Using Neural Networks to Address Nonlinear pH Control in Wet Limestone Flue Gas Desulfurization Plants

Abstract

In previous articles,(1-3) we evaluated the performance of different linear control strategies (decentralized feedback control and multivariable predictive control) in wet limestone flue gas desulfurization (WLFGD) plants based on a pilot plant study. Although these control strategies show good performance, they may not be suitable if the oxidation tank pH is significantly nonlinear, which depends on many plant operating factors. Control of oxidation tank pH is important since it is related to the quality of gypsum, a byproduct of the WLFGD process. In this work, we propose and assess a combined control strategy for dealing with nonlinear pH control in WLFGD plants, based on a decentralized strategy composed of a neural predictive controller and a feedback controller, which control the oxidation tank pH and SO2 concentration of the desulfurized gas, respectively.