In recent years, facing the challenge of global climate change, technologies of power plants with carbon capture have been studied worldwide. Integrated gasification combined cycle (IGCC) plants, which have potential for higher efficiencies than the conventional pulverized coal-fired power plants , are an emerging candidate for carbon capture. In this work, we analyze the implementation of model predictive control (MPC) strategies for an IGCC power plant equipped with a water gas shift membrane reactor (WGS-MR) unit for carbon capture. A model for this IGCC power plant represented by a system of differential algebraic equations was previously developed in MATLAB/Octave . Using this model, two centralized control strategies based on linear and nonlinear MPC algorithms are presented to address different power generation scenarios.
The linear MPC control strategy is based on a dynamic matrix control (DMC) method. For this method, a multiple-input multiple-output step response matrix is obtained employing step tests in the IGCC plant. The nonlinear MPC control strategy, NLMPC [2, 3], is based on the conversion of the differential algebraic equation system into a large-scale nonlinear programming (NLP) problem, which is solved using IPOPT, an efficient interior point-based large-scale nonlinear optimization algorithm. In the proposed multivariate control strategies, the following variables are considered as controlled output variables (CVs): carbon capture rate, power generation, temperature of cooled syngas stream, temperature of cooled permeate stream, steam to CO ratio at the WGS-MR inlet and hydrogen purity in the permeate stream. Also, the selected manipulated variables (MVs) are: water flow rates for syngas and permeate cooling, steam injection flow to syngas for the facilitation of the WGS reaction, total coal/water slurry, oxygen enriched air flow for the gasifier, sweep flow for the WGS-MR and air flow for the gas turbine.
A number of control scenarios for the IGCC power plant are addressed. These scenarios include: (i) setpoint tracking associated with a power generation demand change, in which the IGCC power plant demand imposes a step increase from its original power generation steady point; (ii) disturbance rejection related to the variability in the quality of the coal feed. In this case, the power generation should be kept at the setpoint while the carbon content in the coal/slurry is reduced; and (iii) a combination of both setpoint tracking and disturbance rejection cases. In this presentation, results on the closed-loop responses for these different scenarios will be analyzed and compared considering the advanced linear and nonlinear MPC control strategies.
 Lima, F. V. P. Daoutidis, M. Tsapatsis, and J. 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.
 Lima, F. V., R. Amrit, M. Tsapatsis, and P. Daoutidis. Nonlinear model predictive control of IGCC plants with membrane reactors for carbon capture. In Proceedings of the American Control Conference. Washington, DC, June 17-19 2013.
 R. Amrit, J. B. Rawlings, and L. T. Biegler. Optimizing process economics online using model predictive control. Computers and Chemical Engineering, 58:334-343, 2013.
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