Last modified: 2024-06-18 12:32:12
Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
---|---|---|---|---|---|
Hall K, Day 1 | |||||
(13:20–14:20) (Chair: | |||||
K114 | Monitoring pseudoplasticity modification of rice porridge by ultrasonic in-line rheometry | Rheology In-line measurement Rice porridge | 6-a | 26 | |
K115 | Quality prediction in a small data environment for batch processes | Machine Learning Quality Prediction Small data | 6-a | 286 | |
K116 | Development of a new indicator for comprehending cyclic variations in process behavior by applying frequency analysis | Wavelet Transformation Fault Detection Stability Indicator | 6-a | 673 | |
(14:20–15:40) (Chair: | |||||
K117 | Design and Optimization of an Integrated CO2 Capture, Utilization and Storage System for Large-scale Removal of CO2 emissions | integrated CCUS system large-scale CO2 emission sources process design and optimization | 6-b | 359 | |
K118 | Model-based approach to design space determination in drug substance flow synthesis using Grignard reaction | Hybrid model Flow chemistry Disturbance | 6-b | 234 | |
K119 | Data-driven approach to automated reaction process analysis | Mechanistic model Machine learning Neural network | 6-b | 235 | |
K120 | Flow Analysis of Gravure Coating Room Using Cloud-Native CAE | Gravure Coating Room Cloud-Native CAE CFD | 6-b | 95 | |
(15:40–17:00) (Chair: | |||||
K121 | Modeling and control of systems with large time delay | process control | 6-d | 733 | |
K122 | (withdrawn) | 100 | 189 | ||
K123 | The Utilization of AI in Process Plants | Physical model Machine-learning Plant efficiency enhancement | 6-e | 624 | |
K124 | Dynamic Process Simulation for Green Ammonia Synthesis Considering Wind and Solar Condition | Dynamic simulation Green ammonia Machine learning | 6-c | 695 | |
Hall K, Day 2 | |||||
(13:20–14:00) (Chair: | |||||
K214 | Improved accuracy of MSPC through optimization of scaling factor of multivariable independent of domain knowledge. | Pharmaceutical Continuous Manufacturing Wet Granulation Multivariate Statistical Process Control | 6-f | 239 | |
K215 | A parameter estimation method for chromatographic separation process based on physics-informed neural network | parameter estimation chromatographic process physics-informed neural network | 6-f | 44 | |
(14:00–14:40) (Chair: | |||||
K216 | Model-based design framework for antibody drug production processes considering perspectives from cell characteristics to social requirements | Biopharmaceuticals Process design IDEF0 | 6-g | 92 | |
K217 | Coarse-Grained Force Field Parametrization for Polymers Using Machine Learning | Coarse-Grained Force Field Machine Learning | 6-g | 220 |
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SCEJ 89th Annual Meeting (Sakai, 2024)