(493b) Modeling and Control of IGCC Plants With Membrane Reactors for Carbon Capture | AIChE

(493b) Modeling and Control of IGCC Plants With Membrane Reactors for Carbon Capture


Amrit, R. - Presenter, University of Wisconsin - Madison
Lima, F. V., West Virginia University

In recent years, incentives for cleaner and more efficient coal-based power generation have risen in the United States and worldwide. These incentives are mainly motivated by the regulation of carbon dioxide (CO2) emissions to the atmosphere that are linked to global climate change. Integrated gasification combined cycle (IGCC) plants correspond to an emerging technology for coal-based power generation. When operated with carbon capture, these plants have higher efficiencies than their conventional pulverized coal-fired power plants counterparts. This presentation focuses on the implementation of nonlinear model predictive control (MPC) strategies to coal-based IGCC plants with water gas shift membrane reactors (WGS-MR) for pre-combustion capture of CO2. In this case, the WGS-MR is equipped with a hydrogen (H2)-selective membrane and is integrated downstream of the gasifier. This MR flowsheet position enables the production of retentate (CO2 rich) and permeate (H2 rich) streams that proceed for carbon capture and sequestration (CCS) and power generation by the gas turbine, respectively. Additional power in the IGCC plant is produced by the high and low pressure steam turbines.

Specifically, a first-principles, systems-level nonlinear model of the integrated IGCC-MR process, which consists of the air separation unit, gasifier, WGS-MR, turbines, heat exchangers, and the CCS units, is introduced [1, 2]. The simulation results for a design based on this model with assumed process and MR characteristics are presented. The selected design is used as a starting point for the control studies, as it satisfies the constraints of the IGCC flowsheet related to target specifications and regulations imposed in stream compositions and temperatures, including the CO2 capture goal (≥ 90%) required by the U.S. Department of Energy (DOE). The MPC controllers are then applied to the IGCC-MR process to allow the simultaneous manipulation of several flow rates to reach multiple control objectives associated with power demand and process efficiency, while satisfying the aforementioned process restrictions. Among the manipulated input flows considered are the coal/water slurry and oxygen enriched air to feed the gasifier, air to feed the gas turbine, water for cooling of process streams, and steam for injection to syngas to facilitate the WGS reaction as well as for use as MR sweep gas. The discrete time nonlinear MPC algorithm is implemented using the simultaneous/direct transcription approach, which utilizes collocation-based discretization of the nonlinear model [3, 4]. Different plantwide control strategies based on this algorithm are analyzed (centralized, hierarchical) to address scenarios that include transitions in power demand (setpoint tracking) and variability in feed coal/slurry compositions (disturbance rejection). Preliminary results on the implementation of the centralized MPC strategy indicate that successful power control is attained for different simulation scenarios, while all of the posed constraints are satisfied. The results on the comparison of the application of the analyzed control strategies to the IGCC-MR plant in terms of performance variables, such as process efficiency, power generation and carbon capture, will be presented.

[1] Kendell R. Jillson, Vishnu Chapalamadugu, and B. Erik Ydstie. Inventory and flow control of the IGCC process with CO2 recycles. J. Proc. Cont., 19:1470-1485, 2009.
[2] Fernando V. Lima, Prodromos Daoutidis, Michael Tsapatsis, and John J. Marano. Modeling and optimization of membrane reactors for carbon capture in Integrated Gasification Combined Cycle units. Ind. Eng. Chem. Res., 51(15):5480-5489, 2012.
[3] Rishi Amrit, James B. Rawlings, and Lorenz T. Biegler. Optimizing process economics online using model predictive control. Computers and Chemical Engineering, 2013. Submitted for publication.
[4] L.T. Biegler. Nonlinear Programming. Concepts, Algorithms, and Applications to Chemical Processes. SIAM, 2010.