论文标题
一种基于灵敏度的方法选择过程网络的最佳传感器方法
A sensitivity-based approach to optimal sensor selection for process networks
论文作者
论文摘要
传感器选择对于非线性过程的状态估计，控制和监测至关重要。但是，除非$ m $和$ n $很小，否则评估$ n $传感器的每种可能组合的性能是不切实际的。在本文中，我们提出了一种基于灵敏度的方法，以确定传感器数量的最小数量及其最佳位置以进行状态估计。测得的输出对初始状态的局部灵敏度矩阵用作观察性的度量。最小传感器的数量以使得局部灵敏度矩阵为完整列等级的方式确定。满足全等级条件并提供最大可观察性的传感器子集被视为最佳传感器位置。灵敏度矩阵的连续正交化是在提出的方法中进行的，以显着降低选择传感器的计算复杂性。为了验证该方法的有效性，它应用于两个过程，包括由四个连续搅拌坦克反应器和一个废水处理厂组成的化学过程。在这两种情况下，提出的方法都可以获得最佳传感器子集。
Sensor selection is critical for state estimation, control and monitoring of nonlinear processes. However, evaluating the performance of each possible combination of $m$ out of $n$ sensors is impractical unless $m$ and $n$ are small. In this paper, we propose a sensitivity-based approach to determine the minimum number of sensors and their optimal locations for state estimation. The local sensitivity matrix of the measured outputs to initial states is used as a measure of the observability. The minimum number of sensors is determined in a way such that the local sensitivity matrix is full column rank. The subset of sensors that satisfies the full-rank condition and provides the maximum degree of observability is considered as the optimal sensor placement. Successive orthogonalization of the sensitivity matrix is conducted in the proposed approach to significantly reduce the computational complexity in selecting the sensors. To validate the effectiveness of the proposed method, it is applied to two processes including a chemical process consisting of four continuous stirred-tank reactors and a wastewater treatment plant. In both cases, the proposed approach can obtain the optimal sensor subsets.
