Automatic HAZOP analysis method for unsteady operation in chemical based on qualitative simulation and inference

Abstract Comparing with continuous production process, unsteady operation process, such as startup and shutdown, tends to abnormal situations due to a large number of operations of operators and dynamic state changes involved. To guarantee a safe operation, process hazard analysis (PHA) is very important to proactively identify the potential safety problems. In the chemical process industry, hazard and operability (HAZOP) analysis is the most widely used method. In this paper, based on proposed qualitative simulation and inference method, an automatic HAZOP analysis method for unsteady operation processes is proposed. Mass transfer and relationships among process variables are expressed by Petri net–directed graph model based fuzzy logic. Operating procedure is expressed according to a formal expression. Possible operation deviations from normal operating procedure are identified by using a group of guidewords. Hazards are identified automatically by qualitative simulation and inference when wrong operation process is performed. The method is validated by a rectification column system.

[1]  P. W. H. Chung,et al.  An automated system for batch hazard and operability studies , 2009, Reliab. Eng. Syst. Saf..

[2]  V. Venkatasubramanian,et al.  Automating HAZOP analysis of batch chemical plants: Part I. The knowledge representation framework , 1998 .

[3]  Paul W. H. Chung,et al.  A systematic Hazop procedure for batch processes, and its application to pipeless plants , 2000 .

[4]  Henner Schmidt-Traub,et al.  An integrated approach to early process hazard identification of continuous and batch plants with statechart modelling and simulation , 2001 .

[5]  Venkat Venkatasubramanian,et al.  Automating HAZOP analysis of batch chemical plants : Part II. Algorithms and application , 1998 .

[6]  Ma Xin Consequence analysis strategy for mal-operation in batch processes based on qualitative simulation , 2012 .

[7]  Rajagopalan Srinivasan,et al.  Fault detection during process transitions: a model-based approach , 2003 .

[8]  Venkat Venkatasubramanian,et al.  Digraph-based models for automated HAZOP analysis , 1995 .

[9]  Zhang Yuliang Maloperation risk identification based on digraph models of batch process , 2011 .

[10]  Venkat Venkatasubramanian,et al.  Petri net-Digraph models for automating HAZOP analysis of batch process plants , 1996 .

[11]  Liulin Cao,et al.  Consequence Identification for Maloperation in Batch Process , 2013 .

[12]  Venkat Venkatasubramanian,et al.  Intelligent systems for HAZOP analysis of complex process plants , 2000 .

[13]  En Sup Yoon,et al.  Automation of the safety analysis of batch processes based on multi-modeling approach , 2003 .

[14]  Venkat Venkatasubramanian,et al.  Experience with an expert system for automated HAZOP analysis , 1996 .

[15]  Venkat Venkatasubramanian,et al.  PHASUITE: AN AUTOMATED HAZOP ANALYSIS TOOL FOR CHEMICAL PROCESSES Part I: Knowledge Engineering Framework , 2005 .

[16]  H Graf,et al.  Early hazard identification of chemical plants with statechart modelling techniques , 2000 .

[17]  Wu Chong-guang Novel Qualitative Simulation Technology Based on SDG for Hazard Analysis , 2005 .

[18]  Paul W. H. Chung,et al.  A computer tool for batch hazard and operability studies , 2008 .

[19]  Rajagopalan Srinivasan,et al.  Monitoring transitions in chemical plants using enhanced trend analysis , 2003, Comput. Chem. Eng..