SCEJ 89th Annual Meeting (Sakai, 2024)
Program search result : 予測 : 18 programs
The preprints can be viewed by clicking the Paper IDs.
The ID/PW sent to the Registered participants and invited persons are required.
The ID/PW sent to the Registered participants and invited persons are required.
Title (J) field includes “予測”; 18 programs are found.
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| Day 1 | K115 | Quality prediction in a small data environment for batch processes | Machine Learning Quality Prediction Small data | 6-a | 286 |
| Day 1 | B119 | [Requested talk] Generalized pore-flow model based on chemical potential distribution for the prediction of vapor permeances | Modeling Vapor permeation Gas permeation | K-1 | 440 |
| Day 1 | I120 | Effects of cavity length of consecutive abrupt contraction-expansion microchannels on polymer scission predicted by numerical simulation | Polymer scission Numerical simulation Consecutive abrupt contraction-expansion micro channel | 2-a | 391 |
| Day 2 | PB202 | Viscosity Measurement of CO2/Organic Solvent/Polymer Ternary Solutions for CO2 Painting Method and Viscosity Prediction Based on HSP Theory. | CO2 Painting Viscosity Polymer Solution | 1-a | 255 |
| Day 2 | PB214 | Construction of machine learning models to predict the dielectric constant of epoxy resin and design of monomer structures to achieve low dielectric constant | Epoxy Resin Materials Infomatics Dielectric Constant | 6-b | 55 |
| Day 2 | PB226 | Vapor pressure prediction of amines using molecular descriptors | Machine Learning Physical Property Prediction amine | 6-g | 12 |
| Day 2 | PB286 | Prediction of CO2 desorption performance of amine-based absorbents in vacuum flash desorption using porous hollow fiber membranes | CO2 chemical absorption CO2 stripping machine learning | 13-g | 690 |
| Day 2 | B208 | [Requested talk] TBA | * | F-1 | 798 |
| Day 2 | H219 | Understanding the stability of features for AI cardiotoxicity prediction models using in vitro cardiomyocyte beating data. | cardiotoxicity iPS-derived cardiomyocytes Machine learning | 7-e | 195 |
| Day 2 | D221 | [Invited lecture] Fragment-based Ab Initio Molecular Simulation for Predicting Physicochemical Properties of Functional Liquids | Physicochemical Properties Molecular Simulation Functional Liquids | X-53 | 514 |
| Day 2 | I224 | Method for predicting the size of transition metal complex hydroxides precipitated in a stirred-tank semi-batch reaction crystallizer using CFD and particle agglomeration models | turbulent mixing precipitation agglomeration model | 2-b | 666 |
| Day 3 | J301 | Water temperature prediction for cyanobacteria bloom assessment in the Murou Dam reservoir | Cyanobacteria Water Temperature Murou Dam Reservoir | 13-a | 485 |
| Day 3 | H302 | Improving the performance of fluorescence immunosensor by predicting single mutation effects using protein language model | immunosensor yeast surface display machine learning | 7-a | 13 |
| Day 3 | I304 | Dispersion prediction of surface modified nanoparticles using machine learning model with various molecular descriptors | machine learning dispersion prediction surface modified nanoparticles | 1-b | 242 |
| Day 3 | E305 | Prediction model for solar cell power generation including shadows using irradiance fractions based on real-time cloud image data | Solar cell Energy system Prediction model | 9-e | 623 |
| Day 3 | PE353 | Prediction of mass yield and elemental composition of biochar from pyrolysis of bagasse | Torrefaction Kinetic model Ash content | 5-g | 647 |
| Day 3 | I316 | Physics-informed neural networks for flow in a bubble column | Bubble column OpenFOAM Machine learning | 2-d | 635 |
| Day 3 | I323 | Evaluation of capping tendency by structural analyses at high-speed tableting process | FEM simulation capping tableting | 2-f | 444 |
Technical program
Technical sessions (Wide)
(For narrow screen)
Session programs
Search in technical program
SCEJ 89th Annual Meeting (Sakai, 2024)
© 2024 The Society of Chemical Engineers, Japan. All rights reserved.For more information contact Organizing Committee of SCEJ 89th Annual Meeting
E-mail: inquiry-89a
www4.scej.org