SCEJ 91st Annual Meeting (Kyoto, 2026)
Program search result : 予測 : 33 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 “予測”; 33 programs are found.
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| Hall H 第 1 日 Day 1 | H114 | [Divisional Award] Frontier Prize lecture: Data-Driven Prediction of Particle-Fluid Dynamics inside Fibrous Filter Microstructures | Surrogate model Computational fluid dynamics Filter microstructure | X-51 | 701 |
| Hall P 第 1 日 Day 1 | PA109 | Development of AAV vectors targeting hepatic stellate cells using AI-based protein structure prediction | liver fibrosis AAV protein design | 7-d | 157 |
| Hall K 第 1 日 Day 1 | K117 | Modeling of Zeolite Catalysts for the Prediction of NH3-TPD Profiles | Zeolite Catalyst NH3-TPD | 12-m | 59 |
| Hall F 第 2 日 Day 2 | F208 | [Invited lecture] A study on CFD modeling of turbulent neutralization reaction and particle size prediction methods for metal hydroxides in the scale -up of stirred tank-type reactive crystallizer | Particle agglomeration model Turbulent mixing model Precipitation | K-5 | 215 |
| Hall N 第 2 日 Day 2 | N213 | Development and Demonstration of a Soft Sensor for Predicting Chemical Production Rate and an Estimation-Based Control System in a Bioreactor. | Softsensor Adaptive Control Bioreactor | 6-d | 305 |
| Hall N 第 2 日 Day 2 | N214 | Application of nonlinear model predictive control in antibody drug manufacturing | Antibody drug manufacturing Process Nonlinear model predictive control Optimization of fed-batch cell culture process | 6-d | 327 |
| Hall P 第 2 日 Day 2 | PC205 | Development of vapor-liquid equilibrium prediction model using Raman spectrum informatics | raman spectrum vapor-liquid equilibrium activity coefficient | 1-a | 380 |
| Hall P 第 2 日 Day 2 | PC207 | A Hybrid Prediction Model Combining GCN and PC-SAFT Equation of State with Molecular Shape | Perturbed chain-statistical associating fluid theory Equation of state Graph convolutional network | 1-a | 31 |
| Hall P 第 2 日 Day 2 | PC213 | Raman spectrum informatics for estimation of mixture density toward thermal transport evaluation of micro heat pipes | raman spectrum informatics mixture density micro heat pipe | 1-d | 406 |
| Hall P 第 2 日 Day 2 | PC215 | Prediction of Interaction Parameters for PR-vdW Model via PCM | Peng-Robinson equation of state Artificial neural network Polarizable continuum model | 1-a | 312 |
| Hall L 第 2 日 Day 2 | L215 | Predicting parison shape for plastic fuel tank by using Gaussian process regression | parison Gaussian process regression extrusion | 2-a | 373 |
| Hall N 第 2 日 Day 2 | N221 | A zero-shot quality prediction method for new combinations of materials and processes | Transfer learning Zero-shot regression Process Change | 6-f | 165 |
| Hall J 第 2 日 Day 2 | J223 | Application of transfer learning to product composition prediction model for catalytic cracking reaction of vegetable oils | catalytic cracking transfer learning reaction prediction | 5-a | 454 |
| Hall L 第 3 日 Day 3 | L302 | Scale-Up Prediction of Liquid-Liquid Mixing and Reaction Processes Using Computational Fluid Dynamics | Liquid-liquid mixing Stirred vessel Computational fluid dynamics | 2-b | 12 |
| 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 | 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 | PD358 | Prediction of solubility in CO2 using COSMO-vacancy model | COSMO-vacancy activity coefficient supercritical CO2 | 6-f | 585 |
| 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 | PD367 | Prediction of dielectric constant using calculated infrared spectra | infrared spectra machine learning | 6-f | 120 |
| 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 | PD376 | Fundamental study on disease prediction using brain MRI images by meta-learning | Machine Learning Neurodegenerative diseases Meta-learning | 6-f | 316 |
| 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 | PD379 | Improving the predictive accuracy of drug-drug interaction prediction models using unlabeled, ingredient, and clinical data | Drug-drug interaction Machine learning Positive-unlabeled learning | 6-f | 23 |
| Hall P 第 3 日 Day 3 | PD385 | AI-based Reduced-order Model for Fast Predictive Simulations | Reduced-order model DEM-DNS Slurry | 6-c | 48 |
| 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 | PD387 | Development of a GCN prediction model for the Ames test using improved molecular expression methods | Machine Learning Ames Test Molecule Graph | 6-f | 614 |
| Hall A 第 3 日 Day 3 | A304 | [Invited lecture] Neural Network Application for Reaction Prediction and Real-Time Control in Resin Batch Manufacturing: DIC and Hitachi | Neural Network Resin Batch Manufacturing Reaction Prediction and Real-Time Control | SS-5 | 223 |
| Hall J 第 3 日 Day 3 | J307 | Development of a prediction model for the partition coefficient in supercritical carbon dioxide extraction | Partition coefficient Supercritical carbon dioxide Machine learning | 8-c | 207 |
| Hall P 第 3 日 Day 3 | PE356 | Evaluation of Lithium-Ion Battery Cathode Slurries by Rheological and Impedance Measurements -Prediction of the Effects of Drying Temperature on Film Structure and Volume Resistivity - | Lithium-ion battery slurry Drying conditions | 11-a | 391 |
| 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 |
| Hall P 第 3 日 Day 3 | PE392 | A chemical kinetics model for predicting iron removal from acid mine drainage by microbial oxidation | Passive treatment Acid mine drainage Iron-oxidizing bacteria | 13-b | 496 |
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SCEJ 91st Annual Meeting (Kyoto, 2026)
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