(371ae) Uncertainty and Disturbance Estimator (UDE) Based Model Predictive Control for Optimal Operation of Desulfurization Process

Desulfurization
technology plays a vital role in reducing pollutant emissions, and it is widely
applied to coal-fired power plants in China. The desulfurization control
process aims to reduce desulfurization costs under the premise of ensuring SO2
emission concentration. However, complex reaction mechanism, severe
interferences and complex dynamic features make it difficult to achieve
satisfying control performance of the desulfurization process, resulting in low
efficiency and high costs of the desulfurization system.

Model predictive
control (MPC) has been extensively applied to many industrial processes as one
of the cutting-edge advanced control schemes for its advantages of controlling
complex processes and the capability of handling constraints. However, MPC
control schemes usually cannot achieve satisfactory effects in the presence of
severe disturbances or uncertainties. Uncertainty and disturbance estimator
(UDE) does not require the knowledge of bound on
uncertainties, and it is an effective technique to estimate composite
uncertainties that comprises the effects of system uncertainties and external
disturbances.

In this paper, in
order to improve the disturbance rejection performance of the desulphurization
system, a new MPC control strategy based on UDE for optimal operation is
proposed, where the MPC is employed to generate proper pre-setpoints and the
UDE aims to improve the operation performance by dynamically compensating the
setpoints. The effectiveness of the proposed control scheme is demonstrated
through numerical simulation based on the operating data from a wet flue gas
desulphurization system of a 1000MWe supercritical coal-fired power plant in
China. The simulation results show that the dynamic performance of both the
slurry pH and the desulphurization efficiency under the proposed control
strategy are much better than that under MPC control strategy; the proposed
method has a strong disturbance rejection performance, and it obtains a fast
convergence speed and small amplitudes of fluctuations. The UDE¨CMPC method
provides a feasible design method for advanced control for the desulfurization
system.

Key words: Desulphurization Process Operation Optimization;
Uncertainty and Disturbance Estimation; UDE-MPC

Figure 1: The structure of the proposed UDE¨CMPC scheme
for desulphurization process

Figure 2: Response curves of controlled variables in
the presence of external disturbances under UDE¨CMPC and MPC

Financial support
from the key project of the National Nature Science Foundation of China
(project number: 51736002) and the National key research and development
program of China (project number: 2016YFB0600802).