AAT-Increased Refinery Productivity Through Online Performance Monitoring

7/30/2019 AAT-Increased Refinery Productivity Through Online Performance Monitoring

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Increased Refinery Productivity through OnlinePerformance Monitoring

by

Douglas C. White

Emerson Process ManagementMDC Technology Division

Presented at the

NPRA2001 Computer Conference

October 1 to 3

Dallas, Texas


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Increased Refinery Productivity through Online PerformanceMonitoring

by

Douglas C. White

Emerson Process ManagementMDC Technology Division

Abstract

Refinery staffs have always been concerned with improving the reliability of majorequipment and avoiding unscheduled production slowdowns or shutdowns. Asrefineries get larger, the financial implications of even relatively short productionoutages are quite high. Reliable and efficient operation of equipment is essential forprofitable production. The performance of process equipment, such as compressors,heaters, heat exchangers and columns, often deteriorates with time due to wear andtear and fouling and this deterioration has significant economic implications. In addition,deterioration in process equipment performance in conjunction with traditional conditionmonitoring is often a precursor to actual equipment failure. Even when process data ismonitored online, the actual equipment performance, which is not directly measurable,can be masked by changes in stream compositions, operating conditions, ambient

conditions, and other normal process variations. To correct for these variations it isnecessary to use rigorous engineering models of the performance and the best possibleestimate of the correct values of process input data to the calculation. Fortunately, thecontinuing advances in computers and communication have dramatically reduced thecosts for development and implementation of this rigorous performance monitoring. Anattractive current option is to obtain these applications, as a service, over the Internet,vastly reducing the cost and complexity of implementing and maintaining them. Therequired investment in hardware, operating systems, databases, software licenses, ITstaff and third party support is significantly reduced. In this paper we present the resultsof installation of this technology on processing units including technical details andeconomic benefits. Important associated benefits include the opportunity to benchmark

similar equipment across multiple refinery sites and the ability to use centralizedengineering resources to support multiple refineries.


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Introduction

The refining industry continues to face pressure on operating margins and increasedregulatory oversight, particularly in the health, safety, and environmental (HSE) area.

To improve competitive operation and profitability and to improve HSE performance,enhanced equipment reliability is critical. Maintaining the efficient operation andperformance of refinery equipment is essential to the long term profitability and safeoperation of the refinery.

In a typical refinery, maintenance expenditures are the largest single cost afterfeedstock and utilities. However, the cost of poor reliability is even higher with manyrefineries reporting losses in production capacity of three to seven percent due tounscheduled shutdowns and slowdowns of major process equipment.

Another factor that has a significant effect on the profitability of the refinery is the timing

of maintenance and cleaning schedules for major items of refinery equipment. Thismaintenance and/or cleaning is required since most refinery equipment performancedegrades with time. Not only does performance degrade between overhauls but alsowith each successive overhaul the performance gains may be less.

The collective software technologies that address this area are known as AssetManagement Systems. Asset Management System technologies are designed toensure that refinery production equipment is maintained at the maximum performancelevel for minimum cost. Assets in this context refer to all of the physical equipment inthe refinery - compressors, pumps, distillation columns, heat exchangers, boilers, etc.

There are several approaches to maintenance in the plant. One is to wait until theequipment breaks and then fix it if it is really important. The second, known aspreventative maintenance, uses average times to failure for equipment and schedulesmaintenance before the expected failure time. However, equipment can vary widely inactual performance. Predictive maintenance attempts to find techniques to determinemore precisely if equipment is underperforming or about to fail. Predictive analysismaintenance techniques have been demonstrated to save as much as thirty percent ofunscheduled maintenance costs while simultaneously improving equipment reliability.We are all aware of the tremendous decrease in the physical size of high performancecomputing equipment and the increase in communication bandwidth and capabilities.Sensors on equipment are becoming cheaper with enhanced computing and

communication capabilities included. With the continuing improvement in computingand communication capabilities, predictive maintenance can be based on actual deviceperformance data, obtained and analyzed in near real time. The overall objective is tocatch potential equipment problems early which leads to less expensive repairs andless downtime. Conversely we want to avoid shutting expensive equipment downunnecessarily.


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The figure following illustrates the concept. We would like to detect anomalies early andthen decide what they mean with respect to the equipment. Data from the process andthe equipment is validated and brought to performance models. These calculate theperformance and correct to standard conditions and with economic information calculatethe cost of poor performance. This can be used for predictions of unscheduled removal

(or replacement) of part(s), disruption of service, or delays of capacity.

