Summary of The Artificial Neural Twin — Process Optimization and Continual Learning in Distributed Process Chains, by Johannes Emmert et al.
The Artificial Neural Twin – Process Optimization and Continual Learning in Distributed Process Chains
by Johannes Emmert, Ronald Mendez, Houman Mirzaalian Dastjerdi, Christopher Syben, Andreas Maier
First submitted to arxiv on: 27 Mar 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 The proposed Artificial Neural Twin combines concepts from model predictive control, deep learning, and sensor networks to address the challenges of industrial process optimization and control. By treating interconnected process steps as a quasi-neural network, the approach enables backpropagation of loss gradients for process optimization or model fine-tuning. The concept is demonstrated on a virtual machine park simulated in Unity, consisting of bulk material processes in plastic recycling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Artificial Neural Twin helps optimize industrial processes by combining different technologies like model predictive control and deep learning. It can adjust to changes in data and make the process more efficient. This was tested on a computer simulation of a recycling plant. |
Keywords
* Artificial intelligence * Backpropagation * Deep learning * Fine tuning * Neural network * Optimization
