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Industrial applications of intelligent systems for operating procedure synthesis and hazards analysis for batch process plants
European Symposium on Computer Aided Process Engineering - 10 S. Pierucci (Editor) 9 2000 Elsevier Science B.V. All rights reserved.
787
Industrial Applications o f Intelligent Systems for O p e r a t i n g P r o c e d u r e Synthesis and Hazards Analysis for Batch Process Plants Jinsong Zhao, Shankar Viswanathan and Venkat Venkatasubramanian * Laboratory for Intelligent Process Systems, School of Chemical Engineering, Purdue University, West Lafayette, IN 47907 Operating Procedure Synthesis (OPS) and Hazard and Operability analysis (HAZOP) are two important areas in batch process development. They are time-consuming and knowledge-intensive, and could benefit from automation. Recently, two knowledge-based systems for automating OPS and HAZOP, called iTOPS and BHE respectively, were developed and integrated. The integrated system has the capability to perform consistent HAZOP analysis based on the information in the generated operating procedures. In this paper, the integrated system is illustrated using one large-scale pharmaceutical industrial case study. 1. I N T R O D U C T I O N The increasing trend towards the production of higher-value-added products by chemical or pharmaceutical industries has stimulated considerable interest in batch processes. In the current environment of intense market competition, batch process industries stand to benefit from faster and safer process development. One day of delay to the market may cost millions in potential profits since today's blockbuster drugs may make $500 million to $1 billion a year [ 1]. In the process of batch process development, two important areas in batch process development that take considerable amount of time and effort are operating procedure synthesis (OPS) and process hazards analysis (PHA) as they are often manually performed. Thus there exists substantial motivation to develop computer-based approaches for OPS and PHA for batch processes. In this paper, we discuss the results of fielding several industrial applications of such automated intelligent systems for OPS and PHA. 2. I N T E L L I G E N T S Y S T E M S F O R OPS A N D H A Z O P
OPS is systematic synthesis of a sequence of elementary tasks an operator needs to manage a batch process safely and optimally. PHA is the proactive identification, evaluation, and mitigation of hazards. CCPS defines hazard as "an inherent physical or chemical characteristic that has the potential for causing harm to people, property or the environment" [2]. The importance of OPS and PHA is underlined by OSHA PSM Standard 29 CFT 1910 which was enacted in 1992. This standard regulates that major chemical plants should perform PHA on a regular basis when a new process is launched or any change happens in an existing process. It also requires that at least every five years after the completion of the initial PHA, PHA shall be updated and revalidated to assure that the process hazard analysis is consistent with the current process. It is also regulated by this standard that written operating procedures that provide clear instructions for *Author to whomall correspondence shouldbe addressed
788 safely conducting activities should be developed and implemented for chemical processes covered by the standard. PHA is a time-consuming activity. It is estimated that the complete PHA of a typical process could take 1-8 weeks for a PHA team. Typically, there are tens to hundreds of processes per year to review in a chemical or pharmaceutical company, it is easy to imagine how hard the PHA team would work. Moreover, it is considerably difficult for the team to keep the process hazard analysis consistent and systematic during so long and tiresome a period of time for PHA. An intelligent system which helps in automating the entire PHA study would reduce the time, effort and money involved in a review, make the review more thorough and detailed with human errors minimized. OPS is also a time-consuming activity. It is often manually performed according to the experiences of process engineers. The experience-based method is also short of consistency in process development because different engineers can generate different versions of operating procedures. Once the procedure is generated, PHA has to be performed for this process to identify the potential hazards. Corrections to the procedure have to be made to prevent the potential hazards found out in PHA. For example, according to the results of PHA, safeguards might be added to the process. Values of process variables might also be changed if they were set too high or too low. In addition, human errors in the operating procedures are inevitable so that there are usually some corrections of this kind of mistake. Whenever there is a correction necessary, the whole procedure has to be designed again and the diagram of the procedure has to be drawn again. Recently, Viswanathan et. al. [3] presented