decision-making on pipe stress analysis enabled by knowledge

Knowl Inf Syst (2007) 12(2):255–278DOI 10.1007/s10115-007-0076-4

Knowledge andInformation Systems

REGULAR PAPER

Matıas Alvarado ·Miguel A. Rodrıguez-Toral ·Armando Rosas · Sergio Ayala

Decision-making on pipe stress analysisenabled by knowledge-based systems

Received: 7 February 2006 / Revised: 9 November 2006 / Accepted: 25 November 2006Published online: 21 March 2007C© Springer-Verlag London Limited 2007

Abstract This paper presents engineering decision-making on pipe stress analy-sis through the application of knowledge-based systems (KBS). Stress analysis, aspart of the design and analysis of process pipe networks, serves to identify whethera given pipe arrangement can cope with weight, thermal, and pressure stress atsafe operation levels. An iterative process of design and analysis cycle is doneroutinely by engineers while analyzing the existing networks or while designingthe process pipe networks. In our proposal, the KBS establishes a bidirectionalcommunication with the current engineering software for pipe stress analysis, sothat the user benefits from this integration. The stress analysis knowledge base isconstructed by registering the senior engineers’ know-how. The engineers’ overallstrategy to follow up during the pipe stress analysis, to some extent contained bythe KBS, is presented. Advantages in saving engineering man-hours and useful-ness in guiding experts in pipe stress analysis are the major services for the processindustry.

Keywords Decision making · Pipe stress analysis · Knowledge-based systems

1 Introduction

Engineering practice in the oil and gas industry is a combination of complex andspecialized areas. Nowadays, the use of engineering software by engineers carry-ing out information input or manipulating decision variables is a daily issue. When

M. Alvarado (B)Centre of Research and Advanced Studies (CINVESTAV-IPN), Av. Instituto PolitecnicoNacional, 2508 Col. San Pedro Zacatenco, 07360 Mexico City, MexicoE-mail: [email protected]

M. A. Rodrıguez-Toral · A. Rosas · S. AyalaInstituto Mexicano del Petroleo, Mexico City, Mexico

256 M. Alvarado et al.

an engineering problem requires a specific solution, human engineers manipulateand interpret the input data to a software system and they decide to accept data forimplanted solutions. Whenever the engineering software does not come up withan acceptable solution, engineers manipulate variables and parameters to achievea convenient output that fulfils the mean requirements. The software tools, sim-ulators, largely act like black boxes answering yes or no based on the input datawith respect to some goal solution. However, no suggestion is delivered from thesimulators about what to do if the input data does not assure a required solution.Actually, the relevant human expert task is dealing with the change of input data,typically during an iterative process of successive approximations to solution, sothat new data gets closer to obtaining the required solution.

1.1 Interaction between human expert/engineering software

This interaction process, technical expert–engineering software–technical expert,is common in engineering practice. In engineering companies—as in diverse pro-ductive organizations—it is well known that the human technical skills are finiteresources that should be well managed in order to optimize the company workexecution. In this paper, we show that the usage of modern artificial intelligence(AI) techniques rooted in KBS for specific engineering discipline is a key aspectin boosting productivity. AI soft-computing techniques like genetic algorithms,neural networks, and fuzzy logics do flexible data manipulation and support ro-bust solutions in complex domains like those from engineering fields. Besides,the preservation of the engineering technical knowledge and improvement of theeffective usage of preserved knowledge is strategic to promote sustainability ofengineering services. Based on the knowledge management embedment in engi-neering practice, reuse of the human experts’ know-how for future works can bedone in an effective manner. In this proposal, the effective use of a KBS is withrespect to two main objectives:

• To support a successful transfer of knowledge from senior expert engineeringdesigners to junior designers.

• To allow the human expert to use knowledge skills and experience from otherswithin the organization without sacrificing the productivity and design quality.

Junior designers can use a KBS to efficiently do their job without having topass through a mean steep learning curve. Furthermore, in a broad perspective,KBS can be a powerful and versatile tool for the designer; the KBS allows himor her to reuse the know-how from other organization members, thus preservingdesign quality and augmenting productivity with the (semi-) automated supportfrom the KBS.

A point of view about the so-called expert system (ES) as the former genera-tion of current KBS is that ES was used to automate the usage of information forlow-level expert’s decision-making. Interest in old ES is renewed by the currentchallenge of dealing with complex dynamics in business process re-engineeringas well as in concurrent engineering in an automated fashion. Due to the matu-rity of the AI methods, practical aspects relevant to engineering can be addressedjointly by software developers, knowledge engineers, and domain experts through-out KBS deployment.

Decision-making on pipe stress analysis enabled by knowledge-based systems 257

1.2 Pipe network: critical lines, flexibility, and supports distribution

Opportunities were identified for KBS application in pipe stress analysis (PSA)which is a part of the design process of pipe networks. PSA is used to identifywhether a pipe arrangement will cope with weight, thermal, and pressure stress atacceptable levels under engineering design standards for safe operation. An itera-tive process of design and analysis cycle is done on the existing or to-be designedpipe networks. The KBS can support engineers during the steps of process pipedesign by suggesting pipe design rules or possible solutions, such that design con-ditions for safe and reliable operation will be accomplished in an effective manner.The more the KBS guides the verification and approval of design proposals, themore the pipe stress analysis expert’s time is saved. In addition, KBS can supportPSA experts in routine or preliminary tests of pipe network’s design reliability.

The PSA concerned in this paper, the artefact to be analyzed, is the design ofprocess plant pipe networks in the upstream or downstream petroleum industry.According to our PSA experience, decision-making bears on

• identification of critical pipelines,• top-down ranking of critical pipelines,• flexibility of the piping system for relaxing overstressed points,• proper and balanced supports distribution.

