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Brainsand Brawn

Integrating Digital and Human Asset Intelligence intoComprehensive Power Plant Knowledge Management

A Foundational StrategyAdvocated and Prepared by:

Paul Kurchina, President, KurMeta Inc

Jason Makansi, President, Pearl Street Inc | August 2008© 2008 KurMeta, Inc. and Pearl Street, Inc.



Brains and Braun: A Foundational Strategy | August 2008

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Brains and Brawn:Integrating Digital and Human Asset Intelligence into Comprehensive PowerPlant Knowledge Management

PrefaceThis document represents a collaboration of two recognized leaders in asset and enterprise software anddigital applications with corresponding deep domain expertise in power generation. It offers a vision and astrategy for achieving the highest level of performance from a power production facility through integratedapplication of today’s powerful performance software and communication tools. Fundamentally, all thedigital elements of a power station must be conceived, designed, and managed in an integrated fashion

just like the physical equipment on the ground. This document explains how knowledge management, with

the twin engines of the human element and digital intelligence, drives an asset-based business. Against theonslaught of exorbitantly high global fuel and commodity costs, opposition towards building new capacity,carbon constraints, and other severe pressures, the industry has little choice but to extract as much valuefrom existing assets. That’s what integrated digital asset intelligence is all about.

A follow-up effort by the authors will provide recommended solution sets for achieving the vision and gainingcompetitive advantage in the marketplace. KurMeta and Pearl Street also plan to build as well as participatein on-line communities (website, blogs) and face-to-face events around these concepts, to foster acceptanceamong the many stakeholders and application by the owner/operators of power generating assets. Feel freeto contact either of the authors for comment, discussion, and opinion, and to request information about thefollow-up efforts.

Author Contact Details:Paul Kurchina

403.374.1580

[email protected]

Jason Makansi

314.495.4545

[email protected]






Executive Summary: Meet the Chief Asset Intelligence Guy

(Gal)Managing a power plant asset over its multi-decade lifetime—a continuum that begins with project concep-tion and ends with the dismantling and decommissioning of the facility—requires a comprehensive, inte-grated, and exible knowledge management (KM) strategy with two principal elements:

The digital element--asset intelligence (DAI) embodied by myriad automation and software perfor-•mance solutions

The human element (HE), the people who are assigned responsibility, wholly or in part, for the pow-•er plant asset, the processes through which they work, and the cultural aspects of the organization

In this report, we advance a vision based upon the analogy that the KM system is the plant’s “brain,” con-stantly assimilating information, and requires a design and development strategy distinct from, but closelyaligned with, the physical assets, or the “brawn.” We also demonstrate that executing such a vision, thoughconducted in stages, provides a distinct competitive advantage unmatched in terms of early payback oninvestment. Why? Because it has become so dif cult to expand electricity infrastructure—for example, to ob -tain permits to build new power plants and transmission lines or to justify capital costs for new plant assetswhich have more than doubled in ve years. Every kilowatt-hour that comes from an existing asset growsmore valuable by the day. In addition, the imposition of climate change policy onto power generators meansthat the primary emission from a plant, carbon dioxide, has a nancial value, determined either through atax, cap and trade, or other policy mechanism. This fundamentally changes not only the nancial manage -ment of the plant but also leads to a rethinking about the Environmental, Health, and Safety (EHS) systemsfor the asset.

Fundamentally, superior KM allows the asset owner to extract more value and productivity—that is, morerevenue under a declining cost curve—from the asset in the face of new, more complex constraints, suchas carbon management, a changing workforce, reliability standards imposed by the North American ElectricReliability Council (NERC), European Union, International Union of Producers and Distributors of Electricity(Unipede, now called EurElectric), various Asia-Paci c partnerships, and others. The successful KM strategyis equal parts “digital asset intelligence” and “human elements.” The latter work together to de ne an organi -zation’s culture. One aspect cannot be divorced from the other.

The KM strategy we describe and advocate here acknowledges that information about an asset is becomingmore valuable than the asset itself, or at least equally important. To re ect this, every asset-based orga -nization [see illustration next page] should have the equivalent of a Chief Asset Intelligence Of cer in theexecutive boardroom who works closely with the CIO (Chief Information Of cer) or head IT person and thehead human resources person. Every project design team should have a Lead Asset Intelligence Architectworking in conjunction with the architect-engineer (A/E) or the engineer-procure-construct (EPC) contrac-tor, equipment and automation system suppliers, and so forth. Perhaps most importantly, every power plantshould cultivate and deploy throughout the enterprise asset intelligence specialists who can cross boundar-ies among equipment and IT/software; among maintenance, operations, and nance; and among mechani -cal, electrical, I&C, and chemical disciplines. However, these specialists should ultimately be responsible to,and report through, the operations executives.






Knowledge management, with the twin engines of human element (HE) and digital asset intelligence (DAI),should drive the business. In the past, the industry has talked at length about the trends in cross-training andmulti-skilled workers. Today, leadership organizations must actively cultivate hybrid specialists who interpretthe digital asset intelligence, make the linkages and connections that uncover value, prove that value to theplant operators and managers, and articulate that value to management. The knowledge so gained shouldthen be spread and leveraged over other assets in the portfolio. Importantly, the knowledge base aboutthe asset must transcend individual human elements so that changes to the workforce, to the people whointeract with the knowledge, can be accommodated with the least amount of disruption to the enterprise.