Acquire and

Validate Data

Analyze

Performance

Predict

Degraded

Operation

Take

Corrective

Action

Design

Information

Maintenance

History

Process Data

Temperature

Pressure

Flow

Load

Operating

Mode

Validated Data

Standardized Performance

Economics

Cost/ Benefits for Cleaning

Impending Failures

Proritized MaintenanceWork Orders

Asset Failure Probability

EquipmentDiagnostics


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Model Based Equipment Performance Monitoring andAnalysis

Lets assume you are responsible for a major piece of equipment in the plant such as a

large heater. Suppose you see efficiency go down and fuel usage go up - what does itmean? Variations in air temperature, the load on the equipment, and the fuel gascomposition as well as operating decisions can easily cause the efficiency to vary by20%. Obviously you need some way to bring the performance to standard conditionsthat can be compared with historical, design, and clean conditions.

In the past, performance has been monitored at the refinery by considering eithersimple input / output algorithms or by processing data that has first been fitted to asimple correlation based model of the equipment. While both of these methods canproduce acceptable results, they still require a significant investment in software andhardware technologies and require skilled technicians to oversee the monitoring

infrastructure. Some organizations still continue to retrieve raw refinery data andcalculate performance using a homemade spreadsheet and base the timing ofmaintenance and cleaning schedules on these calculations. Such techniques are hardto maintain long term and are prone to errors.

For a true representation of the current operation of any piece of refinery equipment, amodel-based performance monitoring system provides greatest accuracy. Thistechnique allows the user to extract or infer information about the machine from theoperating data by use of the mathematical representation of the equipment and thecalculation engine.

Technology Description

The figure following shows modern Equipment Performance Monitoring Technology asinstalled in leading refineries today.

Parameters are selected for each of the unit operations being monitored, which have anengineering meaning: for example, the overall heat transfer coefficient (UA value) for aheat exchanger, or the isotropic efficiency for a compressor stage. Each of these canbe expected to respond to fouling in the unit; in both the above cases by decreasing inmagnitude with time.


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The rate at which the parameters are recalculated should be chosen to reflect theanticipated decay in their value. If the frequency is set too high, then there will be nosignificant change from one estimate to the next, if it is too low then it is possible to missa critical shift.

With heat exchangers the rate of fouling is obviously dependent on the fluid beingprocessed, so that the appropriate update may differ from one exchanger to another. A

standard recommendation would be to recalculate the fouling indicators two or threetimes per day. This is a higher frequency than any expected change; however, it isnecessary for the statistical verification that follows. The value that is used for predictionis more likely to be a daily or weekly moving average.

Other types of equipment have very different rates of change: for some compressorsand reactors may vary over many months, while an off-gas CV may change from minuteto minute.

It is necessary to have some idea of the range that the parameter will assume betweenits clean (or design) state and a fully-fouled condition. A usable definition offully-

fouledis when the condition of the unit significantly influences the economics of theprocess.

Model

Updating

DataValidation

Input

Conditioning

BaseCase Model

KPI Generation &

Degradation

Data Server (Data Archive, Performance Calcs, Scheduled Reports)

Plant I/O

Analog Measurements and Digital Signals

InferredMeasurements

& OperatorInformationKPIs &

DegradationIndicators

ValidatedMeasurements

Model Parameters

Plant Data

Base Point &Constraint Approaches

ConditionedData

Remote DataTransfer


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It is important to establish that the chosen parameter is a good indicator ofperformance; i.e. that it has the following properties:

The value should not change dramatically between individual evaluations. It

would be expected to fluctuate about a mean value over a period of, say, oneweek; but these excursions should be small compared with the absolute value, inparticular, with the expected change between the clean and fully-fouledconditions.

The changes in mean value over suitable periods should be monotonic, and inthe anticipated direction. This substantiates the importance of selecting ameaningfulparameter. The average values of the parameters should be plottedagainst time. The value of the indicator is reflected in how useable this curve is:

The curve should be decreasing, continuous and smooth.

It should be possible to extrapolate the curve meaningfully, i.e. without achange in direction in the extrapolated function.

It is not expected that performance deterioration will be uniform. Indeed, the rate offouling often increases with the degree of fouling. The extrapolation should becompared with the actual performance, so that fouling rules can be established.

