AVT Process Systems Engineering, RWTH Aachen University, Germany.
Water Res. 2013 Sep 1;47(13):4774-87. doi: 10.1016/j.watres.2013.04.007. Epub 2013 Apr 15.
Many relevant process states in wastewater treatment are not measurable, or their measurements are subject to considerable uncertainty. This poses a serious problem for process monitoring and control. Model-based state estimation can provide estimates of the unknown states and increase the reliability of measurements. In this paper, an integrated approach is presented for the optimization-based sensor network design and the estimation problem. Using the ASM1 model in the reference scenario BSM1, a cost-optimal sensor network is designed and the prominent estimators EKF and MHE are evaluated. Very good estimation results for the system comprising 78 states are found requiring sensor networks of only moderate complexity.
许多相关的污水处理过程状态是不可测量的，或者它们的测量结果存在相当大的不确定性。这给过程监测和控制带来了严重的问题。基于模型的状态估计可以提供未知状态的估计值，并提高测量的可靠性。本文提出了一种基于优化的传感器网络设计和估计问题的综合方法。在参考场景 BSM1 中使用 ASM1 模型，设计了一个成本最优的传感器网络，并评估了突出的估计器 EKF 和 MHE。对于包含 78 个状态的系统，发现需要具有中等复杂度的传感器网络才能获得非常好的估计结果。
