AVESTAR researchers are developing optimal sensor placement strategies and algorithms required for process state estimation, process disturbance rejection, equipment/process condition monitoring, and process fault diagnosis. The key objectives are to enhance process observability and minimize the estimation error of unmeasured process states; to improve the disturbance rejection capabilities of process control systems; to enhance the monitoring of process operations and performance with respect to key performance indicators; and to increase the reliability of fault diagnosis and fault-tolerant control systems for reducing fault severity and improving overall fault resolution.
Future power plants with CO2 capture will face stricter operational and environmental constraints. Accurate values of relevant states/outputs/disturbances are needed to satisfy these constraints and to maximize the operational efficiency. Unfortunately, a number of these process variables cannot be measured while a number of them can be measured, but have low precision, reliability, or signal-to-noise ratio. AVESTAR researchers are developing a sensor placement algorithm for optimal selection of sensor location, number, and type that can maximize the plant efficiency and result in a desired precision of the relevant measured and unmeasured states. The sensor placement algorithm is developed with the assumption that an optimal Kalman filter will be implemented in the plant for state and disturbance estimation. The algorithm is developed assuming steady-state Kalman filtering and steady-state operation of the plant. The control system is considered to operate based on the estimated states and thereby, captures the effects of the sensor placement algorithm on the overall plant efficiency. The optimization problem is solved by Genetic Algorithm considering both linear and nonlinear equality and inequality constraints.
- Paul, P. , D. Bhattacharyya, R. Turton, and S.E. Zitney, “Sensor Placement for Maximizing Process Efficiency: An Algorithm and its Application,” AIChE 2013 Annual Meeting, San Francisco, CA, November 3-8 (2013).
- Paul, P. , D. Bhattacharyya, R. Turton, and S.E. Zitney, “Adaptive Kalman Filter for Estimation of Environmental Performance Variables in an Acid Gas Removal Process,” Proc. of the 2013 American Control Conference, Washington D.C. , June 17-19 (2013).
- Paul, P. , D. Bhattacharyya, R. Turton, and S.E. Zitney, “State Estimation of an Acid Gas Removal Unit for an IGCC Power Plant with CO2 Capture,” Proc. of the 29th Annual International Pittsburgh Coal Conference, Pittsburgh, PA, October 15-18 (2012).