The Artificial Neural Twin — Process optimization and continual learning in distributed process chains
Emmert J, Mendez R, Dastjerdi HM, Syben C, Maier A (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 180
Article Number: 106647
DOI: 10.1016/j.neunet.2024.106647
Abstract
Industrial process optimization and control is crucial to increase economic and ecologic efficiency. However, data sovereignty, differing goals, or the required expert knowledge for implementation impede holistic implementation. Further, the increasing use of data-driven AI-methods in process models and industrial sensory often requires regular fine-tuning to accommodate distribution drifts. We propose the Artificial Neural Twin, which combines concepts from model predictive control, deep learning, and sensor networks to address these issues. Our approach introduces decentral, differentiable data fusion to estimate the state of distributed process steps and their dependence on input data. By treating the interconnected process steps as a quasi neural-network, we can backpropagate loss gradients for process optimization or model fine-tuning to process parameters or AI models respectively. The concept is demonstrated on a virtual machine park simulated in Unity, consisting of bulk material processes in plastic recycling.
Involved external institutions
Germany (DE)How to cite
APA:
Emmert, J., Mendez, R., Dastjerdi, H.M., Syben, C., & Maier, A. (2024). The Artificial Neural Twin — Process optimization and continual learning in distributed process chains. Neural Networks, 180. https://doi.org/10.1016/j.neunet.2024.106647
MLA:
Emmert, Johannes, et al. "The Artificial Neural Twin — Process optimization and continual learning in distributed process chains." Neural Networks 180 (2024).
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