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Abstract. In order to solve the coal-fired boiler operation optimization problem with multiple main controllable parameters, a fuzzy pattern fusion based on competitive agglomeration (FPF-CA) algorithm is developed and applied in this paper. A simplified mathematical model for coal-fired boiler systems is applied in terms of both historical operational and thermal efficiency data under different load conditions. The FPF-CA algorithm can be applied to perform information fusion in terms of combining fuzzy clusters of some quantitative attributes with generated quantitative association rules. The historical data collected from a 130/2.82-M circulating fluidized bed (CFB) boiler being installed in a production scale thermal power plant is used to specifically analyze, and the simulation experiment results show the application of FPF-CA algorithm is a decision oriented intelligent technology and may provide an efficient results for coalfired boiler operation optimization in thermal power plant. Keywords IntroductionCoal-fired circulating fluidized bed (CFB) boilers are widely used in thermal power plants, with the increase in the size of power plants, the capacity and the number of CFB also increase. But in the aspect of management is more concerned with the safety of boiler operation than its thermal performance. Coal-fired CFB boiler is a multivariable, strong-coupling, time-varying and nonlinear system, the thermal performance of which is strongly influenced by the change of load, coal type and operator inputs. When coal type remains unchanged for a period of time, the influence of operator inputs can be analyzed to optimize the operating procedures on the premise of adapting to changes in load. The heart of the coal-fired boiler operation optimization problem is the determination of the optimization values for the main controllable parameters [1]. Under different load conditions, several mining on operation parameters achieve a set of rules to provide a framework for operation optimization.