关键词: 趋势相似度分析, 多重时滞辨识, 自适应粒子群算法, 加氢, 裂化, 柴油闪点, 预测, 模型

Abstract:

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.

Key words: trend-similarity analysis, multi-delay identification, adaptive particle-swarm algorithm, hydrogenation, cracking, diesel-fuel flash-point, prediction, model