In summary, performance monitoring involves identifying the following elements:

A list of measured values from the plant that are to be used to update model

parameters. These values should represent data that could be used as inputs tothe model and data that corresponds to calculated results of a correspondingmodel run.

A list of parameters that are to be updated. The parameters should have aneffect on the chosen model calculated results.

A list (subset) of the measurement data to which offsets are to be applied.

Data Cond i t ioning

Model output accuracy is obviously dependent on the quality of the data used for the

calculations. The raw data used for the calculations undergoes validation checks toensure its consistency and integrity. These checks are performed prior to the datareconciliation. Gross error detection is performed initially and obviously incorrectreadings are excluded. For excluded data, typical or recent average values can beused.

If the data is OK, a series of calculations are performed for data suitability within thesystem and the data is scaled for unit conversion between the core model and results


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presentation. If an error is detected within the data, the data can be automaticallyrepaired depending on the data error present. Data reconciliation calculates aconsistent and reliable set of reconciled values that force a heat and material balance inthe model and minimize the sum of the difference between measured and reconciledvalues, weighted by meter accuracy.

Use of a predict ive model

The use of a predictive model has several advantages over a model that simplygenerates performance indicators. The primary advantage is that the predictive modelcan be exercised over a different range to the actual refinery operation with confidencethat the predictions are what the actual equipment woulddo, if it were under newoperating conditions.

Secondly the predictive model will inherently be a more detailed representation than asimple performance indicator model, but require less input to generate results.

Finally, a predictive model can be used in multiple forms for the same problem togenerate comparative results simultaneously. This is possible because the predictivemodel has tuning parameters that allow it to account for equipment degradation (seebelow). In effect, these tuning parameters can be used to generate copies of themodel in different scenarios. Some systems calculate this functionality of predictivemodels to allow simultaneous generation of the machine operation in both clean(design) and current modes. However, predictive models can be used in this way for avariety of other calculations (e.g. run at worst condition, run todays conditions at lastmonths degradation, etc.)

Model Tuning

In an ideal world, equipment would perform as delivered until it was decommissioned.However, in the real world performance is not so uniform, and equipment is subject todegraded performance from a variety of causes. In addition, remedial action is oftenundertaken to reverse (or at least reduce) the effects of the degradation. From amathematical model perspective, the net effect is that the model predictions start to driftaway from the real life behavior, since the model still thinks the equipment is at design.

To counter this effect, the application of statistical and optimization techniques to themathematical model tune it against the observed behavior. Effectively thesetechniques adjust the mathematical model, using tuning parameters, to ensure themathematical predictions match the observed behavior.

There are several difficulties associated with model tuning:

the tuning relies on the live data being representative, and as such is subject toerrors in this live data

the procedure is somewhat computationally expensive since it requires the use ofmulti-variable optimization algorithms


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The best approach minimizes the effect of both of these issues by tuning the model overa range of operation, rather than simply the current values. Effectively the equipmentmodel is tuned over its recent history of operation, rather than to a spot value. Thishistory approach allows data outliers to be accounted for within the algorithm, and alsoreduces the overall computational time required to solve the problem. Hence the

equipment is tuned over an active range to correspond to its operation over that activerange.

The historized tuning process allows behavior over the range of operation to bemodified, rather than simply adjusting a spot value. In effect the whole machine istuned (over the operating range), rather than just one instance of its operation.

An important result of implementing the model tuning is the availability of theparameters that are used to update the model. These are the backbone of theperformance monitoring function since monitoring of these factors allows the plantengineers to follow the behavior of equipment as it changes (degrades) and determine

when appropriate maintenance action needs to be taken. In addition to the updatedparameters, certain other aspects of performance monitoring are implemented by modelconfigurations. It is possible to run the model with design values for the parameterssimultaneously with the current values. Comparison gives the effect of the equipmentdegradation through difference in results and economic functions. Performancemonitoring also allows the generation of Key Performance Indicators that can be chosento represent important aspects of the equipment operation.

Histor ical Data Storage and Data Comm unicat ion

Historical data gathered from the refinery for each item of equipment to be monitoredcan be collected from a variety of sources, and indeed from more than one source for a

piece of equipment.

Problem Identification

As mentioned initially, the objective for performance monitoring is to detect problems inequipment before they become serious. This is done by the following:

Out of normal operating range data is detected and tabulated.

