The Artificial Neural Twin -- Process Optimization and Continual Learning in Distributed Process Chains

arXiv:2403.18343v1 Announce Type: new
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 differentiable …

abstract artificial arxiv continual control cs.lg data data-driven data sovereignty distributed economic efficiency expert however implementation industrial knowledge optimization process process optimization sensory twin type