SCEJ 54th Autumn Meeting
Sep. 11 (Mon) - Sep. 13 (Wed), 2023
Fukuoka University, Nanakuma Campus
| Shimada Iori (Shinshu Univ.), Kim Sanghong (Tokyo Univ. of Agri. and Tech.), Toya Yoshihiro (Osaka Univ.), Kaneko Shogo (Sumitomo Chemical), Mukaida Shiho (Mitsui Chemicals), Muroga Shun (AIST) |
Data science has been rapidly developing in recent years as the fourth science following experimental science, theoretical science, and computational science. In the field of chemical engineering, data-driven science, which derives superior materials and processes by making full use of a large amount of accumulated data and information, is becoming increasingly important, and many efforts are being made. This symposium will have speakers who are making pioneering efforts toward a data-driven society from various viewpoints and discuss future research and development.
Most recent update: 2024-04-11 13:19:01
 The keywords that frequently used in this topics code. | Keywords | Number | |
|---|---|---|---|
| Materials Informatics | 6 |  | |
| Machine learning | 5 | | |
| Bayesian optimization | 4 | | |
| DX | 3 | | |
| Model based metabolic pathway design | 1 | | |
| ACKN No. | Title/Author(s) | Keywords | Style |
|---|---|---|---|
| 33 | Deep learning model for predicting all protein-protein interactions from sequence data | Cross attention deep learning prediction | O |
| 167 | Utilization of Bayesian optimization in the process development of drug substance | DX Bayesian optimization Simulation | O |
| 187 | Bayesian Optimization Framework for Polymer Composites Design Using High Dimensional Past Materials Data | Bayesian optimization DX Materials informatics | O |
| 203 | Development of a soft sensor and a controller system of hydrogen concentration in the exhaust gas in fuel cell systems | PEFC Hydrogen control soft sensor | O |
| 204 | Gaussian Process Regression Approaches for Process Optimization: A Case Study of Interface State Density Prediction between Insulator and Semiconductor | Gaussian process regression length-scale Metal-oxide-semiconductor | O |
| 225 | Development of microbial production process by model based metabolic design and directed evolution | Model based metabolic pathway design Directed evolution Metabolic engineering | O |
| 235 | Machine learning guided enzyme’s molecular recognition specificity conversion | enzyme design machine learning | O |
| 353 | Predicting Physical Properties of Structurally Unknown Polymers Using Spectroscopy Data | Machine Learning Predict Descriptor | O |
| 385 | Batch Bayesian optimization method for goal-oriented multi-objective functional materials design | Bayesian Optimization | O |
| 445 | Development of machine learning model for CO2 absorption performance of blended amine solutions | CO2 absorption machine learning amine | O |
| 450 | Construction of MI platform for functional materials | Materials informatics DX | O |
| 480 | Application of reaction mechanism search method using chemical reaction neural network to glycerol oxidation reaction | physics informed neural network kinetics model data-driven | O |
| 550 | Novel encoding method for high dimensional power consumption data in distributed energy system for short-term electricity demand forecasting | electricity demand prediction distributed energy system big data | O |
| 602 | Multimodal Deep Learning for Predictions of Various Properties of Composite Materials | multimodal deep learning materials informatics generative deep learning | O |
| 646 | Calculation of Tokyo's Photovoltaic Potential and Study of the Effects of Reducing Daily Power Fluctuations from Facade Installations | Facade installation Photovoltaic power potential Power fluctuation | O |
| 686 | High accuracy prediction of edible oil oxidation stability by multivariate analysis incorporating chemiluminescence information | multivariate analysis oxidative stability edible oil | O |
| 728 | Development of mechanistic cell cultivation models in monoclonal antibody production using data-driven insights | Biopharmaceuticals Lactate consumption Glutamine | O |
| 741 | Elucidation of appropriate data acquisition conditions for API concentration prediction by NIR | NIR Spectrum diffuse reflectance measurement API concentration prediction | O |
| 784 | [Invited lecture] Material exploration and process optimization by digital technology | Materials Informatics Process Informatics Quantum Chemistry | O |
| 805 | [Invited lecture] Prediction and control of bacterial evolution through high-throughput automated experiments using robots | Laboratory automation Laboratory evolution Escherichia coli | O |
| 816 | Design of integrated upstream and downstream monoclonal antibody production processes using surrogate models | Surrogate model Machine learning Bayesian optimization | O |
| 948 | Applicational study of symbolic regression to exploring new materials and constructing kinetics models | Machine learning Materials Informatics Reaction Kinetics | O |
| 976 | [Invited lecture] Data-driven Approaches for Functional Materials Development in SEKISUI CHEMICAL. | Data-Driven Development Functional Materials Materials Informatics | O |
| 979 | [Invited lecture] Remote Operation Support and Automatic Plant Operation Technology In Waste-to-Energy Plants | Remote operation Automatic operation AI and Data analysis | O |
easp 2.51b; update.pl 2.37 (C)1999-2015 kawase