Specific sets of process operating conditions likely to indicate pending faults areconfigured, monitored and alarmed.

Statistical analysis of operating data can be performed to detect sudden changesin performance

Pract ical Con siderat ions

When using parameter updating (tuning) for a model it is important to have a largeenough history of results. There are two reasons for this requirement.

Firstly, it is impossible to update a performance curve, such as the efficiency / flowcurve on a compressor, from a small clump of data from the same operating region.Depending on the technique, the best that can happen is that the curve will move up or


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down, the worst that can happen is that the curve becomes totally wrong towards theextremes.

Secondly, it is impossible to calculate a goodness-of-fit parameter. This makesdetermination of faulty measurements difficult.

When these problems are anticipated, it is better to adopt a strategy where an updatedcorrection factor is applied to the model curve instead of updating the performancecurve itself.

It should be noted that certain fitting procedures are dependent on the availablemeasurement data, and failing minimum requirements, updating or reconciliation cannotbe performed. For example, reconciliation on a compressor cannot be performed unlessan estimate of the compressor power is known. Without a power measurement there isno unique solution. It is possible to fit the compressor to a variety of biasedmeasurements and retain the existing tuning parameters the biases simply serve to

shift the total relationship up or down. The power measurement ties the varioussolutions of the efficiency / head calculations to a single power number. In essence, itoccupies the remaining degree of freedom of the compressor calculations.

Using a performance monitoring system based on rigorous model-based technologyprovides highly accurate information to users, reducing the effect of instrument noiseand bad data on the accuracy of the data used in the calculations.


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Internet Based Equipment Performance Monitoring

Using a structured approach to performance monitoring, with model-based technology,improves decision-making based on derived performance and economic data, andreduces uncertainty in maintenance scheduling. Combining the use of this technology

with the latest software delivery of applications through the Internet provides aneconomically attractive method to implement the technology.

Recently the increase in the availability of Application Service Providers (ASP's) hastransformed many traditional custom-coded applications or off-the-shelf softwareproducts into net-centric applications. Companies no longer pay large upfront licensefees to install a complex application. Instead customers lease the application or service,usually for a monthly fee, that includes service support. The end client no longer ownsthe application and the hardware required or the responsibilities and costs associatedwith initial and ongoing maintenance. The client, through a standard Internet browser,accesses remote, centralized computer servers hosting the application. The client only

manages the results from the application locally. The figure below illustrates theprocedure for performance monitoring.

ProcessProcessProcessProcessDataDataDataData

RequirementsRequirementsRequirementsRequirements

ProcessProcessProcessProcessDataDataDataData

RequirementsRequirementsRequirementsRequirements

Plant DataPlant DataPlant DataPlant DataHistorianHistorianHistorianHistorianPlant DataPlant DataPlant DataPlant DataHistorianHistorianHistorianHistorian

CustomerCustomerCustomerCustomerDatabaseDatabaseDatabaseDatabaseCustomerCustomerCustomerCustomerDatabaseDatabaseDatabaseDatabase

AccessAccessAccessAccessDesign DataDesign DataDesign DataDesign DataSheetSheetSheetSheet

AccessAccessAccessAccessDesign DataDesign DataDesign DataDesign DataSheetSheetSheetSheet

PrePrePrePre----processprocessprocessprocessDataDataDataData

(Validate)(Validate)(Validate)(Validate)

PrePrePrePre----processprocessprocessprocessDataDataDataData

(Validate)(Validate)(Validate)(Validate)

PerformancePerformancePerformancePerformanceCalculatorCalculatorCalculatorCalculator

PerformancePerformancePerformancePerformanceCalculatorCalculatorCalculatorCalculator

ValidateValidateValidateValidateResultsResultsResultsResultsValidateValidateValidateValidateResultsResultsResultsResults

DownloadDownloadDownloadDownloadUniqueUniqueUniqueUnique

TemplateTemplateTemplateTemplate

DownloadDownloadDownloadDownloadUniqueUniqueUniqueUnique

TemplateTemplateTemplateTemplate

Notify CustomerNotify CustomerNotify CustomerNotify Customerof Completionof Completionof Completionof Completion

Notify CustomerNotify CustomerNotify CustomerNotify Customerof Completionof Completionof Completionof Completion