The PSA engineers’ decisions are founded on the assessment and interpreta-tion of design information of the piping system. Such information is provided fromthe engineering workgroups at previous steps of the pipe system design. Whenevera formal PSA of the pipe design indicates an overstressed point, decision-makingover possible solutions imply modifications of the pipe pathway, supports distri-bution or both. If none of these modifications solve the pipe overstress, a redesigncoming from the steps of basic engineering would be necessary. The KBS has abidirectional communication with the PSA software simulator. Through the restof the paper, the overall strategy followed during the development of the KBS forPSA and the advantages in its application are presented. Beyond saving PSA engi-neering man-hours, KBS increases productivity and becomes useful in the processof training new technical experts. As an antecedent of our aims, we mention thatKBS technology has successfully provided intelligent support to humans duringthe process of database analysis and design [36]. However, skepticism remainedon the capacity to simulate the diagnostic competence of human designers. Ex-pert human designers employ the so-called knowledge of the real world in car-rying their design activities. Last advances in AI on knowledge management andknowledge representation techniques in modern KBS apply on implementing suchcapacity. Herein this advance is advantaged by linking the capabilities of highlyexperienced people on pipe stress analysis with those from AI. This has method-ological advantages including project development management, human expertuse, and acceptance (most likely, since they are contributors too), and practical aswell as realistic user requirements for the KBS. In the next section, a summaryof the state-of-the-art KBS in engineering for the process industry as well as thepresentation of the AI techniques for decision-making is presented. Throughoutthe rest of the sections, our pipe stress analysis decision-making perspective isintroduced.


2 Engineering decision-making

After mid-1970s, interest in ES decreased; they recovered some popularity inthe 1990s with the application of techniques like case-based reasoning andconstraint-based reasoning. These were mainly associated with business processre-engineering and knowledge-based software engineering. Expert systems, nowrecognized as KBS, can capture the (senior) expert’s know-how on design solu-tions; they involve the construction and usage of the following modules:

• Knowledge base (KB),• Inference engine,• User’s interface.

An engineering KB contains basic as well as complex facts forming the knowl-edge about the engineering domain, e.g., chemical, mechanical, civil, and so on—at the basic or detailed levels of engineering. The inference engine codifies theway the expert engineers do reasoning in such a way that from the set of KB in-formation, new relevant information can be induced, deduced or abducted. Theknowledge and reasoning process is the qualitative part dealing with semantic ob-jects that require definition or translation to numerical values. In addition, the KBSshould perform some of its inference based upon dynamic changes in knowledge;the execution order of the system should not be defined solely by data, as withmany algorithms. The user interface sets the way the input information must becaptured for material problem situations.

2.1 KBS for process industry

KBS on engineering applications for the process industry herein is reviewed,specifically in relation to piping systems, process plants, and equipment. Applica-tions include the analysis of metallurgical failures in an ES for recognizing modesof failure like stress corrosion and hydrogen embrittlement [31]. Later, the devel-opment of an ES designed to assist technical personnel in the evaluation of thephysical integrity of process equipment by generating diagnosis and explanatoryinformation was reported in [17]. In this ES, the knowledge base draws in the threeexpertise domains required to evaluate the integrity of the equipment: inspectionof the equipment, numerical analysis of critical defects, and recommendation ofcorrective actions. A KBS for material selection in an engineering design processis described in [44]. It discusses the development of material databases to be usedas material selection packages. Examples were shown for the use of a KBS in ma-terial selection in the domain of polymeric-based composite. The importance ofKBS in the context of concurrent engineering is also explained.

2.1.1 Computer aided design tools

The concept of so-called intelligent computer-aided design (CAD) systems hasbeen identified as an approach toward integrated engineering environments. Theexperience of designers is the main tool in the process of finding an optimum


route of ship pipes, which is a complicated and time-consuming process. To re-duce the amount of design man-hours and human errors, an ES shell and a geo-metric modeling kernel were integrated for design process automation [23]. Theyimplemented methods of ES to find the routes of ship pipes on the main deck ofa bulk carrier. A framework of the intelligent CAD system for pipe auto-routingwas suggested. The CADDS 5 of Computervision is used as the overall CAD en-vironment, the Nexpert Object of Neuron Data is used as the ES shell, and theCADDS 5 ISSM is used to build user interface through which geometric modelsof pipes are created and modified.

2.1.2 Knowledge-based engineering

Competitive pressures are forcing equipment manufacturers to reduce product de-velopment times, minimize design iterations, and react rapidly to changing mar-kets. Concurrent engineering replaces the traditional sequential design processwith parallel efforts in multiple disciplines, increasing the product quality whilereducing the work time. Knowledge-based engineering captures product and pro-cess knowledge contained in the ‘corporate memory’ to enhance and acceleratethe design process. Linking these two together provides a wide variety of syner-gistic effects. A general description of the process used to create a knowledge-based engineering (KBE) for concurrent engineering (CE) is given in [27]. Theuse of the system to solve real world design problems in compressor rotor designis discussed.

Selecting a shell-and-tube heat exchanger type and geometry is an applica-tion, where a wide range of specialized knowledge is available as qualitative rulesthat can be incorporated in an ES. Abou-Ali and Beltagui [1] present a techniquefor building a KBS utilizing object-oriented ES shells. The constructed prototypeassists the designer in making decisions about fluid allocation, selection of Tubu-lar Exchanger Manufacturers Association (TEMA) shell type, bundle, heads, andvarious geometrical details. The final aim of developing an ES of this type is toachieve an integrated design procedure, from initial selection to the final thermo-hydraulic and mechanical design.

Kim et al. reported an ES called NPiES for nuclear piping integrity. In thiswork, the structure and development strategy of the ES including, user interface, adatabase (where nuclear piping material properties are stored and the unknownmaterial properties are provided through inferring the known material proper-ties), a knowledge base (with rules for inferring material properties), expert part(where the most appropriate evaluation method for given input condition is rec-ommended), and finally, an integrity part (where they plan to do the evaluation ofpiping integrity), is described.

