Abstract
We propose a grid-like computational model of tubular reactors. The architecture is inspired by the computations performed by solvers of partial differential equations which describe the dynamics of the chemical process inside a tubular reactor. The proposed model may be entirely based on the known form of the partial differential equations or it may contain generic machine learning components such as multi-layer perceptrons. We show that the proposed model can be trained using limited amounts of data to describe the state of a fixed-bed catalytic reactor. The trained model can reconstruct unmeasured states such as the catalyst activity using the measurements of inlet concentrations and temperatures along the reactor.
| Original language | English |
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| Title of host publication | 2021 IEEE 19th International Conference on Industrial Informatics (INDIN) |
| Publisher | IEEE |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-7281-4395-8 |
| DOIs | |
| Publication status | Published - 21 Jul 2021 |
| MoE publication type | A4 Conference publication |
| Event | IEEE International Conference on Industrial Informatics - Palma de Mallorca, Spain, Palma de Mallorca, Spain Duration: 21 Jul 2021 → 23 Jul 2021 https://2021.ieee-indin.org/ |
Conference
| Conference | IEEE International Conference on Industrial Informatics |
|---|---|
| Abbreviated title | INDIN |
| Country/Territory | Spain |
| City | Palma de Mallorca |
| Period | 21/07/2021 → 23/07/2021 |
| Internet address |
Keywords
- Catalyst Sctivity
- Deep learning
- Fixed-Red Catalytic Reactor
- Multi-Layer Perceptron
- Soft Sensor
- Tubular Reactor
- Temperature Measurement
- Computational Modeling
- Partial Differential Equations
- Catalysts
- Mathematical Models
- Data Models