  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.
