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.

Keywords

• Clustering
• feature selection
• flue gas desulfurization
• multiple models
• neurocontrol