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Economic Benefits ofUpTime Early Fault

Detection

The material in this document is proprietary to InSyst Ltd.Any unauthorized reproduction, use, or disclosure of this

document or any part thereof is strictly prohibited.

ClearView Monitoring Solutions19 Hartum St, SuiteHar Hotzvim Science ParkJerusalem 91450 IsraelTelephone: +972 2 5400920Fax: +972 2 5400044E-mail:[email protected] www.clearviewmonitorin .com

Economic Benefits ofUpTime

Early Fault Detection

The material in this document is proprietary to Clearview Monitoring Solutions,Ltd. Any unauthorized reproduction, use, or disclosure of this document or any

part thereof is strictly prohibited.


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Economic Benefits of UpTime

Subject to Proprietary Notice on Cover 2 of 8

Economic Benefits of UpTime Early Fault Detection:

Increased Generation Improved Efficiency Reduced Losses

Introduction

Faced with deregulation, increasing retail competition and pressures to keep boilers online, many coal-fired power generating stations have adopted business strategies centered on increasing unitavailability, reliability and increasing the operational life of critical equipment. However, boiler tubefailures continue to be the number one cause of forced outages in fossil plants today. These costlyforced outages are responsible for an estimated six percent overall loss of unit availability.1

Annual loss of generation revenue for a 645 MW coal plant can exceed $650,000. ClearView

Monitoring Solutions, Ltd. has developed leading edge early fault detection software to provideowner/operators of coal units unprecedented insight into their boilers beyond the traditionaldistributed control system (DCS). Operators can consider UpTime as insurance, protecting their plantagainst significant lost revenue.

Table 12

shows the age of the fleet of coal-fired power plants in the United States. More than 75% ofthese plants are more than 30 years old.

Table 1: Age of U.S. Coal Plants

1Chris Harley,Senior Applications Engineer, Conforma Clad,Advanced Erosion Protection Technology for Steam BoilerTubes,Power Engineering, August 2006.

2 Energy Information Administration (EAI), Existing Electric Generating Units in the United States, 2005.Note: This report was originally written by Insyst Intelligent Systems, Ltd., all copyrights and intellectual property are now theproperty of ClearView Monitoring Solutions, Ltd.

Year Built # of

Plants

Total

Capacity

2005 Power

Production

20002004 15 1,837 MW 8,649 GWh

19951999 27 4,524 MW 26,465 GWh

19901994 67 8,638 MW 57,831 GWh

19851989 104 23,577 MW 158,594 GWh

19801984 119 55,887 MW 368,744 GWh

19751979 126 55,845 MW 364,872 GWh

19701974 136 66,334 MW 397,062 GWh

19651969 166 42,142 MW 245,121 GWh

19601964 162 25,240 MW 142,300 GWh

19551959 221 29,568 MW 159,378 GWh

19501954 234 18,674 MW 88,613 GWh

19401949 111 3,194 MW 8,006 GWh

19301939 20 132 MW 365 GWh

19211929 14 87 MW 182 GWh


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These plants are highly susceptible to expensive boiler leaks and equipment failures. In a report to theCalifornia legislature in 2003, the California Public Utilities Commission found that the mostcommon causes of outages and lost capacity in power plants are boiler-related problems such as boiler

tube leaks. Plant age may be a contributing factor since the majority of the plants in California usingboilers are at least 30 years old.3

Boiler Leaks Economic Impact

Given the large number of coal-fired plants more than 30 years old, boiler-related failures figureprominently in the plant business models.Table 2

4highlights the massive economic losses associated with boiler leaks at coal plants in the

United States.