Introduction & Overview: The Vision of the Chief Asset

Intelligence Of cerAlthough there are many reasons why a KM strategy is necessary, the most compelling is that the per-formance constraint envelope within which a power plant must operate today is simply too dynamic andvariable to be optimized by the “human element” alone. In addition, the human element has also changedirrevocably. With today’s wired and wireless communications technologies, data and information about theasset can be propagated to whoever, wherever, whenever, literally at the speed of light. Having stated this,the human element is an essential component of the KM strategy. The IT (information technology) or “digital”component of the strategy must be designed for optimum interaction with, and exibility around, human ele -ment resources [see text box below].

Battle eld intelligence proves the point.

One of our white paper reviewers made an interesting observation. US Army units, usually at thebattalion level, begin to build a knowledge base of battle eld intelligence soon after they enter anew area of operations (AO). There is a standard template for building this base of knowledge.Of cers and others add data about key terrain, enemy habits, enemy personalities, areas needing special attention, friends, and so forth. All of the intelligence that can be gathered is input. Whena new unit is rotated in, the knowledge base is passed on, for continuity of operations, safety, and effectiveness.

All of cers and NCOs are graded on their contributions to the knowledge base, and these gradesare then tied to their annual review, pay, promotion, and awards. Medical and health professionalsare also beginning to share patient data among geographically dispersed locations, or treat patientsremotely from a central facility. There are reports that at least one defense contractor is preparing to bring its command and control military platform to the power industry.

Each power plant asset represents, at a minimum, several thousand process and sensor data points—a eetof generating units for a mid-sized utility represents, according to one utility’s count, 300,000 process datapoints, or tags. Now consider the millions of interactions and relationships among all of these “tags,” at leastwith respect to extracting actionable intelligence from them, as well as the multiple locations at which thisdata and information can be bene cially used—trading and markets, purchasing and stores, managementoversight and tracking, supplier contracts, regulatory compliance, and on and on—and it becomes obviouswhy a digital KM platform is essential. To make it seemingly even more complicated, overlay the data fromexternal sources that can help the plant run its business—fuel market prices, real-time electricity prices,emissions allowances, emissions data to compliance authorities, and so on. All of this must be carefullyconsidered and crafted as an overall knowledge management strategy that drives the business.

The essential challenges to this vision are three-fold:

The digital elements of this brain, from a commercial viewpoint, are fragmented among multiple,1.disparate software and automation suppliers

The traditional functions within the asset owner/operator organization often are not recon gured to2.best interact with the asset “brain”






The sensors and nal control devices at the plant are not advancing commensurate with the soft -3.ware and presiding KM, or digital, elements

Our objectives in preparing this document are:

To encourage suppliers to cooperate and collaborate to overcome the rst challenge•

To convince owner/operators that both top-down and bottom up strategies are necessary to over-•come the second challenge

To make a compelling argument that designers and project developers should think harder and•deeper about the third challenge

In understanding the depth of these challenges, consider this: When you “buy” a power plant, you typicallypurchase a boiler island, a gas turbine island, and/or a steam turbine island. One supplier assumes respon-sibility for the island even though all of the components could come from many different “shops.” An architectengineer (A/E) or EPC contractor is responsible for tting the islands together, along with the balance of

plant. The control and automation supplier contracted by the developer, owner/operator, or A/E rm, designsa system which includes the sensors and nal control elements that meet the control strategy, not necessar-ily the KM strategy . [See illustration below.]






There is nothing like this “island supply concept” for the KM system. In principle, the automation or DCS sup-plier could be the supplier of the “brain” island; in practice, the KM system is actually built up outside of theDCS, often on an ad hoc basis. However, as a caveat, the digital movement and management capabilities of the Internet, intranets, and associated storage and applications software is becoming an enabling platformfor an integrated KM system distinct from the DCS.

An analogy to the personal computer (PC) is appropriate. Originally, you bought a PC and then had toinstall myriad software applications to make it do anything. Today, you buy a PC that is “pre-loaded” with allkinds of software—word processing, spreadsheets, printer drivers, database managers, photo- le handlers,Internet access, modems and wireless drivers, storage devices and ports, and so on. Most of this “stuff”actually works together pretty well in an integrated fashion. However, these software “shells” then evolveto re ect how an individual user, family, business,and the like, manages things. A simple example might beusing an of cial power point “template” required by your company. Few people actually think very hard abouta knowledge management strategy, and then go purchase a “system” that executes that strategy! However,this is the critical missing element with knowledge management around an asset or portfolio of assets, takinginto account that the system must be exible and be capable of handling the dynamic nature of information

ow.

The strategy that we advocate, and present in this document, is that the knowledge management strategymust be the foundational framework for the entire asset design, build, and operating enterprise. This isthe best and most intelligent way to continually extract more value from the asset. The name of the gameis faster, better, cheaper, or doing something better tomorrow than you did today, while at the same timeimproving the quality of the product or service.

We also want to caution against the danger of pursuing the more glamorous aspects of KM and sacri cingthe small things that nevertheless have great payback. For example, plant personnel in several owner-operator organizations report that their rms are spending millions for new control and information systems

but neglect the basics, like replacing worn bearings on burner tilts. Apparently, it hasn’t dawned on somemanagement that the most sophisticated control system in the world won’t move a burner with a frozenbearing, and therefore heat rate and emissions performance could suffer. No one should read this think-ing that proper investment in the “brawn” should ever be sacri ced for more sophisticated investment in aKM platform. There is, however, a need for balance and to avoid turf wars among those responsible for thephysical equipment and those responsible for the bits and bytes.