an intelligent tool for operating procedure synthesis (iTOPS) by using grafchart-based methods. The input to iTOPS included process materials, process equipment and high level process description called Block Process Sequence Diagram (PSD) indicating the sequence of the main tasks. Then process sequence diagram (PSD) and master operation records (MOR) were automatically generated. MOR is a series of detailed operation instructions. As HAZOP is the most widely used PHA method, an automated HAZOP analysis expert s y s t e m Batch HAZOPExpert (BHE) for batch processes was developed [4-5]. Batch recipe was represented by two-tier Petri-Nets. One was Recipe Petri Net (RPN) representing the sequence of main tasks such as reaction, separation and etc. Associated with each main task, there was a lower level Petri Net called Task Petri Net (TPN) containing a series of subtasks. Associated with each subtask, there was a digraph-based qualitative model that captures the cause-effect relationships of the variables in the subtask. The subtask digraph models are process-generic, which means that they can be used in different processes. Since PSD contains the process specific information such as process materials, equipment, operating conditions and so on that is required by BHE for performing HAZOP analysis, Sriniwasan and Zhao et. al. [6] integrated iTOPS and BHE together. In the integrated system, once the OPS is done, i. e. after the PSD and MOR are generated, the PSD-based representation of batch processes is translated into the Petri Net-based representation required by BHE. Meanwhile, during the translation, the digraph models are automatically associated with the corresponding subtasks. Then BHE can perform HAZOP analysis based on the process information derived from OPS. To get process-specific knowledge such as the hazard critical properties of the process materials, material and equipment databases are connected with the integrated system. Material interaction database is used to capture hazards caused by possible side reactions. Block PSD can be determined based on the chemist's process description, iTOPS
789 generates PSD and MOR based on the Block PSD. Through the OPS-PHA interface, block PSD and PSD are converted into the Petri Nets., and therefore, process specific information flows from PSD into Petri Nets. Then BHE can automatically analyze all the potential hazards resulting from abnormal deviations to the process variables, and the final HAZOP analysis report is output to a Word file. 3. I N D U S T R I A L A P P L I C A T I O N S OF ITOPS AND
BHE
The industrial applications are based on the intelligent systems iTOPS and BHE mentioned above. This paper focuses on the applications of these two intelligent systems in pharmaceutical industry. In the following, one large-scale pharmaceutical industrial case study is addressed. A B C
C + E +F waste
y
I
~k
el F ~~ Reaction
C + E +F waste
E
~
R2 ~_~ Vacuum Distillation
C + E +F waste
I=l E
R2 Vacuum Distillation
Vacuum Distillation
C
~ ~] F1 "]Filtratioi
E waste
I Vacuum R3 "l Distillation
R4 Reaction ~
K
~ H waste
I
+ G Disposal
H F2 Filtration
R6 IVacuum Distillation
R5 Neutralization
I
~
H waste
R6 Vacuum Distillation
[
L M ~ N
R7 Extraction
+
waste
R7
I
Extraction ]
,--
p
q, waste Fig. 1 Process flow diagram
This industrial case study compromises of OPS and HAZOP analysis of a large-scale pharmaceutical process. According to the chemist's process description, to produce the final product P, thirteen tasks including two main exothermic reactions, one neutralization, six vacuum distillations, two filtrations and two extractions were performed. The first reaction is:
790 A+B
(1)
c+D ) O + G
Then the intermediate product 0 was transformed to the final product in the second main reaction: O+J
(2)
I )P+L
Separations were used to separate out the solvents, catalysts and by-products involved in this process. Totally, twenty eight process materials and nine pieces of equipment such as reactors and filters were involved in the process. Fig. 1 shows the process flow diagram of the process.
Operating Procedure Synthesis by iTOPS To perform OPS, iTOPS provides friendly user interface/workspace to specify the following process-specific information: process materials with necessary physical properties such as density, molecular weight and so on; process equipment with capacity information; process chemistry (reactions and separations) and the high level process description - Block PSD. The workspace for the property specification of a process material is illustrated in Fig. 2. Once the process specific information is input, OPS can be done in the order of 2-3 minutes by iTOPS. According to the OPS results, 148 operations were generated by iTOPS in order to complete the thirteen tasks. These operations include twenty three different kinds of operations such as charge, heat, cool, hold, distillation, filtration, extraction and so on. Due to the length limitation of the paper, the whole picture of the PSD generated by iTOPS can not be shown in this paper.
