数据驱动的蒸发器在线建模方法

针对蒸发器离线建模方法对变量运行工况范围要求较大的问题,利用K-means算法对辨识模型的观测数据进行聚类筛选处理,提出一种基于数据的蒸发器在线建模方法。首先利用DB准则和PSO算法提出K-means算法中最优分类数K*和最优初始聚类中心的确定方法,提高算法的收敛速度,并使用改进的K-means算法获得各簇聚类中心来代替辨识模型的观测数据,减少模型辨识的数据量。然后利用已有的蒸发器模型结构以及模型辨识方法,对模型进行辨识。实验结果表明：利用聚类筛选前、后的观测数据所辨识的模型精度基本相当,分别在±3%和±3.5%以内。最后利用在线观测数据到各聚类中心欧氏距离的分析判断,提出蒸发器的在线建模方法。该方法可以先采用小工况范围的少量离线数据辨识模型,再利用在线数据修正模型参数,扩大模型的适用范围。

A Data-driven Online Modeling Method for Evaporators

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.