Reliability monitoring of fluorochemical process operation unit based on GLSAFIS

doi:10.11949/0438-1157.20210712

CIESC Journal ›› 2021, Vol. 72 ›› Issue (11): 5696-5706.DOI: 10.11949/0438-1157.20210712

• Process system engineering • Previous Articles Next Articles

Reliability monitoring of fluorochemical process operation unit based on GLSAFIS

Feng XUE^1,²(),Xintong LI¹,Kun ZHOU¹,Zhiqiang WEI³,Xiaoxia GE³,Zhiqiang GE⁴,Kai SONG¹()

^1.Tianjin Key Laboratory of Chemical Safety and Equipment, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
^2.Suzhou Chuangyuan Industrial Investment Co. , Ltd. , Suzhou 215000, Jiangsu, China
^3.Health, Safety and Environmental Protection Department, Juhua Group Co. , Ltd. , Quzhou 324004, Zhejiang, China
^4.State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China

Received:2021-05-26 Revised:2021-08-16 Online:2021-11-12 Published:2021-11-05
Contact: Kai SONG

基于GLSAFIS的氟化工过程操作单元可靠性监测

薛峰^1,²(),李欣铜¹,周琨¹,魏志强³,葛晓霞³,葛志强⁴,宋凯¹()

^1.天津大学化工学院，天津化工安全与装备重点实验室，天津 300350
^2.苏州创元产业投资有限公司，江苏苏州 215000
^3.巨化集团有限公司健康安全环保部，浙江衢州 324004
^4.浙江大学控制科学与工程学院，工业控制技术国家重点实验室，浙江杭州 310027

通讯作者: 宋凯
作者简介:薛峰（1995—），男，硕士，3014207238@tju.edu.cn
基金资助:
国家重点研发计划项目(2018YFC0808600)

Abstract

Abstract:

The highly toxic characteristics of fluorochemical products make it extremely important to monitor the operational reliability of fluorochemical process equipment. For this reason, this paper proposes a fuzzy inference system based on global-local structural analysis (GLSAFIS) to evaluate the operational reliability of fluorochemical process operation units online. After selecting the process variables of the operating unit according to the fluorochemical process flow, the global-local feature extraction of the operating unit process variables is carried out through the global-local structure analysis algorithm (GLSA). More importantly, GLSA transferred process data into a much lower feature space, which made it much more efficient to design fuzzy logics for a fuzzy inference system (FIS) and less dependent on expert knowledge. The effectiveness of the proposed method is evaluated through the fluorochemical R-22 production process located in East China and the Tennessee Eastman benchmark process. The results show that this method can accurately reflect the operating conditions of the actual chemical process operation unit, and plays an important role in monitoring the chemical process operation.

Key words: reliability estimation, global-local structure analysis, fuzzy inference system, fluorochemical process, Tennessee Eastman Process

摘要：

氟化工产物的剧毒特性，使得对于氟化工过程设备的运行可靠性监控异常重要。为此，提出了基于全局-局部结构分析的模糊推理系统（GLSAFIS）在线评估氟化工过程操作单元运行可靠性。依据氟化工工艺流程选取操作单元过程变量后，通过全局-局部结构分析算法（GLSA）对操作单元过程变量进行全局-局部特征提取。这些低维的全局-局部特征代替原始变量作为模糊推理系统（FIS）的输入，不仅可以克服噪声的影响，降低对专家知识的依赖，同时可以通过特征空间的降维压缩，大大加速后续模糊推理系统的逻辑设计。最后模糊推理系统的实施，使得本方法可以对化工过程操作单元可靠性进行在线评估。国内某氟化工厂二氟一氯甲烷（R-22）生产过程的实际应用以及田纳西伊斯曼模拟过程的仿真结果均证实了所提方法可以准确地反映实际化工过程操作单元的运行状况，大大提高了对实际化工过程的安全监控力度。

关键词: 可靠性评估, 全局-局部结构分析, 模糊推理系统, 氟化工过程, 田纳西伊斯曼过程

CLC Number:

TP 274

Feng XUE, Xintong LI, Kun ZHOU, Zhiqiang WEI, Xiaoxia GE, Zhiqiang GE, Kai SONG. Reliability monitoring of fluorochemical process operation unit based on GLSAFIS[J]. CIESC Journal, 2021, 72(11): 5696-5706.

