Working condition-based optimization framework for operational patterns and its application in petrochemical industry

doi:10.3969/j.issn.0438-1157.2012.12.034

CIESC Journal ›› 2012, Vol. 63 ›› Issue (12): 3978-3984.DOI: 10.3969/j.issn.0438-1157.2012.12.034

Previous Articles Next Articles

Working condition-based optimization framework for operational patterns and its application in petrochemical industry

JIANG Baihua¹, LIU Wei², DAI Zhijun², WANG Hong'an²

1. Petro-Cyber Works Information Technology Co., Ltd., Beijing 100007, China;
2. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

Received:2012-08-07 Revised:2012-08-17 Online:2012-08-29 Published:2012-12-05
Supported by:
supported by the National Science and Technology Supporting Program(2012BAE05B03)and the National Basic Research Program of China(2009CB320804).

基于工况的操作模式优化及在石化工业中的应用

蒋白桦¹, 刘伟², 戴志军², 王宏安²

1. 石化盈科信息技术有限责任公司, 北京 100007;
2. 中国科学院软件研究所, 北京 100190

通讯作者: 刘伟
作者简介:蒋白桦(1961-),男,硕士,高级工程师。
基金资助:
国家科技支撑计划项目(2012BAE05B03);国家重点基础研究发展计划项目(2009CB320804)。

Abstract

Abstract: A new working condition-based optimization framework for operational patterns was proposed. The framework is composed of three components which are data preprocessing,the creation of library of optimized operational patterns and real-time optimization based on working conditions.On the basis of optimization and control framework,the application of online operational optimization for the dry-point temperature of FCCU was focused.Base on the FCCU properties,a two-step optimization approach for operational patterns which combines SVM and AdaBoost was proposed.Experimental results show that the new approach has higher prediction performance.

Key words: operational pattern, soft sensor, SVM, AdaBoost, pattern discovery

摘要： 提出了一种基于工况的操作模式优化框架,框架包括三部分:数据预处理、优化操作模式库的形成和基于工况的实时优化。在操作模式优化控制的框架和相关概念的基础上重点研究了催化过程中干点温度指标在线优化的应用。针对催化的工业特点,提出SVM与AdaBoost相结合的两步结合的操作模式的发现方法,试验证实该方法具有较高的预测性能。

关键词: 操作模式, 软测量, 支持向量机, AdaBoost, 模式发现

CLC Number:

TP273

JIANG Baihua, LIU Wei, DAI Zhijun, WANG Hong'an. Working condition-based optimization framework for operational patterns and its application in petrochemical industry[J]. CIESC Journal, 2012, 63(12): 3978-3984.

蒋白桦, 刘伟, 戴志军, 王宏安. 基于工况的操作模式优化及在石化工业中的应用[J]. 化工学报, 2012, 63(12): 3978-3984.

References 21

[1]	Hu Zhikun(胡志坤),Gui Weihua(桂卫华),Peng Xiaoqi(彭小奇),Yao Junfeng(姚俊峰),Zhang Weihua(张卫华).Intelligent optimization of optimal operational pattern in the process of copper converting furnace[J].Control Theory and Applications(控制理论与应用),2005,22(2):243-247
[2]	Hu Zhikun(胡志坤).Research on intelligent optimization methods of operational pattern in the complex process of nonferrous metallurgy smelting[D].Changsha:Central South University,2005
[3]	Gui Weihua(桂卫华),Yang Chunhua(阳春华),Li Yonggang(李勇刚),He Jianjun(贺建军),Yin Linzi(尹林子).Data-driven operational-pattern optimization for copper flash smelting process[J].Acta Automatica Sinica(自动化学报),2009,35(6):717-724
[4]	Yan Aijun(严爱军),Chai Tianyou(柴天佑),Yue Heng(岳恒).Multivariable intelligent optimizing control approach for shaft furnace roasting process[J].Acta Automatica Sinica(自动化学报),2006,32(4):636-640
[5]	Du Wenli(杜文莉),Guan Zhenqiang(官振强),Qian Feng(钱锋).Dynamic soft sensor modeling based on time series error compensation[J].CIESC Journal(化工学报),2010,61(2):439-443
[6]	Wang Jingfang(王景芳),Liao Yifan(廖亦凡).Soft sensor of the solidifying point of catalytic cracking light cycle oil based on the support vector machines[J].Automation in Petro-Chemical Industry(石油化工自动化),2008,44(2):44-47
[7]	Xue Xiuli(薛秀莉),Miao Rong(苗荣).The soft sensoring for dry point of light naphtha on the atmospheric tower in the hydrogenation and cracking process[J].Automation in Petro-Chemical Industry(石油化工自动化),2009,45(6):54-56
[8]	Feng Dachun(冯大春),Lu Hong(鲁红).Application of data-based techniques for petrochemical industry process[J].Automation in Petro-Chemical Industry(石油化工自动化),2010,46(6):28-35
[9]	Qiao Jinghui(乔景慧),Zhou Xiaojie(周晓杰),Chai Tianyou(柴天佑),Zheng Xiuping(郑秀萍).Soft measurement model of raw meal decomposition rate in cement clinker production process[J].Control Engineering of China(控制工程),2011,18(4):495-499
[10]	He Peng(何鹏),Wang Yalin(王雅琳),Xie Yongfang(谢永芳),Gui Weihua(桂卫华).Optimal selection of parameters of LSSVM for predicting time series of returned material compositions’ in alumina production[J].Control Engineering of China(控制工程),2011,18(4):605-609
[11]	Wang Xiaohong(王孝红),Liu Wenguang(刘文光),Yu Hongliang(于宏亮).Research on industrial process soft-sensor[J].Journal of Jinan University:Science and Technology(济南大学学报:自然科学版),2009,23(1):80-86
[12]	Yu Jinshou(俞金寿).Soft sensing technology and its application[J].Process Automation Instrumentation(自动化仪表),2008,29(1):1-7
[13]	Dana Angluin,Philip Laird.Learning from noisy examples[J].Machine Learning,1988,2(4):343-370
[14]	Fabien Lauer,Gérard Bloch.Incorporating prior knowledge in support vector regression[J].Machine Learning,2008,70(1):256-264
[15]	Suykens J A K.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9:293-300
[16]	Adams N M,Hand D J.Comparing classifiers when the misallocation costs are uncertain[J].Pattern Recognition,1999,32(7):1139-1147
[17]	Yoav Freund.Boosting a weak learning algorithm by majority[J].Information and Computation,1995,12(2):256-285
[18]	Schapire R,Singer Y. BoosTexter:a boosting-based system for text categorization[J].Machine Learning,2000,39(2/3):135-168
[19]	Friedman H,Hastie T,Tibshirani R.Additive logistic regression:a statistical view of Boosting[J].Annals of Statistics,2000,28(2):337-343
[20]	Vapnik V.Overview of statistical learning theory[J].IEEE Transactions on Neural Networks,1999,10(5):988-999
[21]	Vapnik V.The Nature of Statistical Learning Theory[M].2nd ed.New York:Springer-Verlag,1998:360-374