Forces Demanding Continuous Performance ImprovementGlobal power generation has transformed from a parochial regulated electric-utility operation to a sophisti-cated regional, national, and even global business, especially from the perspective of asset management.As one measure, 40% of the power generation capacity in the U.S. is now categorized as “non-utility,”meaning that it functions as an “independent generator” (long-term contract for output), merchant generator (sales based on market conditions), or has been divested from a utility as a result of prior deregulation andcompetitive electricity supply programs. Another measure is the growing in uence of regional electricity mar -kets like PJM, NY-ISO, New England ISO, CAISO (California), ERCOT (Texas), and MISO (many Midwestand western states), and their attendant reporting and compliance requirements. A third measure is thatpower stations are largely either part of either regional or national portfolios of assets owned by a Canadian,U.S. or foreign-owned “generating company” or are owned by “energy companies” that have multi-state or regional portfolios participating in different markets.

In a sense, power plants have become factories or re neries, and electricity is almost a byproduct, or atleast one of many products. Several of the products are not products in the conventional sense of the word.They are products that represent compliance with environmental restrictions for air, water, and solid wastedischarges. However, at coal- red plants, processes to remove emissions often result in saleable products,such as y ash, gypsum, slag, and so forth. Even the primary output, electricity, is sold as differentiatedproducts in the marketplace, such as capacity, energy, reserve capacity, reactive power for voltage support,spinning reserve, and so on.

The primary product, electricity, is sold where it commands the best price, and may be sold as energy,capacity, black start capability, regulation, and other needs of the market or grid. The fuel, raw materials,other inputs, and labor are sourced on commodity and labor markets. The emissions from the plant alsohave nancial value through the emissions trading mechanisms, including potentially the biggest emissions

market of all, carbon dioxide, or are cost items (compliance penalties) if emissions limits are exceeded. Eventhe discharges from the plant, like y ash, slag, bottom ash, and ue gas desulfurization (FGD) productgypsum, can be sold for bene cial use. All of the input costs, revenue generating outputs, and emissions/discharges vary to some degree. Most coal is purchased through long-term contracts, but more and moresmart operators buy some coal on the spot, or short-term, market. Natural gas is also supplied under somecombination of long-term and short-term contract, and many gas- red plants arbitrage their gas instead of generating power. And electricity is now subject to real-time market pricing. Even labor may be more vari-able and transient with the shortage of skilled engineers and workers and the prospect of foreign labor llingin gaps.

In addition to the nancial and technical constraints, there are new requirements for reliability andcyber-security. In North America, new requirements are being issued by the Federal Energy Regulatory

Commission—through its of cial electricity reliability organization, the North American Electric ReliabilityCorporation—and the old requirements to meet federal OSHA standards, safety standards, insurancerequirements, local and state environmental laws, emergency preparedness, national security, and so forth,have not gone away. Global climate change and carbon management will add another dense layer onto thealready complex emissions pro le management of an asset.






Optimizing the technical and nancial performance of an asset within this massive “constraint envelope” isbeyond the capabilities of the unaided “human element.” The optimization “routines” necessary to dynami-cally balance the above constraints in real time are too complicated. The need to parse and distribute plantinformation about process, equipment, emissions, safety, reliability, output, spares and inventory within theplant (operations, maintenance, engineering, EHS), outside the plant to the owner/operator organization (as-set manager, electricity marketers, outsourced services partners), and to regulatory and community organi-zations (NERC, FERC, EPA, OSHA, NRC, etc) is too vast to be handled by armies of paper-handlers.

On top of all this, the skills and talent pools in the industry are being depleted. Although this isn’t necessarilya permanent, chronic problem—all resource constraints tend to be a temporary dislocation between supplyand demand—it is an acute problem today and is expected to be so for at least several years, and perhapsa decade or more. The result may not be a systemic lack of people to do the jobs, but extraordinarily highlabor and salary rates needed to attract and retain them.

The value of a streamlined KM capability shows up in less than obvious places as well. For example, insur-

ance companies are considering lowering their rates to power plant customers which have remote monitor-ing capability, at least for certain machine con gurations. Some owner/operators are able to run machineswith some “issues” by closely monitoring them, rather than assuming they have to be taken out of serviceat the wrong times, such as when electricity prices are best in the markets. Scheduled major outages for the main steam turbine/generator have now been extended from one year to three to ve years at manypower stations, primarily as a result of having more knowledge about the condition of the machine. There isincremental and continuous value to be extracted by having as much intelligence as possible about a plantand its components.

Despite all of the apparent sophistication of automation, software, and telecom tools, and the complexitiesof the optimization routines, the power industry is no closer to some of the most important ways to manage

nancial performance than it was twenty years ago. For example, there is still no real ability to correlate

coal characteristics to downstream performance in process control, or to match coal prices and quality withelectricity prices. The so-called “dark spread,” reducing coal costs when electricity prices are highest in themarket, is probably the fastest way to make money at a coal- red plant. The need for new sensing and initialand nal control devices is addressed in a later section. And, regarding participation in electricity markets,most plants acknowledge that the heat rates being used for scheduling units in and out of the queue do notre ect reality, but are either design basis numbers or the result of a rarely conducted American Society of Mechanical Engineers (ASME) performance test. Real-time, or at least updated heat rate calculations, aregoing to be necessary to properly account for carbon allowances, and this also means that instrumentationmust be properly maintained.






The “Brain”: Mastering the Digital Elements and Integrating

Islands of AutomationSo, what constitutes this power plant KM system, or brain? Management consultants, automation sys-tem suppliers, software companies, and IT gurus in owner/operator organizations have all kinds of prettyPowerPoint slides to depict an “advanced” strategy or vision. But what are we really talking about?

