The Integration of HAZOP Expert System and Piping And

7/29/2019 The Integration of HAZOP Expert System and Piping And

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Process Safety and Environmental Protection 8 8 ( 2 0 1 0 ) 327334

Contents lists available at ScienceDirect

Process Safety and Environmental Protection

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / p s e p

The integration of HAZOP expert system and piping and

instrumentation diagrams

Lin Cui a,1, Jinsong Zhao a,, Ruiqi Zhang b,2

a Department of Chemical Engineering, Tsinghua University, Beijing 100084, Chinab Sinopec Engineering Institute, Chaoyang District, Beijing 100101, China

a b s t r a c t

The main purpose of hazard and operability (HAZOP) analysis is to identify the potential hazards in the process

design which nowadays is generally developed through a computer aided design (CAD) package. Due to the time

and effort consuming nature of HAZOP, it is not done in every engineering firm for every design project. To make

HAZOP an integral part of process design, an integration framework is proposed in this paper to seamlessly integrate

the commercial process design package Smart Plant P&ID (SPPID, Intergraph) with one of the HAZOP expert systems

(named as LDGHAZOP) developed by authors. This integration makes it possible to perform HAZOP analysis easily

at anytime of the whole lifecycle of a chemical plant as long as the process design is available, which might help the

improvement of design quality. One industrial case study is used to illustrate the ability of the integrated system.

2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Keywords: HAZOP; P&ID; Process design; Expert system

1. Introduction

Hazard andoperability (HAZOP) study is a widelyused process

hazard analysis (PHA) approach (Swann and Preston, 1995). It

is usually applied when detailed process piping and instru-

mentation diagrams (P&ID) are ready but not yet frozen, so

that the potential process hazards can be identified, evalu-

ated and mitigated in the design stage (Venkatasubramanian

et al., 2000). But HAZOP study is still an expensive process

due to its highly time consumption and effort consumption

for the HAZOP human team (Khan and Abbasi, 1997a). There-

fore, many engineering firms prefer limiting the number ofHAZOP studies which are usually performed at the end of

the design project. However, this approach carries a possi-

ble risk of identifying a safety risk late in the design stage,

which might result in costly changes, for example, re-ordering

of equipments. Therefore, the sooner the HAZOP is done, the

less time and resources will be spent on the changes resulted

from HAZOP findings. On the other hand, some of the chemi-

cal plant managers do not accept HAZOP yet because HAZOP

Corresponding author. Tel.: +86 10 62783109.E-mail addresses: [email protected](L. Cui), [email protected] (J. Zhao), [email protected]

(R. Zhang).Received1 January2010; Receivedin revisedform 19 April 2010; Accepted 27 April2010

1 Tel.: +86 10 62783109.2 Tel.: +86 10 84876913.

studies require the key personnel away from their daily jobs

to attend the HAZOP meetings which might last for weeks

or months. Sometime it is almost a mission-impossible for

chemical plants running lean.

Many HAZOP automatic reasoning approaches (McCoy et

al., 1999; Kanga et al., 2003; Bartolozzi et al., 2000) and expert

systems (Zhao et al., 2005; Viswanathan et al., 1999; Khan and

Abbasi, 1997b) have been developed in thepast two decades to

improve the HAZOP teams efficiency. Although the automatic

analysis process done by the expert systems is quick, the pro-

cess of data input to theexpert systems is long because HAZOP

analysis is also a highly data consuming process. The processtopological information, process chemistry, equipment design

parameters and chemical material properties are needed to

run the expert systems. Computer aided design (CAD) had

been introduced to chemical process design activity for a long

time all around the world, therefore most of the process spe-

cific information is available in the CAD system used by the

process designers. Unfortunately, directly utilizing the CADs

database is rarely reported in the chemical process field. A

0957-5820/$ see front matter 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.doi:10.1016/j.psep.2010.04.002
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328 Process Safety and Environmental Protection 8 8 ( 2 0 1 0 ) 327334

Fig. 1 Chemical process life cycle.

great deal of extra effort on manual transformation and reor-ganization of the neededdatais still needed in order to use the

automaticHAZOP systems.This dilemma makes it rather diffi-

cult to agilely perform HAZOP study during the process design

stage where frequent changes might be made to the design.

In addition, manual input of the process specific data into

a HAZOP expert system is prone to human error. Therefore,

there exists a considerable motivation to automatically gen-

erate HAZOP reports from the process design data.

