Removal of Sulfur Dioxide in Flue Gas Using Invasive Weed Optimization-Based Control Method

This study focuses primarily on sulfur dioxide (SO2) emissions control problem in a wet flue gas desulfurization (WFGD) process, and our objective is to design an intelligent control system so that the outlet SO2 concentration satisfies the SO2 emission standard. In our approach, a multimodel control framework, which is made up of a linear robust controller and a neural controller, is integrated with the invasive weed optimization (IWO) algorithm in an elegant fashion and used for SO2 emissions control purposes. A case study is carried out based on operation data from a 600 MW coal-fired unit, and simulation results show that IWO-based automatic clustering can identify different operating modes in the WFGD process with high accuracy. Further, the established multimodel control system can remove SO2 emissions effectively. Experimental results show that SO2 emissions can be removed effectively with the proposed method, and this could provide engineering guidance to design a WFGD control system.