Integration of First Principles and Empirical Modeling Technologies for Improved Plant Reliability...

1 EtaPRO™, EPReporter™, EPTrendSetter™, EPAlert™, and Virtual Plant™ are trademarks of GP Strategies.

POWER-GEN MIDDLE EAST 2012 February 6-8, 2012 Doha, Qatar

INTEGRATION OF FIRST PRINCIPLES AND EMPIRICAL

MODELING TECHNOLOGIES FOR IMPROVED PLANT

RELIABILITY AND EFFICIENCY

Dr. Jeff Parmar

International Operations Director

GP Strategies

UK

Dr. Elmer Hansen

Principle Engineer

GP Strategies

USA

Mr. Ron Griebenow

Director, Energy Services

GP Strategies

USA

ABSTRACT

As power generating companies seek to improve

plant reliability, maintain efficiency, and increase

outage intervals, many are turning to on-line

performance and condition monitoring to augment

traditional high-low control system alarms. Such

monitoring systems employ advanced modeling

techniques for automated early detection of incipient

problems and are an essential element in an effective

asset management program. Early warning provides a

reduction in repair costs, an increase in equipment

run times, reduction in fuel costs, reduction in

replacement power cost and economical use of

planned or unplanned outages.

Traditional first principles models provide the ability

to detect of abnormal behavior based on engineering

relationships related to heat transfer, conservation of

mass and energy, and fluid dynamics. Empirical

models use historical data to accomplish the same

goal on equipment where a first principles model is

unavailable or overly complex, (turbine shaft, fan

bearings, damper settings, etc.). This paper describes

a new method which uses hybrid equipment models

based on both empirical and first principles

techniques. Such hybrid models incorporate elements

unique to each method to provide comprehensive

monitoring of all plant equipment.

The on-line monitoring system to be discussed uses

hybrid physical-empirical models to detect

abnormalities and alert plant personnel. Asset

managers use the refined detection information from

models to oversee the six major plant concerns of

reliability, efficiency, environmental, chemistry,

fouling, and cycle leakage. Numerous case studies

demonstrating the results of the hybrid models at a

coal-fired power plant are included.

INTRODUCTION

As power generating companies seek to improve

plant reliability, maintain efficiency and increase

outage intervals, they are turning to on-line

monitoring to assist in this task. On-line monitoring

has greatly enhanced the traditional high-low control

system alarms. Automatic early detection of incipient

problems is now an essential tool in the hands of

asset managers. It alerts managers to sensors in

equipment that have moved outside of the normal

operating ranges so that they can make good timely

decisions as shown in Figure 1.

Figure 1 Management tool to turn Data into

Decisions

Companies have successfully used first principle-

based performance monitoring systems to track

parameters such as boiler efficiency, turbine cycle

heat rate, condenser pressure as shown in Figure 2,

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and steam turbine efficiency, as well as overall plant

heat rate. General Physics’ EtaPRO™ and

VirtualPlant™ technologies utilize first principle

engineering relationships such as heat transfer,

thermodynamics, and fluid dynamics to quantify

degradation and need for overhaul. These same

companies have also successfully used empirically

based condition monitoring systems such as EtaPRO

APR™ (Advanced Pattern Recognition) to detect

anomalies associated with rotating equipment bearing

behavior, Figure 3.

Figure 2 First Principle calculation of condenser

pressure

Figure 3 Empirical calculation of vibration

The behavior of bearings, shafts, damper positions,

etc. is currently beyond the reach of first principle

models. VirtualPlant™ and EtaPRO APR™ are two

very different modeling methods that have one thing

in common: they both provide the user with an

expected value that can be compared with the actual

value. EtaPRO APR uses normal historical data as

the basis of the empirical model. Localized modeling

is used to provide the expected values of model

points. The APR algorithm selects historical time

slices that are near the current time slice and uses a

weighted average of these to predict the expected

values. Figure 4 is a comparison of these two

modeling technologies:

Figure 4 Hybrid Modeling using first principle

and empirical modeling

The goal of the hybrid models is to monitor the six

major areas of plant concerns: reliability,

environmental, chemistry, efficiency, fouling, and

cycle leakage, (field examples in each of these

categories are discussed further on in this paper).

