The Artificial Neural Twin -- Process Optimization and Continual Learning in Distributed Process Chains
This addresses process optimization for industrial sectors like recycling, though it appears incremental as it combines existing concepts.
The paper tackles the challenge of optimizing and controlling distributed industrial processes by proposing the Artificial Neural Twin, which integrates model predictive control, deep learning, and sensor networks to enable differentiable data fusion and backpropagation for process optimization and model fine-tuning, demonstrated on a virtual plastic recycling simulation.
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 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.