Most of the nowadays widely used commercial process

design software packagesprovide external softwareinterfaces

in various forms, which make it feasible for other programs

to communicate with them, extract the process design infor-

mation, or manipulate the data within them. An integrationframework was proposed centering on design rationale and

integrated tools by Zhao et al. (2004) for safer batch chem-

ical processes. A HAZOP expert system empowered with

such information extracting capability can perform automatic

analysis repeatedly and frequently in the whole lifecycle of

a chemical plant shown in Fig. 1, especially in the design

stage, the operation stage and the modification stage, which

might provide more reliable life long guarantee of process

safety.

Although combining HAZOP expert system with process

CAD packages is necessary, relevant researches or develop-

ment attempts are rarely reported. An et al. (2009) proposed

methods for indentifying the plant items boundaries and theinstruments from P&ID created through Intergraphs Smart

Plant P&ID (SPPID) process design package. As a result, two

computer tools were developed based the methods. One

was to help with the task of identifying hazards related to

maintenance work and the other was to carry out cause

and effect analysis automatically. To the best knowledge

of authors, HAZID Technologies Ltd. partners with Inter-

graph Corporation, a worldwide leader in process plant

design software for the integration of the HAZIDs HAZOP

expert system and the SPPID (Joop, 2007). However, their

detailed integration methodology has not been readily avail-

able yet.

Thepurposeof this paper is to present an approach of inte-grating a process design package with the layered directed

graph (LDG) model based HAZOP expert system (LDGHAZOP)

developed by authors for continuous petroleum, petrochemi-

cal and chemical processes.

Fig. 2 The framework of LDGHAZOP.

2. Brief introduction of SPPID and

LDGHAZOP

2.1. SPPID

As a key part of the Intergraph SmartPlant Enterprise, the

chemical process design package SmartPlant P&ID (SPPID)

is an asset-centric, rule-driven CAD system that can help to

efficiently create and improve plant configurations. Running

in a clientserver environment, the software focuses on the

plant data instead of on drafting, with all P&ID information

stored in the data model, a central database, which brings

many conveniences for accessing its internal data for other

applications such as safety analysis, hydraulic analysis opti-

mization and process optimization. HAZID Technologies Ltd.has partnered with Intergraph Corporation for the integration

of the HAZIDs HAZOP expert system and SPPID. However,

their detailed integration methodology has not been readily

available yet. In addition, the prototype of the HAZIDs HAZOP

expert system was initiated by researchers from Loughbor-

ough Universitys chemical engineering and computer science

departments. In that system, the plant was described as a

network of interconnected units and each unit is described

in terms of a model based on signed directed graphs (SDGs)

(McCoy et al., 1999).

2.2. LDGHAZOP

To overcome the shortcomings of signed directed graph (SDG)

model in knowledge representation, LDG model was proposed

by the authors (Cui et al., 2008). Basically LDG model quali-

tatively captures cause-effect relationships between process

deviations generated by using all types of HAZOP guidewords.

Recently a HAZOP expert system named LDGHAZOP has been

developed based on LDG models. LDGHAZOP is a web based

multi-client expert system for HAZOP study developed with

Java programming language mainly. It consists of document

(DOC) module, layered directed graph (LDG) module, database

abstract module and some accessory modules as shown in

Fig. 2.

The DOC module is an intelligent word processing module,

which can assist the experts editing HAZOP results, making

the printable HAZOP reports and so on. All the text gen-

erated during the HAZOP process is saved and reorganized

by DOC module. Based on those data and utilizing a certain


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Process Safety and Environmental Protection 8 8 ( 2 0 1 0 ) 327334 329

search algorithmic, the DOC module also provides hints dur-

ing HAZOP analysis to the human experts. Aided by the hints

about different HAZOP components such as causes, conse-

quences and suggestions, the efficiency and completeness of

the HAZOP analysis can be improved significantly.

The LDG module consists of several sub-modules. LDG

model library contains a serial of LDG models of various types

of equipment which are organized in a tree-like structure. Theprocess description (PD) sub-module describing the chemical

processes to be studied is used to generate process specific

data packages also named as reasoning configuration (RC)

packages. Based on the data defined by the PD module, a

model matching algorithmic is developed to match each piece

of process equipment to a proper LDG model in theLDG model

library. The models are then linked based on the equipment

interconnection information specified in the RC packages.