薛峰, 李欣铜, 周琨, 魏志强, 葛晓霞, 葛志强, 宋凯. 基于GLSAFIS的氟化工过程操作单元可靠性监测[J]. 化工学报, 2021, 72(11): 5696-5706.

Figures/Tables 10

Fig.1 Schematic diagram of membership function design method

Table 1 The restriction of operating unit input variables

输入变量	低	中	高
Z₁	否	是	否
Z₂	否	是	否
Z₃	否	是	否

Fig.2 Flow chart of GLSAFIS algorithm

Fig.3 Schematic diagram of R-22 production process

Fig.4 Application example of GLSAFIS in R-22 reactor operation unit

Fig.5 Three cases of R-22's important raw material feed volume and reactor operating unit reliability

Fig.6 Comparison of reliability estimation results of R-22 reactor operating unit

Fig.7 Flow chart of TE process

Fig.8 Membership function of TE reactor and separator operating unit

Fig.9 Three cases of reactor operating unit and stripper operating unit reliability in TE

References 31

1	Harkat M F, Mansouri M, Nounou M N, et al. Fault detection of uncertain chemical processes using interval partial least squares-based generalized likelihood ratio test[J]. Information Sciences, 2019, 490: 265-284.
2	Wang Y H, Hou X Q, Zhang P, et al. Reliability assessment of multi-state reconfiguration pipeline system with failure interaction based on Cloud inference[J]. Process Safety and Environmental Protection, 2020, 137: 116-127.
3	Jia L L, Ren Y, Yang D Z, et al. Reliability analysis of dynamic reliability block diagram based on dynamic uncertain causality graph[J]. Journal of Loss Prevention in the Process Industries, 2019, 62: 103947.
4	Jiang Q, Gao D C, Zhong L, et al. Quantitative sensitivity and reliability analysis of sensor networks for well kick detection based on dynamic Bayesian networks and Markov chain[J]. Journal of Loss Prevention in the Process Industries, 2020, 66: 104180.
5	Senol Y E, Aydogdu Y V, Sahin B, et al. Fault Tree Analysis of chemical cargo contamination by using fuzzy approach[J]. Expert Systems with Applications, 2015, 42(12): 5232-5244.
6	Wang W B. A simulation-based multivariate Bayesian control chart for real time condition-based maintenance of complex systems[J]. European Journal of Operational Research, 2012, 218(3): 726-734.
7	Tao T, Zio E, Zhao W. A novel support vector regression method for online reliability prediction under multi-state varying operating conditions[J]. Reliability Engineering & System Safety, 2018, 177: 35-49.
8	Rebello S, Yu H Y, Ma L. An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model[J]. Reliability Engineering & System Safety, 2018, 180: 124-135.
9	BahooToroody A, de Carlo F, Paltrinieri N, et al. Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation[J]. Reliability Engineering & System Safety, 2020, 201: 106966.
10	Zhou X B, Peng T. Application of multi-sensor fuzzy information fusion algorithm in industrial safety monitoring system[J]. Safety Science, 2020, 122: 104531.
11	Yan L J, Zhang L B, Liang W, et al. Key factors identification and dynamic fuzzy assessment of health, safety and environment performance in petroleum enterprises[J]. Safety Science, 2017, 94: 77-84.
12	Ojha V, Abraham A, Snášel V. Heuristic design of fuzzy inference systems: a review of three decades of research[J]. Engineering Applications of Artificial Intelligence, 2019, 85: 845-864.
13	Subbaraj P, Kannapiran B. Fault detection and diagnosis of pneumatic valve using Adaptive Neuro-Fuzzy Inference System approach[J]. Applied Soft Computing, 2014, 19: 362-371.
14	Zhao X, Xie L P, Wei H K, et al. Fuzzy inference systems based on multi-type features fusion for intra-hour solar irradiance forecasts[J]. Sustainable Energy Technologies and Assessments, 2021, 45: 101061.
15	Troussas C, Chrysafiadi K, Virvou M. An intelligent adaptive fuzzy-based inference system for computer-assisted language learning[J]. Expert Systems with Applications, 2019, 127: 85-96.
