Hydrogen is one of the most important by-products in catalytic reforming process

a hydrogen purity soft sensor will contribute to guiding production. However

the working condition of catalytic reforming process is complex and changeable

a single model soft sensor is hard to ensure the prediction accuracy. Aiming at this problem

this paper present a combined soft sensor model based on modified fast search clustering algorithm and Gaussian processes regression (GPR). The history sample are classified by the novel clustering algorithm and then each sub-model is built through GPR with the classified sub sample. Meanwhile the class identification model has been built by GPR as well. Finally

the combined model soft sensor is established in a switcher form. The combined is applied to a catalytic reformer and the result indicates that the proposed method has a good result and has certain practical value.