PDPSO优化多阶段AR-PCA间歇过程监测方法

doi:10.11949/j.issn.0438-1157.20180369

化工学报 ›› 2018, Vol. 69 ›› Issue (9): 3914-3923.DOI: 10.11949/j.issn.0438-1157.20180369

PDPSO优化多阶段AR-PCA间歇过程监测方法

高学金^1,2,3,4, 黄梦丹^1,2,3,4, 齐咏生⁵, 王普^1,2,3,4

1. 北京工业大学信息学部, 北京 100124;
2. 数字社区教育部工程研究中心, 北京 100124;
3. 城市轨道交通北京实验室, 北京 100124;
4. 计算智能与智能系统北京市重点实验室, 北京 100124;
5. 内蒙古工业大学电力学院, 内蒙古呼和浩特 010051

收稿日期:2018-04-03 修回日期:2018-06-17 出版日期:2018-09-05 发布日期:2018-09-05
通讯作者: 齐咏生
基金资助:
国家自然科学基金项目（61640312，61763037）；北京市自然科学基金项目（4172007）；北京市教育委员会资助项目。

Batch process monitoring using multiphase AR-PCA optimized with PDPSO

GAO Xuejin^1,2,3,4, HUANG Mengdan^1,2,3,4, QI Yongsheng⁵, WANG Pu^1,2,3,4

1. Department of Information Science, Beijing University of Technology, Beijing 100124, China;
2. Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China;
3. Beijing Laboratory for Urban Mass Transit, Beijing 100124, China;
4. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;
5. School of Electric Power, Inner Mongolia University of Technology, Huhhot 010051, Inner Mongolia, China

Received:2018-04-03 Revised:2018-06-17 Online:2018-09-05 Published:2018-09-05
Supported by:
supported by the National Natural Science Foundation of China (61640312,61763037), the Natural Science Foundation of Beijing (4172007) and Beijing Municipal Education Commission Fund.

摘要/Abstract

摘要：

针对间歇过程固有的多阶段特性和动态性，提出基于种群多样性的自适应惯性权重粒子群算法（PDPSO）优化的多阶段自回归主元分析（AR-PCA）间歇过程监测方法。该方法引入了PDPSO算法指导AP聚类偏向参数的选取，避免了传统方法依据聚类评价指标选取参考度时的盲目性。对PDPSO优化AP聚类的多阶段发酵过程的数据样本建立AR-PCA模型能够消除各阶段的动态性及变量之间的自相关和互相关影响。最后，对自回归（AR）模型的残差矩阵建立主成分分析（PCA）模型用于发酵过程监测。将该方法应用到青霉素发酵过程，并与传统方法进行对比，结果表明，该方法能够有效进行间歇过程阶段划分并降低故障的漏报和误报。

关键词: 间歇过程, 种群多样性, 粒子群优化, 仿射传播聚类, 自回归主元分析

Abstract:

To overcome the problem of batch process caused by the traditional process dynamics multistage characteristic, the multiphase auto regression-principal component analysis (AR-PCA) monitoring method is proposed based on affine propagation (AP) clustering optimized with a population diversity-based particle swarm optimization algorithm (PDPSO). The method introduced PDPSO method to improve the AP clustering. It avoided the blindness of common method that indirectly chose the preference based on the clustering evaluation index. Then we established the AR-PCA model for the data samples of the multiphase fermentation process to eliminate the dynamic characteristics of each stage and the auto-and-cross-correlation between variables. Finally, the PCA model is established for the residual of the AR model for fault monitoring of the batch process. The method is applied to the process of penicillin fermentation. Experiments show that the method can effectively divide the process into different phases and reduce the false and leak alarms.

Key words: batch process, population diversity, particle swarm optimization, affine propagation clustering, autoregressive principal component analysis

中图分类号:

TP277

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.

GAO Xuejin, HUANG Mengdan, QI Yongsheng, WANG Pu. Batch process monitoring using multiphase AR-PCA optimized with PDPSO[J]. CIESC Journal, 2018, 69(9): 3914-3923.