Data for the plant assets are compared to the various

asset models and abnormalities are brought to the

attention of asset managers for timely decisions, as

shown in Figure 5.

Figure 5 APR Process - Data, Model, Concerns,

Reporting

Equipment behavior is shown to be abnormal when

the actual measured value moves away from the

historical (or expected) value of the model. In Figure

6 the IB (inboard) motor bearing temperatures are

well correlated with the motor stator temperature.

However, the OB (outboard) bearing temperature has

some points that are outside of the normal range of

operation. These are abnormal.

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Figure 6 Normal and abnormal reference data

The automatic early detection of both performance

and integrity issues gives the operator or analyst the

ability to concentrate on solving problems rather than

searching for a potential problem. Performance and

integrity calculations are brought together in hybrid

asset models. These models allow the user great

flexibility in that actual values can come from sensor

measurements or calculations and real-time expected

values can come from an APR model, a Virtual Plant

model, or an EtaPRO Point ID.

Source of Actual Value

Sensor Measurement

EtaPRO calculation

Manually input value

Source of Expected Value

APR model

EtaPRO calculation

Virtual Plant model

For example actual and expected values pairs might

be:

Actual Value Source Expected Value Source

Measured HP turbine

exhaust pressure

HP turbine exhaust pressure

calculated from VirtualPlant™

Measured HP Turbine

exhaust pressure

HP turbine exhaust Pressure

from EtaPRO APR™

Main steam temperature Expected Main steam

temperature from EtaPRO

APR

Calculated HP turbine

efficiency

HP turbine design efficiency

calculated in EtaPRO

Calculated HP turbine

efficiency

HP turbine efficiency from

EtaPRO APR

PRESENTING RESULTS

Having the right combination of modeling techniques

to accurately predict expected behavior forms the

foundation of effective monitoring for anomalies.

Equally important is the ability to display, organize,

and manage such anomalies as they occur. The

EtaPRO Concerns Viewer shown in Figure 7 is one

such implementation. Each row represents a

measured or calculated point along with its

corresponding model attributes (expected value,

normal operating range and deviation). The red and

blue bars show the time and direction of the anomaly,

(red is high and blue is low). Pre-defined trends are

available for every modeled parameter and can be

displayed beneath any selected point row.

Figure 7 Concerns Viewer

When enlarged, the trend displays a yellow line as

the actual value along with a light blue line

representing the predicted value on a gray zone of the

normal range of operation and a purple line showing

the deviation plotted at the bottom. A concern is

triggered when the actual value is outside the gray

zone for a user-specified period of time, with the

concern marked with a red cross, as shown in Figure

8.

The early warning presented through the EtaPRO

Concerns Viewer can help produce a reduction in

repair costs, a life extension of equipment, a

reduction in fuel costs, a reduction in replacement

power cost and a reduction in EFOR (Equivalent

Forced Outage Rate) through the use of planned or

unplanned outages.

Concerns typically fall into these six major areas:

reliability, environmental, chemistry, efficiency,

fouling, and cycle leakage.

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Figure 8 Region of normal operation and

indication of abnormality

These are not exclusive categories but have overlap

and interaction. The following sections provide

specific examples of applying hybrid models to a

working coal-fired generating plant.

RELIABILITY

Ash Pump Thrust Bearing. Reliability is important

to rotating equipment. The first example, Figure 9,

shows the heating of an ash pump thrust bearing. The

source of the problem was found to be a lack of

cooling water. The auxiliary cooling water was shut

down to replace a line to the PA fans but the cooling

water was not correctly swapped on this pump. Had

the screen below been available to operations, as it

now is, the pump situation would have been corrected

without turning the pump off.

Figure 9 Abnormal bearing temperature

Service Air Compressor. This air compressor has

been shutdown to replace a motor bearing which the

station staff believe is causing the high bull gear

vibration seen in Figure 10. Since there are three air

compressors, this one can be taken out of service and

repaired at the plant’s convenience. Having early

warning of the change in vibration, allows the

compressor to be repaired before the problem

becomes more costly.

