AVESTAR R&D to be presented at the 2013 ISA Power Industry Division (POWID) Symposium and 2013 IFAC American Control Conference
AVESTAR Presentations/Abstracts * - Denotes Speaker
Wilbers*, D., “Invensys and DOE Partnering to Create Two Generic Simulators for Carbon Capture Studies,” Presented at 56th Annual ISA Power Industry Division (POWID) Symposium, Orlando, FL, June 2–7 (2013).
Abstract: NETL's AVESTAR Center and Invensys Operations Management (IOM) established CRADAs to specify, develop, test, and deploy a high-fidelity, full-scope, real-time dynamic simulator and operator training systems for a generic natural gas combined cycle (NGCC) power plant and a generic supercritical once-through (SCOT) pulverized coal power plant. This presentation will highlight recent progress on these two dynamic simulators targeted for use in power plant and carbon capture operations and control R&D, as well as industry workforce training and engineering education.
Jones*, D., D. Bhattacharyya, R. Turton, and S.E. Zitney, “Optimal Selection of Primary Controlled Variables for an Acid Gas Removal Unit as part of an IGCC Plant with CO2 Capture,” Proc. of the 2013 American Control Conference, Washington D.C., June 17-19 (2013).
Abstract: This work is focused on the development of a rigorous, model-based approach for the selection of primary controlled variables as part of a plant-wide control system design methodology. Selected controlled variables should exhibit good self-optimizing control performance while not sacrificing controllability. The approach developed in this work for the controlled variable selection problem results in a large-scale, mixed-integer optimization problem. For solution of this problem, a parallelized, bi-directional branch and bound (BNB) algorithm has been developed so as to solve the problem on large computer clusters, taking advantage of MATLAB Distributed Computing Server. The proposed method is then applied to an acid gas removal (AGR) unit as part of an IGCC power plant with CO2 capture.
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).
Abstract: In this paper, adaptive Kalman filter (KF) algorithms are implemented in an acid gas removal (AGR) process for estimating the key environmental performance variables. It was found that by using a KF where the measurement noise covariance matrix (R) is adopted based on the residual sequence, the composition of the top and bottom streams from the H2S absorber in the AGR process could be estimated accurately even in the presence of large noise-to-signal ratio and poor initial guesses for . Estimation accuracy of a KF, where the process noise covariance matrix (Q) is adopted, is found to be superior in comparison to the traditional KF, even in the presence of large mismatches between the linear and nonlinear models and a poor initial guess for Q.