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.
@article{arxiv.2403.18343,
title = {The Artificial Neural Twin -- Process Optimization and Continual Learning in Distributed Process Chains},
author = {Johannes Emmert and Ronald Mendez and Houman Mirzaalian Dastjerdi and Christopher Syben and Andreas Maier},
journal= {arXiv preprint arXiv:2403.18343},
year = {2024}
}