Data-driven modelling and fuzzy multiple-model predictive control of oxygen content in coal-fired power plant.

In the combustion system of a boiler, oxygen content in the flue gas is a significant economic parameter for combustion efficiency. As a combustion system is highly complex and there are many constraints in a real process, traditional control cannot achieve satisfying performance in the practical oxygen content tracking control problem. In this paper, we build a combustion process model with a data-driven method and present a multiple-model-based fuzzy predictive control algorithm for the oxygen content tracking control. The combustion process model is presented as a multiple-model form, which can represent the real process more accurately. A data-driven method with fuzzy c-means clustering and subspace identification is used to identify the model parameters. Then, model predictive control integrated with a fuzzy multiple-model is used to control the oxygen content tracking problem. As the coal manipulated variable is decided by the load demand in the real process, a real-time measured value is applied to the process. All data used to obtain the process model is historical real-time data generated from a 300-MW power plant in Gui Zhou Province, China. Real-time simulation results on the 300-MW power plant show the effectiveness of the modelling and control algorithms proposed in this paper. [ABSTRACT FROM AUTHOR]