Multi-delays exist largely in variables between production units of complex industrial processes and are difficult to detect. A trend-similarity analysis was proposed to identify such multi-delays. The trend-similarity was defined via data of variable derivatives after a polynomial least square fitting on key variables with strong correlation between production units. Multi-delay identification was described by minimizing trend-similarity after sampling delay translation. L2 norm was used to quantify trend-similarity vector and thus multi-delay identification was transformed into L2 norm minimization. The optimal sampling delays on variables were determined by fast optimization with improved adaptive particle-swarm algorithm. The proposed method was applied to identify variable sampling delays in a hydrocracking process. Based on these identified sampling delays

a prediction model for flash point of diesel fuel was established with assistance of local weighted kernel principal component regression. The experimental results show that the multi-delay prediction model improves accuracy by 19.05%

which verifies effectiveness of the proposed multi-delay-identification method.