2.1.3 Case-based reasoning for experience recording

Case-based reasoning (CBR) employs past problem-solving experiences whensolving novel problems. In [40], CBR has been applied to mechanical bearingdesign. Their system retrieves previous design cases from a case repository anduses adaptation techniques to modify them for satisfaction of problem require-ments. The approach combines (a) parametric adaptation, to consider parameter


substitution and the interrelationships between the problem definition and its so-lution; (b) constraint satisfaction, to globally check the design requirements toassess case adaptability. The system was implemented and tested in the domain ofrolling bearings.

The application of KBS to the task of failure analysis and design against fail-ure is reviewed in [20]; they emphasized on the reasoning methodologies and theknowledge domains. Case-based reasoning techniques were considered to be themost suitable for generic failure analysis due to the complexity of knowledge re-quired. They concluded that future trends in diagnostic expert systems will bebased on the holistic hybrid rule-case-based reasoning approach combined with anumber of stand-alone engineering failure-analysis calculating software tools anda multimedia-type KBS for different failure modes.

2.1.4 Knowledge management practice

Knowledge management (KM) grants effective methods to organize and reuseinformation for executing business procedures [33]. In the process industry, thecomplex interrelationship between design stress and environment often makes thisan overwhelming task, even for engineers with considerable know-how and expe-rience in failure analysis. Usage of information systems as complementary frame-works for the knowledge-based ones, facilitates the analysis task. The stored andsubsequently accruing expert’s experience is available to be reused for junior en-gineers. Furthermore, organized information can be used for planning, decision-making, and process optimization. As an instance, the building of a KM systemin the Mechanical and Industrial Department at DAR AL HANDASAH, a lead-ing consulting firm in the Middle East, as well as lessons learned in building thesystem and the steps needed to improve it are reported in [29].

2.2 Decision-making representation

2.2.1 Artificial intelligence techniques

Nowadays, the codification of autonomous agent’s knowledge as well as the rea-soning capacities is strongly based in AI techniques like multivalued logics, ge-netic algorithms and neural networks. The autonomous agent system’s applicationin any discipline carries on the usage of AI methods to deal with diverse informa-tion in a flexible manner. In engineering, the expectation is the advancement in theknowledge-based engineering added value by applying tools that are integrated inautonomous computational modules.

Currently, in engineering, the use of simulators is ubiquitous making easierthe test of designed solutions. The input data configuring a proposed solution areprocessed in the simulator for validation if conditions for safe operation are ful-filled. Simulators process huge volumes of data that typically implies hard andcomplicated calculus, in a precise manner, making it easy—to some extend—forthe human analysis of the solutions.

However, there is no commercial system guiding the assessment of alternativesolutions, either at the preliminary tests or during the iterative steps looking for


a convenient solution. Such guiding system is thought to suggest possible adjust-ment of parameters in order to fit the design of an intended solution. In pipe stressanalysis, for instance, the designer has to decide about the pipe flexibility as wellas the distribution of balanced pipe supports.

The intensive usage of AI methods supporting the interpretation and assess-ment of technical and administrative data is required, such that the data becomesuseful information through the analysis of novel solutions for either well-knownor novel problems. Furthermore, a step forward in this direction is to be able tocodify in computational systems, the experiences of engineers participating in rou-tine operations, but specially, the acquired know-how of engineers providing novelsolutions to challenging problems in their expertise area.

2.2.2 Architecture

Decisions in engineering are related to an artefact being designed, constructed,operated, or maintained [5]. Therefore, there should be a link that exposes the de-cisions made, but explicitly with respect to the issues in artefact decision-making.Systems like QOC [28], KBDS [6], n-DIM [47], DraMa [12], and CLiPS [7]have addressed this relationship and have proposed different solutions. A relevantconclusion is that the relationship between the designed device and the decision-making about such a design must be a part of the representation.

Moreover, in [2], is considered the automation of a framework to embody de-cision representation by integrating

1. technical information about device and issues to attend,2. workflow diagram,3. the knowledge that is being shared between members of the organization.

According to such architecture, in our approach to pipe stress analysisdecision-making, the software simulator contains technical information about thepiping system (1) to be analyzed with respect to (2) static and dynamic effects,whereas (3) the human expert’s cognitive abilities are coded and then appliedthroughout the KBS (see Fig. 1). Furthermore, execution of tasks throughout theworkflow carries on decision-making processes at different levels of complexity.In turn, the decision-making process is largely based on the participants’ mutualknowledge.

The base of architecture combines the widely accepted state task network(STN) representation that associates one or more issues to each transition in thenetwork [4]. Every issue has a number of possible solutions (options), and a de-cision involves the selection of one of these options. A decision is implementedthroughout an action that is the embodiment of the transition between states. Thus,decisions/actions are the transitions linking the issues/states.

In addition, the record of the decisions and their components (e.g., the optionsconsidered and the criteria used for their evaluation), also known as decision ra-tionale, could be the most convenient way to record best practices and lessonslearned. Decision rationale would be an essential part of knowledge management,the key principle of which is capturing intellectual assets for the tangible benefit ofthe organization. The suitability of KM based on decision-rationale to improve the


Fig. 1 PSA decision-making by KBS

competitive edge of engineering is allowed so that the aim of any design engineer-ing organization may produce projects with high quality and make them availablein less time.

3 Pipe stress analysis

PSA is a complex engineering discipline which covers the design, analysis, andidentification of piping problems by ensuring that weight, thermal, and pressurestresses are at acceptable levels specified in engineering design standards. PSAincludes the calculation of piping code stresses, loads, and deflections under staticand dynamic loading conditions. The stress analysis of pipe networks is normallydone using the finite element method (FEM) [35].