After the LDG models are linked, the reasoning machine sub-

module can be used to perform automatic HAZOP analysis.

However, the analysis results generated by LDGHAZOP are still

far from completion before thehuman experts validation and

modification. These reasoning results are stored in a separatereference base (REF base) and utilized by the DOC module.

There are two usages of the REF base. Firstly, DOC module

cangeneratethe HAZOP report bystraightlyimporting theREF

base whenever it is required by the human experts. Secondly,

the data in the REF base is used to make intelligent online

hints during the discussion of the human experts.

An external database system is used to store all the data

including HAZOP analysis results, LDG library, REF base, etc.

The database operations are performed via the database

abstract (DBA) module based on SQL language and Java

Database Connector (JDBC). The DBA module envelops the

detail operations on the database system, supplying high level

interfaces for performing data manipulations.Besides the core modules described above, some accessory

modules such as userinterface (UI) module, usermanagement

module are all shown in the top block in Fig. 2. Although those

modules are necessary for the system, they are not detailed

here because their functionshave nothing to do with thetopic

of this paper.

3. The framework and implementations ofthe integration

3.1. The framework

To integrate LDGHAZOP and SPPID, a heterogeneous systems

integration framework is presented as shown in Fig. 3. There

are five modules for the data transfer and data process-

ing between the LDGHAZOP and SPPID. The SPPID Interface

(SPI) module accesses process specific information in the

SPPIDs database. Then the Data Acquisition (DAQ) module

classifies and reorganizes the data into five categories includ-

ing equipment, valve, chemical, instrument, interlink. The

normalization (NORM) module performs data regularization

operations which translate the items by using ontologies

(Zhao et al., 2008) and unify units. The Interface module (IM)

acts as a bridge which allows some other application accessthe extracted data via Intranet or Internet. Those four mod-

ules should be all deployed on the host computer in which

SPPID resides. Finally LDGHAZOPs host gets the SPPIDs data

through the RC package generator (RCPG) module.

3.2. Data acquisition module

The main function of the data acquisition module is actually

data extraction of the corresponding object instances inside

P&IDs for obtaining the process specific information required

byLDGHAZOP. In SPPID, allthe drawings arestoredin a generic

database system provided by other database manufacturers.

Currently there are two database supported by SPPID: Oracle

9i and Microsoft SQL Server. As the database adopted by

SPPID is a generic database management system (DBMS),

accessing the database directly bypassing the SPPID might be

the most rapid data acquisition method. Unfortunately, this

methodis not recommendedfor at least two reasons. The first

reason is that the detailed inner data structure is not read-

ily available, which makes this method hardly practical. The

other reason is that this method is not officially supported by

INTERGRAPH, which might lead to compatibility problems.

3.2.1. The SPPIDs Llama interface

SPPID provides developers with a set of Application Program-

ming Interfaces (APIs) named as Llama that are officiallysupported by Intergraph Corp. for data communication

between SPPID and other systems. The Llama APIs are pre-

sented under the middleware technology framework named

as Microsoft COM. Their functionalities are defined implic-

itly through the methods associated with them. They provide

standard mechanisms of interactions between SPPID and

other systems or programs regardless of the program lan-

guages, the type of machine in a computer network or the

operating system used. Under the mechanisms, the inner data

of SPPID is encapsulated into a set of classes according to

Object-Oriented programming principles.

The Llama classes have a hierarchy that organizes the

classes in an class-tree representing the objects used in theP&IDs. In the Llama object model, the top level abstract classs

name is Model Item (MI). Class MI has a set of subclasses

organized as a class tree corresponding to each kind of the

drawing object. One of the subclasses of class MI, named as

Plant Item (PI), represents all the concrete objects such as

equipment, instruments, piping components and so on. PI has

a set of subclasses representing the above objects respectively.

For example a water tank is an instance of the class Vessel, a

subclass of Equipment which is a subclass of PI, once again a

subclass of MI.

3.2.2. Data acquisition method

The DAQ classifies the data sets extracted from SPPIDs P&IDsinto five categories as shown in Fig. 3, of which equipment

infomation, interlinks among equipments are indispensable

for automatic HAZOP reasoning. The equipment type infor-

mation is used to match LDG models, while the interlinks

are primarily utilized to link the corresponding LDG mod-

els. Although the other data categories including equipment

detail description, instrument and chemical are optional, they

can aid the reasoning machine to improve the quality of the

HAZOP analysis results.

