SCEJ 86th Annual Meeting (2021)
Program search result : 予測 : 15 programs
The preprints(abstracts) are now open (Mar. 8th). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants (excludes free registration) and invited persons are required.
Title (J) field includes “予測”; 15 programs are found.
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| Day 1 | I114 | [Divisional Award] Unpredictable Dynamics of Polymeric Reacting Flow by Comparison between Pre- and Post-Reaction Fluid Properties:Hydrodynamics Involving Molecular Diagnosis via ATR-FTIR Spectroscopy | Fe3+ aqua complex Henderson-Hasselbalch equation Weissenberg effect | X-51 | 702 |
| Day 1 | PA103 | API concentration prediction by NIRS measured off-line and in-line in powder mixing process | powder mixing PAT API concentration prediction | 6-d | 148 |
| Day 1 | PA120 | Product yield prediction using machine learning in co-processing of bio-oil and heavy oil in catalytic cracking | machine learning catalytic cracking bio-oil model compound | 5-a | 525 |
| Day 1 | PA132 | Application of machine learning in yield prediction of biomass liquefaction using solvolysis | machine learning bio-oil solvolysis | 5-g | 408 |
| Day 1 | L124 | Development of predictive method of partition coefficient of organics between high-pressure carbon dioxide and water using machine learning | machine learning partition coefficient high-pressure carbon dioxide | 8-b | 310 |
| Day 2 | PB203 | Protease cleavage site prediction by multivariate analysis using peptide array | protetase | 7-a | 40 |
| Day 2 | K204 | Product composition prediction of catalytic cracking reaction with machine learning and feature engineering | machine learning feature engineering catalytic cracking | 5-a | 403 |
| Day 2 | L204 | Product quality prediction from small manufacturing process data based on brain-inspired bayesian attractor model | soft sensors product quality prediction machine learning | 6-d | 503 |
| Day 2 | L215 | Important stage selection for yield rate prediction in multi-process production systems | Statistical modeling Optimization | 6-f | 638 |
| Day 3 | I301 | Comprehensive index for predicting the occurrence of self-induced vibration in a pillow-type bubble column | bubble column self-induced vibration superficial velocity | 2-d | 238 |
| Day 3 | PD314 | Clustering of energy data using K-means method and DBSCAN for electricity demand forecasting | distributed generation renewable energy machine learning | 9-e | 334 |
| Day 3 | N314 | Estimation and Prediction of NOx Concentration Using Simplified Reaction Mechanism in CH4/NH3 Co-Combustion | Turbulent Combustion NOx CFD | 9-c | 252 |
| Day 3 | N317 | Prediction of heating efficiency using dimensionless number (Asakuma number) for microwave absorbance | Microwave dimensionless number heat efficiency | 3-a | 146 |
| Day 3 | G317 | [Invited lecture] New materials Ddevelopment based on property prediction models | Machine learning AI Inverse analysis | SS-5 | 67 |
| Day 3 | Q305 | [Invited lecture] New materials development based on property prediction models | Machine learning AI Inverse analysis | SP-7 | 78 |
Technical program
Technical sessions (Wide)
(For narrow screen)
Session programs
Search in technical program
SCEJ 86th Annual Meeting (2021)
© 2023 The Society of Chemical Engineers, Japan. All rights reserved.For more information contact Organizing Committee of SCEJ 86th Annual Meeting (2021) and IChES 2021
E-mail: inquiry-86a
www3.scej.org