Working condition-based optimization framework for operational patterns and its application in petrochemical industry

基于工况的操作模式优化及在石化工业中的应用

PDF (PC)

Knowledge

Cited

Abstract

Cite this article

share this article

References 21

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Linqi YAN, Zhenlei WANG. Multi-step predictive soft sensor modeling based on STA-BiLSTM-LightGBM combined model [J]. CIESC Journal, 2023, 74(8): 3407-3418.
[2]	Weiming SHAO, Wenxue HAN, Wei SONG, Yong YANG, Can CHEN, Dongya ZHAO. Dynamic soft sensor modeling method based on distributed Bayesian hidden Markov regression [J]. CIESC Journal, 2023, 74(6): 2495-2502.
[3]	LIU Cong, XIE Li, YANG Huizhong. Multi-model soft sensor development for penicillin fermentation process based on improved density peak clustering [J]. CIESC Journal, 2021, 72(3): 1606-1615.
[4]	Xianyi YU, Jianghong WU, Yunhui GAO. Research on refrigerant leakage identification for heat pump system based on PCA-SVM models [J]. CIESC Journal, 2020, 71(7): 3151-3164.
[5]	Yuhao DU, Gaowei YAN, Rong LI, Fang WANG. Multiple working conditions soft sensor modeling method of geodesic flow kernel based on locally linear embedding [J]. CIESC Journal, 2020, 71(3): 1278-1287.
[6]	YANG Yijun,WANG Zhenlei,WANG Xin. Soft sensor modeling method based on hybrid model of nearest neighbor and neural network [J]. CIESC Journal, 2020, 71(12): 5696-5705.
[7]	Xuezhi DAI,Weili XIONG. A fast active learning method based on kernel extreme learning machine and its application for soft sensing [J]. CIESC Journal, 2020, 71(11): 5226-5236.
[8]	Xiaoqin LIAN, Liwei WANG, Sa AN, Wei WEI, Zaiwen LIU. On soft sensor of chemical oxygen demand by SOM-RBF neural network [J]. CIESC Journal, 2019, 70(9): 3465-3472.
[9]	Jing WU, Yiqi LIU, Jian LIU, Daoping HUANG, Yu QIU, Guangping YU. Study on the soft sensor of multi-kernel relevance vector machine based on time difference [J]. CIESC Journal, 2019, 70(4): 1472-1484.
[10]	Rui MU, Gaoyang LE, Huizhong YANG. Estimation method of dissolved gas quantity in COD determination based on O₃/UV [J]. CIESC Journal, 2019, 70(2): 730-735.
[11]	Jianyong ZHU, Xuqian ZHANG, Hui YANG, Rongxiu LU. Soft-sensing of Pr/Nd component content under different single illumination conditions [J]. CIESC Journal, 2019, 70(2): 780-788.
[12]	Wenpeng JI, Huizhong YANG. Multi-manifold soft sensor based on modified expanding search clustering algorithm [J]. CIESC Journal, 2019, 70(2): 723-729.
[13]	Rongrong ZHAO, Zhonggai ZHAO, Fei LIU. Gaussian process regression modeling of fermentation process based on k-nearest neighbor mutual information [J]. CIESC Journal, 2019, 70(12): 4741-4748.
[14]	Bao ZHU, Junfei QIAO. Features extracted from auto-encoder based echo state network and its applications to process modeling [J]. CIESC Journal, 2019, 70(12): 4770-4776.
[15]	QIU Yu, LIU Yiqi, WU Jing, HUANG Daoping. A self-adaptive multi-output soft sensor modeling based on deep neural network [J]. CIESC Journal, 2018, 69(7): 3101-3113.