled.
(Aug. 24,27) Schedule of SY-74 (X306, X307) and HQ-11 (D301) has been changed.
Preprints(Abstracts) are now open. Click the Paper IDs. (Registered participants and invited persons only)
The ID/PW was sent on Sept. 10 (for earlybird registered participants) or on Sept. 23 (for on-site registered participants).
(Aug. 8) Flash session of SY-69 has been cancelled.
(Aug. 24,27) Schedule of SY-74 (X306, X307) and HQ-11 (D301) has been changed.
Title (J) field includes “予測”; 14 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 | T104 | Design of high-selective toluene hydrogenation process using semi-empirical prediction model for byproducts formation behavior | Hydrogen energy Renewable enegy Process intensification | SY-66 | 393 |
| Day 1 | Y104 | Prediction of melting point depression in high pressure CO2 using molecular interaction energy in equation of state from molecular information | melting point depression high pressure CO2 molecular information | SY-51 | 607 |
| Day 1 | N106 | Prediction of Gas Dissolution Rate in Gas-Liquid Stirred Tank by Using Empirical Correlation and CFD Calculation | Stirred Tank Gas Dissolution Bubble Size | SY-53 | 162 |
| Day 1 | T106 | Methods of spatiotemporal data analysis for development of semi-empirical prediction model for generation of trace hydrocarbon | Hydrogen carrier Spatiotemporal data analysis semi-empirical prediction model | SY-66 | 461 |
| Day 1 | E114 | [Invited lecture] Catalyst Informatics Approach for the Prediction of Catalytic Reaction Yields | Catalyst Informatics Machine Learning Catalyst Informatics | SY-80 | 81 |
| Day 1 | K117 | [Requested talk] Development of Prediction Model for Environmental Condition of Deterioration Phenomena for Asset Integrity Management | Asset Integrity Environmental Condition Deterioration Phenomena | SY-76 | 518 |
| Day 1 | PB141 | Development of a power generation simulation model for solar cells including shadow effect by combining operation principles of solar cell and machine learning | Energy system Solar cell Simulation | ST-24 | 668 |
| Day 2 | PB240 | Estimate ethanol fermentation and the significant components in cultivation media by deep neural network | deep neural network biofuel yeast | SY-69 | 194 |
| Day 2 | PB275 | Estimate growth phases of yeast by AI for image recagnition | Yeast Predicting culture Convolutional Neural Network | SY-69 | 593 |
| Day 3 | W306 | Evaluation and prediction of occurrence of micro-collapse for quality assessment of a freeze-dried product | freeze-drying micro-collapse X-ray CT | SY-71 | 852 |
| Day 3 | I307 | Thermal Decomposition and Coking Prediction Technologies for Heavy-Hydrocarbon Fuels | Heavy-Hydrocarbon Fuels Thermal Decomposition and Coking Prediction Technologies detailed chemical kinetic mechanism | SY-63 | 525 |
| Day 3 | I308 | Application of machine learning to product composition prediction in catalytic cracking reaction | machine learning feature engineering catalytic cracking | SY-63 | 533 |
| Day 3 | I309 | [The Outstanding Paper Award] Prediction of catalytic activity for supported metal catalyst using d-band center theory and HSAB concept | supported metal catalyst d-band center theory HSAB concept | SY-63 | 35 |
| Day 3 | D317 | Thermodynamic model for predicting regeneration energy in the CO2 absorption method with amine absorbents | CO2 capture Thermodynamic model | HQ-11 | 266 |
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SCEJ 51st Autumn Meeting (2020)
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