Equipment information is primarily used in the LDG model

matching (LDGMM) process preformed by the RCPG module.

The LDG library stores a set of organized LDG models corre-

sponding to a certain type of equipment or operation. The

LDGMMs objective is to select the most suitable LDG model

for the equipment found in P&IDs. To select the model accu-

rately, the extracted equipments type information is firstly

applied to roughly determine the category of the model, and


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Fig. 3 The framework of the integrated system.

then the name and the properties of the equipment are uti-

lized to distinguish the exact LDG model from the library.The properties of the equipments, including the contained

chemicals, the materials, design parameters, working condi-

tions and so on, are needed by the reasoning machine and

the DOC module, so that they should be extracted and stored,

too.

The equipments are most important for HAZOP study as

mentioned above. The information about the equipment type,

constructionmaterials, operating conditionparameters, etc.is

all needed. Llama defines a class named Equipment derived

from the class PI, having a set of properties correspond-

ing to the information listed above. Furthermore, the class

Equipment has a set of subclasses corresponding to various

types of equipments such as Vessel, Heat Exchanger, etc.,while each subclass contains more specific properties. For the

convenience of programming, all equipment are regarded as

Equipment at firstand the generic properties of Equipment

areextracted,and then, based on theextracted data theequip-

ments type can be determined and the specific properties

corresponding to the specific equipments types are extracted.

This step by step data extraction methodis usually used in the

whole extracting process.

Another important item group is the interconnections

amongequipments, instrumentsand valves.The interconnec-

tion data is firstly used to link theselectedLDG models, for the

sake of the cross-model reasoning performed by the LDG rea-

soning machine. Based on the linked models, the reasoningmachine can search the HAZOP deviation propagation routes

and deduce the aftermaths of each deviation in the equipment

network. Secondly, the interconnection data helps define the

service or action targets of the instruments and valves.

Although it is easy for human beings to visually judge

whether two items are connected together by directly watch-

ing the P&ID, its sometimes rather difficult for computer to

recognize the interconnections as the equipments are often

connected indirectly, via a chain consisting of instruments or

piping components such as control valves andflanges. We use

a browse strategy simulating human cognitive behavior to deal

with it. The extraction of interconnection is an equipment-

centered approach. By browsing all of P&IDs, all equipmentcan be listedby theDAQby using the methoddescribed above.

To find all items andconnections attachedto each equipment,

the DAQ searches the items along all the pipelines or signal

lines departing from the equipment. When an item is founded

along a certain route, the DAQ makes a decision depending on

the items type. If it is equipment, the searching along thisroute is done, and searching along another route is started

unit all routes from the equipment are searched. Otherwise

the searching is continued until another equipment is found

or an end point is reached. Finally, the DAQ creates object

instances for the interconnections, instruments and valves

found in theabove routes. In many cases,the pipingand signal

lines interconnections constitute a complicated network. The

strategy described above will sometimes encounter infinite

loops. However, as HAZOP study does not need high resolu-

tion of the interconnections map, only the networks major

profile needs to be extracted. Recording all searched routes

and skippingthem whenencountering themagain can resolve

this problem.Although represented by different classes in Llama, in the

DAQ module, the process accessories such as sensors, actua-

tors, controllers and the process safeguards such as alarms,

check valves and pressure relief valves are all included in the

category of instrument for the reason that they all might be

able to change the probability of abnormal situation emer-

gence. Although they are simple items, they play a key role in

hazard evaluation performed by the LDG reasoning machine.

The relationships between instruments and their effects are

described by rules, similar to the reasoning rules introduced

by An, Chung, et al. for Cause & Effect (C&E) Table generation

(An et al., 2009). For example, a check valve in a pipeline can

reduce the likelihoods of consequences caused by the devia-tion reverse flow in the pipeline. Another example is that if

three instrument elements including a level sensor, a control

valve and a controller are found linked by signal lines, then a

level control loop certainly exists and the consequence likeli-

hood of the levels deviations should be decreased. Therefore

identification and utilization of the instruments informa-

tion during automatic LDG model reasoning help improve the

results quality.

