Abstract: According to the problem that the offline modeling method of the evaporator requires a large range of variable operating conditions， a data-based evaporator online modeling method is proposed by using K-means algorithm to perform clustering and screening on the observation data of the identified model. Firstly， a method to determine the optimal classification number K* and the optimal initial clustering center in the K-means algorithm is proposed using the DB criterion and the PSO algorithm to improve the convergence speed of the algorithm and the observation data of the identified model are replaced by the cluster centers obtained by the improved K-means algorithm to reduce the data amount of the model identification. Then the existing evaporator model structure and the model identification method are employed to identify the model. The experimental results show that the accuracy of the models identified by the observation data obtained before and after clustering and screening is approximately equal and their errors are within ±3% and ±3.5% respectively. Finally， an online modeling method for the evaporator is proposed by analyzing and judging the Euclidean distance between the online observation data and each cluster center. This method can firstly use a small amount of offline observation data obtained from a small range of working conditions to identify the model， and then use the online data to modify model parameters to expand the scope of application for the model.

Key words: online modeling, clustering-algorithm, data screening, evaporator, model