It begins with a monitored parameter which could come from a temperature sensor, pressure transmitter,motor controller, valve positioner, or damper motor, for example. This represents one of thousands of piecesof data available at any given time that describes the “state” of the plant. This data may be a critical part of the plant’s automation and control system, or it may be informational only. It is then transmitted and used inthe control room to monitor and control the unit, but it is also transmitted to various software programs thatcan convert the data into information for equipment diagnostics, maintenance and outage planning, compli-

ance reporting, capital projects budgeting, etc.

Information is created when data points or tags are aggregated and analyzed for trends, when multipledata points are used in a calculation, or when data points are correlated, analyzed, and compared to earlier data to detect patterns, identify anomalies, and so on. Information is then acted upon (“actionable” informa-tion), either in the form of the control and automation system making a change to the “state” of the unit (bymoving a valve, a damper, a burner, or a pump), an operator making a decision to do something or not, or an engineer determining the condition of a piece of equipment, for example. At the plant itself, the braininterfaces with a “ nal control device” to implement a decision or action automatically—closed loop—or withan operator (human element) who adjusts something manually—open loop.






In practice, many power plants are moving towards an integrated, comprehensive KM system, or brain.In particular, there’s been important progress in moving from a plant-by-plant concept to a “ eet” concept,where expertise and management resources are shared by multiple plants. As just one of many examples, avibration analysis expert can now monitor data remotely and serve multiple plant locations, rather than hav-ing dedicated specialists at each plant. This trend, by the way, could be considered a modern manifestationof an earlier trend to move from control systems for individual units at multi-unit power stations to one controlroom for the entire plant.

However, the transition from plant to eet has had unintended consequences:

Some utilities have established centralized performance monitoring facilities, in which information•from multiple plants is collected, analyzed, and acted upon in a “war room” or dispatch-center style.However, these facilities typically are “constructed” using a software “stack,” different softwarepackages that are “layered” (as opposed to integrated) within the monitoring computer screens.

The KM system is usually separate from the DCS system, even though virtually every DCS vendor •claims to provide all of the capability that individual software suppliers provide—including datahistorian or repository, neural network or process optimization, condition monitoring and analytics,emissions monitoring and reporting, computerized maintenance management system, execu-tive and management dashboards for key performance indicators (KPI), project and schedulingmanagement, outage management, inventory and procurement interfaces, simulators for training,engineering and process models of the plant, and so on.

Some of the software digital elements are selected by function and department of the owner/opera-•tor organization, rather than being streamlined for the organization as a whole. For example, anasset management software package may be tailored to the needs of the VP of asset managementbut duplicates many of the functions found in the DCS or in other software packages used by theplant or other staff. Some companies issue edicts to standardize on a software “platform,” eventhough that software may be better for one type of plant and not another, or the Internet may in factbe the “platform.”

Many KM systems for older plants are the equivalent of “baling wire and spit.” They are built out•by functional silos within the owner/operator organization for speci c narrow purposes, to solveemergent, pressing problems, or to deal with obsolescence. These are often also referred to “is-lands of automation,” perhaps more aptly referred to as “digital islands.” Often, these systems areabandoned or “turned off” when staf ng changes or because the capability to use the software wasnever properly transferred to the plant staff.

In fact, if you look closely at a power plant or portfolio asset level KM system today, you nd three essentialelements:

The DCS or automation system1.

The data historian (usually a PI system) that pulls the data from the DCS and converts it into open-2.system data that can be handled and transferred to any and all PCs and consoles

The many independent software packages that take the historian data and convert it into informa-3.tion, trends, analytics, actionable information, etc.






In practice, there’s no reason why many, most, or even all of these software functions could not be embed-ded into the DCS controllers, although there are reasons to limit the integration of some pieces of the KMpuzzle with the DCS. In addition, the DCS data-gathering operations are intended primarily to support theclosed-loop control needs of the automation system. From the corporate side, an enterprise-wide informa-tion network now interfaces with the plant and asset-level KM system.

Within each software package, you also often nd that its full capabilities are not being used, or being abused . Many neural network process optimization packages can, in theory, optimize the whole boiler, eventhe whole plant, but usually are bought and employed for minimizing NOx emissions. While the intent isto minimize sacri ce in heat rate, often plant equipment deteriorates even faster because one goal, NOxreduction, becomes paramount. Condition-based predictive analytics packages may be used for a few of thehighest value components, such as the main turbine and boiler feed pumps, even though the capability canapply to virtually any component where two or more monitored parameters can be correlated in real time.Emissions monitors are used strictly for compliance, even though the information could be very useful for

feedback process control for open or closed loop.






Today, data and information is often propagated far and wide without adding much value at all forthe enterprise.

Sometimes, centralized performance monitoring is in place mostly as an oversight function on the plant man-agement. In this regard, information is simply shifted from one place to another, without adding much value.The real objective should be to gain additional insight into the operation of the asset and gain value fromhaving actionable intelligence. In fact, a close “under the hood” examination of an organization’s informationsystems usually reveals that there has to be a more expedient way to integrate and share the various dataplatforms. It is non-optimum to hammer away at the legacy plant control and information systems for existingassets and get them to t with a sophisticated enterprise information system. It is unrealistic to expect half adozen small suppliers of critical performance and asset management software to collaborate and integratetheir offerings for the bene t of a customer. Moreover, it takes time—time that an enterprise doesn’t have inthe brutally competitive world electricity markets—to get older employees to conform to new KM systems.