16	Zhang Z, Song K, Tong T P, et al. A novel nonlinear adaptive Mooney-viscosity model based on DRPLS-GP algorithm for rubber mixing process[J]. Chemometrics and Intelligent Laboratory Systems, 2012, 112: 17-23.
17	Oluwasegun A, Jung J C. The application of machine learning for the prognostics and health management of control element drive system[J]. Nuclear Engineering and Technology, 2020, 52(10): 2262-2273.
18	Lee J, Wu F J, Zhao W, et al. Prognostics and health management design for rotary machinery systems—reviews, methodology and applications[J]. Mechanical Systems and Signal Processing, 2014, 42(1/2): 314-334.
19	Moradi R, Groth K M. Modernizing risk assessment: a systematic integration of PRA and PHM techniques[J]. Reliability Engineering & System Safety, 2020, 204: 107194.
20	Peng X, Tang Y, Du W L, et al. Performance monitoring of non-Gaussian chemical processes with modes-switching using globality-locality preserving projection[J]. Frontiers of Chemical Science and Engineering, 2017, 11(3): 429-439.
21	Gui J, Jia W, Zhu L, et al. Locality preserving discriminant projections for face and palmprint recognition[J]. Neurocomputing, 2010, 73(13/14/15): 2696-2707.
22	Xie X, Shi H B. Multimode process monitoring based on fuzzy C-means in locality preserving projection subspace[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1174-1179.
23	Wang A G, Zhao S H, Liu J J, et al. Locality adaptive preserving projections for linear dimensionality reduction[J]. Expert Systems with Applications, 2020, 151: 113352.
24	Zhang M G, Ge Z Q, Song Z H, et al. Global–local structure analysis model and its application for fault detection and identification[J]. Industrial & Engineering Chemistry Research, 2011, 50(11): 6837-6848.
25	Shao Y. Supervised global-locality preserving projection for plant leaf recognition[J]. Computers and Electronics in Agriculture, 2019, 158: 102-108.
26	Zadeh L A. Fuzzy sets[J]. Information and Control, 1965, 8(3): 338-353.
27	Dey S, Jana D K. Application of fuzzy inference system to polypropylene business policy in a petrochemical plant in India[J]. Journal of Cleaner Production, 2016, 112: 2953-2968.
28	Tiri A, Belkhiri L, Mouni L. Evaluation of surface water quality for drinking purposes using fuzzy inference system[J]. Groundwater for Sustainable Development, 2018, 6: 235-244.
29	Downs J J, Vogel E F. A plant-wide industrial process control problem[J]. Computers & Chemical Engineering, 1993, 17(3): 245-255.
30	Bathelt A, Ricker N L, Jelali M. Revision of the Tennessee Eastman process model[J]. IFAC-PapersOnLine, 2015, 48(8): 309-314.
31	Udugama I A, Gernaey K V, Taube M A, et al. A novel use for an old problem: the Tennessee Eastman challenge process as an activating teaching tool[J]. Education for Chemical Engineers, 2020, 30: 20-31.

[1]	YANG Zhengyong, WANG Xin, WANG Zhenlei. LTSA and combined index based non-Gaussian process monitoring and application [J]. CIESC Journal, 2015, 66(4): 1370-1377.
[2]	YE Lubin, SHI Xiangrong, LIANG Jun. A Multi-level Approach for Complex Fault Isolation Based on Structured Residuals [J]. , 2011, 19(3): 462-472.
[3]	S. M. Khazraee, A. H. Jahanmiri. Composition Estimation of Reactive Batch Distillation by Using Adaptive Neuro-Fuzzy Inference System [J]. , 2010, 18(4): 703-710.
[4]	SONG Kai, WANG Haiqing, LI Ping, FENG Zhigang. Quality Based Prioritized Sensor Fault Monitoring Methodology [J]. , 2008, 16(4): 584-589.

Reliability monitoring of fluorochemical process operation unit based on GLSAFIS

基于GLSAFIS的氟化工过程操作单元可靠性监测

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 31

Related Articles 4

Recommended Articles

Metrics

Comments