CustomerCustomerCustomerCustomerCustomerCustomerCustomerCustomer

The PlantThe PlantThe PlantThe Plant

TemplateTemplateTemplateTemplatePopulated withPopulated withPopulated withPopulated withProcess DataProcess DataProcess DataProcess Data

TemplateTemplateTemplateTemplatePopulated withPopulated withPopulated withPopulated withProcess DataProcess DataProcess DataProcess Data

StoreStoreStoreStoreResultsResultsResultsResultsStoreStoreStoreStore

ResultsResultsResultsResults


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Data is sent from the site to a central processing point where the data is cleansed andthe models are maintained. Current model tuning parameters and equipmentperformance is calculated. The results are then stored in a historical database forretrieval via a browser based interface.

The growing convergence of software and IT infrastructure towards an Internet-based orcentrally focused environment has enabled information on refinery equipmentperformance to be pushed direct to the desktop for monitoring and analysis. This kindof system provides all the benefits of a traditional system but, in addition, will enable arefinery to reduce costs further by following a similar business model to an ASP -eliminating the need to purchase hardware and software.

A fully outsourced performance monitoring solution allows customers to accessequipment performance results at anytime from anywhere in the world as shown in thefigure below. Large corporations with many geographically dispersed manufacturingoperations can aggregate online resources over the Internet and realize significant

benefits including sharing of information. Customers can assess the effect theperformance of refinery equipment is having on the efficiency of the refinery in terms ofthroughput, downtime, stability etc. It may also show the cost of the degradation inperformance in monetary terms. Performance monitoring enables users to makeessential decisions based on hard facts. Maintenance schedules can be optimized toextend run times and plan activities accurately.


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Example

In the graphs and data below, we see an example of the use of this technology on alarge compressor. The compressor processes a gas stream that varies substantially incomposition and throughput. Decisions on the relative performance of the machine are

very difficult without use of an online model that brings operation back to standardconditions. The calculation of performance, in this case compressor efficiency, alloweddetermination that the compressor was fouled in May 2000. The compressor wasshutdown and cleaned. The improvement in performance is clear. Also clear is thefouling of the compressor over the next year. Calculation of the economic penalty forthis reduced performance shows the surprisingly high cost of the fouling.


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DeviationFromDesign


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Benefits

Benefits from implementation of the asset management technology include:

Improved refinery uptime

Earlier detection of potential abnormal situations in the refinery

Reduced loss and faster recovery from process upsets

Enhanced productivity with troubleshooting tasks

Increased safety for personnel and refinery

The need for on-line performance monitoring is usually identified following some form ofcatastrophic refinery failure, which would have been predicted in advance if the state ofthe equipment had been more closely monitored. Obviously, the simplest criterion ofsuccess for the online, model-based performance monitor is that there should be norecurrence of the shutdown events.

The figure below shows the trended degradation of a unit versus the fixed maintenancecost for the unit from being brought online to its shutdown and maintenance. Thenegative sloped line is the total (fixed) cost of the maintenance for the equipmentspread over each day (cost per day online). Obviously the longer the equipmentremains in service the more cost effective it is in terms of maintenance cost (i.e. thefixed cost divided by the number of days online). To counter this, the positive line is thedegradation cost of the equipment (compared to design or day zero conditions) thatwill increase as the unit deteriorates. A composite of these two cost curves shows aperiod when the unit reaches its most cost effective (the composite curve minimum).

After this time the unit starts to become less (cost) effective and maintenance could beeconomically justified. The actual maintenance is also being subject to availability andprocess engineering judgment.

The performance monitoring aspects of this example would allow each unit to bemonitored separately, and the exact time of maintenance identified. Since each unitmay degrade differently, (the positive sloped line will have different gradients,maintenance engineers can make quantitative decisions rather than just use onlyqualitative judgment. In addition, the system is regularly updated to reflect changes indegradation that may be the result of operational changes, maintenance of relatedequipment, etc. In this manner, the maintenance schedule is updated based on theactual equipment behavior, rather than a rule of thumb.


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Unit Degradation

Time

MaintenanceCost(spreadover

time)

Maintenance Cost Degradation Cost Composite Cost

However, for the many other potential uses of the model it is necessary only that itfollows the true performance of the process closely all of the time. A PerformanceMonitor can be regarded as successful when:

It is using a good indicator, as defined above, i.e. it follows the trueperformance of the equipment

The unit itself exhibits deterioration to a significant extent during the normal runtime of the process between major maintenance shutdowns, so that the systemis providing worthwhile advice.

