A sensitivity-based approach to optimal sensor selection for complex processes

Abstract

Sensor selection is critical for state estimation, control and monitoring of nonlinear processes. However, it is impractical to evaluate the performance of all the combinations of the potentially available sensors using exhaustive search unless the sensor set is small. In this paper, we present a sensitivity-based approach for determining the minimum number of sensors and their optimal locations for state estimation. The observability is measured using the local sensitivity matrix of the output measurements to initial states. Based on this matrix, we determine the minimum number of sensors required to achieve full column rank. The optimal sensor placement is thought to be the sensor set that provides the maximum degree of observability among all sets that meet the full-rank requirement. The computational complexity of sensor selection is significantly reduced by successive orthogonalization of the columns of the sensitivity matrix. To validate the effectiveness of the proposed method, it is applied to two processes: four continuous stirred-tank reactors and a wastewater treatment plant. In both cases, the proposed approach can obtain the optimal sensor subset.