参考文献 30

[1]	GUO H, LI H G. On-line batch process monitoring with improved multi-way independent component analysis[J]. Chinese Journal of Chemical Engineering, 2013, 21(3):263-270.
[2]	NOMIKOS P, MACGREGOR J F. Monitoring batch processes using multiway principal component analysis[J]. AIChE Journal, 1994, 40(8):1361-1375.
[3]	MACGREGOR J F, JAECKLE C, KIPARISSIDES C, et al. Process monitoring and diagnosis by multiblock PLS methods[J]. AIChE Journal, 1994, 40(5):826-838.
[4]	LOU Z J, SHEN D, WANG Y Q. Preliminary-summation-based principal component analysis for non-Gaussian processes[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 146:270-289.
[5]	贾润达, 毛志忠, 王福利. 基于KPLS模型的间歇过程产品质量控制[J]. 化工学报, 2013, 64(4):1332-1339. JIA R D, MAO Z Z, WANG F L. KPLS model based product quality control for batch processes[J]. CIESC Journal, 2013, 64(4):1332-1339.
[6]	GE Z Q, SONG Z H, ZHAO L P, et al. Two-level PLS model for quality prediction of multiphase batch processes[J]. Chemometrics and Intelligent Laboratory Systems, 2014, 130:29-36.
[7]	QIN S J. Survey on data-driven industrial process monitoring and diagnosis[J]. Annual Reviews in Control, 2012, 36(2):220-234.
[8]	LUO L J, BAO S Y, MAO J F, et al. Quality prediction and quality-relevant monitoring with multilinear PLS for batch processes[J]. Chemometrics and Intelligent Laboratory Systems, 2016, 150:9-22.
[9]	LIU Y, ZHAO S, WANG Q, et al. Learning more distinctive representation by enhanced PCA network[J]. Neurocomputing, 2017, 275:924-931.
[10]	LIU Y, LIANG Y, GAO Z, et al. Online flooding supervision in packed towers:an integrated data-driven statistical monitoring method[J]. Chemical Engineering and Technology, 2018, 41(3):436-446.
[11]	LIU Y, HSEUH B F, GAO Z, et al. Flooding prognosis in packed columns by assessing degree of steadiness (DOS) of process variable trajectory[J]. Industrial and Engineering Chemistry Research, 2016, 55(40):10744-10750.
[12]	LI H G, SONG R Q, LIU J W. Low-dimensional feature fusion strategy for overlapping neuron spike sorting[J]. Neurocomputing, 2018, 281:152-159.
[13]	WANG F, WANG Q, NIE F, et al. Efficient tree classifiers for large scale datasets[J]. Neurocomputing, 2018, 284:70-79.
[14]	WANG Z, HU H, ZHANG L, et al. Discriminatively guided filtering (DGF) for hyperspectral image classification[J]. Neurocomputing, 2017, 275:1981-1987.
[15]	GE Z Q, SONG Z H, GAO F R. Review of recent research on data-based process monitoring[J]. Industrial and Engineering Chemistry Research, 2013, 52:3543-3562.
[16]	CAI F, TIAN X M, CHEN S. A process monitoring method based on noisy independent component analysis[J]. Neurocomputing, 2014, 127:231-246.
[17]	QIN Y, ZHAO C H, WANG X Z, et al. Subspace decomposition and critical phase selection based cumulative quality analysis for multiphase batch processes[J]. Chemical Engineering Science, 2017, 166:130-143.
[18]	FEI Z S, LIU K L. Online process monitoring for complex systems with dynamic weighted principal component analysis[J]. Chinese Journal of Chemical Engineering, 2016, 24(6):775-786.
[19]	张子羿, 胡益, 侍洪波. 一种基于聚类方法的多阶段间歇过程监控方法[J]. 化工学报, 2013, 64(12):4522-4528. ZHANG Z Y, HU Y, SHI H B. Multi-stage batch process monitoring based on a clustering method[J]. CIESC Journal, 2013, 64(12):4522-4528.
[20]	QIN S J. Process data analytics in the era of big data[J]. AIChE Journal, 2014, 60(9):3092-3100.
[21]	LÜ Z M, YAN X F, JIANG Q C. Batch process monitoring based on multiple-phase online sorting principal component analysis[J]. ISA Transactions, 2016, 64:342-352.
[22]	LU N Y, GAO F R, WANG F L. Sub-PCA modeling and on-line monitoring strategy for batch processes[J]. AIChE Journal, 2004, 50(1):255-259.
[23]	ZHANG C, ZHAO H T. Order clustering based sub-stage division and its application to batch process monitoring[C]//Control Conference. IEEE, 2016:6482-6487.
[24]	YU J, QIN S J. Multiway Gaussian mixture model based multiphase batch process monitoring[J]. Industrial and Engineering Chemistry Research, 2009, 48(18):8585-8594
[25]	FREY B J, DUECK D. Clustering by passing messages between data points[J]. Science, 2007, 315:972-976.
[26]	CHOI S W, MORRIS J, LEE I B. Dynamic model-based batch process monitoring[J]. Chemical Engineering Science, 2008, 63:622-636.
[27]	胡永兵, 高学金, 李亚芬, 等. 基于仿射传播聚类子集主元分析的间歇过程监测方法[J]. 化工学报, 2016, 5(5):1989-1997. HU Y B, GAO X J, LI Y F, et al. Clean production of pharmaceutical crystallization[J]. CIESC Journal, 2016, 5(5):1989-1997.
[28]	王志文, 刘毅, 高增梁. 时变间歇过程的2D-PID自适应控制方法[J]. 化工学报, 2016, 67(3):991-997. WANG Z W, LIU Y, GAO Z L. 2D-PID adaptive control method for time-varying batch processes[J]. CIESC Journal, 2016, 67(3):991-997.
[29]	高学金, 崔宁, 张亚潮, 等. 基于粒子群优化MICA的间歇过程故障监测[J].仪器仪表学报, 2015, 36(1):152-159. GAO X J, CUI N, ZHANG Y C, et al. Subset multiway principal component analysis monitoring for batch process based on affinity propagation clustering[J]. Chinese Journal of Scientific Instrument, 2015, 36(1):152-159.
[30]	YU H H, CHEN Y Y, HASSAN G S, et al. Prediction of the temperature in a Chinese solar greenhouse based on LSSVM optimized by improved PSO[J]. Computers and Electronics in Agriculture, 2016, 122:94-102.

