SCEJ 88th Annual Meeting (Tokyo, 2023)
Program search result : Machine learning : 22 programs
The preprints(abstracts) are now open (Mar. 1st). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants in Period I/II and invited persons are required.
Keywords field exact matches “Machine learning”; 22 programs are found.
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| Day 1 | I119 | Quantitative Comparison of Reinforcement Learning and Model-based Optimal Control for Chemical Processes | process control machine learning optimal control | 6-d | 52 |
| Day 1 | D124 | [Invited lecture] Graph theory approach to data-driven energy planning | P-graph energy planning machine learning | K-2 | 620 |
| Day 2 | PB225 | Machine learning application for the directed evolution of antibody fragments | Antibody fragments Phage display Machine learning | 7-a | 551 |
| Day 2 | PB237 | Machine-learning assisted evolution of fungal cellulase | machine learning enzyme biorefinery | 7-a | 710 |
| Day 2 | F207 | Hybrid modelling of active pharmaceutical ingredient flow synthesis in ring-opening reaction of an epoxide with a Grignard reagent | Flow chemistry Machine learning Random forest regression | 5-i | 167 |
| Day 2 | PC229 | Simulation and design of integrated upstream and downstream monoclonal antibody production processes | Surrogate model Bayesian optimization Machine learning | 6-b | 496 |
| Day 2 | PC230 | Application of machine learning and physical modeling for detecting hydrogen leakage from hydrogen pipeline | hydrogen pipeline leak detection machine learning | 10-e | 269 |
| Day 2 | PC236 | Machine Learning Study for Identifying key factors that determine the Corrosion Resistance of Stainless Steels | Machine Learning Corrosion | 6-g | 420 |
| Day 3 | D301 | Prediction of surface-modified iron oxide nanoparticles extraction from reaction field using solubility parameters and machine learning | nanoparticle extraction solubility parameter machine learning | IS-1 | 475 |
| Day 3 | PD311 | Prediction of nanoparticle dispersion by machine learning with Hansen parameters as input | Hansen solubility parameter nanoparticle dispersion machine learning | 1-b | 486 |
| Day 3 | PD333 | Prediction of product composition using machine learning in co-processing of bio-oil and heavy oil in catalytic cracking process | bio-oil co-processing machine learning | 5-a | 539 |
| Day 3 | H305 | High-speed computing of powder mixing using machine learning with random motion model | Powder mixing High-speed computing Machine learning | 2-f | 112 |
| Day 3 | PD346 | The development of Porous polymer monolith catalyst with the application of machine learning | Immobilized Catalyst Monolith Machine Learning | 5-a | 317 |
| Day 3 | H306 | [Featured presentation] Machine learning-based calibration of physical properties in bulk material simulations | Discrete element method Machine learning Model identification | 2-f | 284 |
| Day 3 | I307 | Dipeptide property analysis for the prediction of liquid chromatography retention time | peptide LC-MS/MS machine learning | 7-h | 450 |
| Day 3 | R306 | [Requested talk] Theory-driven Machiene Learning for Chemical Engineering | Machine learning Artificial Intelligence Big data | HQ-21 | 471 |
| Day 3 | PE302 | Polymer structure generation using generative adversarial networks and its application to separation membrane design | machine learning polymer membrane gas separation | 4-a | 625 |
| Day 3 | PE340 | Machine Learning-assisted Large-scale Screening of Metal-organic Frameworks for CO2/CO Separation | Metal-organic frameworks CO2/CO separation machine learning | 4-e | 487 |
| Day 3 | B319 | [Requested talk] Practical use of digital technology at chemical plant | chemical plant digital transformation machine learning | SS-5 | 349 |
| Day 3 | Q306 | [Requested talk] Practical use of digital technology at chemical plant | chemical plant digital transformation machine learning | SS-7 | 348 |
| Day 3 | Q307 | Inverse design of functional separation materials using deep generation models. | machine learning polymer membrane gas separation | SS-7 | 357 |
| Day 3 | Q314 | [Invited lecture] AI use cases in predictive maintenance that have entered the practical stage | AI Machine Learning Predictive Maintenance | SS-7 | 393 |
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SCEJ 88th Annual Meeting (Tokyo, 2023)
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