Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas Desulfurization

Abstract

Flue gas emission is an inevitable procedure in the course of electricity generation, which would pose a severe threat to human health, and has an adverse effect on our environment. Due to the fact that the environment in practical flue gas desulfurization system fluctuates frequently, system parameters tend to vary constantly during the operating process, thus control performance with traditional strategies tends to be suboptimal in most cases. To address this problem, some insight into operating conditions must be gained prior to taking proper control strategy. Therefore, in this article, based on actual measurements in 1000 MW Unit Wet Limestone FGD System for a coal-fired power plant, a kind of intelligent operating condition partition method is combined with the multi-model adaptive control strategy. Specifically, analysis and partition of operating condition is carried out in the first place, then adaptive multi-model control is implemented with the combination of parallel dynamic neural network and partition results. Additionally, the applicability of proposed control mode is investigated through different simulation examples. At the same time, to further enhance the flexibility of multi-model control structure, some possible improvements on it is also discussed.

Constrained nonlinear model predictive control of pH value in wet flue gas desulfurization process

“…The basic principle of WFGD in this study is the chemical reaction between SO 2 in flue gas and alkaline sorbent, limestone slurry. 25 The overall chemical reaction is as follows.…”

“…The limestone gypsum WFGD system is mainly composed of limestone slurry preparation system, flue gas heat exchange system, absorption system, gypsum dehydration system, waste water treatment system, and so forth. The basic principle of WFGD in this study is the chemical reaction between in flue gas and alkaline sorbent, limestone slurry 25 . The overall chemical reaction is as follows.…”

Multi‐model predictive control of converter inlet temperature in the process of acid production with flue gas

“…Multi‐model adaptive control has proven to be a highly efficient approach in addressing control issues within complex systems 10 . In literature 11, aimed at the control problem of wet‐desulfurization, a dynamic neural network is used to intelligently divide working conditions and multi‐model adaptive control was realized. And the effectiveness of this method was proved by experiments.…”