Figure 10 High bearing vibration

ENVIRONMENTAL

Boiler Air Damper Maintenance. Correct boiler

combustion is essential to maintaining proper

emission levels. Correctly functioning boiler dampers

deliver the desired amount of air and fuel to maintain

correct boiler combustion. Boiler dampers can fail by

sticking in one position and by dragging behind the

command signal. The Boiler damper model uses the

damper position as the actual value and the damper

demand as the expected value (an EtaPRO point).

When the two values are different for a sufficient

time, a concern is triggered. As seen in Figure 11,

the first damper is stuck in one position as the

demand tries to move it. The second damper shows a

damper that drags behind the demand. It responds but

not all the way.

Figure 11 Abnormal boiler damper operation

Four months of keeping up with damper repairs have

almost all the dampers operating properly. The blue

and red bars indicate the times when the damper was

in concern. The time period of the bar chart is from 1

February to 6 May 2011. By looking at the bars one

can see the progress that has been made, as depicted

in Figure 12.

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Figure 12 Increase in correct boiler damper

operation

CHEMISTRY

Boiler chemistry can also be monitored as shown in

Figure 13 to obtain two benefits. First, bad readings

from instruments can be quickly detected and repairs

made. Second, on-line instruments give quicker

indication of abnormal chemistry than do daily

samples. This allows operations to take immediate

corrective action to mitigate damage.

Figure 13 Boiler Chemistry

EFFICIENCY

High Energy Drain to Condenser. The condenser

pressure as seen in Figure 14, suddenly increased.

The problem was found to be that the cold reheat

drain to condenser opened up because of a bad level

switch. The level switch is isolated and will be fixed

on the upcoming outage. If the plant had temperature

sensors on the drain lines before the drain valve that

would have provided an additional indication of the

source of the problem. Lacking such instrumentation

the condenser must be walked down to find the

source of the problem.

This condenser pressure rise was also confirmed

using hotwell related temperature sensors, as shown

in Figure 15.

Figure 14 High condenser pressure

Figure 15 Confirming hotwell temperatures

Condenser Air In-leakage. The second condenser

example (Figure 16) looks very similar to the first

but had a different root cause. Some drains were

leaking into the condenser and a rupture diaphragm

and other minor areas were leaking air into the

condenser. They have been temporarily fixed and

will be repaired during outage. The condenser

diagnostics module pointed to the air inflow problem.

The cost of the high condenser pressure in the first

example was calculated using the integral over time

of the data shown in Figure 17 below with the

accumulated cost between 8 to 12 March 2011 of US

$67,000. This is exacerbated by low hot reheat temps.

Reheat Temperature Control Problem at Low

Loads. A third efficiency related item deals with low

hot reheat temperature and hot reheat spray seen in

Figure 18. Each item itself causes a heat rate penalty.

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Figure 16 High condenser pressure

Figure 17 Cost of high condenser pressure

They should not occur together but in this case they

do. The model uses an EtaPRO calculation to provide

the expected values of spray and hot reheat

temperature. At very low loads hot reheat

temperature becomes difficult to maintain. During the

same time, reheat sprays are active and which should

not be needed. This only increases the loss.

Based on the cost of being off target shown in Figure

18, the loss due to low hot reheat and reheat sprays is

US $886 per day (Figure 19).

High Pressure Feedwater Heater Tube Leak. The

fourth example looks at a leak in a high pressure final

feedwater heater. The leak started on 16 January

2011. A jump is seen in heater 7 valve demand and

heater 6 valve demands (Figure 20).

When the valve demand is converted to a flow, the

jump in demand represents a flow of about 30 klb/hr

(11 Tonnes/hr). This corresponds well to the sonic

flow at saturation conditions for one open tube. There

is a second jump on 18 January 2011 of the same

magnitude indicating a second tube has opened. The

additional flow drove the heater 6 drain valve fully

open and began to open the emergency drain valve.

There are corresponding increases in heater levels for

both heaters. Finding a leak early and isolating the

heater can reduce the collateral damage and extend

the life of the heater. This heater has been in service

34 years and has 27 of 1,010 tubes plugged.

Figure 18 Excessive reheat spray

Figure 19 Cost of RH sprays and low RH

temperature

The heater isolation valves are leaking so that the

heater cannot be totally isolated. The heater will

remain out of service until a several day offline

outage is available to plug the tubes. The cost of

being out of service is about US $130 per hour or US

$3,120 per day as seen in Figure 21.