Fig. 2 Workspace for material property specification
HAZOP Analysis by BHE BHE converts the Block PSD and PSD into Petri Nets-based representation of the product recipe. In HAZOP, hazard-critical properties of process materials and equipment are necessary. The hazard-critical properties of a process material include quantitative properties such as boiling point, flash point, melting point and decomposition point as well as qualitative properties such as flammability nature, corrosive nature, toxic nature
791 and so on. The properties of process equipment needed by HAZOP include the design temperature and design pressure. Most of the quantitative information can automatically derived from the database if the material or equipment is available in the database. However, the qualitative information and the quantitative information of the materials and equipment not present in the database should be specified by users. BHE instructs users to specify the information it needs. Instructions BHE gives are then listed on a spreadsheet. For example, if the decomposition temperature of material A is not available in the database, the instruction such as "Please specify the decomposition temperature of A" will be given by BHE. Clicking on the instruction can activate a property dialog of the material where users can specify the decomposition temperature. Similarly, dialog-based interfaces are designed to help users complete all other instructions BHE gives. Table 1 Node-1 HAZOP Results Reported by the Team and BHE Deviations Safety Hazards from the Team Safety Hazards from BHE Low None 1. Incomplete reaction 2. Highly hazardous reactant not Temperature consumed Vaporization of volatile materials High none Temperature Low Agitation Too low an agitation may cause 1. Incomplete reaction an incomplete reaction within 2. Highly hazardous reactant not the specified time consumed 3. Non-uniform concentration due to poor mixing 4. Poor temperature distribution High Agitation None Level close to the maximum volume Short Time Too short a reaction will result 1. Incomplete reaction in incomplete reaction 2. Highly hazardous reactant not consumed High Level None Close to the maximum volume High None Operator exposure to hazardous Concentration material B when sampling As PSD only indicates the sequence of operations, it is hard to accurately identify the position of a reaction. BHE also gives an instruction to ask users to insert necessary reaction subtasks into the Petri Nets of the corresponding reaction tasks. After all the instructions given by BHE are completed by users, HAZOP can be automatically performed by BHE in about 15 minutes for this case study. Totally 1148 possible high/low/zero deviations to the process variables were analyzed by BHE, and 68 potential safety hazards were reported. All of the twelve safety hazards reported by the HAZOP team were captured by BHE. Table 1 compares the safety hazards of Task-1 reported by the team and BHE. From the table, it can be found that some important safety hazards were neglected by the team. For example, the reactant B is a highly hazardous material. To control the product quality, sampling is required in the operating procedure. Therefore, BHE indicates a
792 potential operator exposure to hazardous material B during sampling. However, the team was not able to flag this out. In equivalent situation, BHE can reproduce similar results. For example, BHE reported that high Temperature could cause emission of volatile materials from condenser in all of the six vacuum distillation tasks while the human team only flagged this hazard in Task-2. Up to now, sixty industrial processes have been generated by using iTOPS in one of our industrial partners since January 1998. Twelve pharmaceutical processes have been tested with BHE, of which eight processes were tested with the integrated system. Similar observations were obtained. Due to the length limitation of the paper, more case study results can not be shown here. If interested, please contact authors. 4. CONCLUSIONS Compared with the OPS and HAZOP analysis done by engineers without the aid of such automation tools, up to 50% savings in time and effort have been seen in all of the industrial applications. BHE generated more consistent HAZOP results in the process variable deviation analysis than the team did. Documentation of PSD and MOR becomes much easier. BHE considered many more potential hazardous scenarios thus performing a more comprehensive analysis. Faster and safer process development can be achieved by using the intelligent tools- iTOPS and BHE. REFERENCES
1. Basu P. K., "Pharmaceutical Process Development is Different!", Chem. Eng. Progress, 98(9) 75-82 (1998). 2. Center for Chemical Process Safety (CCPS), Guidelines for hazards evaluation procedures, 2nd edition with worked examples. American Institute of Chemical Engineers, New York, 1992. 3. Viswanathan S., Johnsson C., Srinivasan R., Venkatasubramanian V., and Arzen K., Automating operating procedure synthesis for batch processes - I&II Implementation and application, Computers and Chemical Engineering, 22(11) 1673-1696 (1998). 4. Srinivasan R., Venkatasubramanian V., Automating HAZOP analysis of batch chemical plants: Part II Algorithms and Application, Computers and Chemical Engineering, 22 (9) 1357-1370 (1998). 5. J. Zhao, S. Viswanathan, R. Srinivasan and V. Venkatasubramanian, Automated process hazard analysis of batch chemical plants, AIChE Annual Meeting, Miami, Florida, Nov. 1998. 6. S. Viswanathan, J. Zhao and V. Venkatasubramanian, Integrating operating procedure synthesis and hazards analysis automation tools for batch processes, Computers & Chem. Eng., 23 $747-750, (1999).
