SCEJ 91st Annual Meeting (Kyoto, 2026)
Program search result : モデル : 49 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 “モデル”; 49 programs are found.
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| Hall L 第 1 日 Day 1 | L115 | Carbonation rate, reaction mechanism, and reaction rate model of γ-C2S-type cement special admixture | CO2-absorbing concrete Real-time rate analysis Reaction model | 13-g | 76 |
| Hall K 第 2 日 Day 2 | K201 | Heat-Transfer Model for Wound Rolls in Roll-to-Roll Processing | Roll-To-Roll Heat transfer wound roll | 12-h | 25 |
| Hall G 第 2 日 Day 2 | G202 | Evaluation of Excess Surface Tensions at High Pressure Using Wilson-SurTen Model | excess surface tension high pressure ASOG-SurTen model | 1-a | 104 |
| Hall K 第 2 日 Day 2 | K202 | Slot Coating Edge-Flow Model for Thickness Control | Slot coating edge thickness Roll-To-Roll | 12-h | 26 |
| Hall N 第 2 日 Day 2 | N202 | Simulation Model Construction for an Integrated E-Fuel Plant with SOEC Co-Electrolysis and Fischer-Tropsch Synthesis | e-fuel Fischer&Tropsch SOEC co-electrolysis | 6-b | 461 |
| Hall P 第 2 日 Day 2 | PB205 | Development of DEM proxy model for flow, mixing, and segregation of polydisperse powders of different particle sizes | DEM proxy model polydisperse powders segregation | 2-f | 767 |
| Hall P 第 2 日 Day 2 | PB276 | Development of a Numerical Model for Anion Exchange Membrane Water Electrolysis | hydrogen production numeical simulation water electrolysis | 4-j | 515 |
| Hall N 第 2 日 Day 2 | N203 | Development of an estimation method for CO2 capture amount using reduced chemical reaction models | CCS Absorption Bayesian optimization | 6-b | 150 |
| Hall N 第 2 日 Day 2 | N204 | Application of an Inferential Property Model to Actual Process Data with Variable Time Delays | process control | 6-d | 716 |
| Hall N 第 2 日 Day 2 | N205 | [The SCEJ Award for Outstanding Young Researcher] Development of novel mathematical models for solid drug product manufacturing and application to process design | 0-c | 786 | |
| Hall K 第 2 日 Day 2 | K208 | A model for analyzing particle deposition in slurry coated onto a porous substrate | Suspension Filtration Numerical simulation | 12-h | 610 |
| Hall E 第 2 日 Day 2 | E212 | [The SCEJ Award for Outstanding Women's Activity] Global Project Management Education and As a Role Model for Female Engineers --In the age of diversity | HC-11 | 370 | |
| Hall L 第 2 日 Day 2 | L213 | Transfer Learning and Multi-Nozzle Extension of a CFD-Based Surrogate Model for Spray Flow Fields | CFD Surrogate Spray | 2-a | 284 |
| Hall L 第 2 日 Day 2 | L214 | Experiments and Mathematical Model Development for the Drying Process in Food Freeze-Drying | Freeze-Drying Heat and mass transfer Numerical simulation | 2-a | 311 |
| 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 | 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 | 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 P 第 2 日 Day 2 | PC224 | Molecular Simulation of Metal-Organic Framework Formation via Coarse-grained Modeling | Metal-organic frameworks Nucleation MD simulation | 12-a | 519 |
| Hall M 第 2 日 Day 2 | M215 | Techno-Economic Evaluation of Facade-Integrated Photovoltaic Deployment to Increase Renewable Energy Penetration in Urban Areas | Perovskite solar cell Energy system modeling Techno-economic assessment | 9-e | 746 |
| Hall L 第 2 日 Day 2 | L220 | Development of a model-free facile viscometer based on flow inverse analysis | Rheology Fluid Mechanics Viscosity | 2-a | 447 |
| Hall N 第 2 日 Day 2 | N220 | In-line spectroscopic monitoring of phase structure for polymer processing using a light scattering model | In-line analysis polymer blends phase structure | 6-g | 200 |
| Hall I 第 2 日 Day 2 | I221 | Sequential HETP Computational Model for Determining the Column Height of Structured Packing Columns in a Continuous Distillation | Structured Packing Column Sequential HETP Computation Continuous Distillation | 4-c | 11 |
| 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 N 第 2 日 Day 2 | N223 | Accelerating Automated Physical Model Building by Partitioning Equation-Variable Graphs | Automated physical modeling Equation-based modeling Graph partitioning | 6-f | 663 |
| Hall P 第 3 日 Day 3 | PD309 | Extending Chemical Reaction Neural Networks for Reaction-Diffusion Modeling in Porous Catalysts | Reaction mechanism Reaction-diffusion system Data-driven | 5-a | 452 |
| 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 | 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 | PD365 | Modeling and design of antibody drug manufacturing processes with culture media recycling | Biopharmaceuticals Recycling Process design | 6-b | 463 |
| 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 | 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 | PD380 | Modeling and design of cultivation processes considering glycosylation in monoclonal antibody manufacturing | Mechanistic model Monoclonal Antibody Cultivation Glycosylation | 6-b | 400 |
| 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 | 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 P 第 3 日 Day 3 | PD390 | Construction of a Physical Model for Risk Analysis of Water Electrolysis Systems | Water Electrolysis Systems Risk Analysis Physical Model | 10-e | 508 |
| 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 L 第 3 日 Day 3 | L308 | Development of inter-particle flux models for numerical simulation of transport phenomenon. | numerical simulation transport phenomenon multiphase flow | 2-b | 754 |
| Hall K 第 3 日 Day 3 | K314 | Development of a Novel Scheme for Constructing Physical Models from MD Simulations | Symbolic Regression Molecular Dynamics Crystalline Structure | 12-i | 70 |
| Hall P 第 3 日 Day 3 | PE360 | Modeling of Ir recycling processes and application to proton exchange membrane water electolysis | hydrometallurgy process modeling hydrogen | 13-e | 570 |
| Hall P 第 3 日 Day 3 | PE381 | Experimental and modeling study of atmospheric CO2 capture using a hollow fiber membrane contactor | DAC chemical absorption process simulation | 13-g | 769 |
| 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 |
| Hall J 第 3 日 Day 3 | J320 | Validation of reaction models for the SiC-CVD process using Quadrupole Mass Spectrometry | SiC CVD Quadrupole Mass Spectrometry | 5-h | 672 |
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SCEJ 91st Annual Meeting (Kyoto, 2026)
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