(373c) Development of a Sensor Placement Algorithm for Maximizing the Efficiency of an Acid Gas Removal (AGR) Unit for an IGCC Power Plant with CO2 Capture

Future integrated gasification combined cycle (IGCC) 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. In this work, a sensor placement (SP) algorithm is developed 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/unmeasured states.

In this work, an SP algorithm is developed for an selective, dual-stage Selexol-based acid gas removal (AGR) unit for an IGCC plant with pre-combustion CO₂ capture.  A comprehensive nonlinear dynamic model of the AGR unit is developed in Aspen Plus Dynamics^® (APD) and used to generate a linear state-space model that is used in the SP algorithm. The SP 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 SP algorithm on the overall plant efficiency. The optimization problem is solved by Genetic Algorithm (GA) considering both linear and nonlinear equality and inequality constraints.

Due to the very large number of candidate sets available for sensor placement and because of the long time that it takes to solve the constrained optimization problem that includes more than 1000 states, solution of this problem is computationally expensive. For reducing the computation time, parallel computing is performed using the Distributed Computing Server (DCS^®) and the Parallel Computing^® toolbox from Mathworks^®.  In this presentation, we will share our experience in setting up parallel computing using GA in the MATLAB^® environment and present the overall approach for achieving higher computational efficiency in this framework.