(706c) Design and Implementation of Model Predictive Control Strategies for IGCC Power Plant Cycling with Carbon Capture
AIChE Annual Meeting
2016
2016 AIChE Annual Meeting
Computing and Systems Technology Division
Process Control Applications I
Thursday, November 17, 2016 - 1:06pm to 1:24pm
Specifically, 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 by performing step tests in the IGCC plant. The proposed nonlinear MPC strategy is an extension of NLMPC [2, 4, 5], which is based on the conversion of the differential algebraic equation system into a large-scale nonlinear programming (NLP) problem. The resulting NLP is solved using IPOPT, an efficient interior point-based large-scale nonlinear optimization algorithm [6]. Also, the ADOL-C [7], an open-source automatic differentiation package, is employed for improved accuracy and speed of the calculations of system derivatives. For additional NLMPC computational improvements, sequential quadratic programming (SQP) algorithms are analyzed for solving the posed MPC NLP problem.
The proposed MPC algorithms are implemented for the IGCC-MR power plant as centralized plantwide control strategies. For such implementations, the selected control structure consists of the following controlled output variables (CVs): carbon capture, power generation, temperatures of cooled syngas and cooled permeate streams, steam to CO ratio at the WGS-MR inlet and hydrogen purity in the permeate stream. Also, the selected manipulated input 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 including: (i) trajectory tracking associated with a power generation demand cycling, in which the IGCC power plant demand follows a certain profile from its original power generation setpoint; (ii) disturbance rejection related to the variability in the quality of the coal feed. In this case, the power generation should still track the cycling profile while the carbon content in the coal slurry is reduced. In this presentation, different NLP solving methods will also be discussed when the nonlinear MPC controllers are implemented for this high-dimensional system. Results on the closed-loop responses for the different scenarios will be analyzed and compared considering the advanced linear and nonlinear MPC control strategies.
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