The reasons for the analysis of Pipe stress on a piping system is essentialto ensure that the piping is well supported and does not fall or deflect under itsown weight; the deflections are well controlled when thermal and other loads areapplied; the loads and moments imposed on machinery and vessels by the ther-mal growth of the attached piping are not excessive; and that the stresses in thepipework in cold and hot conditions are under the range allowed. PSA addressesproblems such as thermal analysis (analysis for free and restrained thermal growthconditions); deadweight analysis (analysis at ambient temperature with a systemof hangers at specific locations to support the weight of the system, for allow-able stress and reactions at equipment connections); seismic analysis (static ordynamic); wind load analysis (static stress analysis); transient analysis (for vari-ous transient loading conditions such as, turbine trip, pipe whip, safety relief valvetrip, etc.). Static analysis in PSA includes the use of hangers, wind load sets, nozzleflexibilities and stresses, equipment load check (under engineering standards, forexample, for steam turbines (NEMA SM23), centrifugal compressors (API 617),air-cooled exchangers (API 661), etc.), flange leakage and stresses, fatigue analy-


sis and cumulative usage (to calculate the remaining life based on material fatiguecurve data and an assigned number of cycles), offshore piping analysis (for ana-lyzing individual pipe elements experiencing loading due to hydrodynamic effectsof ocean waves and ocean currents).

Dynamic analysis in PSA considers dynamic data such as lumped masses, im-posed vibration, snubbers, and spectrum definitions. Dynamic analysis includesaspects such as mode shape and natural frequency calculations (for reviewing thesystems natural modes of vibration), harmonic forces and displacements (to eval-uate the vibration response of a damped system to a range of harmonic forces ordisplacements to simulate mechanical and acoustic line vibrations), shock spec-trum analysis and independent support motion (including anchor movements),force spectrum analysis (for the analysis of general impact loads such as waterand steam hammer, slug flow and relief valve discharge), modal time history anal-ysis, relief valve load synthesis (to calculate the dynamic thrust load and transientpressures from relief valves in open discharge systems).

Engineers should also combine different static/dynamic loads in order to prop-erly address the occasional load requirements of the piping codes.

3.1 PSA: from early to current computer applications

Early applications of PSA on microcomputers are reported in [3]. In 1955, thestress concept for evaluating thermal expansion stress was recognized by an in-ternational engineering code, for pressure piping [38]. Applications of pipe stressanalysis include pipelines with soil forces and longitudinal/lateral pipe movement[37], pipe stress/support analysis to establish extra safety margin [26], and under-ground pipes in granular or sandy soil using a pipe stress program for the evalua-tion of thermal and pressure effects [42].

The idea of a fully integrated engineering software has been reported [13] onthe AUTO-PIPE CAE System that allows the user to perform the entire sequenceof piping analysis and design in a streamlined work flow process. Tasks in thisautomatic process include pipe stress analysis, pipe support location optimization,stress isometric drawing generation, pipe support pattern selection and memberdesign, 3-D interference detection for support. At the core of the system is theAUTO-PIPE (relational) database which contains all the static (project-specific)and dynamic (model-specific) data required for all of the mentioned tasks. TheAUTO-PIPE CAE System has been used for pipe system design of nuclear powerplants in Japan to achieve substantial manpower reduction and cost savings. Now,there is a commercial software, AutoPIPE, as a stand-alone computer-aided engi-neering (CAE) program for the calculation of piping stresses, flange analysis, pipesupport design, and equipment nozzle loading analysis under static and dynamicloading conditions.

In [45], stresses of a pipe flange connection with a spiral-wound gasketunder internal pressure were analyzed. It acknowledged the nonlinearity andhysteresis of the gasket by using an axisymmetric theory of elasticity and theFEM.

Knowledge advances are still active in this area; for example, when branchconnections in low-pressure large-diameter piping systems are designed, as re-ported in [43], the flexibility factors in ASME B31.31 for branch connections do


not assist the designer in taking credit for flexibility that may exist in a large di-ameter intersection. The author reports that since the stress intensification factors(SIFs) are relatively high for large-diameter piping, many stub-in branch connec-tions will require a pad to meet the code displacement stress limits.

3.2 Engineering software

Pipe stress analysis can be done using analysis software such as AutoPIPE orCAESAR II. The model is constructed from piping general arrangement, pipingisometric drawings, and piping and valve specifications. Once the system is mod-eled and the boundary conditions are set, comprehensive stress analysis calcula-tions are done by the engineering software, and modifications to the model can bemade to ensure compliance with the design requirements.

Many engineering and energy organizations, around the world engaged in ser-vices on design and analysis of process pipe networks use the CAESAR II engi-neering software, first introduced in 1984 by a company named COADE; today,it is perhaps the most used in the engineering area [14]. CAESAR II allows theanalysis of piping systems subject to weight, pressure, thermal, seismic, and otherstatic and dynamic loads. The code compliance report generated by CAESAR IIdefines the overstressed points in the system.

CAESAR II begins a static analysis by recommending load cases necessaryto comply with piping code stress requirements. As a comprehensive program forpipe stress analysis, it includes a full range of the latest international piping codes.It provides static and dynamic analysis of pipe and piping systems, and evaluatesfiber-reinforced plastic (FRP); buried piping; wind, wave, and earthquake loading;expansion joints, valves, flanges, and vessel nozzles; pipe components; and nozzleflexibilities. The program automatically models structural steel and buried pipe,and provides spectrum, time history analysis, and automatic spring sizing. CAE-SAR II includes component databases and an extensive material database withallowable stress data. It also includes a bidirectional link to COADE’s CADWorxPlant drafting package.

The program’s interactive capabilities permit easy evaluation of input and out-put, a valuable match for the iterative ‘design and analysis’ cycle, and has an easy-to-use menu-driven interface. Context-sensitivity helps provide instant technicalassistance. Data values depicted in the help screens are automatically presented inthe current set of units to make input easier.

3.2.1 About exchanging data from CAESAR II

CAESAR II offers a link to CADWorx/PIPE, COADE’s AutoCAD based pipedrafting and design software. This is a fully functional, bidirectional link betweenCAD and the PSA program. CAESAR II has a neutral data file format for inde-pendent use in exchanging data with other programs such as CadCentres PDMSand Jacobus 3DM. Piping input and output can be directed to an ODBC database,e.g., MS Access R©, for data review and manipulation outside CAESAR II.


