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SCEJ 86th Annual Meeting (2021)
Program search result : Machine Learning : 12 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.
Keywords field exact matches “Machine Learning”; 12 programs are found.
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| 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 | 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 | PB230 | Highly Efficient Screening of Biocompatible Ionic Liquids Based on Machine Learning | Ionic liquid Machine learning Drug delivery system | 7-e | 336 |
| 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 | D313 | Pharmaceutical cocrystal screening by machine learning with molecular surface charge distribution | cocrystal machine learning molecular surface charge distribution | IS-1 | 136 |
| Day 3 | G317 | [Invited lecture] New materials Ddevelopment based on property prediction models | Machine learning AI Inverse analysis | SS-5 | 67 |
| Day 3 | PE318 | Determination of parameters in discrete element method for non-spherical particles by machine learning | Discrete Element Method Machine Learning Non-spherical particle | 2-f | 273 |
| Day 3 | G319 | [Invited lecture] Hitachi Materials Informatics Solution:proposal of a new methology to accelerate a R&D, based on Materials Informatics | materials informatics machine learning chemoinformatics | SS-5 | 68 |
| Day 3 | Q305 | [Invited lecture] New materials development based on property prediction models | Machine learning AI Inverse analysis | SP-7 | 78 |
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SCEJ 86th Annual Meeting (2021)
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