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.

工业过程优化和控制是提高经济和生态效益的关键。我们提出了人工神经双胞胎，结合了模型预测控制、深度学习和传感器网络的概念，以解决数据主权、不同目标或实施所需的专业知识等问题。通过将相互连接的过程步骤视为准神经网络，我们可以对过程优化或模型微调进行损失梯度的反向传播，从而调整过程参数或人工智能模型。该概念在Unity中模拟的虚拟机群中进行了演示，其中包括塑料回收中的散粒材料处理过程。

人工神经孪生——分布式工艺链的过程优化和持续学习