3.3. Data normalization module

The SPPID has private taxonomy and standards of data rep-

resentation while LDGHAZOP has its own taxonomy and

standards. The problems of interoperability between inter-

acting computer systems have been well researched. A

comprehensive classification of the different kinds of inter-

operability problems can be found in literature (Sheth, 1998)


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Process Safety and Environmental Protection 8 8 ( 2 0 1 0 ) 327334 331

where the system, syntactic, structural and semantic levels of

heterogeneity were depicted. The semantic level refers to the

meaning of terms in the interchange.

Although some standards were presented for system inte-

gration and data unification, such as the CAPE-OPEN for

process simulation module interoperation(Pons, 2003), Chem-

ical Industry Data Exchange Association (CIDX) eStandards

for chemical data exchange in e-Business (Lampathaki, 2009),there is no widely used one for chemical process data

exchange between heterogeneous computer systems.

To resolve the semantic heterogeneity problem between

LDGHAZOP and SPPID, a Data Map based an ontology library

is created to specify different terms from the two systems and

define the translations between the terminologies. The ontol-

ogy library being used is a chemical process safety ontology

developed by our team (Zhao et al., 2008), defining a com-

prehensive set of concepts in chemical engineering including

chemical process, unit operation, equipment, chemical mate-

rials and so on. Based on the data map, NORM module unifies

the textual data from DAQ and the units of numerical data in

SPPID are also standardized into international units which areadopted by LDGHAZOP.

3.4. Interface

For the reason that the SPPID system normally resides in a

standalone server, while LDGHAZOP is usually deployed on

another host, the integration is a distributed heterogeneous

system interlink. There are several data transfer frameworks

coping with that situation, in which the Service Oriented

Architecture (SOA) is a widely adopted one (Bell, 2002). Under

SOA, the data operations are presented in form of standard-

ized services, while any application can request the services

via computer network to access the data despite of the appli-cations platforms and program languages. In our integration,

the Representational State Transfer (REST), one of the SOA

implementations (Fielding and Taylor, 2002), is applied. REST

affords the services in the form of Unified Resource Identifier

(URI), allowing any request by using the Hypertext Transfer

Protocol (HTTP), which is widely used on World Wide Web

(WWW). REST also uses XML as its data representation lan-

guage, being capable of containing various kinds of data.

Under such a mechanism, LDGHAZOP can access the data

through IM without any external efforts on data transferring

details.

3.5. The RC package generator

Data transferred through IM finally reaches the RCPG mod-

ule and a RC package is generated to get ready for automatic

HAZOP reasoning. The RCPGs main work is to perform

LDGMM process, matching LDG models, as mentioned above.

During this process, RCPG compares the extracted equip-

ments name,type andpropertieswiththe contents in theLDG

model library. The textual comparison algorithm which calcu-

lates the Levenshtein Distance (Levenshtein, 1965) is adopted

here to determine the textual similarities. The chemicals and

the equipments function, if provided, are also considered in

the matching process. However, the automatic model match-

ing is failed or partly failed in some situation due to the lack

of data or LDG models, manually matching the models maybe

necessary.

After data extraction, the whole P&IDs are simplified into

a data set with an LDG model-centered structure shown in

Fig. 4 Extracted data structure.

Fig. 4. Every LDG model has a set of properties describing

the corresponding equipments. No matter how complex the

interconnection between two equipments is, there is only a

single connector linking the matched LDG model. The other

accessory items such as instruments and piping components

are treated as the attachments of the equipments. They

are classified into two categories. The first category includes

attachments with an explicit host and the second one cov-

ers attachments without an explicit host. The attachmentswith an explicit host are the items linked directly to the

equipments such as level sensors. The attachments with-

out an explicit host means they are linked to the connector

between the equipments such as a flow meter attached to a

pipeline, through which two equipments may be connected.

For those items connected to several equipments simulta-

neously instead of being attached to connectors, their hosts

are indistinguishable either. They will be assigned to the sec-

ond category. In the RC package, all of the attachments are

recorded for the risk evaluation, so that they are all treated as

preliminary safeguards of the equipments.

Although much information of a process is eliminated

through the above extraction simplification method, theessential data is still kept to fulfill the requirements of HAZOP

study.