3.3 Expert systems in pipe stress analysis and design

The identification of piping design rules and how these rules can be incorporatedinto an expert system using a common subset of LISP was reported for an expertsystem that was then interfaced with a computer-aided design package [21]. Also,applications in engineering companies like Brown and Root, Inc., U.S.A., reportedthe opportunity for AI techniques in diagnosing high-energy piping problems [41],arguing the need of an expert system for efficiently using their company’s experi-ence on many high-energy piping systems in fossil power plants, technical papers,procedures, reports and reviews with a large database, including the accumulatedexperience of senior engineering specialists. The challenge was how to best usethis expanding base of valuable knowledge and experience. They saw an oppor-tunity for piping data and expertise to be used more efficiently, comprehensively,and accurately with an expert system.

An expert system environment designed to integrate multiple sources ofknowledge required to analyze the internal structure of flexible pipes namedFRAES is reported in [9]. There, numerical algorithms, databases, and expertknowledge are explored by the inference mechanism of the system to assist thetechnical personnel of petroleum companies in the analysis, design, and diagnosisof flexible pipes; these are used as flowlines or risers in offshore applications.

Diab and Morand (2005) proved that a safety factor principle is enough to an-alyze safety reserves in buried pipes because of the variation of the phenomenaacting on the behavior of the pipe sewers. Their decision support system is used toboost the efficient use of existing resources as it integrates all of the informationinvolved in a decision-making process. They report a semiprobabilistic approachto diagnose urban sewers, which is divided into two parts: one based on a sim-plified probabilistic method (concerns only the mechanical behavior of the pipe);the other part is based on the established rules to integrate the impact of the pipebehavior on its environment. They insist that their method will permit to establisha rational diagnosis of urban sewers.

Actual decisions that address the human expert and the expert systems forpipe networks and pipelines design include a range of complex and specializedknowledge like the one outlined in the next section, thus showing a necessity forthe development of a KBS as proposed here.

3.4 Issues and options on piping system design

As in many engineering disciplines today, the expert uses computer software forengineering calculations, then he or she may need to decide on the modificationsrequired to apply to the computer model until a satisfactory solution comes up.Technical expert in PSA should know when and how to use specific restraints (orsupport types) for piping systems and these include the following.

Restraints: A device that prevents, resists, or limits the free thermal movementof the pipe. Restraints can be either directional, rotational, or a combination ofboth.


Anchors: A restraint that provides substantial rigid strength, ideally allowing nei-ther movements nor bending moments. There are also anchors with displace-ments.

Expansion loops: A purpose designed device that absorbs thermal growth; usu-ally used in combination with restraints and cold pulls.

Cold pull or cold spring: It is used to pre-load the piping system in cold conditionin the opposite direction of the expansion, so that the effects of expansionare reduced. Cold pull is usually 50% of the expansion of the pipe run underconsideration. Cold pull has no effect on the code stress but can be used toreduce the nozzle loads on machinery or vessels.

Spring hangers: They are used to support a piping system that is subjected tovertical thermal movements. Variable effort spring hangers are usually incor-porated for vertical thermal movements up to approximately 50 mm. The vari-ation between the preset and operating loads should not be more than 25% ofthe operating load. Constant effort spring hangers are usually incorporated forvertical thermal movements in exceeding 50 mm.

Solid vertical support: It is used in places where vertical thermal movement doesnot create undesirable effects or where vertical movement is intentionally pre-vented or directed.

Human expert should also know aspects of solid supports in the form of rodsor pipe shoes, the importance of free horizontal movement of the pipe as not beingimpeded unless the horizontal restraint is desired, harmonic forces and displace-ments influencing the vibration response of a damped system, etc.

The expert should be able to transfer his design to/from hydraulic analysisdepartment (using commercial software too, e.g., Stoners LIQT and Sunrise Sys-tems PIPENET or even by hand, expressing the piping isometric into his ownengineering software, e.g., CAESAR II. He or she should know specific topicsfrom engineering codes, as well as effects like single or double acting transla-tional, single or double acting rotational, translational with bilinear stiffness, useof snubbers (shock absorbers), guides and limit stops, bottomed-out springs, tierod assemblies, gaps and friction, connecting nodes for nodal interdependenceand large rotation rod supports.

During the piping design stage, a choice of (algebraic) combination of dis-placements, forces, and stresses results in the modification of load cases. Somechoices are indicated as obliged, but there are others that admit alternative solu-tions, and the criteria to select them is a key judgment by senior engineers. On theother hand, to pipe the support’s distribution should be decided on the position ofeach support, the type of support, the distribution along the process plant, etc.

4 The knowledge-based system

The PSA experts’ decision-making concerns with the artefact to be analyzed,which is the design of process plant pipe. The steps to PSA decision-making arethe ones mentioned in Sect. 1.2:

• identification of pipe critical lines,• ranking of critical lines according to the level of risk or danger,


• flexibility of the piping system as for not having overstressed points,• proper and balanced supports distribution.

PSA engineers assess and interpret the information on the design of the pip-ing system delivered from the basic engineering (process and design) technicians.PSA engineers do decide whether according to the given specifications, the pipepathway is reliable or not, as well as if the distribution of pipe supports is wellsuited, then will that pipe operate safe. Identification of pipe critical lines indi-cates the part of the pipe to put special attention considering the top-down rankingof risk and danger of the pipelines. Whenever PSA of the proposed design derivesin pipe overstress, the decision-making compels to modify pipe pathway or sup-ports distribution or both, as the solution that the PSA engineers can introduce toachieve safe operation.