Once again, coming back to the “brawn,” a new vendor’s DCS may be interacting with the last DCS ven-dor’s hardware in the plant. Cooperation and sharing between the two competitors is necessary to expeditethe project and make it a success. In fact, the owner/operator may have to invest in upgrading the legacyequipment, such as an analog eld controller, to take advantage of the digital capability. At the same time,the information about both the DCS and the plant-installed hardware must be sent to the enterprise-widesoftware system as well as other plant or eet-centered software systems. Often, the monitoring of major equipment is now the responsibility of the supplier, whose interests—selling services and replacements andspares—are not necessarily aligned with the plant’s—avoiding unnecessary costs.

The transition from plant/asset and eet to enterprise poses another set of issues. In this realm, you canthink of it like this: Trying to marry up the corporate information systems to the asset information systems,both of which are likely a combination of legacy and customized systems and software—such as for ac-counting, human resources, nance, for example—and implementation of an enterprise-wide softwaresystem. In a sense, the corporate IT department is responsible for one side of the house, and the techni-cal managers, engineers, and specialists are responsible for the other. Thus, the need for the Chief AssetIntelligence Of cer and the asset intelligence architects we identi ed earlier to pull these often competingand disparate requirements into a seamless whole becomes clear.

Characterizing Today’s BrainOriginal research conducted by Pearl Street last year reveals that power plant digital and software applica-tions have moved closer together but each piece in the “stack” continues to work independently. An inte-grated platform is still elusive. In addition, there is much disparity of opinion regarding the value of speci csoftware applications. Most every plant or diagnostic & monitoring center has, or is interfaced to, several of the following digital elements:

Plant data repository and data historian that captures data from the DCS and non-DCS control and•monitoring systems

Enterprise resource planning (ERP) systems•

A management level dashboard and/or “portal,” or data/information window onto the asset(s) gener-•ally accessible by many others beyond the plant staff






Maintenance systems, computerized maintenance management system (CMMS), or task manage-•ment of some form, usually interfaced with the enterprise asset management (EAM) software

Process and equipment diagnostic and asset analytics package•

Intelligent soot-blower software (coal plants only)•

Thermal performance monitoring (heat rate)•

Combustion, boiler, or process optimization neural network, or equivalent•

Outage and project management and scheduling software for capital budget projects and outages•

Environmental health and safety (EHS) monitoring, documentation, and reporting•

Corporate governance risk and compliance (GRC), a la Sarbanes Oxley•

Emissions compliance reporting and monitoring•

Cycling and dispatch cost estimation software•

Fuel cost and characteristics tracking•

Electricity trading and marketing software tracking output, availability history, and other key perfor-•mance indicators important to the market interface

Electronic noti cation and alerts distributed to PCs and personal digital assistants—cell phones,•blackberries, and so on

Most of these applications reside outside of the plant DCS system. Even more interesting, all of these soft-ware applications together, even with an apportioned cost for the usually expensive enterprise software, maycost a fraction of what is invested in the DCS at the plant. If anything, the PI system that pulls the data out of the DCS and makes it available to the other software packages could be considered the true KM hub.

In addition to these systems, which can be said to be associated with the continuous processes at the plant,there are the controls, monitoring, and software associated with the batch (or discontinuous) processes,such as coal yard operations, water treatment, and so forth. These systems can also provide valuableinformation that can be propagated and shared among the enterprise, but often are constrained by manualcontrol systems and even local control, where a plant s taff person must physically go to the area and pushbuttons. Local, disconnected control continues to plague power stations.

Propagating Information—the Meta-OrganizationEarlier, we mentioned that information from the plant can be propagated to whoever, wherever, whenever.Because of this, the expertise best capable of analyzing the information available from the data can belocated remotely from the plant. This is why owner/operators have gone to centralized monitoring and

diagnostic centers, and many equipment suppliers, especially those associated with today’s combined cycleplants and wind energy facilities, provide outsourced maintenance and even operations services based onthis data and information. Today, eet owner/operators interact with local plant personnel and asset manag -ers, transmit revenue requirements, share information that helps them understand operating costs beforecommitting assets in open markets, analyze plant performance trends, and compare unit performance at theportfolio level.






In other words, the actual nancial management of the plant is taking place elsewhere, while the plant staff essentially follows instructions for how to bid into the market and cycle the unit up and down in load. Theresponsibilities of the plant staff become more focused on day-to-day tasks, such as maintaining equipment,buying consumables, overseeing the controls, rebuilding circuits, locating hard to obtain spares, tending tolabor and workforce problems, and so forth. For critical components, such as the gas turbine/generator or steam turbine/generator, the full expertise of the vendor’s organization or performance engineering rm canbe brought to bear through remote machine monitoring, diagnostics, and analytics.

Thus, for today’s plant, the plant “staff” isn’t constrained to the people assigned to the plant location. Thestaff includes those individuals, of course, but also the equivalent of people from the water treatment vendor,chemical analysis laboratory, one gas turbine specialist located at the vendor’s headquarters, some frac-tion of a team of asset analytics experts residing at the software vendor’s location, the equivalent of threeengineers from corporate headquarters, some fraction of the asset portfolio’s vibration expert, who servesmultiple plants, and so on. The important rule of thumb is this: The information and data must be made avail-able to those with the expertise to best make use of it.

Information and data must migrate to where the expertise can best make use of it.

