Consequence Identification for Maloperation in Batch Process

doi:10.1016/S1004-9541(13)60620-5

Chinese Journal of Chemical Engineering ›› 2013, Vol. 21 ›› Issue (12): 1347-1359.DOI: 10.1016/S1004-9541(13)60620-5

• 过程系统工程与过程安全 • 上一篇下一篇

Consequence Identification for Maloperation in Batch Process

张玉良, 张贝克, 马昕, 曹柳林, 吴重光

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

收稿日期:2012-06-18 修回日期:2012-12-18 出版日期:2013-12-28 发布日期:2013-12-27
通讯作者: MA Xin

Consequence Identification for Maloperation in Batch Process

ZHANG Yuliang, ZHANG Beike, MA Xin, CAO Liulin, WU Chongguang

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Received:2012-06-18 Revised:2012-12-18 Online:2013-12-28 Published:2013-12-27
Contact: MA Xin

摘要/Abstract

摘要： Batch processes are important in chemical industry, in which operators usually play a major role and hazards may arise by their inadvertent acts. In this paper, based on hazard and operability study and concept of qualitative simulation, an automatic method for adverse consequence identification for potential maloperation is proposed. The qualitative model for production process is expressed by a novel directed graph. Possible operation deviations from normal operating procedure are identified systematically by using a group of guidewords. The proposed algorithm is used for qualitative simulation of batch processes to identify the effects of maloperations. The method is illustrated with a simple batch process and a batch reaction process. The results show that batch processes can be simulated qualitatively and hazards can be identified for operating procedures including maloperations. After analysis for possible plant maloperations, some measures can be taken to avoid maloperations or reduce losses resulted from maloperations.

关键词: maloperation, consequence identification, batch process, hazard and operability study, qualitative simulation

Abstract: Batch processes are important in chemical industry, in which operators usually play a major role and hazards may arise by their inadvertent acts. In this paper, based on hazard and operability study and concept of qualitative simulation, an automatic method for adverse consequence identification for potential maloperation is proposed. The qualitative model for production process is expressed by a novel directed graph. Possible operation deviations from normal operating procedure are identified systematically by using a group of guidewords. The proposed algorithm is used for qualitative simulation of batch processes to identify the effects of maloperations. The method is illustrated with a simple batch process and a batch reaction process. The results show that batch processes can be simulated qualitatively and hazards can be identified for operating procedures including maloperations. After analysis for possible plant maloperations, some measures can be taken to avoid maloperations or reduce losses resulted from maloperations.

Key words: maloperation, consequence identification, batch process, hazard and operability study, qualitative simulation

张玉良, 张贝克, 马昕, 曹柳林, 吴重光. Consequence Identification for Maloperation in Batch Process[J]. Chinese Journal of Chemical Engineering, 2013, 21(12): 1347-1359.

ZHANG Yuliang, ZHANG Beike, MA Xin, CAO Liulin, WU Chongguang. Consequence Identification for Maloperation in Batch Process[J]. Chin.J.Chem.Eng., 2013, 21(12): 1347-1359.

参考文献

1 Lee, K.S., Lee, J.H., “Iterative learning control-based batch process control technique for integrated control of end product properties and transient profiles of process variables”, J. Proc. Cont., 13, 607-621 (2003).

2 Scenna, N.J., “Some aspects of fault diagnosis in batch processes”, Reliab. Eng., 70, 95-110 (2000).

3 Srinivasan, R., Venkatasubramanian, V., “Petri net-digraph models for automating HAZOP analysis of batch process plants”, Computers Chem. Eng., 20, S719-S725 (1996).

4 Palmer, C., Chung, P.W.H., “A computer tool for batch hazard and operability studies”, J. Loss Prev., 21, 537-542 (2008).

5 Vaidhyanathan, R., Venkatasubramanian, V., “Experience with an expert system for automated HAZOP analysis”, Computers Chem. Eng., 20, S1589-S1594 (1996).

6 Mushtaq, F., Chung, P.W.H., “A systematic Hazop procedure for batch processes, and its application to pipeless plants”, J. Loss Prev., 13, 41-48 (2000).

7 Srinivasan, R., Venkatasubramanian, V., “Automating HAZOP analysis of batch chemical plants: Part Ⅰ. The knowledge representation framework”, Computers chem. Eng., 22 (9), 1345-1355 (1998).

8 Srinivasan, R., Venkatasubramanian, V., “Automating HAZOP analysis of batch chemical plants: Part Ⅱ. Algorithms and application”, Computers chem. Eng., 22 (9), 1357-1370 (1998).