Table 2: Cost of Boiler Tube Leaks at Coal Plants

Unit Forced Outages

(per Unit-Year or per Event)

Total Lost

Generation

10% Lost

Generation

20% Lost

Generation

Size (MW) #/UY MWh/UY Hr/UY Hr/Event

Day/Event

MWh/Event

Per Event PerUnit-Year

Per Event Per/Unit-Year

Per Event PerUnit-Year

1000 3.18 290,876 236.86 74.48 3.10 91,470 $4,573,522 $14,543,800 $457,352 $1,454,380 $914,704 $2,908,760

800999 1.99 98,769 119.39 59.99 2.50 49,633 $2,481,633 $4,938,450 $248,163 $493,845 $496,327 $987,690

600799 2.76 128,606 184.68 66.91 2.79 46,596 $2,329,819 $6,430,300 $232,982 $643,030 $465,964 $1,286,060

400599 3.38 101,521 196.98 58.28 2.43 30,036 $1,501,790 $5,076,050 $150,179 $507,605 $300,358 $1,015,210

300399 2.94 56,065 168.99 57.48 2.39 19,070 $953,486 $2,803,250 $95,349 $280,325 $190,697 $560,650

200299 2.98 36,871 164.38 55.16 2.30 12,373 $618,641 $1,843,550 $61,864 $184,355 $123,728 $368,710

100199 2.25 20,439 147.49 65.55 2.73 9,084 $454,200 $1,021,950 $45,420 $102,195 $90,840 $204,390

1 99 2.14 8,559 138.82 64.87 2.70 4,000 $199,977 $427,950 $19,998 $42,795 $39,995 $85,590

Operators can reduce economic loss due to forced outages by addressing latent boiler problemsdetected early. The last four columns of

Table 2 show the amazing reduction in lost generation revenue if the costs could have been reduced toonly 10% or 20%, by mitigating the damage and reducing the repair time.

Early Fault Detection

A significant factor in determining the cost of a problem is the length of time that it existed.Unfortunately during a significant portion of this time, the problem was not even detected by the plantstaff.

A typical Potential Failure (P-F) curve as shown in Figure 1 illustrates this situation. This graphrepresents the monitoring of a single measured or calculated parameter versus time. The scale of thevertical axis shows the degree of satisfactory operation the parameter of interest, where 100% signifies

normal operation and 0% indicates total failure. The horizontal axis represents time.

3 California Public Utilities Commission, Electricity Generation Power Plant Performance Program: Progress Report to the

Legislature on the Implementation of Senate Bill SB 39 of the 2001-02 Second Extraordinary Section, November, 14, 2003, p. 6.

4 Federal Energy Regulatory Commission, Boiler Tube Leakages for Coal Plants: Forced Outages and Derates by Size of Plant.


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Figure 1: Preventive Failure (P-F) Curve

The marked points on the P-F curve are as follows:

P1 Point where UpTime generates an alarm

P2 Point where an observant operator might notice degradation from normal

P3 Point where the control system generates an alarm F Point of failure of the equipment or system without any corrective action

If the plant staff had been alerted about the slight deterioration of this particular parameter at point P1,the successive operating period could have progressed much differently. Plant staff could have begunto monitor an impending failure, taken corrective action, planned preventive maintenance, swapped ina redundant device, or prepared for a planned shutdown.

Boiler Leaks Early Detection by UpTime

UpTime early fault detection software discovers developing equipment failures and excursions fromnormal plant operating conditions, hours or even days before a conventional DCS triggers alarms.This early warning capability translates into significant economic saving through less downtime,thereby enabling the generation of more revenue. UpTime can quickly and accurately locate the causeof an impending failure, allowing the plant operator or maintenance technician sufficient time to takepreventive action, thus avoiding costly downtime, equipment damage, or reduced efficiency.

The UpTime technology considers the relationships among hundreds of variables that reflect plantconditions. The UpTime algorithms combine mathematical principles, heuristic rules, and first-


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principle engineering models. This real-time, statistics-driven system automatically identifies possibleinstrument failures and system faults by detecting when one or more of these parametric relationshipsare outside the normal range. This approach enables diagnosis of developing system problems beforethe plant DCS can identify them.

UpTime correlates the readings of many instruments and uses the values to perform engineeringanalyses that reflect the performance and health of the equipment and process. These performancecalculations include heat rate, thermodynamic efficiency, flow power, heat transfer rate, pressuredifference, temperature difference, and saturation conditions.