Although this is not yet prevalent at power stations, information from the KM system can be propagatedout another level into the meta-organization: The wider supply chain that serves the enterprise. Automatedsearches could be conducted for suppliers of components, consumables, and services ahead of scheduledequipment overhauls. Plant KM systems can be wired into stores and inventory databases and to automati-cally “order” replacements when necessary. Plants of identical or similar design can share databases for critical spares, or hard to obtain parts for legacy equipment. This would help companies reduce carryingexpenses by sharing inventory. Eventually, the system could even be smart enough to identify heat ratelosses—impacting the carbon footprint—and make recommendations on what components to consider during the next outage! Of course, one has to ensure that the base information driving the supply chain is

correct and adequately updated. Often, in most facilities, it is neither.






Con guring the Human Element: Avoiding Islands of

CommunicationIn some ways, optimizing the human element to the digital elements is no different than, twenty years ago,optimizing an organizational chart. Every military and organizational leader knows that power resides inknowledge, and there is always resistance to sharing power—and knowledge—and therefore there is a ten-dency to hoard knowledge. In this regard, the “digital” part of the KM strategy is at the mercy of the businessobjectives and the human element. Another thing to keep in mind when con guring the human element isthat the digital elements are most logical for repetitive tasks, for identifying and mapping important patternswithin and among individual data streams.

Overall, the industry must concede that the impact of the digital elements on the human element is two-fold:on one hand, fewer people are needed to perform those repetitive tasks or search for those patterns and

correlations; on the other, some of the labor is simply displaced to others in the organization, or the meta-organization.

Ten years ago, it appeared that the IT and computer/automation revolution was going to empower plantsand allow them to run autonomously as pro t centers. Plant management would simply interact with theowners as necessary and “send the money” to headquarters. It would be the corporate owners who wouldbe “hollowed out” because the IT systems would allow the plant to manage their own affairs. In truth theopposite happened. The higher value functions of engineering, management, and nancial operations weredisplaced elsewhere. Maintenance specialists are no longer dedicated to a single asset. On-site manage-ment of the asset is being dis-intermediated, meaning that the value of the function has been stripped awayso that it is no longer necessary. There are still plant people with these titles, but when you remember therule about knowledge and power, well, the power is located elsewhere because the information is available

elsewhere.

Optimizing the human element around the digital elements requires an understanding of the continuum of knowledge provided from the digital side, that is, data, trended data, correlations and analytics based onthe trended data, advisories provided to those who can do something with the knowledge, and immediate,short-term, and long-term decisions and actions based on the knowledge. Certain functions required on acontinuous or frequent basis will always remain at the plant s ite. Some actions and activities will be handledby teams assembled and sent to the plant site. Many functions can be located virtually anywhere in theworld—monitoring, trending, analysis of long-term impacts, and others.

What is important is not so much that the IT systems shatter the traditional boundaries drawn in organi-zations, that a reduced staff does more with less by sharing the knowledge contained in the enterpriseinformation system, or that costly preventive maintenance work is avoided or postponed because predictivetechniques now prevail because of sophisticated analytics. All of these outcomes are a given. What is impor-tant is that the human elements are properly con gured around the digital elements, and vice versa. It’s notabout one or the other; it’s about a KM system where the digital elements are an extension of the brains of the human elements.






Enduring Challenges and Risks: What Stands in the WayAchieving the vision of an integrated, comprehensive KM platform poses challenges beyond the generalinertia prevalent in large organizations, the characteristics of the IT and software business, and the duplica-tion of functions between the software “stack” and the DCS.

Topping the list is cyber-security, the unwanted, unintended consequence of the move to the open PC-basedcomputing environment. Related to this, something that plant managers complain bitterly about, is versionproliferation—new software versions with re-adjusted security management that requires everyone to beretrained. Also high on the list is user-friendliness. Pearl Street’s research also suggested that most soft-ware, even products that have a high perceived value, does not pass the means test for the user interface.So-called “data fog” threatens the utility of the KM program. Workers simply go numb from having too muchinformation and not knowing what to do with it. Plus, they are worried about the energy and time it takes toshare their knowledge, or maintain the asset intelligence base.

A less obvious challenge is that the ability to pass information around the meta-organization also makes iteasier for people to “pass the buck.” An analogy can be made here to email traf cking:

People spend a great deal of time today managing email rather than managing their job or business1.

People often forward emails irresponsibility, in the hopes that someone else will handle an un-2.wanted task

There are computer hardware issues as well. One of these, also uncovered in the Pearl Street research, isthe inability of the PC environment to handle the real-time data environment at the plant. The shear volumeof data and/or the processing times required are reported to overwhelm PC capability.

Often, problems shift or move, rather than go away. For example, the immature KM platform, in its un-

integrated state, has spawned a crisis of sorts in alarm management. Plant managers note that from 25-50%of the alarms handled at the plant are “system “ alarms, or alarms pertaining to the DCS or control systemitself, not the actual equipment being controlled. There are vastly more alarms and they are functionallydifferent.

It should also be acknowledged that one reason the cost for the “software stack” mentioned earlier is solow relative to, for example, the DCS, is that the initial cost of digital element software is much smaller thanthe “ nal” cost of software. Usually software implementations require continuing consulting and services. Infact, the research referenced earlier showed that the initial vs. nal cost could be anywhere from 1:2 to 1:20depending on what was included and paid for initially, the capability of the owner/operator staff managing thesoftware, and so forth.

While these issues are relevant and important, we believe that most of them can be managed precisely bythe strategy advocated here. For example, a common user interface for multiple software applications wouldgo a long way towards alleviating the issue of user-friendliness. Having a KM “island” supplied by one ven-dor at least means that there would be one source of responsibility that the owner/operator would have tocommunicate with. As a minimum, perhaps, the number of vendors could be limited and the strategy couldbe to at least work towards one common graphical user interface (GUI).






