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

摘要：

针对复杂工业流程生产单元间变量存在多重时滞且检测困难，提出一种基于趋势相似度分析的多重时滞辨识方法。选取单元间相关性强的关键变量，利用多项式最小二乘拟合后的变量导数数据定义趋势相似度，以经采样时滞平移后的趋势相似度最小，描述多重时滞辨识问题；用L2范数量化趋势相似度向量，将多重时滞辨识问题转化成L2范数最小化问题；并用改进的自适应粒子群算法快速寻优，确定各变量的最优采样时滞。所提方法被应用于加氢裂化流程中，辨识出各变量的实际采样时滞，由此建立了基于局部加权核主元回归的柴油闪点预测模型。实验结果表明：考虑多重时滞的预测模型准确率提高了19.05%，验证了所提时滞辨识方法的有效性。

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