4. Case study

The P&ID shown in Fig. 5 is a process segment of a catalyst

preparation process. It is a batch operation. Firstly, the pow-

der of slightly overdosed chemical B is manually dumped into

tank T1 via a manhole on the top of T1. After the manhole is

sealed, solvent C is pumped into T1 to dissolve B. Then a lit-

tle amount of desalinized water and chemical A are pumped

into T1 to react with chemical B to make catalyst D. Once the

reaction is completed, the mixture is transferred to tank T2for precipitation separation. The waste residue in T2 is trans-

ferred into tank T3 where it is hydrolyzed by high pressure

water and thereafter drained. The remaining solution from

T2 containing catalyst D is transferred to downstream equip-

ments. During maintenance, tanks T1, T2, and T3 should be

thoroughly cleaned up.

As the original digital P&ID is confidential, the P&ID shown

in Fig. 5 is drafted by authors. There are three main equip-

ments, two motors, two agitators and several instruments,

including indicator, valves and so on, in this process segment.

To retrieve the datawithin, the LDGHAZOPprovides users with

the interface shown in Fig. 6. In Fig. 6, the left region is the

SPPIDs drawing list which is read from SPPID. Once the P&ID

is selected, its equipment list is retrieved and shown in the

right region as is shown in the figure. It can be seen that all

equipments including the five unnamed equipments whose

names are all null as shown are all listed. Then the inter-


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Fig. 5 P&ID of the catalyst preparation.

Fig. 6 The extracted equipment list from P&ID.

links among the equipments can be read and displayed using

the user interface shown in Fig. 7. The cells with L mark

means the corresponding two equipments on the vertical and

horizontal coordinates are connected directly or indirectly via

piping, instrument, signal line and so on. During the inter-

linkacquisition process the equipment attachmentsincluding

instruments, safeguards and so on are also extracted by back-

ground processes.

The whole data extraction process including scanning the

P&ID and extracting the meaningful elements lasts for 6 min

25 s to complete. During the scanning process 331 graphic

elements are scanned, most of them are pipe line segments

and signal line segments, among them three major equip-ments, five accessorial equipment parts and 24 instruments

are found and imported. The system test was executed on a

common personal computer having a dual core 2.4 GHz CPU

and 2G RAM, on which a SPPID system and the LDGHAZOP

system were installed together. The average data extrac-

tion speed is about two important equipment or instruments

per minute. Compared with the manual data preparation

done by authors, about 50% of the data input time can be

saved.

After the data retrieved by the processes described above

is fed into the LDGHAZOP via NORM and IM modules shown

in Fig. 6, the corresponding RC package is produced by the

RCPG Module. As the final data acquisition result, the RC

package contains a set of LDG modules corresponding to the

equipments in the P&ID, respectively, the attachments of the

equipment and the connections among the equipments are

also recorded by the RC package.The RC package can be represented by a graphic interface

shown in Fig. 8. In the figure, the center box represents the

currently focused equipment; the connected equipments of

it are represented by the smaller boxes placed on the left or


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ing machine. Although the automatic reasoning results are

much more than human experts results, most of them are

redundant or meaningless. After data rearrangement and

examination, the result comparison is shown in Table 1. The

lost results, which were concerned by human expert but did

not be deduced by reasoning machine, are shown in Table 2.

It can be concluded than LDGHAZOP identifies 77% of the

results from the human experts. However, the quality of theautomatic reasoning results depends on the LDG models

knowledge representation ability and the algorithmic in the

reasoning machine, which is little concerned with the system

integration.

5. Conclusions and future works

In this paper, the idea of integrating HAZOP expert systems

with commercial process design software packages SPPID is

introduced. The framework of the integration is presented

including the function modules of the integrated system, the

essential data needed by the integration, the integration strat-

egy and so on. It can be seen that the automatic featureextraction application can easily and rapidly translate the

P&IDs into data with proper structure that a HAZOP expert

system can understand and utilize. The integration will save

much effort and time in specifying the process, which signifi-

cantly eases HAZOP study in the whole lifecycle of a chemical

plant.

Although SPPID and LDGHAZOP are used as the examples

for system integration in this paper, the strategies and meth-

ods proposed in the paper are generic, such as the ontology

and the platform independent SOA, so that the system has

potential of integration with some other CAD systems without

major modifications. The future work may include extending

the ontology library, developing newdata acquisition modules

for other CAD systems. Besides that, since the current LDG

model librarys scaleis small,expanding it is also an important

future task that should be done.

Acknowledgement

The work is supported by the National Nature Science Foun-

dation of China, 20776010.

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The Integration of HAZOP Expert System and Piping And