787
Industrial Applications o f Intelligent Systems for O p e r a t i n g P r o c e d u r e Synthesis and Hazards Analysis for Batch Process Plants Jinsong Zhao, Shankar Viswanathan and Venkat Venkatasubramanian * Laboratory for Intelligent Process Systems, School of Chemical Engineering, Purdue University, West Lafayette, IN 47907 Operating Procedure Synthesis (OPS) and Hazard and Operability analysis (HAZOP) are two important areas in batch process development. They are time-consuming and knowledge-intensive, and could benefit from automation. Recently, two knowledge-based systems for automating OPS and HAZOP, called iTOPS and BHE respectively, were developed and integrated. The integrated system has the capability to perform consistent HAZOP analysis based on the information in the generated operating procedures. In this paper, the integrated system is illustrated using one large-scale pharmaceutical industrial case study. 1. I N T R O D U C T I O N The increasing trend towards the production of higher-value-added products by chemical or pharmaceutical industries has stimulated considerable interest in batch processes. In the current environment of intense market competition, batch process industries stand to benefit from faster and safer process development. One day of delay to the market may cost millions in potential profits since today's blockbuster drugs may make $500 million to $1 billion a year [ 1]. In the process of batch process development, two important areas in batch process development that take considerable amount of time and effort are operating procedure synthesis (OPS) and process hazards analysis (PHA) as they are often manually performed. Thus there exists substantial motivation to develop computer-based approaches for OPS and PHA for batch processes. In this paper, we discuss the results of fielding several industrial applications of such automated intelligent systems for OPS and PHA. 2. I N T E L L I G E N T S Y S T E M S F O R OPS A N D H A Z O P
OPS is systematic synthesis of a sequence of elementary tasks an operator needs to manage a batch process safely and optimally. PHA is the proactive identification, evaluation, and mitigation of hazards. CCPS defines hazard as "an inherent physical or chemical characteristic that has the potential for causing harm to people, property or the environment" [2]. The importance of OPS and PHA is underlined by OSHA PSM Standard 29 CFT 1910 which was enacted in 1992. This standard regulates that major chemical plants should perform PHA on a regular basis when a new process is launched or any change happens in an existing process. It also requires that at least every five years after the completion of the initial PHA, PHA shall be updated and revalidated to assure that the process hazard analysis is consistent with the current process. It is also regulated by this standard that written operating procedures that provide clear instructions for *Author to whomall correspondence shouldbe addressed
788 safely conducting activities should be developed and implemented for chemical processes covered by the standard. PHA is a time-consuming activity. It is estimated that the complete PHA of a typical process could take 1-8 weeks for a PHA team. Typically, there are tens to hundreds of processes per year to review in a chemical or pharmaceutical company, it is easy to imagine how hard the PHA team would work. Moreover, it is considerably difficult for the team to keep the process hazard analysis consistent and systematic during so long and tiresome a period of time for PHA. An intelligent system which helps in automating the entire PHA study would reduce the time, effort and money involved in a review, make the review more thorough and detailed with human errors minimized. OPS is also a time-consuming activity. It is often manually performed according to the experiences of process engineers. The experience-based method is also short of consistency in process development because different engineers can generate different versions of operating procedures. Once the procedure is generated, PHA has to be performed for this process to identify the potential hazards. Corrections to the procedure have to be made to prevent the potential hazards found out in PHA. For example, according to the results of PHA, safeguards might be added to the process. Values of process variables might also be changed if they were set too high or too low. In addition, human errors in the operating procedures are inevitable so that there are usually some corrections of this kind of mistake. Whenever there is a correction necessary, the whole procedure has to be designed again and the diagram of the procedure has to be drawn again. Recently, Viswanathan et. al. [3] presented an intelligent tool for operating procedure synthesis (iTOPS) by using grafchart-based methods. The input to iTOPS included process materials, process