Perhaps the one KM issue that cannot be managed by the strategy advocated here is the need for better and more accurate original data inputs from the plant. The old saw, “garbage in garbage out,” is a real threatto KM effectiveness. It is important to think through the wide range of data needs for the life of the plant dur-ing the design stage and make a larger effort to justify the expenditures for better sensors and nal controldevices, and new real-time monitors and measuring devices. Many times, software suppliers advocate usingtheir analytical capability to determine where bad sensors are, rather than using good data from the sensor to gain more insight into operations. And, optimization software may simply camou age the lack of attentionto the “brawn” with respect to plant performance.






The Next Ten Years: Transforming Digital Asset Intelligence

into Knowledge ManagementToday, it is true that KM strategies are being adapted to the assets at hand. Existing assets, with oldhardware and control/automation systems, are like having a modern airplane cockpit run a ’58 Chevy. Thecontrols are far more sensitive than the ability to actually move a nal control element (motor, damper, or valve, for example). In thinking ahead to the next ten years, it is likely that the asset will have to adapt tothe enterprise KM strategy . For example, how well and how quickly an asset’s KM can be adapted to theenterprise KM platform could well be one of the highest priority considerations for an acquisition.

In addition, Pearl Street and KurMeta anticipate the KM strategy expanding to include a real-time assess-ment of an enterprise’s “carbon footprint.” Forward-thinking energy companies now accept that carbonmanagement will be required—it’s just a matter of when. The rst step will be a comprehensive evaluation

to develop the baseline case. Cap & trade legislation and/or carbon taxes will be instituted. Regulatorsand even lenders/investors will require detailed information about global warming and carbon “risks” to thebusiness. Carbon management has already become an issue in nancing coal plants, much less permittingthem. Reducing the carbon footprint will become a part of doing business. The immense tracking, monitor-ing, reporting, and analytical requirements associated with this ramp up the necessity of an overall KMstrategy.

There is likely to be more outsourcing than less. The upcoming crop of young engineers is different fromcurrent engineers. Reports are that they are resistant to working in the remote environments where manyplants are located—and new ones will be as well. Many plant engineers, especially in nuclear plants, weremembers of the nuclear navy, who were happy to simply work in one place, rather than travel underwater their entire career. The next crop of engineers isn’t like them.

The KM system can be best thought of not only as a brain, but one that matures and grows as the assetproceeds from project conception through design, engineering, construction, commissioning, startup, andlife-cycle operation. Let’s face it, the best training simulator for a plant should be a somewhat simpli ed ver -sion of the actual plant that’s being built, with the ability to adapt as the plant ages and the design character-istics change [see diagram next page].






Experience tells us that power plants either do not proceed through the early startup phase without dif cul -ties (nuclear plants), or end up operating in modes not envisioned in original design—for example, coalplants built for base-load in the late 1970s ended up being heavily cycled because the base-loaded nukescame on line, and the combined cycles of the last ten years mostly run in peaking mode because the marketfor mid-merit electricity collapsed. The KM system has much more exibility to adapt to such disruptions andcan therefore minimize the cost impact when they occur. However, this will require that project developers,and those who nance projects, understand that new instrumentation and devices are necessary to measurethe right parameters and input the data into the KM system.

Some of the devices designers must consider seriously include:

On-line fuel analysis: Evaluations conducted by Pearl Street, Inc. and others clearly show that ad -•vanced plant instruments that monitor in real time the important constituents in coal—ash elementsthat lead to slagging, water, sulfur, and so on—






lead to improved process control of the entire combustion process. Advanced gas turbines are•extremely sensitive to impurities in fuel gas feed and therefore bene t from on-line analysis.

Corrosion monitors: Weaknesses in metal often lead to catastrophic failure or unplanned downtime.•Devices available today can monitor corrosion and erosion—for example, metal loss—directly andreport the data through the KM system.

Cycling and dispatch impacts: Every plant needs more instrumentation and control exibility to mini -•mize the signi cant cost impacts of cycling and start-stop operations. Gas- red combined cycles,in particular, are suffering from the effects of cycling, because they were designed for base load or mid-merit operation.

Tube failures: 50% of all forced outages at coal- red plants, and a substantial source of problems•at combined cycle plants, are boiler—or HRSG—tube failures. The industry is no closer to a solu-tion to this problem today than it was twenty ve years ago. This is an area screaming for better sensor and detection technology, or asset analytics that identify problems in other ways.

Emissions monitors, carbon in y ash monitors,and so on.•

Thus, the evolution of the “design” of the KM system, or brain, has multiple objectives. One is to ensure anintegrated software stack that is designed and built in conjunction with the physical assets, and makes bestuse of software and tools applied during the design and construction phases of the project. Another is theopportunity to improve on the raw data that is the source for all the enterprise asset knowledge. Importantly,it is essential to remember that many key components at the power plant are not well-instrumented in the

rst place, especially in older plants. In some cases, it will be necessary to add basic sensing capability,not even advanced sensors. In other cases, so-called soft sensor technology can be employed to providemonitoring of poorly instrumented equipment.

At the other end of the spectrum, the technologies for collecting and propagating information continue

to advance and are making their way into the plant environment, where, just a few years ago, they wereconsidered inappropriate (for example, wireless) or ineffective. Some of these communications technologiesinclude radio-frequency identity (RFID) tags, wide area networks (WiFi) with multiple communication chan-nels, mesh networks, digital bus (sensors embedded in equipment with diagnostic capability), and smartdigital meters.

