Dynamic Analysis and Identification of a Wet Limestone Flue Gas Desulfurization Pilot Plant

Abstract

In order to meet more stringent SO 2 regulations that have come into force recently, the operating conditions of wet limestone flue gas desulfurization (WLFGD) plants must be changed from design conditions to others where a higher SO 2 removal is obtained; this results in higher operating costs. Furthermore, because of process disturbances, the SO 2 removal target is usually set somewhat higher than necessary to provide a safety margin for meeting SO 2 emission limits. Thus, the larger the safety margin, the higher the operating costs. How much larger the safety margin needs to be depends on the proper tuning of the control strategy and dynamic properties of the WLFGD plant to be controlled. Both aspects can only be studied if a dynamic model of the plant is available. However, dynamic modeling of WLFGD plants for control purposes has not been addressed in the literature so far. To deal with with this issue, in this paper we propose an identification methodology for control purposes applicable to full-scale plants. The identification methodology is put into practice in a WLFGD pilot plant, and the empirical dynamic model obtained is suitable for predicting the dynamic behavior of the pilot plant in a wide range of operating conditions. The dynamic behavior of the pilot plant is further analyzed from the perspective of the physical-chemical phenomena of the WLFGD process.

Wet Industrial Flue Gas Desulfurization Unit: Model Development and Validation on Industrial Data

“…Since its large deployment during the 1980s, the wet FGD unit has been modeled extensively. Some models are based on a statistical approach, , but most of them are based on a phenomenological description. The large majority of these FGD models emphasize, logically, a detailed representation of the SO 2 absorption in a limestone slurry. − A few of them − link the absorption model with the column bottom reactor and take into account fine limestone dissolution, sulfite oxidation, or gypsum precipitation models in order to represent the interaction of these models on the desulfurization process.…”

“…Since its large deployment during the 1980s, the wet FGD unit has been modeled extensively. Some models are based on a statistical approach, 1,2 but most of them are based on a phenomenological description. The large majority of these FGD models emphasize, logically, a detailed representation of the SO 2 absorption in a limestone slurry.…”

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

“…The oxidation tank pH must also be controlled because it has a significant influence on limestone utilization and, therefore, on gypsum quality. 1 The main manipulated variables are the fresh limestone slurry flow rate to the oxidation tank and the slurry recycle flow rate in the absorber. The main control problem in a WLFGD plant is the rejection of its most important disturbance, the inlet SO 2 load to the absorber.…”

“…Recently, more stringent SO 2 regulations have come into force, both in Europe and the U.S.A. 1 Meeting the new SO 2 emission limits entails higher operating costs related to limestone consumption and pumping of absorbent slurry. In fact, the outlet SO 2 set point is lower than the SO 2 emission limit to provide a safety margin against changes in the inlet SO 2 load to the absorber that could result in violation of the SO 2 emission limit.…”

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In previous articles, [1][2][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.

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