SCEJ 91st Annual Meeting (Kyoto, 2026)
Program search result : 機械 : 25 programs
The preprints(abstracts) are now open (Mar. 3rd). 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 “機械”; 25 programs are found.
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| Hall E 第 1 日 Day 1 | E114 | [Requested talk] Heat exchanger network optimization with mechanical vapor recompression and streams with soft data | heat exchanger network synthesis mechanical vapor recompression mixed-integer linear programming | K-3 | 690 |
| Hall R 第 1 日 Day 1 | R106 | [Requested talk] Rheo-optical behavior of cellulose nanofiber dispersions defibrillated by the water jet method | Cellulose nanofibers Rheo-optics Polarized light imaging | HQ-21 | 808 |
| Hall P 第 2 日 Day 2 | PB231 | Development of a Particle Tracking Method from Low-Quality Videos Using Machine Learning | Softsensor Particle tracking Machine learning | 2-g | 679 |
| Hall P 第 2 日 Day 2 | PB259 | Evaluation of CO2 Separation Performance of Novel Covalent Organic Frameworks via Machine Learning-Based Structural Screening | Covalent Organic Framework Grand Canonical Monte Carlo Machine Learning | 4-a | 331 |
| Hall B 第 2 日 Day 2 | B205 | [Invited lecture] Human Resource Development in Chemical Engineering within Integrated Businesses of Materials, Machinery, Engineering and Power : Current Status and Initiatives | Human Resource Development Integrated Business Cross-functional Networking | SS-3 | 703 |
| Hall P 第 2 日 Day 2 | PC211 | Development of a Gradient-Constrained Machine Learning Model for Thermophysical Property Estimation of Ternary Mixtures | Ternary mixture Gradient constraint Machine learning | 1-a | 322 |
| Hall P 第 2 日 Day 2 | PC214 | Thermophysical Property Estimation of Multicomponent Mixtures Using Mixture Descriptors and Machine Learning | Multicomponent mixture Molecular descriptor Machine learning | 1-a | 678 |
| Hall H 第 2 日 Day 2 | H219 | Design of a Versatile Humanized VHH Scaffold via Machine Learning: Simultaneous Optimization of Developability and Affinity Acquisition | Antibody VHH | 7-a | 755 |
| Hall N 第 2 日 Day 2 | N222 | Combining Universal Machine Learning Interatomic Potential with Rare Event Sampling Methods for Polymer Polymerization and Degradation | uMLIP reaction rare event | 6-f | 336 |
| Hall P 第 3 日 Day 3 | PD305 | Co-processing of plastic and bio-oil in catalytic cracking process -Prediction of yields and investigation of feedstock interactions using machine learning- | co-processing plastic bio-oil | 5-a | 286 |
| Hall P 第 3 日 Day 3 | PD319 | Design of polymeric palladium catalysts for solid-state Suzuki-Miyaura type cross-coupling reaction with machine learning | Machine learning Suzuki-Miyaura cross coupling Pd catalyst | 5-a | 47 |
| Hall P 第 3 日 Day 3 | PD331 | Analysis of the Dushman Reaction Using Physics-Informed Machine Learning | Dushman reaction Mixing evaluation Machine learning | 5-i | 323 |
| Hall P 第 3 日 Day 3 | PD338 | Extrapolative Prediction of Dushman Reaction Kinetics Using a Hybrid Machine Learning Model | Dushman reaction Extrapolation Machine learning | 5-i | 680 |
| Hall P 第 3 日 Day 3 | PD354 | Proposal of Crystal Structures for Novel Solid Electrolytes with High Ionic Conductivity Using Machine Learning | Solid Electrolyte Machine Learning Materials Informatics | 6-f | 8 |
| Hall P 第 3 日 Day 3 | PD357 | Suggesting new drug candidates for schizophrenia using machine learning models and generative adversarial network | Machine learning Drug design Schizophrenia | 6-f | 52 |
| Hall P 第 3 日 Day 3 | PD360 | Machine Learning Interatomic Potentials Reveal Surface Proton Diffusion on Perovskite Oxide | Machine Learning Interatomic Potential Proton Diffusion Surface Conduction | 6-g | 183 |
| Hall P 第 3 日 Day 3 | PD361 | Prediction of Microfiltration Membrane Fouling and Proposal of Optimal Operating Conditions with Machine Learning | Machine Learning Microfiltration Membrane Fouling | 6-d | 163 |
| Hall P 第 3 日 Day 3 | PD366 | Development of a Machine Learning Model for Species-Specific MIC Prediction of Antimicrobial Peptides | Machine Learning Antimicrobial Peptides Minimum Inhibitory Concentration (MIC) | 6-g | 627 |
| Hall P 第 3 日 Day 3 | PD368 | A Comparative Study of Molecular Descriptors to Improve Machine Learning Prediction of Ames Test Result | Ames test Machine learning | 6-f | 175 |
| Hall P 第 3 日 Day 3 | PD373 | Development of a machine learning model for predicting water vapor sorption of polysaccharides | Machine learning Polysaccharides Water vapor sorption | 6-f | 387 |
| Hall P 第 3 日 Day 3 | PD378 | Improving Reaction Prediction Accuracy Using Machine Learning and Reaction Networks | machine learning chemical reaction network reaction pathway prediction | 6-g | 352 |
| Hall P 第 3 日 Day 3 | PD383 | Design of novel plastic-degrading enzymes with high thermostability and degradation activity using machine learning models | machine learning bioinformatics plastics-degrading enzymes | 6-f | 538 |
| Hall P 第 3 日 Day 3 | PD384 | Optimization of BiVO4 photocatalyst synthesis conditions with machine learning and experiments | Bismuth Vanadate Synthesis conditions Machine learning | 6-e | 51 |
| Hall P 第 3 日 Day 3 | PD386 | Molecular generation to lower reorganization energy of organic semiconductors with an emphasis on the reliability of predictions of machine learning model | Machine learning Generative adversarial networks Organic semiconductor | 6-f | 139 |
| Hall P 第 3 日 Day 3 | PE366 | Prediction of carbon dioxide solubilities in deep eutectic solvents and ionic liquids using molecular information and machine learning | CO2 absorption COSMO-SAC machine learning | 13-g | 379 |
Technical program
Technical sessions (Wide)
(For narrow screen)
Session programs
Search in technical program
SCEJ 91st Annual Meeting (Kyoto, 2026)
© 2026 The Society of Chemical Engineers, Japan. All rights reserved.For more information contact Organizing Committee of SCEJ 91st Annual Meeting
E-mail: inquiry-91a
www4.scej.org