equipment and high level process description called Block Process Sequence Diagram (PSD) indicating the sequence of the main tasks. Then process sequence diagram (PSD) and master operation records (MOR) were automatically generated. MOR is a series of detailed operation instructions. As HAZOP is the most widely used PHA method, an automated HAZOP analysis expert s y s t e m Batch HAZOPExpert (BHE) for batch processes was developed [4-5]. Batch recipe was represented by two-tier Petri-Nets. One was Recipe Petri Net (RPN) representing the sequence of main tasks such as reaction, separation and etc. Associated with each main task, there was a lower level Petri Net called Task Petri Net (TPN) containing a series of subtasks. Associated with each subtask, there was a digraph-based qualitative model that captures the cause-effect relationships of the variables in the subtask. The subtask digraph models are process-generic, which means that they can be used in different processes. Since PSD contains the process specific information such as process materials, equipment, operating conditions and so on that is required by BHE for performing HAZOP analysis, Sriniwasan and Zhao et. al. [6] integrated iTOPS and BHE together. In the integrated system, once the OPS is done, i. e. after the PSD and MOR are generated, the PSD-based representation of batch processes is translated into the Petri Net-based representation required by BHE. Meanwhile, during the translation, the digraph models are automatically associated with the corresponding subtasks. Then BHE can perform HAZOP analysis based on the process information derived from OPS. To get process-specific knowledge such as the hazard critical properties of the process materials, material and equipment databases are connected with the integrated system. Material interaction database is used to capture hazards caused by possible side reactions. Block PSD can be determined based on the chemist's process description, iTOPS
789 generates PSD and MOR based on the Block PSD. Through the OPS-PHA interface, block PSD and PSD are converted into the Petri Nets., and therefore, process specific information flows from PSD into Petri Nets. Then BHE can automatically analyze all the potential hazards resulting from abnormal deviations to the process variables, and the final HAZOP analysis report is output to a Word file. 3. I N D U S T R I A L A P P L I C A T I O N S OF ITOPS AND
BHE
The industrial applications are based on the intelligent systems iTOPS and BHE mentioned above. This paper focuses on the applications of these two intelligent systems in pharmaceutical industry. In the following, one large-scale pharmaceutical industrial case study is addressed. A B C
C + E +F waste
y
I
~k
el F ~~ Reaction
C + E +F waste
E
~
R2 ~_~ Vacuum Distillation
C + E +F waste
I=l E
R2 Vacuum Distillation
Vacuum Distillation
C
~ ~] F1 "]Filtratioi
E waste
I Vacuum R3 "l Distillation
R4 Reaction ~
K
~ H waste
I
+ G Disposal
H F2 Filtration
R6 IVacuum Distillation
R5 Neutralization
I
~
H waste
R6 Vacuum Distillation
[
L M ~ N
R7 Extraction
+
waste
R7
I
Extraction ]
,--
p
q, waste Fig. 1 Process flow diagram
This industrial case study compromises of OPS and HAZOP analysis of a large-scale pharmaceutical process. According to the chemist's process description, to produce the final product P, thirteen tasks including two main exothermic reactions, one neutralization, six vacuum distillations, two filtrations and two extractions were performed. The first reaction is:
790 A+B
(1)
c+D ) O + G
Then the intermediate product 0 was transformed to the final product in the second main reaction: O+J
(2)
I )P+L
Separations were used to separate out the solvents, catalysts and by-products involved in this process. Totally, twenty eight process materials and nine pieces of equipment such as reactors and filters were involved in the process. Fig. 1 shows the process flow diagram of the process.
Operating Procedure Synthesis by iTOPS To perform OPS, iTOPS provides friendly user interface/workspace to specify the following process-specific information: process materials with necessary physical properties such as density, molecular weight and so on; process equipment with capacity information; process chemistry (reactions and separations) and the high level process description - Block PSD. The workspace for the property specification of a process material is illustrated in Fig. 2. Once the process specific information is input, OPS can be done in the order of 2-3 minutes by iTOPS. According to the OPS results, 148 operations were generated by iTOPS in order to complete the thirteen tasks. These operations include twenty three different kinds of operations such as charge, heat, cool, hold, distillation, filtration, extraction and so on. Due to the length limitation of the paper, the whole picture of the PSD generated by iTOPS can not be shown in this paper.