4.1 Device information and experts know-how

Technical data about the piping system to make decisions is broadly containedin the PSA simulator jointly with other engineering software tools. PSA sim-ulator uses the design information of the piping system to test if it suits stressenough below the allowed limits. If design data input to simulator fulfils the spec-ified restraints and there are no overstressed points, then the simulators output isOK. Otherwise, reasons for a negative answer are not shown. However, no rec-ommendation is indicated about possible changes to introduce in the proposedpipe design. Actually, this is the current difficulty that PSA engineers deal with.Currently, assessment and interpretation about what to do is being obtained fromhuman experts’ know-how. The more the engineer’s experience, the more quicklythe required design is found out: junior engineers can spend a lot of man-hoursto get the right solution, usually by a trial–error cycle or by asking senior PSAengineers for some guidance.

Alternatively, we experienced that large part of the routine and/or finedecision-making can reside in the KBS. When the PSA software simulator findsoverstressed points in the piping system, the human expert feeds the simulatorwith alternative data. The experts’ recommendations, besides the processed infor-mation to find them, can be coded inside the KBS for PSA. Like the human ex-perts, KBS will support decision-making; thus, it should deal with the assessmentand interpretation of information on the design of the piping system to addressan enough-flexible pathway as well as a proper distribution of supports as humanengineers deal with. As a human supporter, KBS should guide, to some extent, theeventual changes that could be introduced in the pipe design.

KBS takes as input data the simulator output, and fashions possible changesto avoid overstress; then, this new data of design is the next input to simulator inturns. This way, an interacting cycle—simulator/KBS/simulator—receives aftersome iteration, a well-suited pipe design. Symmetrically, the initial data that feedsthe simulator can be previously assessed by a KBS so that a KBS/simulator/KBScycle works as well. Then, KBS offers a possible design of the solution as theinput for the simulator, or that the simulator output is the input to be assessed andweighted for the KBS.


Fig. 2 Developers of the pipe stress analysis KBS

4.2 Pipe stress analysis reasoning

Based on the concepts of [16], people involved in the development of KBS for pipestress analysis are (a) senior experts with engineering experience, (b) knowledgeengineers, and (c) the end users. In all the cases, more than one person wouldparticipate, since the complexity and magnitude of each PSA matter is wide andcomplex enough (see Fig. 2). The knowledge acquisition tool (KAT) is for KBconstruction, and the KAT shell serves the purpose of constructed knowledge base[46].

Backend of the KAT is based on fuzzy sets and logic that provides a powerfulsupport to KBS inference engine. Because of fuzzy sets, the parameters used tomodel or simulate an engineering situation can have an ad hoc range of values.Fuzzy logic furnishes the parameter’s combination in such a way that a globalassessment of the engineering problem is available. There, a sample of rules thathuman expert uses for decision-making in designing pipe networks is presented.They are being implemented in the knowledge base and grouped in the steps men-tioned in this section.

4.2.1 Critical lines

In the very first step are identified the parts of the pipe that, due to the flow con-ditions, material, and size of the pipe as well as the type of connected devices, isespecially dangerous and needs extra care. Flow conditions refer to temperature,pressure, type, toxicity, density, regime, among others. Pipe size involves diam-


Table 1 Rules to identify critical lines

Rule 1. On the selection of critical line subject to PSA because high flow pressure andtemperature.

IF pressure is >15 kg/cm2 man. OR operating temperature is > 150 ◦C.OR. below −10 ◦C.THEN. critical line . AND. do PSA

Rule 2. On the selection of critical line subject to PSA due to big diameter size.IF pipe line diameter >20 in .THEN. critical line .AND. do PSA

Rule 3. On the selection of critical line subject to PSA based on pipe material of construction.IF pipe material of construction is different from carbon steel .THEN. critical line .AND. do

PSA

eter and length. Connected equipments to the pipe are furnaces, bombs, thermalchangers, turbines, and compresors, among others. Some example rules for thisstep decisions are presented in Table 1.

4.2.2 Top-down ranking of critical lines

The second step is the creation of a top-down scoring of lines’ criticalness leveldue to the characteristics of the flow each line transports as well as the pipe di-ameter or the equipment that is being connected. Most combinations making pipecritical lines involve high flow temperature, medium or big pipe diameter as wellas more fine equipment being connected. The more the level of each flow tem-perature, pipe diameter size or equipment fineness, the more the level of line crit-icalness. By the process of Cartesian coordinates, Fig. 3 illustrates that point Acorresponds to a more critical line than the one represented by point B becauseA contains flow with a higher temperature, has a bigger diameter, and connectsmore fine equipment than does B. Also with equal temperature, point D sets amore critical line than does E because D’s diameter is bigger.

As a recommended practice, the most critical lines must be first OK designedand then constructed. This way, the pipe pathway space needed to locate criticallines or the facilities construction required to keep them together with all compul-

Fig. 3 Combination provoking critical lines


sory conditions can be allowed without restrictions. As much as most critical linesare designed or constructed, the lines of next minor level of criticalness should bedesigned; the design of less critical lines can be adapted to the left conditions afterthe design and construction of the most critical ones. Experiences doing the de-sign and some times the construction—regardless of this order—advice that harddifficulties in constructing extreme critical lines may occur.

4.2.3 Pathway modification

Modifications about pathway aim to benefit pipe flexibility by introducing pipearchitectural elements like the following among others:

• expansion joints,• expansion loops,• thermal changers.

Typically, a long straight section in pipe is not recommended; it facilitates theincrement of flow pressure or force, thus increasing pipe stress. In this case, itis suggested to modify the long section architecture by introducing an expansionloop or omega (for the letter shape). The loop diminishes the flow inertia and re-duces the pressure or temperature. An alternative is the introduction in the middleof the pipe long section of an expansion joint made of a more flexible materialwith shape of folds or biome that augments the pipe flexibility. Actually, both ofthe mentioned resources augment the pipe flexibility in order to cope with thestress flow induced by pressure or temperature. Also it guarantees a safe pipe op-eration in front of the flow turbulence or the so-called flow ram touch.

Other usual pipe circumstances concern with the flow regime, namely liquid,gas, or even solid. Connection to a specific equipment to modify other flow regimeis required. In these cases, it is a condition to attend vendor’s specifications aboutthe devices to guarantee a right usage. Other aspect to be attended is the corrosionthat pipe is exposed to. Special materials covering the pipe’s inner surface shouldbe considered such that enough pipe resilience is assured.

