含时滞测量值下间歇过程的双维状态估计

doi:10.11949/j.issn.0438-1157.20151894

化工学报 ›› 2016, Vol. 67 ›› Issue (9): 3784-3792.DOI: 10.11949/j.issn.0438-1157.20151894

含时滞测量值下间歇过程的双维状态估计

祁鹏程, 赵忠盖, 刘飞

江南大学轻工过程先进控制教育部重点实验室, 江苏无锡 214122

收稿日期:2015-12-14 修回日期:2016-04-28 出版日期:2016-09-05 发布日期:2016-09-05
通讯作者: 赵忠盖
基金资助:
国家自然科学基金项目（61134007，61573169）。

Two dimensional state estimation in batch process with delayed measurements

QI Pengcheng, ZHAO Zhonggai, LIU Fei

Key Laboratory of Advanced Control for Light Industry Processes, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China

Received:2015-12-14 Revised:2016-04-28 Online:2016-09-05 Published:2016-09-05
Supported by:
supported by the National Natural Science Foundation of China (61134007, 61573169).

摘要/Abstract

摘要：

基于粒子滤波研究了间歇过程的状态估计问题。根据间歇过程双维动态特性,针对关键参数在线检测精度低、离线分析时滞大等问题，分别建立一种双维状态转移模型和时滞测量模型，并利用贝叶斯方法及前/后向平滑，提出一种含时滞测量值下的双维状态估计算法。该算法通过融合先前批次和时滞测量值的信息提高估计精度，并且克服了离线采样周期和时滞时间不确定的问题。在数字仿真和啤酒发酵过程中的仿真应用验证了该算法的有效性。

关键词: 间歇过程, 双维状态空间模型, 时滞, 粒子滤波, 贝叶斯方法, 前/后向平滑

Abstract:

This work investigates the state estimation in a batch process based on the particle filter method. Considering that the two dimensional dynamics and the key parameters are obtained online with low accuracy or offline with large time delay, a two dimensional state transition model and delayed measurement model are developed. In addition, a two dimensional state estimation algorithm is proposed by using Bayesian method and the forward-backward smoothing algorithm. The proposed algorithm improves the estimation accuracy by fusing information of previous batches and delayed measurements, and overcomes the influence of the uncertainty of offline sampling period and time delay. The applications in a numerical example and a beer fermentation show the effectiveness of the proposed method.

Key words: batch process, two dimensional state space model, time delay, particle filter, Bayesian method, forward-backward smoothing

中图分类号:

TQ920.6

祁鹏程, 赵忠盖, 刘飞. 含时滞测量值下间歇过程的双维状态估计[J]. 化工学报, 2016, 67(9): 3784-3792.

QI Pengcheng, ZHAO Zhonggai, LIU Fei. Two dimensional state estimation in batch process with delayed measurements[J]. CIESC Journal, 2016, 67(9): 3784-3792.

参考文献 22

[1]	侯迪波. 间歇生产过程中的知识发现方法研究[D]. 杭州:浙江大学, 2004. HOU D B. Research on knowledge discovery method in batch process[D]. Hangzhou:Zhejiang University, 2004.
[2]	李慧娟. 青霉素发酵过程建模与优化[D]. 沈阳:东北大学, 2010. LI H J. Modeling and optimization of penicillin fermentation process[D]. Shenyang:Northeastern University, 2010.
[3]	姜蕾蕾. 啤酒发酵过程状态监测及控制策略的研究[D]. 哈尔滨:哈尔滨理工大学, 2014. JIANG L L. Study on state monitoring and control strategy of beer fermentation process[D]. Harbin:Harbin University of Science and Technology, 2014.
[4]	KOZUB D J, MACGREGOR J F. State estimation for semibatch polymerization reactors[J]. Chemical Engineering Science, 1992, 47(5):1047-1062.
[5]	OISIOVICI R M, CRUZ S L. State estimation of batch distillation columns using an extended Kalman filter[J]. Chemical Engineering Science, 2000, 55(20):4667-4680.
[6]	WANG J L, ZHAO L Q, YU T. On-line estimation in fed-batch fermentation process using state space model and unscented Kalman filter[J]. Chinese Journal of Chemical Engineering, 2010, 18(2):258-264.
[7]	CHEN T, JULIAN M, ELAINE M. Particle filters for state and parameter estimation in batch processes[J].Journal of Process Control, 2005, 15(6):665-673.
[8]	YAO Y, GAO F. Batch process monitoring in score space of two-dimensional dynamic principal component analysis (PCA)[J]. Industrial & Engineering Chemistry Research, 2007, 46(24):8033-8043.
[9]	ZHAO Z G, HUANG B, LIU F. Bayesian method for state estimation of batch process with missing data[J]. Computers and Chemical Engineering, 2013, 53(11):14-24.
[10]	ZHAO Z G, HUANG B, LIU F. State estimation in batch process based on two-dimensional state-space model[J]. Industrial & Engineering Chemistry Research, 2014, 53(50):19573-19582.
[11]	GUO Y F, ZHAO Y, HUANG B. Development of soft sensor by incorporating the delayed infrequent and irregular measurements[J]. Journal of Process Control, 2014, 24(11):1733-1739.
[12]	GOPALAKRISHNAN A, KAISARE N S, NARASIMHAN S. Incorporating delayed and infrequent measurements in extended Kalman filter based nonlinear state estimation[J]. Journal of Process Control, 2011, 21(1):119-129.
[13]	GUO Y F, HUANG B. State estimation incorporating infrequent, delayed and integral measurements[J]. Automatica, 2015, 58:32-38.
[14]	FERDINAND E U. State-space estimation with a Bayesian filter in a coupled PDE system for transient gas flows[J]. Original Research Article, 2015, 39(2):682-692.
[15]	ANDRIEU C, DOUCET A, SINGH S, et al. Particle methods for change detection, system identification, and control[J]. Proceedings of the IEEE, 2004, 92(3):423-443.
[16]	DOUCET A, GODSILL S, ANDRIEU C. On sequential Monte Carlo sampling methods for Bayesian filtering[J]. Statistics and Computing, 2000, 10(3):197-208.
[17]	STORVIK G. Particle filters for state-space models with the presence of unknown static parameters[J]. IEEE Trans. Signal Processing, 2002, 50(2):281-289.
[18]	RIGATOS G G. particle filtering for state estimation in nonlinear industrial systems[J]. Instrumentation and Measurement, 2009, 58(11):3885-3900.
[19]	NURMINEN H, RISTIMAKI A, ALI-LOYTTY S, et al. Particle filter and smoother for indoor localization[C]//4th International Conference on Indoor Positioning and Indoor Navigation. Montbéliard, France:IEEE, 2013:1-10.
[20]	ORGUNER U, GUSTAFSSON F. Storage efficient particle filters for the out of sequence measurement problem[C]//11th International Conference on Information Fusion. Piscataway, NJ, USA:IEEE, 2008:1-8.
[21]	KOTTAKKI K K, BHARTIYA S, BHUSHAN M. State estimation of nonlinear dynamical systems using nonlinear update based unscented Gaussian sum filter[J]. Journal of Process Control, 2014, 24(9):1425-1443.
[22]	ZHAO Z G, HUANG B, LIU F. Parameter estimation in batch process using EM algorithm with particle filter[J]. Computers and Chemical Engineering, 2013, 57(15):156-172.

