The Artificial Neural Twin — Process optimization and continual learning in distributed process chains

• Published in: Neural networks 2024-12, Vol.180, p.106647, Article 106647
• Main Authors: Emmert, Johannes, Mendez, Ronald, Dastjerdi, Houman Mirzaalian, Syben, Christopher, Maier, Andreas
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Computer Simulation; Continual learning; Data-fusion; Decentralized and distributed control; Deep Learning; Distributed learning; Humans; Industry; Internet of things; Model predictive control; Multi sensor systems; Neural Networks, Computer; Process optimization
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2024.106647

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. •Integration of process optimization, sensor networks, and continual learning.•Process optimization and continual learning solved by distributed backpropagation.•Differentiable data fusion provides derivates for backpropagation.•Simple integration via process interface.•Demonstration in a virtual plastic sorting process chain.