4.2.4 Supports modifications

Balance and equilibrium on pipe’s weight and stress also concerns the supportspipes keep. Right distribution of supports as well as the adequate support at therequired place contribute to a safer pipe operation. Actions that PSA engineers cantake with regard to pipe supports in order to achieve safe operation as follows:

• add supports,• change supports separation or distribution,• change the type of support.

By adding supports or changing the separation between them, a well-suited weightdistribution can be obtained. On the other hand, when the pipe height is a variable,a below-fix-pipe support is not suggested but a pipe above the flexible one is.Pipe section’s height varies due to pipe expansion or distension from variabletemperature from the inside flow or environment. This is a typical situation ofweather conditions, like the ones of desert, where severe changes occur from day


Fig. 4 Overhead piping system for a distillation tower

to night; variation in temperature is high during a 24-h cycle such that specialsupports adaptable to different conditions are needed.

Example. One of the first process operations in a petroleum refinery is per-formed in an atmospheric distillation column, whose separated vapour goes to acondenser through a piping system as the one shown in Fig. 4. The piping sys-tem consists of one feed and one discharge connection pipe segments, one rigid Ysupport, three shoe supports, and four long-radius elbows. The upper end is con-nected to the top of the distillation column while the piping system’s lower end isconnected to the overhead condenser. The resultant pipe stress analysis done withCAESAR II determined that forces in the piping system exceed the limit specifiedin the ASME B31.3 code for process piping. The pipe system does not have suffi-cient flexibility to accommodate the elongation of the atmospheric tower resultingfrom the temperature variations.

The engineering expert solution incorporates a variable spring hanger to per-mit upward movement caused by the elongation of the tower; then, CAESAR IIresulted in no warning or error messages for exceeding code limits. An alternativesolution was the introduction of an expansion joint, it was an expensive solution—therefore not practiced—due to the pipe diameter. Nor was an expansion loop in-troduced, because it would be necessary as an additional support to a great height,which is expensive too.

4.3 Major redesign

Whenever none of the PSA proposals solves the pipe overstress, a redesign mustbe practiced by the process and/or design engineers. It passes on major modifica-tions of the pipe pathway concerning such steps of the pipe deployment.

4.4 Intelligent Chat KBS/PSA

As introduced in Sect. 2.2, the integration of technical information about thedesigning issues, in this case, the process workflow and the experts’ know-


Fig. 5 KBS/PSA chatting

how, harmonize the holistic solutions that benefits decision-making for our pur-poses. As mentioned in Sect. 4.1, the technical information on device underconsideration is modeled in the software engineering PSA simulator. On theother hand, the expert engineers’ information is partially included in the KBSas long as it captures the human expert’s know-how. Rules to identify criti-cal lines as well as for the top-down scoring fashion or the ones about thepipe pathway modifications and support distribution codify the PSA engineers’know-how.

This way automation of the interaction between the parts of the proposed ar-chitecture is arranged. The managed knowledge about pipe stress analysis is cou-pled with the pipe network being designed. The KBS becomes a smart mediatorbetween the simulators modeling and the PSA engineer experiences adjusting themodel (see Fig. 5). KBS can suggest alternatives in order to achieve an adequatepiping system design under the advanced stress analysis.

4.5 Advantages

In addition to human expert’s time being saved and used for finer deci-sions, the advantage of KBS application is to be able to code specializedknow-how such that the knowledge base turns out to be a significant exper-tise from senior experts. There are significant advantages in its applicationin terms of saving engineering man-hours, increasing productivity, and beinguseful in training new technical experts. Furthermore, the medium extent ofKBS deployment should deal with high-level decision-making of keen expert’sknow-how.

This KBS for pipe stress analysis together with similar tools for strategic areasis a key aspect in augmenting productivity, preserving, and incrementing orga-nization technical knowledge and being strategic in promoting sustainability ofengineering services organizations.


5 Ongoing technologies for decision-making

5.1 Ontologies and business process languages

Nowadays, a competent method to represent information useful to model, designand then implement computational systems is the ontologies. Ontologies providethe elements to precisely define the entities and relationships among entities incertain domain. Ontologies set the methodology and basic elements to construct arepresentation language in such a domain. By using ontologies, decision tools of alanguage devoted to make clear communication among a community of users canbe constructed [32]. The incorporation of ontology engineering tasks is a must inknowledge-empowered organizations [25], as is the case in engineering companiesrelated to the energy sector.

According to ontologies classification, the knowledge ontologies describe do-mains and facts that characterize the system, whereas the service ontologies depictthe abilities each computing module (agent) provides. There are interaction on-tologies specifying the protocol attending declarative or actionable message inter-change as well as the shared understanding of information contents. The interop-erability ontologies specify the layers making communications and coordination(collaboration or cooperation) among heterogeneous applications.

The accurate description of structures and pieces alongside a process entailsthe usage of ontologies. The ontologies-based representation allows a smart designof complex information system, as illustrated in [18]. This ontological descriptionsets easier process integration from structural, functional, or teleological perspec-tive. For our purpose, the PSA ontology embraces description of the plant of pro-cesses entities like temperature, diameter size, and connected equipment. Com-plementarily, the ontological description of physical and chemical cause–effectrelationships among the mentioned entities describe the functional and teleolog-ical facts regarding pipe stress analysis. Some KBS rules are the computationalprogramming of these physical/chemical relations. There are teleological relationspertaining to the experts’ know-how that are not being programmed yet, and couldbe considered in further ontological descriptions.