PDPSO优化多阶段AR-PCA间歇过程监测方法

Batch process monitoring using multiphase AR-PCA optimized with PDPSO

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 30

相关文章 15

编辑推荐

Metrics

本文评价

[1]	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.
[2]	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.
[3]	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.
[4]	白竣仁, 易军, 李倩, 吴凌, 陈雪梅. 面向反应再生过程的量子粒子群多目标优化[J]. 化工学报, 2019, 70(2): 750-756.
[5]	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.
[6]	周新志, 邵伦, 李荣昆, 赵成萍, 董晨龙. 基于小波去噪和改进型PSO-SVM的微波加热温度预测模型研究[J]. 化工学报, 2018, 69(S2): 291-299.
[7]	徐文星, 王万红, 王芳, 刘才, 景邵星, 赵国新. 基于PSOGSA前向神经网络的石化控制系统入侵检测[J]. 化工学报, 2018, 69(S2): 350-357.
[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]	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.
[10]	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.
[11]	华丽, 于海晨, 邵诚, 巩师鑫. 基于SVM-BOXPLOT的乙烯生产过程异常工况监测与诊断[J]. 化工学报, 2018, 69(3): 1053-1063.
[12]	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.
[13]	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.
[14]	王建林, 刘伟旻, 邱科鹏, 张维佳, 于涛. 基于LWPT-DTW的间歇过程不等长时段数据同步化[J]. 化工学报, 2017, 68(7): 2866-2872.
[15]	仇力, 栾小丽, 刘飞. 基于主元相似度的间歇过程操作曲线递推优化[J]. 化工学报, 2017, 68(7): 2859-2865.