Multiscale modeling and nonlinear model predictive control for flue gas desulfurization

Dabadghao, Vibhav; Biegler, Lorenz T.; Bhattacharyya, Debangsu

doi:10.1016/j.ces.2022.117451

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Multiscale modeling and nonlinear model predictive control for flue gas desulfurization

Dabadghao, Vibhav·Biegler, Lorenz T.·Bhattacharyya, Debangsu

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Abstract

The primary source of sulfur dioxide (SO 2 ) emissions is flue gas from fossil fuels-based power plants. SO 2 emissions are known to not only cause health issues, but also have an adverse effect on the environment in various ways. Several Flue Gas Desulfurization (FGD) technologies have been incorporated in power plants. The most popular technology is Wet FGD, where a limestone slurry is used to absorb SO 2 from the flue gas. A detailed droplet scale model describing the instantaneous and finite rate chemistry is developed. The ill-posed Differential-Algebraic Equation (DAE) droplet model is reformulated to a well-posed index-1 DAE through index reduction. The droplet model is integrated with the bulk phase by incorporating gas-liquid mass transfer, and an oxidation reactor model to simulate the dynamic operation of the counter-current spray scrubber. As a result, the model has a well-conditioned Jacobian and overcomes the modeling challenges of previous works and enables numerical solution without requiring carefully selected initialization or specialized solution procedures. The model is successfully validated using power plant measurements, and nonlinear model predictive control (NMPC) studies are demonstrated to optimize recycle stream flowrates to minimize pumping costs.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY flue gas desulfurization index reduction nonlinear model predictive control

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Dabadghao, Vibhav, Biegler, Lorenz T., Bhattacharyya, Debangsu. 2022-02-10. Multiscale modeling and nonlinear model predictive control for flue gas desulfurization. https://doi.org/10.1016/j.ces.2022.117451

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