Rather than use simple univariate analysis (monitoring data versus time), ClearViews multivariatesoftware provides a more comprehensive view into the plant equipment and systems. Figure 2 shows atypical relationship, or banana, graph that displays two parameters plotted against each other.

Figure 2: Relationship Map in Alert Mode (red background)

This graph clearly shows the red points outside the normal region of the operation for this pair ofparameters (the banana). The blue trend line shows that the process is steadily moving farther awayfrom the normal region. This warning occurs long before the UpTime-calculated statistical limits forthe process have been crossed. In fact, these statistical limits are still well within any alarm set pointsfor these two sensor measurements.

UpTime Case Study: Leak in Boiler Steam Line (Jun 2007)

This case study provides a good example of how the early detection capabilities of ClearViewsUpTime warned the operators of a 560 MW, pulverized coal-fired, drum-type, balanced draft boiler ofa leak long before the plants DCS. This warning provided the plant with time to locate this small leakalong with the assistance of representatives from the boiler manufacturer.


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The boiler leak detection instrumentation at the plant includes sixteen acoustic monitors placed nearthe boiler. These monitors are special microphones that measure noise intensity in areas around theboiler. These noises include the sound of leaking of steam and water.

During the night of 3 June 2007, four acoustic monitors near the top of the boiler (monitors 3, 4, 5,and 6) and one on the roof (monitor 16) began indicating high noise levels, subsequently triggeringUpTime alarms. Finally by 10 June (seven days later), the intensity of these acoustic signals reachedthe set points of the plant control system. Figure 3 displays two cluster graphs that show the elevatedreadings of acoustic monitors 5 and 6 versus the unit load on 14 June.

Figure 3: Acoustic Monitors 5 and 6 vs. Unit Load

Figure 4 shows the steady increase of noise as registered on acoustic monitors 5 and 6 through the first

half of June.

Figure 4: Acoustic Monitors 5 and 6 vs. Time


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During this time, UpTime triggered many alerts (lavender arrow shows the first UpTime alert for eachmicrophone), but the plant control system was silent for several more days until the values reached theplant alarm set point of 90 dB (red arrow).The two sets of elevated readings between the two arrows(Figure 4) correspond to the concentrations of red dots in the two graphs in Figure 3.

At the end of June, the representative of the boiler manufacturer discovered a small steam leak on theattemperation spray line where the steam pipes exit the building at the roof. The unit continued tooperate normally through early July when a shutdown was ordered to repair the problem.

Other UpTime cluster graphs verify the existence of the boiler leak. Part of the UpTime modelincludes a water/steam mass balance around all the boiler components. Under normal conditions theboiler circulation loop loses a small amount of water, approximately 50 metric tons per hour (t/h) dueto blowdown, venting, etc. Figure 5 shows two cluster graphs of unit load versus the boiler waterbalance.

Figure 5: Unit Load vs. Boiler Water Balance Graph (A) in May 2007, (B) in July 2007

Although both graphs have similar shapes, the right graph (dated 9 July) shows a clear shift in thenegative direction on the X-axis compared to the left graph (dated 24 May). This shift occurredbecause the adaptive model learned this increased leakage as the new normal condition. This shiftindicates that the average net leakage from the system has slowly increased from approximately 50 t/hto 60 t/h. Because the plant control system does not calculate the mass balance on line, this

information was only available from UpTime or offline calculations by the plant maintenance staff.

Conclusion

The US fleet of coal-fired power plants is aging. Under current economic conditions there are fewopportunities to build new plants or significantly invest in upgraded subsystems. Boiler problems suchas steam leaks are a major cause of economic loss. To increase the life of their boiler assets, powergeneration companies must maintain their equipment by detecting problems early before they degrade


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into catastrophes that force long shutdowns and lost revenue. ClearViews UpTime offers real value toowner/operators by enabling full utilization of assets to generate maximum revenue.

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