9 Kang, B., Shin, D., Yoon, E.S., “Automation of the safety analysis of batch processes based on multi-modeling approach”, Control Engineering Practice, 11, 871-880 (2003).

10 Palmer, C., Chung, P.W.H., “An automated system for batch hazard and operability studies”, Reliab. Eng., 94, 1095-1106 (2009).

11 Wang, Y., Zhang, B.K., Wu, C.G., “An improved HAZOP analysis module of batch processes”, Computer Simulation, 24 (2), 57-61 (2007). (in Chinese)

12 Xu, L.J., Zhou, J.T., Cao, Q., “Study of HAZOP analysis for batch processes based on dynamic SDG model”, Journal of Safety Science and Technology, 7 (9), 162-164 (2011). (in Chinese)

13 Zhang, B.K., Cai, X.C., Zhang, Y.L., “Maloperation risk identification based on digraph models of batch process”, CIESC Journal, 62 (10), 2805-2812 (2011). (in Chinese)

14 Zhang, Y.L., Zhang, B.K., Ma, X., “Consequence analysis strategy for mal-operation in batch processes based on qualitative simulation”, CIESC Journal, 63 (1), 203-215 (2012). (in Chinese)

15 Yang, F., Shah, S.L., Xiao, D.Y., “Signed directed graph based modeling and its validation from process knowledge and process data”, Int. J. Appl. Math. Comput. Sci., 22 (1), 41-53 (2012).

[1]	Racheal Aigbe, Doga Kavaz. Unravel the potential of zinc oxide nanoparticle-carbonized sawdust matrix for removal of lead (II) ions from aqueous solution[J]. 中国化学工程学报, 2021, 29(1): 92-102.
[2]	Aihong Li, Changzhan Liu, Zhiyong Liu. A heuristic design method for batch water-using networks of multiple contaminants with regeneration unit[J]. 中国化学工程学报, 2019, 27(5): 1103-1112.
[3]	Li Jia, Wendan Tan. Just-in-time learning based integrated MPC-ILC control for batch processes[J]. Chinese Journal of Chemical Engineering, 2018, 26(8): 1713-1720.
[4]	Yongyong Hui, Xiaoqiang Zhao. Batch process monitoring based on WGNPE-GSVDD related and independent variables[J]. Chinese Journal of Chemical Engineering, 2018, 26(12): 2549-2561.
[5]	Xuan Yang, Xiao'e Ruan. Robustness of reinforced gradient-type iterative learning control for batch processes with Gaussian noise[J]. Chinese Journal of Chemical Engineering, 2016, 24(5): 623-629.
[6]	Dazi Li, Ningjia Meng, Tianheng Song. Learning control of fermentation process with an improved DHP algorithm[J]. , 2016, 24(10): 1399-1405.
[7]	Saima Noor, Shamsul Qamar. The space time CE/SE method for solving one-dimensional batch crystallization model with fines dissolution[J]. , 2015, 23(2): 337-341.
[8]	Li Jia, Kai Yuan. Auxiliary error and probability density function based neuro-fuzzymodel and its application in batch processes[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 2013-2019.
[9]	Yuliang Zhang, Wentao Zhang, Beike Zhang . Automatic HAZOP analysis method for unsteady operation in chemical based on qualitative simulation and inference[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 2065-2074.
[10]	Chen Chen, Zhihua Xiong, Yisheng Zhong. Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory[J]. , 2014, 22(7): 762-768.
[11]	杨国军, 李秀喜, 钱宇. A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation[J]. Chinese Journal of Chemical Engineering, 2014, 22(3): 318-329.
[12]	王立敏, 陈曦, 高福荣. An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes[J]. Chinese Journal of Chemical Engineering, 2013, 21(4): 401-411.
[13]	池荣虎, 张德霞, 刘喜梅, 侯忠生, 金尚泰. An Anticipatory Terminal Iterative Learning Control Approach with Applications to Constrained Batch Processes[J]. Chinese Journal of Chemical Engineering, 2013, 21(3): 271-275.
[14]	郭辉, 李宏光. On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis[J]. Chinese Journal of Chemical Engineering, 2013, 21(3): 263-270.
[15]	赵露平, 赵春晖, 高福荣. Phase Transition Analysis Based Quality Prediction for Multi-phase Batch Processes^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1191-1197.

Consequence Identification for Maloperation in Batch Process

Consequence Identification for Maloperation in Batch Process

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价