Abstract：Hydrocracking is a complicated long-term process resulting from many coupled variables that affect manufacturing schedule and create loud noises. This paper presents a dynamic interval identification method for hydrocracking based on principal component derivative feature clustering to accurately identify the dynamic changes from data. Firstly, a principal component analysis (PCA) is used to extract the principal components of the key hydrocracking operating parameters. Then, the first-order derivatives are obtained from fitting polynomials of the principal components with sliding windows. After that, the K-means algorithm is used to identify the dynamic adjustment intervals for the principal component derivative feature clustering with the density peak technique used to determine the initial centers. The flexibility and effectiveness of this method are validated on an industrial petrochemical process. The results show that this method can avoid the influence of variable errors and accurately identify the dynamic adjustment intervals without priori knowledge.
陈晓方, 钱荧灿, 王雅琳, 阳春华. 基于主元导数特征聚类的加氢裂化动态调整区间识别[J]. 清华大学学报（自然科学版）, 2018, 58(1): 81-86.
CHEN Xiaofang, QIAN Yingcan, WANG Yalin, YANG Chunhua. Dynamic adjustment interval identification of hydrocracking based on principal component derivative feature clustering. Journal of Tsinghua University(Science and Technology), 2018, 58(1): 81-86.
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2018, Vol. 58 
