(186d) Optimal Control System Design of an Acid Gas Removal (AGR) Unit for an IGCC Power Plant with CO2 Capture

Dustin Jones^a,b , Debangsu Bhattacharyya^a,b, Richard Turton^a,b, and Stephen E. Zitney^b

^a Department of Chemical Engineering, West Virginia University, Morgantown, WV, USA.
^b AVESTAR^TM Center, National Energy Technology Laboratory, Morgantown, WV, USA

Abstract:

Future IGCC plants with CO₂ capture should be operated optimally in the face of disturbances without violating operational and environmental constraints. To achieve this goal, a systematic approach is taken in this work to design the control system of a selective, dual-stage Selexol-based acid gas removal (AGR) unit for a commercial-scale integrated gasification combined cycle (IGCC) power plant with pre-combustion CO₂ capture. The control system design is performed in two stages with the objective of minimizing the auxiliary power while satisfying operational and environmental constraints in the presence of measured and unmeasured disturbances.

In the first stage of the control system design, a top-down analysis is used to analyze degrees of freedom, define an operational objective, identify important disturbances and operational/environmental constraints, and select the control variables.  With the degrees of freedom, the process is optimized with relation to the operational objective at nominal operation as well as under the disturbances identified.  Operational and environmental constraints active at all operations are chosen as control variables.  From the results of the optimization studies, self-optimizing control variables are identified for further examination.  Several methods are explored in this work for the selection of these self-optimizing control variables.  Modifications made to the existing methods will be discussed in this presentation. Due to the very large number of candidate sets available for control variables and due to the complexity of the underlying optimization problem, 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^®. 

The second stage is a bottom-up design of the control layers used for the operation of the process.  First, the regulatory control layer is designed followed by the supervisory control layer.  Finally, an optimization layer is designed. 

In this paper, the proposed two-stage control system design approach is applied to the AGR unit for an IGCC power plant with CO₂ capture.  Aspen Plus Dynamics^® is used to develop the dynamic AGR process model while MATLAB is used to perform the control system design and for implementation of model predictive control (MPC).