Identification of MSWI Furnace Temperature Model Based on Weighted Adaptive Particle Swarm Optimization

In order to obtain the accurate dynamic model of the furnace temperature of a solid waste incineration plant, the intelligent algorithm based on weighted adaptive particle swarm optimization algorithm is used for parameter identification. First, the transfer function model of furnace temperature is determined by burning characteristic analysis. Then, the model parameters to be identified are determined. Finally, the parameters of primary air-furnace temperature channel transfer function model are identified by weighted adaptive particle swarm optimization using preprocessed data; In the iteration process, the algorithm can obtain the optimal weight value for each iteration adaptively according to the objective function value and maximum and minimum value of the weight, which improves the identification accuracy of the algorithm. The simulation results show that the established transfer function model can fully describe the dynamic characteristics of this condition, and proves the effectiveness of the weighted adaptive particle swarm optimization algorithm, which provides a basis for subsequent multi-channel model identification of furnace temperature.