Dynamic adjustment interval identification of hydrocracking based on principal component derivative feature clustering

doi:10.16511/j.cnki.qhdxxb.2018.22.011

Download: PDF(2192 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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.

Keywords hydrocracking adjustment process principal component analysis (PCA) dynamic interval identification derivative feature clustering

ZTFLH:

TE624

Issue Date: 15 January 2018

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	CHEN Xiaofang
	QIAN Yingcan
	WANG Yalin
	YANG Chunhua

Cite this article:

CHEN Xiaofang,QIAN Yingcan,WANG Yalin, et al. Dynamic adjustment interval identification of hydrocracking based on principal component derivative feature clustering[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(1): 81-86.

URL:

http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.22.011 OR http://jst.tsinghuajournals.com/EN/Y2018/V58/I1/81

[1]	张淑美, 王福利, 谭帅, 等. 多模态过程的全自动离线模态识别方法[J]. 自动化学报, 2016, 42(1):60-80.ZHANG S M, WANG F L, TAN S, et al. A fully automatic offline mode identification method for multi-mode processes[J]. Acta Automatica Sinica, 2016, 42(1):60-80.(in Chinese)
[2]	NARASIMHAN S, MAH R S H, TAMHANE A C, et al. A composite statistical test for detecting changes of steady states[J]. AIChE Journal, 1986, 32(9):1409-1418.
[3]	李博, 陈丙珍, 胡惠琴. 稳态过程在线数据校正技术的工业实施[J].石油化工, 2000, 29(10):768-771.LI B, CHEN B Z, HU H Q. Industrial application of steady state online data reconciliation[J]. Petrochemical Technology, 2000, 29(10):768-771.(in Chinese)
[4]	LI G H. Inverse lag synchronization in chaotic systems[J]. Chaos Solitons and Fractals, 2009, 40(3):1076-1080.
[5]	CAO S L, RHINEHART R R. Critical values for a steady-state identifier[J]. Journal of Process Control, 1997, 7(2):149-152.
[6]	陈文驰, 刘飞. 一种改进的基于多项式滤波的稳态检测方法[J]. 控制工程, 2012, 19(2):13-15, 20.CHEN W C, LIU F. An improved steady state identification method based on polynomial filtering[J]. Control Engineering of China, 2012, 19(2):13-15, 20.(in Chinese)
[7]	TAO L L, LI C C, KONG X D, et al. Steady-state identification with gross errors for industrial process units[C]//201210th World Congress on Intelligent Control and Automation (WCICA). New York, USA, 2012:4151-4154.
[8]	吕游, 刘吉臻, 赵文杰, 等. 基于分段曲线拟合的稳态检测方法[J]. 仪器仪表学报, 2012, 33(1):194-200.LV Y, LIU J Z, ZHAO W J, et al. Steady-state detecting method based on piecewise curve fitting[J]. Chinese Journal of Scientific Instrument, 2012, 33(1):194-200.(in Chinese)
[9]	JIANG T W, CHEN B Z, HE X R, et al. Application of steady-state detection method based on wavelet transform[J]. Computers and Chemical Engineering, 2003, 27(4):569-578.
[10]	季一丁. 多变量复杂系统的稳态检测和提取方法研究[D]. 杭州:浙江大学, 2016.JI Y D. Research on steady state detection and extraction methods for multivariable complex system[D]. Hangzhou:Zhejiang University, 2016.(in Chinese)
[11]	MENG J L, SHANG H K, BIAN L. The application on intrusion detection based on K-means cluster algorithm[C]//International Forum on Information Technology and Applications. New York, USA, 2009, 1:150-152.
[12]	CHOI Y, PARK C, KWEON I S. Accelerated K-means clustering using binary random projection[C]//Asian Conference on Computer Vision. Singapore, 2014:257-272.
[13]	RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191):1492-1496.
[14]	WANG S L, WANG D K, LI C Y, et al. Clustering by fast search and find of density peaks with data field[J]. Chinese Journal of Electronics, 2016, 25(3):397-402.