Condensate Pump Recirculation Valve. The next

two events deal with the recirculation valves of

pumps. Figure 22 shows strip charts for a condensate

pump which had the recirculation valve left in

manual. The valve was closed and the system

returned to normal. In his case there was no point to

indicate the percent open of recirculation valve.

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Figure 20 Feed water heater tube leak

Figure 21 Cost of heater 7 (top heater) out of

service

Boiler Feed Pump Recirculation Valve. The boiler

feed pump recirculation valve of the same unit had to

be controlled manually because of a failure in

maintaining the desiccant in an instrument air dryer

at a regular interval; which contaminated the air

system with (now) powered desiccant. Figure 23

shows that the recirculation valve was open when it

should have been closed.

This valve had to be controlled locally in the field

during load drops. In this case it was not closed after

the load had increased which went unnoticed by the

operators until the speed controls on the feed pump

maxed out bringing an alarm to the DCS. This valve

will be changed out during upcoming outage as well

as purging the remaining desiccant out of the

instrument air system.

FOULING

One area of fouling that requires constant attention is

motor filters. These filters get dirty regularly in a

coal-fired power plant and are very dependent upon

local condition such as field and construction dust for

outside motors and coal dust for inside motors.

Figure 24 shows the development of a dirty filter.

Figure 22 Recirculation valve left in manual

Figure 23 Recirculation valve in manual

On the afternoon of the 4th, a concern is triggered in

the heat of the afternoon. On the afternoon of the 6th,

the temperature is much higher. The expected values

drop down; because the model is set to remove

sensors from actively contributing to the model once

the measured values are too far from the expected.

The value of catching a filter change early is that it

keeps the motor cooler and gives extra time to change

the filter before reaching an alarm or shut down

point. In this case, motor filter change out is based on

actual condition, not time or personal perceptions.

LEAKAGE

The air heater X-ratio calculated in an EtaPRO point

(Figure 25) is used to look at boiler and boiler

ducting air in leakage. At this time there has not been

a change in the X-ratio. Cycle make-up flow is also

calculated by an EtaPRO derivative of the make-up

tank level. It will be used in the detection of a boiler

tube leak.

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Figure 24 Development of a dirty motor air filter

Figure 25 X-Ratio monitoring to detect excess air

in leakage

SENSOR PROBLEMS

The type of problem encountered most frequently is a

sensor that gives a reading within the normal range of

operation but is not correct. When a sensor fails to

read correctly it may fail near an ambient

temperature, it may read high or low, or it may be

erratic. The DCS and the historian often indicate that

the signal is good even though it is not. Hybrid

models have the ability to predict expected sensor

values taking into account design performance

(VirtualPlant) and past performance (EtaPRO APR).

These models consider changes in ambient

conditions, load, and equipment in service to better

estimate a sensor’s true value. This information aids

in instrument maintenance.

Regulator Control Problem. A hybrid model will

often confirm a sensor’s reliability even when it

appears to be faulty. Figure 26 shows an example of

an erratic sensor initially thought to be in error.

The erratic behavior, first thought to be a sensor

issue, turned out to be a regulator having difficulty

controlling the leak-off temp on the outboard bearing

of the boiler feed pump drive turbine. A bypass valve

opened up around the regulator making the issue

harder to identify.

Figure 26 Temperature abnormality

CONCLUSIONS

The hybrid models available in EtaPRO bring

together two tool sets that have up to this point

existed separately. The combination of first principle

and empirical models has been able to provide the

ability to monitor the entire range of power plant

equipment from thermal performance, operations and

maintenance perspectives. Presenting the results of

the hybrid models in a real-time environment with

appropriate display and concern management tools,

allows the operator or analyst the ability to monitor

performance and condition from the same screen. It

provides a way to quickly identify abnormal

equipment behavior and direct resources to resolve

the problem in addition to determining the resultant

financial impact. The system described has proven

highly effective at detecting abnormal plant and

equipment behavior and for targeting limited

manpower resources to specific problem areas.

Integration of First Principles and Empirical Modeling Technologies for Improved Plant Reliability...

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