Ultimately, the model that we’re aiming for isn’t another continuous process industry, perhaps, but a discretemanufacturing one like the automobile industry. For example, sensors in today’s automobiles know secondby second the critical performance parameters—where it is going, how fast, traction at the tires, whether it israining, darkness levels, and so on. Braking systems and safety systems are well automated today, as arethe combustion and engine timing systems—even a sound system can automatically adjust for higher levelsof background noise in the interior! More than 20% of the value of an automobile today, according to reports,

is embodied by the electronics, sensors, and software. And, they can send alerts and maintenance noti ca -tions back to the dealership so they can badger you about bringing the car in for scheduled maintenance!






Conclusion: The Payoff from an Integrated Knowledge

Management PlatformRe-orienting the asset-based organization towards asset intelligence is the rst strategic step in leveraging

the bene ts of today’s performance software, digital technologies, and advanced communication practices.Once a cadre of asset intelligence specialists and architects are in place, cultivated by the Chief AssetIntelligence Of cer and his/her department, they will readjust and execute on the vision of a streamlinedasset software stack and work with the appropriate vendors to customize the system and make it happen for the organization. The payoff has been shown to be enormous, even if implementing the early stages of thevision advocated here.

Islands of automation often spawn islands of communication!

The fact is, software and digital technologies are very low cost compared to equipment “brawn” and work-force labor, and the real value for your money only comes when all the software elements are con gured asa streamlined whole and the knowledge propagated across the enterprise to those who can best make useof the information. Unfortunately, digital software and communication solutions are usually implemented assilos within departments, or end up creating a new function or layer on top of old ones. Islands of automa-tion spawn islands of communication! However, by thinking in terms of our analogy of brains and brawn, theasset-based organization can visualize the importance of taking the long view, of ensuring that the digitaland human elements of the asset brain are conceptualized, designed, built, modi ed, and retro tted tore ect the needs of the physical assets and the long-term strategy of the organization.

Only when the digital asset intelligence is truly propagated and shared across the human element can anasset-based business claim to be a knowledge management enterprise.






Paul KurchinaPaul Kurchina is an Information Technology visionary and a leader in leveraging applications and technologyincluding Plant, Enterprise Asset Management, and Mobile Solutions. Over his career, Paul has workedfor government, crown corporations, public corporations, and consulting organizations. Much of Paul’scareer has been spent working in the utility industry, including a variety of leadership roles at Ontario Hydro,TransAlta and PriceWaterhouseCoopers. Over the years, he has developed and launched numerous ITinitiatives, including ERP implementations and Plant focused solutions. Paul now runs KurMeta, an ITecosystem development practice.

Paul has also been involved in working with a number of utility industry companies and technology partnersaround the world on plant and eet focused solutions with a strong focus on customer value. He has beena technology advisor to a number of leading utilities and non-utility organizations. Paul served as StrategicTechnology Advisor to the Executive Vice President of a power generation company, leading the transfor-mation of the power generation business through integrated software and plant performance applications.These efforts culminated in the awarding of Power magazine’s “Power Plant of the Year Award” in 2005 tothe company.

Paul has been the chair of a number of user groups focused on various subjects areas including utilities,power generation, plant maintenance, enterprise portals, and service orientated architecture. He is currentlya chair of an Enterprise Architecture Special Interest Group.

Paul has been the co-author of a number of books including: Mashup Corporations: The End of Business as Usual , and most recently In Pursuit of the Perfect Plant: A Business and Technical Guide . Paul is a frequentspeaker at utility and non-utility events around the world.

Jason MakansiJason Makansi is a recognized thought leader in the electricity industry and has worked with leading clientsand businesses involved with power plant management, performance, and operations. Knowledge man-agement and asset intelligence is a key Pearl Street practice area. He is the author of three books, mostrecently the highly acclaimed Lights Out: The Electricity Crisis, the Global Economy, and What It Means To You (John Wiley & Sons, June 2007). Earlier books include An Investor’s Guide to the Electricity Economy ,(John Wiley, April 2002), and Managing Steam: A Guide to Industrial, Commercial, and Utility Systems .

Current and recent work with plant knowledge management tools include predictive analytics, neuralnetwork process optimization, on-line fuel analysis, cost of power plant dispatch and cycling, equipmentdamage estimation, simulators, distributed control systems, and others. In 2000, Jason lead the power industry business segment for Myplant.com, an innovative on-line community for information and Internet-

enabled services and software applications. Also at that time, Jason published the seminal executive report,Information Technology for Power Plant Management , distributed through The McIlvaine Company. Prior tothat effort, Jason was the Editor in Chief of Power magazine, the industry’s leading trade publication, from1994-2000 and was with the publication since 1981. In 1989, Jason became Editor in Chief of a companionmagazine, Electric Power International , and began covering the worldwide electric power industry. In theearly 1990s, he launched a series of events, The Power Plant Managers Workshop and the Power Plant




Evolved Technologist KurMeta, Inc. and Pearl Street, Inc. | ®Copyright 2008 24

Information Technology Symposia; special reports and articles, focused on integrating controls and perfor-mance software.

Jason has been an active member of numerous engineering and technical societies, and currently is on theExecutive Committee of the ISA/EPRI Power Industry Symposium and the Program Planning Committee of the Electric Power series of Conferences and Expositions. He earned a BS in Chemical Engineering fromColumbia University.

Feel free to contact us for comment, discussion, and opinion or for further information on our continuing work on integrating digital and human asset intelligence into comprehensive power plant knowledge management systems.

Paul Kurchina

403.374.1580

[email protected]

Jason Makansi

314.495.4545

[email protected]

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