A state-space representation of water quality dynamics describing disinfectant (e.g., chlorine) transport dynamics in drinking water distribution networks has been recently proposed. Such representation is a byproduct of space- and time-discretization of the PDE modeling transport dynamics. This results in a large state-space dimension even for small networks with tens of nodes. Although such a state-space model provides a model-driven approach to predict water quality dynamics, incorporating it into model-based control algorithms or state estimators for large networks is challenging and at times intractable. To that end, this paper investigates model order reduction (MOR) methods for water quality dynamics. The presented investigation focuses on reducing state-dimension by orders of magnitude, the stability of the MOR methods, and the application of these methods to model predictive control.

中文翻译：

---

水质动力学模型降阶

最近已经提出了描述饮用水分配网络中消毒剂（例如氯）运输动态的水质动态的状态空间表示。这种表示是PDE建模传输动力学的时空离散化的副产品。即使对于具有数十个节点的小型网络，这也会导致较大的状态空间维度。尽管这种状态空间模型提供了一种模型驱动的方法来预测水质动态，但是将其合并到大型网络的基于模型的控制算法或状态估计器中仍然具有挑战性，并且有时难以解决。为此，本文研究了用于水质动力学的模型降阶（MOR）方法。提出的调查着重于将状态维减少几个数量级，MOR方法的稳定性，