(628a) Optimal Secondary Controlled Variable Selection: Methodology and Its Application in An Acid Gas Removal Unit

Optimal Secondary Controlled Variable Selection: Methodology and its Application in an Acid Gas Removal Unit

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^® Center, National Energy Technology Laboratory, Morgantown, WV, USA

Plant-wide control system design has been a field of active research over the past decade with most research focused on controller design.  Very little research has been conducted on developing systematic approaches to overall control system design, and the limited research that has been done to date is based upon heuristic methods that require extensive use of process knowledge.  For systematic, model-based control system design, a two-stage method consisting of a top-down analysis followed by a bottom-up design has been proposed by several researchers.  In this presentation, we will present our on-going work to extend this two-stage approach to control system design for large-scale, plant-wide applications.  Recently we have completed the first stage of such an approach in which a list of manipulated, controlled, and disturbance variables has been generated by considering a scalar operation objective and other process constraints.  These results are then used in the second stage, which is a bottom-up approach for simultaneous design of the control structure and the controllers. 

In this presentation, we will describe the design of the regulatory layer as the first step in the bottom-up design.  The focus is on the development of a rigorous, model-based approach for the selection of secondary controlled variables as part of the regulatory layer design.  These secondary controlled variables are intermediate variables between the manipulated variable and the primary controlled variables. A partially controlled plant analysis is undertaken to determine the optimal secondary controlled variables.  Secondary controlled variables are selected so as to optimize the indirect and servo control performance.  Additionally, constraints are included to ensure satisfactory control performance for local disturbance rejection and servo control.  Finally, performance measures such as gains and time constants between input and output variables are included within this formulation to ensure that the closed loop control of the secondary controlled variables is satisfactory.

The approach developed in this work results in a large-scale, mixed-integer optimization problem.  A parallelized, bi-directional branch and bound (BB) algorithm has been developed to solve the problem on large computer clusters, taking advantage of the MATLAB^® Distributed Computing Server^TM.  The proposed method is then applied to 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. Aspen Plus Dynamics^® is used to develop the dynamic AGR process model. A linearized process model is then used for secondary controlled variable selection in MATLAB.