SCEJ 88th Annual Meeting (Tokyo, 2023)
Program search result : 機械学習 : 13 programs
The preprints(abstracts) are now open (Mar. 1st). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants in Period I/II and invited persons are required.
Title (J) field includes “機械学習”; 13 programs are found.
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| Day 1 | J119 | Machine learning assisted molecular evolution for the selectin of antibody mimetics | Antibody mimetics Phage display Machine-learning | 7-a | 667 |
| Day 2 | PB225 | Machine learning application for the directed evolution of antibody fragments | Antibody fragments Phage display Machine learning | 7-a | 551 |
| Day 2 | PB237 | Machine-learning assisted evolution of fungal cellulase | machine learning enzyme biorefinery | 7-a | 710 |
| Day 2 | E205 | Morphological Classification of Electron Microscopic Images of Carbon Black with Machine Learning Assistance | Carbon Black Morphology Convolutional Neural Network | 3-b | 297 |
| Day 2 | PB246 | Machine-learning guided mutagenesis for humanization of camel antibody fragment | VHH Machine-learning Evolutionary molecular engineering | 7-a | 409 |
| Day 2 | I207 | [Featured presentation] Prediction of Olefin Metathesis Reactivity Using Machine-Learning Model | machine-learning olefin metathesis catalytic reaction | 6-d | 69 |
| Day 2 | PC230 | Application of machine learning and physical modeling for detecting hydrogen leakage from hydrogen pipeline | hydrogen pipeline leak detection machine learning | 10-e | 269 |
| Day 2 | G218 | Machine learning-based multi-objective optimization for two-stage CO2 membrane separation process | CCUS Bi-objective optimization membrane separation | 4-a | 277 |
| Day 3 | PD311 | Prediction of nanoparticle dispersion by machine learning with Hansen parameters as input | Hansen solubility parameter nanoparticle dispersion machine learning | 1-b | 486 |
| Day 3 | PD333 | Prediction of product composition using machine learning in co-processing of bio-oil and heavy oil in catalytic cracking process | bio-oil co-processing machine learning | 5-a | 539 |
| Day 3 | H305 | High-speed computing of powder mixing using machine learning with random motion model | Powder mixing High-speed computing Machine learning | 2-f | 112 |
| Day 3 | PD346 | The development of Porous polymer monolith catalyst with the application of machine learning | Immobilized Catalyst Monolith Machine Learning | 5-a | 317 |
| Day 3 | R306 | [Requested talk] Theory-driven Machiene Learning for Chemical Engineering | Machine learning Artificial Intelligence Big data | HQ-21 | 471 |
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SCEJ 88th Annual Meeting (Tokyo, 2023)
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