含时滞测量值下间歇过程的双维状态估计

Two dimensional state estimation in batch process with delayed measurements

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 22

相关文章 15

编辑推荐

Metrics

本文评价

[1]	黄健, 赵众. 延迟焦化加热炉热效率的机理建模与实时估计应用[J]. 化工学报, 2020, 71(7): 3140-3150.
[2]	郑必鸣, 史彬, 鄢烈祥. 多因素不确定条件下的间歇生产调度优化[J]. 化工学报, 2020, 71(3): 1246-1253.
[3]	蔡涛, 杨博, 李宏光. 基于时延挖掘模糊时间认知图的化工过程多变量时序预测方法[J]. 化工学报, 2020, 71(3): 1095-1102.
[4]	于蕾, 邓晓刚, 曹玉苹, 路凯琪. 基于变量分组DTW-MCVA的不等长间歇过程故障检测方法[J]. 化工学报, 2019, 70(9): 3441-3448.
[5]	王立敏, 卢丽彬, 高福荣, 周东华. 多阶段间歇过程无穷时域优化线性二次容错控制[J]. 化工学报, 2019, 70(2): 541-547.
[6]	郭孝正, 刘琳琳, 张磊, 都健. 基于截断器半连续操作的间歇过程性质集成[J]. 化工学报, 2019, 70(2): 516-524.
[7]	高学金, 黄梦丹, 齐咏生, 王普. PDPSO优化多阶段AR-PCA间歇过程监测方法[J]. 化工学报, 2018, 69(9): 3914-3923.
[8]	张雷, 张小刚, 陈华. 基于Gath-Geva算法和核极限学习机的多阶段间歇过程软测量[J]. 化工学报, 2018, 69(6): 2576-2585.
[9]	黄卫清, 谭桂平, 钱宇. 化工反应器系统的最大时滞可耐受度指数求解与应用分析[J]. 化工学报, 2018, 69(3): 974-981.
[10]	王雅琳, 夏海兵, 袁小锋, 桂卫华. 基于趋势相似度分析的多重时滞辨识及其在加氢裂化流程中的应用[J]. 化工学报, 2018, 69(3): 1149-1157.
[11]	常鹏, 乔俊飞, 王普, 高学金, 李征. 基于MKECA的非高斯性和非线性共存的间歇过程监测[J]. 化工学报, 2018, 69(3): 1200-1206.
[12]	刘晓凤, 栾小丽, 刘飞. 基于隐变量空间载荷余弦相似度的间歇过程递推优化[J]. 化工学报, 2018, 69(3): 1167-1172.
[13]	张浩, 刘振娟, 李宏光, 杨博, 路洁. 基于关联变量时滞分析卷积神经网络的生产过程时间序列预测方法[J]. 化工学报, 2017, 68(9): 3501-3510.
[14]	王建林, 马琳钰, 刘伟旻, 邱科鹏, 于涛. 基于核相似度支持向量数据描述的间歇过程监测[J]. 化工学报, 2017, 68(9): 3494-3500.
[15]	刘伟旻, 王建林, 邱科鹏, 于涛, 赵利强. 基于多证据融合决策的间歇过程测量数据异常检测方法[J]. 化工学报, 2017, 68(8): 3183-3189.