Fig. 2 Workspace for material property specification
HAZOP Analysis by BHE BHE converts the Block PSD and PSD into Petri Nets-based representation of the product recipe. In HAZOP, hazard-critical properties of process materials and equipment are necessary. The hazard-critical properties of a process material include quantitative properties such as boiling point, flash point, melting point and decomposition point as well as qualitative properties such as flammability nature, corrosive nature, toxic nature
791 and so on. The properties of process equipment needed by HAZOP include the design temperature and design pressure. Most of the quantitative information can automatically derived from the database if the material or equipment is available in the database. However, the qualitative information and the quantitative information of the materials and equipment not present in the database should be specified by users. BHE instructs users to specify the information it needs. Instructions BHE gives are then listed on a spreadsheet. For example, if the decomposition temperature of material A is not available in the database, the instruction such as "Please specify the decomposition temperature of A" will be given by BHE. Clicking on the instruction can activate a property dialog of the material where users can specify the decomposition temperature. Similarly, dialog-based interfaces are designed to help users complete all other instructions BHE gives. Table 1 Node-1 HAZOP Results Reported by the Team and BHE Deviations Safety Hazards from the Team Safety Hazards from BHE Low None 1. Incomplete reaction 2. Highly hazardous reactant not Temperature consumed Vaporization of volatile materials High none Temperature Low Agitation Too low an agitation may cause 1. Incomplete reaction an incomplete reaction within 2. Highly hazardous reactant not the specified time consumed 3. Non-uniform concentration due to poor mixing 4. Poor temperature distribution High Agitation None Level close to the maximum volume Short Time Too short a reaction will result 1. Incomplete reaction in incomplete reaction 2. Highly hazardous reactant not consumed High Level None Close to the maximum volume High None Operator exposure to hazardous Concentration material B when sampling As PSD only indicates the sequence of operations, it is hard to accurately identify the position of a reaction. BHE also gives an instruction to ask users to insert necessary reaction subtasks into the Petri Nets of the corresponding reaction tasks. After all the instructions given by BHE are completed by users, HAZOP can be automatically performed by BHE in about 15 minutes for this case study. Totally 1148 possible high/low/zero deviations to the process variables were analyzed by BHE, and 68 potential safety hazards were reported. All of the twelve safety hazards reported by the HAZOP team were captured by BHE. Table 1 compares the safety hazards of Task-1 reported by the team and BHE. From the table, it can be found that some important safety hazards were neglected by the team. For example, the reactant B is a highly hazardous material. To control the product quality, sampling is required in the operating procedure. Therefore, BHE indicates a
792 potential operator exposure to hazardous material B during sampling. However, the team was not able to flag this out. In equivalent situation, BHE can reproduce similar results. For example, BHE reported that high Temperature could cause emission of volatile materials from condenser in all of the six vacuum distillation tasks while the human team only flagged this hazard in Task-2. Up to now, sixty industrial processes have been generated by using iTOPS in one of our industrial partners since January 1998. Twelve pharmaceutical processes have been tested with BHE, of which eight processes were tested with the integrated system. Similar observations were obtained. Due to the length limitation of the paper, more case study results can not be shown here. If interested, please contact authors. 4. CONCLUSIONS Compared with the OPS and HAZOP analysis done by engineers without the aid of such automation tools, up to 50% savings in time and effort have been seen in all of the industrial applications. BHE generated more consistent HAZOP results in the process variable deviation analysis than the team did. Documentation of PSD and MOR becomes much easier. BHE considered many more potential hazardous scenarios thus performing a more comprehensive analysis. Faster and safer process development can be achieved by using the intelligent tools- iTOPS and BHE. REFERENCES
1. Basu P. K., "Pharmaceutical Process Development is Different!", Chem. Eng. Progress, 98(9) 75-82 (1998). 2. Center for Chemical Process Safety (CCPS), Guidelines for hazards evaluation procedures, 2nd edition with worked examples. American Institute of Chemical Engineers, New York, 1992. 3. Viswanathan S., Johnsson C., Srinivasan R., Venkatasubramanian V., and Arzen K., Automating operating procedure synthesis for batch processes - I&II Implementation and application, Computers and Chemical Engineering, 22(11) 1673-1696 (1998). 4. Srinivasan R., Venkatasubramanian V., Automating HAZOP analysis of batch chemical plants: Part II Algorithms and Application, Computers and Chemical Engineering, 22 (9) 1357-1370 (1998). 5. J. Zhao, S. Viswanathan, R. Srinivasan and V. Venkatasubramanian, Automated process hazard analysis of batch chemical plants, AIChE Annual Meeting, Miami, Florida, Nov. 1998. 6. S. Viswanathan, J. Zhao and V. Venkatasubramanian, Integrating operating procedure synthesis and hazards analysis automation tools for batch processes, Computers & Chem. Eng., 23 $747-750, (1999).