Sensor network design for post-combustion CO[formula omitted] capture plants: Computational efficiency and robustness

Abstract

State estimation is crucial for the monitoring and control of post-combustion CO2 capture plants (PCCPs). The performance of state estimation is highly reliant on the configuration of sensors. In this work, we consider the problem of sensor selection for PCCPs and propose a computationally efficient method to determine an appropriate number of sensors and the corresponding placement of the sensors. The objective is to find the (near-)optimal set of sensors that provides the maximum degree of observability for state estimation while satisfying the budget constraint. Specifically, we resort to the information in the sensitivity matrix calculated around the operating region of a PCCP to quantify the degree of observability of the entire system corresponding to the placed sensors. The sensor selection problem is converted to an optimization problem and is efficiently solved by a one-by-one removal approach through sensitivity analysis. Next, we extend our approach to study the fault tolerance of the selected sensors to sensor malfunction. The robust sensor selection problem is to find a sensor network that gives good estimation performance even when some of the sensors fail, thereby improving the overall system robustness. The robust sensor selection problem is formulated as a max–min optimization problem. We show how the proposed approach can be adapted to solve the sensor selection max–min optimization problem. By implementing the proposed approaches, the sensor network is configured for the PCCP efficiently.