Modeling of complex industrial process based on active semi-supervised clustering

Abstract Since industrial processes have a wide range of operating conditions, it is difficult to build a single global model that describes a process. One solution that is widely used in control engineering practice is to combine multiple models based on collected process data. For this approach to be successful, it is important to cluster the data before the modeling. In this study, pairwise constraints and an active-learning method were incorporated into the affinity propagation algorithm, resulting in a new method called active semi-supervised affinity propagation (ASSAP) clustering. To apply ASSAP to the modeling of industrial processes, an active-learning strategy is firstly used to obtain constraints on data based on the angle of change between two data points and the probability of their belonging to the same class, and then the constraints are used to adjust the clustering process so as to improve the clustering precision. Finally, the least-squares-support-vector-machine (LS-SVM) is used to build a submodel for each cluster of data points, and then all the sub-models are integrated into a model for the whole data set. Verification of the ASSAP method was carried out on data from the UCI (University of California, Irvine) Machine Learning Repository and Olivetti dataset. In addition, ASSAP and LS-SVM are combined to be applied to the data of the combustion process of a coke oven. The result shows the effectiveness of the method of modeling of complex industrial process based on ASSAP.