The along dynamic has an actionable modeling through business processesmodeling language [10] that extends the workflow language. The workflow lan-guage models the automation of the whole and a part of business process: accord-ing to procedural rules, tasks information is passed between participants for ac-tion. In actionable perspective, the sequence/parallel distribution of subprocessesand tasks are fine modeled such that business process modeling allocates a quickmanagement of the dynamic and teleological relationships during engineering pro-cesses, complementary to the ontological description of related entities. Further-more, BPML places manage technical information about the process, on partic-ipant’s profile data, as well as on the workflow process documents, namely in-put/output information, used/provided for/from the parts of process PSA analysisherein. BMPL family extends deployment facilities, e.g., the business process ex-ecution language for Web services (BPEL4WS). BPEL4WS, 2006, specificationis a recent option enabling Web services standard for composition such that it al-lows creating complex processes by wiring together activities including data ma-nipulation, correlation, fault handling, compensation, and begin/end of structuredactivities alongside processes.


5.2 Autonomous agents and rationale

The autonomous agent systems technology enables social interaction so as tomanage input/output data through sequential or parallel steps, without a wholecentralized control. Autonomy on computing, meaning the capacity of a system’smodule to self-manage the processing and answer (output) attending environmentrequirements (input), is a distinctive characteristic of agents as components ofthe new generation of distributed systems. Autonomy entails agent’s processingof incoming messages or effects from external actions, such that own-managingon time and conditions of reply is practiced. The autonomous task executions toagent’s goals-achieving are coordinated on the base of individual or shared agent’splan actions. In addition to autonomy, the abilities defining an autonomous agent(AA) are pro-activity so that agents plan and act to reach their own no-external-demanded goals; knowledge-reasoning process: information an agent has aboutown and the system’s states that are processed through internal inference mecha-nisms (agents mental structure). The Workflow Automation Agent-Based Reflec-tive Process (WARP) approach proposes a methodology and frame to implementcomplex process attending the structural, dynamic and functional facets [8]. Mul-tiagent systems supported on Web services cross through the whole activities andactors through the organized workflow. Actually, business process specificationgets an effective autonomous-agents-based decision-making deployment [33].

Systems of autonomous agent’s self-managing participation enable next KBSgeneration based on the use of ontologies that organize and do context mak-ing meaningful information [34]. Recording the know-how and the best lessonslearned is fundamental in order to be able to reuse previous information and ex-periences in decision-making. Questions to attend, on one hand, involved what torecord from the partial results; on the other hand, how to ensure that the resultingrecords are not only easily retrievable and in a format that allows its reconstruc-tion by future users, but also amenable to be processed by a computer in order toachieve a degree of automation in an effective fashion [2].

6 Conclusions

Engineering decision-making mediated by KBS for pipe stress analysis is an op-portunity to contribute to the effectiveness, the increase in productivity, the en-hancement of technical knowledge, but as a major strategy to promote sustain-ability of engineering services organizations. This area of engineering has beenfocussing on AI techniques since the late 1980s with variable interest, but we be-lieve the current advances both in the engineering discipline (including knowledgeand commercial software) and in AI, methods are in a strategic position as to becombined to enhance the productivity in engineering practice.

Acknowledgements This work was done under the collaboration of Project Engineering Di-rection and PIMAyC (Applied Mathematical and Computing Research Program), both from theIMP. We would like to express our gratitude to the anonymous reviewers for their comments andsuggestions. M. Alvarado and M. A. Rodrguez-Toral would like to thank the Mexican NationalResearchers System for supporting their research activities.


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Author Biographies

Matıas Alvarado is a Research Scientist at the Centre of Re-search and Advanced Studies (CINVESTAV-IPN, Mexico).He got a Ph.D. degree in computer science at the Techni-cal University of Catalonia with a major in artificial intelli-gence. He has a B.Sc. degree in mathematics from the Na-tional Autonomous University of Mexico. His interests inresearch and technological applications include knowledgemanagement and decision-making, autonomous agents andmultiagent systems for supply chain disruption management,concurrency control, pattern recognition, and computationallogic. He is the author of about 50 scientific papers, the GuestEditor of journal Special Issues on topics of artificial intel-ligence and knowledge management for the oil industry, andan Academic, invited to the National University of Singapore,Technical University of Catalonia, University of Oxford, Uni-versity of Utrecht, and Benemerita Universidad Autonoma dePuebla.

Miguel A. Rodrıguez-Toral is a Chemical Engineer edu-cated at the University of Edinburgh, U.K. (Ph.D.), UMIST,U.K. (M.Sc.), and UNAM, Mexico (B.Sc.). He has 13 yearsof work experience at the Mexican Petroleum Institute (IMP)in the areas of engineering design of heat transfer equipment,cogeneration, and process engineering for the oil, gas, andpetroleum refining industry. He is currently the topside leaderof the Deepwater program at the IMP. He has interest in theapplications of mathematical optimization and knowledge-based systems for the solution of process engineering and en-ergy efficiency design problems.

Armando Rosas Elguera is a Civil Engineer working at theIMP. He has 27 years of experience as a Specialist in flexi-bility and support of critical piping systems for the processindustry. In 1979, he was a piping stress and flexibility Spe-cialist, then an Office Head of piping flexibility, Coordinatorand Representative of the IMP in the Laguna Verde project (anuclear power plant in Mexico). He was also the Head of thepipe stress analysis department from 1994 to 1998. Currently,he is a Researcher in the applications of pipe stress analy-sis. He has deep practical experience in pipe stress analysisfor nuclear power projects, for process and power plants in-volving all the different phases of engineering projects, fromengineering design to plants start-up and operation.


Sergio Ayala got a B.Sc. degree in civil engineering fromthe Mexican National Polytechnic Institute (IPN). He is nowretired from the IMP. He has more than 30 years of industrialexperience gained at the IMP in the area of pipe stress anal-ysis of process plants. He has extensive practical experiencein the engineering design and technical advice during start-upand operations of piping systems for the upstream and down-stream sectors of the Mexican petroleum industry. He is aSenior Specialist in pipe stress analysis. He has interest in theapplications of computer science for the implementation of acorporate memory in his area of speciality.

decision-making on pipe stress analysis enabled by knowledge

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