Application of hybrid prediction model for dry point soft sensing of aviation kerosene

In this paper, an approach to build soft sensing prediction model for the dry point of aviation kerosene in a complex non-linear hydrocracking unit is proposed. PLS, RBFN and PLS-RBFN soft sensor models are first established for dry point of aviation kerosene. On this basis, the three sub-models are combined into a hybrid soft sensing prediction model through principal components regression. The performance of the soft sensor models based on hybrid soft sensing prediction model is compared with that of PLS, RBFN and PLS-RBFN respectively. Numerical results showed that soft sensor model by hybrid prediction model has better forecast accuracy and model stability than the other three methods, and can be well adaptive to the changing working conditions.