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State estimation is crucial for the monitoring and control of post-combustionCO2 capture plants (PCCPs). The performance of state estimation is highlyreliant on the configuration of sensors. In this work, we consider the problemof sensor selection for PCCPs and propose a computationally efficient method todetermine an appropriate number of sensors and the corresponding placement ofthe sensors. The objective is to find the (near-)optimal set of sensors thatprovides the maximum degree of observability for state estimation whilesatisfying the budget constraint. Specifically, we resort to the informationcontained in the sensitivity matrix calculated around the operating region of aPCCP to quantify the degree of observability of the entire system correspondingto the placed sensors. The sensor selection problem is converted to anoptimization problem, and is efficiently solved by a one-by-one removalapproach through sensitivity analysis. Next, we extend our approach to studyfault tolerance (resilience) of the selected sensors to sensor malfunction. Theresilient sensor selection problem is to find a sensor network that gives goodestimation performance even when some of the sensors fail, thereby improvingthe overall system robustness. The resilient sensor selection problem isformulated as a max-min optimization problem. We show how the proposed approachcan be adapted to solve the sensor selection max-min optimization problem. Byimplementing the proposed approaches, the sensor network is configured for thePCCP efficiently.

Sensor network design for post-combustion CO2 capture plants: economy, complexity and robustness