The change in performance can be communicated to the higher level functions,such as online optimization or planning, in a form that causes them to take

appropriate actions. The deterioration curve, and its associated extrapolation rules, has been shown

to predict the behavior of the equipment accurately.

Customers can assess the effect the performance of refinery equipment is having onthe efficiency of the refinery in terms of throughput, downtime, stability etc. It may alsoshow the cost of the degradation in performance in monetary terms. Performancemonitoring enables users to make essential decisions based on hard facts.Maintenance schedules can be optimized to extend run times and plan activitiesaccurately. Efficient management of refinery assets reduces unplanned equipmentbreakdowns, improves shutdown efficiency and optimizes the maintenance budget.

Translating this into economic factors it is expected that full implementation of thetechnology would result in an increase in refinery production from existing equipment ofbetween two and five percent. A reduction in unplanned maintenance costs of ten tothirty percent is also expected.


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Benchmarking

As stated elsewhere, benchmarking is a technique for achieving continuousimprovement by comparing multiple installations of similar equipment. It allows us toanalyze and improve key processes, eliminate waste, improve performance, andprofitability. Its strength is that it allows analytical decisions based on facts. The

Benchmark is used to provide the common denominator: the measured performance ofeach real unit is compared with how the Ideal would behave under the same conditions.It allows us to leverage scarce engineering resources across multiple sites and toidentify best practices and propagate these across multiple installations. Corporationscan compare performance of different assets at the same sit or at different sites on acontinuous basis.

Measurement is the key knowing where we are today and where we need to betomorrow. Benchmarking compares individual processes and functions, to show whatthe best of the best are doing. Clearly we need relative performance indicators toeffectively compare equipment across sites. The reliable way to do this is to use a

model that provides an independent indicator and a degree of reconciliation for dataquality. For example, we can use a model to provide benchmark performance for anytype of feedstock, any likely throughput or other variable.

The next requirement is a way to easily get the information to the desktop in a formatthat the users can easily access. The figure below illustrates this concept. Use ofInternet based performance monitoring obviously facilitates the comparison.

INTERNET ZONE

Existing

Performance

Database

EXISTING

INFRASTRUCTURE

Internet

Performance

Monitoring

Internet

Performance

Monitoring

Internet

Performance

Monitoring

REAL-TIME

DATABASERTD RTD

COMPARISON

ENGINE

Customer Performance

Comparison


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Future Developments

Performance monitoring and predictive maintenance techniques are still in their relativeinfancy. One of the most interesting development areas includes techniques forvisualizing the large quantities of data produced by modern smart instrumentation. Anassociated development area is advanced statistical techniques to extract informationfrom the data. A promising statistical approach is the use of "classification andregression trees" to actually identify conditions leading to problems in the plant.

Conclusion

Implementing the approach of Internet-based performance monitoring deliversnumerous business and financial benefits over alternative methods of monitoringrefinery equipment. As previously explained the use of rigorous model-basedtechnology significantly contributes to the highly accurate results presented to the end-user.

Equipment performance monitoring through the Internet can significantly reduceimplementation and maintenance costs by offloading onsite administration costs whilesimultaneously providing valuable online tools for complex technical reporting andanalysis. Refinery personnel in remote locations will be able to gain access to expertisethat normally would not be available allowing the experts to troubleshoot problemsremotely and avoid unnecessary visits to site. This technology has already beenapplied to compressors, pumps, gas turbines, and other major processing equipment togive operators a more accurate picture of the condition of each machine. Informationpresented through the Internet allows informed up-to-date decisions on asset operation,improved availability and production capacity and minimizes unscheduled equipmentdowntime

The Internet is changing company management and operations in ways that wereunlikely a few years ago. Organizations that take advantage of this latest technologycan expect to gain significant competitive advantages. Potential savings could exceedseveral million dollars per year in increased production by improving equipmentreliability and efficiency while simultaneously reducing maintenance costs.

Acknowledgement

e-fficiency is the internet based equipment performance monitoring technology offeredby MDC Technology, a subsidiary of Emerson Process Management. More informationon the technology is available at www.e-fficiency.com. This paper is partially based on

an earlier paper - Equipment Performance Monitoring Over the Internet presented atISA2001.

AAT-Increased Refinery Productivity Through Online Performance Monitoring

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