Summary of Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations Among Multivariates, by Gang Su et al.
Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates
by Gang Su, Sun Yang, Zhishuai Li
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the application of Transformer-based models for predicting steam drum water levels in a boiler plant. The authors aim to develop an accurate and robust predictive framework to anticipate fluctuations and facilitate proactive control strategies. To achieve this, they propose a pipeline involving data preprocessing, causal relation analysis, delay inference, variable augmentation, and prediction. The effectiveness of the Transformer-based approach is evaluated through extensive experimentation and analysis, highlighting its potential to enhance operational stability and optimize plant performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For predicting steam drum water levels in a boiler plant, researchers developed a predictive framework using Transformer-based models. These models can accurately anticipate fluctuations and help control strategies to improve plant performance. |
Keywords
* Artificial intelligence * Inference * Transformer
