isa expo 06 dynamicnoxheatrateoptimization entergywb oct2006

7/23/2019 ISA EXPO 06 DynamicNOxHeatRateOptimization EntergyWB Oct2006

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Copyright 2006 by ISA, www.isa.orgPresented at ISA EXPO 2006, 17-19 October 2006, Reliant Center Houston, Houston, Texas

Dynamic NOx/Heat Rate Optimization

Don Labbe Bill RayBill Hocking Jon AndersonInvensys, Foxboro, MA Pat [email protected] Entergy White Bluff, Redfield, [email protected]

KEYWORDS

Neural Net, Model Predictive Control, NOxEmissions, Boiler Optimization, Steam TemperatureControl, O2 Control, Heat Rate, Dispatch Rate

ABSTRACT

Entergy White Bluff Units 1 & 2 are split furnace 800 MW PRB coal fired drum units constructed inthe early 80s. The plants were recently retrofitted with a modern DCS and achieved significant controland ramp rate improvement. Entergy desired to further improve unit heat rate and lower NOxemissionswhile enhancing ramp rate capability. This required a dynamic optimization approach that addressedunit limitations such as O2and steam temperature control during unit ramping, coal mill changes andsoot blowing. A dynamic optimization system combining Model Predictive Control and Neural Netsoperating at high execution rates was integrated with the DCS. The system provides tighter regulationof the critical ramping variables thus allowing the reduction of operator margin for heat rateimprovements approaching 1% and NOx reductions in excess of 15%. Through the dynamic multi-

variable control structure these improvements are maintained during dispatch operation, which isnearly continuous for this unit. The system provides the added benefit of lower peak steamtemperatures while lowering the standard deviation, thus enhancing ramp rate capability whileimproving heat rate.

INTRODUCTION

Entergy operates two 800 MW units at their White Bluff Station in Redfield, Arkansas. The units havetangentially fired drum type dual furnace boilers with eight coal mills supplying PRB coal. Designturbine throttle conditions are 1000F/1000F and 2400 psig.

The units were recently retrofitted with distributed control systems for the boiler and auxiliarycontrols. These modifications enhanced unit reliability, improved thermal performance and providedcontinuous dispatch capability.

With the ramp rate and basic performance parameters provided by the DCS, Entergy initiated anoptimization project in 2005. The results of a similar project at Entergy Independence were presented


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last year (Reference 1), however that project included a smart soot blow system as part of theoptimization system.

Due to budget constraints, the smart soot blow portion of this project was delayed. Since soot blow hasa dominant impact on the boiler energy distribution, this project was not initially able to capitalize on

the associated benefits. A comparison to the results presented in reference 1 is included to illustrate theexpected impact of the smart soot blow system. The installation of a smart soot blow system into eachof the White Bluff units was completed during the summer of 2006 and provided additional heat ratebenefits.

The primary objectives in this project were the reduction of NOx emissions and extracting heat ratebenefits while trimming the frequency and duration of high steam temperatures. The distribution ofenergy between the superheat and reheat sections of the boiler as the unit varies in load promotes achallenging steam temperature control problem. This paper discusses these challenges and the resultsfrom Unit 1.

DYNAMIC OPTIMIZATION REQUIREMENTS

The optimization objectives defined by Entergy were:

Lower NOxemissions

Reduce unit heat rate

Minimize steam temperature frequency and duration above 1010F

Support faster unit ramp rate

Since NOx reduction was a prime project objective, prior to initiating the optimization project at thestation, a full retrofit of the 152 drives for the air dampers was undertaken. These pneumatic drives

with internal I/P provided independent control of each air damper resulting in increased flexibility inadjusting the air distribution. The cost impact for these additions was minimized by utilizing a fieldbusinterface to the DCS.

The heat rate parameters available to the optimization system included the prime control variables ofsuperheat and reheat steam temperatures, superheat and reheat spray flow, excess O2 and air heaterexit gas temperature. The objective of the optimization system was to drive these parameters towardstheir optimum values by reducing variability and maintaining adequate margin to alarm conditions.

The station had assigned a significant performance penalty to high steam temperature conditions due topast experiences with boiler tube failures and issues with high temperature steam components. A

paramount objective was to minimize the frequency and duration of steam temperatures above the1010F threshold, designated a Plant Operational Excellence limit. Due to these temperature issues, thedispatch rate was sometimes lowered below 20 MW/min. Another objective was to sustain the dispatchrate of 20 MW/min over the load range and extend the target rate to 25 MW/min in the near future.

The challenge of meeting these objectives was intensified by the recent operating mode transition towide load swings. A shortage in coal supply had forced the unit into a coal conservation mode. During


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daily off-peak power periods the unit loads were lowered to less than 50%, then raised to near full loadduring peak periods. The units were required to dispatch quickly between these load demands and werein nearly continuous dispatch mode at all loads.

This mode of operation amplified the difficulties of steam temperature control. Drum units have a

characteristic issue associated with the distribution of energy between superheat and reheat sections asa function of load. The ability to provide sufficient reheat steam temperature at low load conditionsrequires a large reheat surface area due to the low cold reheat steam temperature. As load is increased,the cold reheat steam temperature increases linearly, thereby increasing the hot reheat temperaturecapability. This can result in excessive reheat sprays at high load to maintain reheat setpoint. On theflip side, the proportion of energy absorbed by the superheat sections drops as load increases, tendingto lower superheat potential. When burner tilts or other direct energy distribution mechanism isprovided, it typically functions to strike a balance between superheat temperatures controlled by spraysand reheat temperatures controlled by sprays.

For these boilers, the ratio of superheat to reheat heat absorption resulted in low reheat temperatures at

low load and low superheat temperatures at high load with high reheat spray flow. This characteristiccombined with the dispatch mode of operation, challenged the regulatory steam temperature controlsystem. During dispatch the steam temperatures would quickly accelerate from a low temperaturecondition (zero spray) through set point to a high steam temperature as the spray system responded.Similarly, the steam temperatures would drive down, with no control as sprays shut off. Thesecharacteristics made this unit a prime candidate for a model predictive approach which can anticipateincreasing steam temperature and take appropriate control steps in advance and thereby prevent highpeaks without excessive spray at lower steam temperatures.

The commissioning and subsequent operation was to be accomplished with the unit operating indispatch mode with load cycles from near minimum load to near maximum several times per day.Figure 1 presents the load profile over a 20 day period following system commissioning.

FIG. 1- LOAD PROFILE


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Typical steam temperature control response prior to the commissioning of the optimization system isillustrated in Figure 2. The combination of the load variations and boiler thermal characteristicschallenged the ability of the DCS to catch peak superheat temperatures and hold setpoint.

FIG. 2- TYPICAL PRIOR STEAM TEMPERATURE CONTROL

This trend presents the load, A/B side superheat temperatures and setpoint, and A/B side reheattemperatures and setpoint for a one week period. At low loads the superheat and reheat setpoints arelowered to provide temperature control capability. At high loads, the reheat temperature easily makessetpoint, but the superheat temperatures fall off due to the boiler characteristics.

The high temperature spikes in superheat temperature typically correspond to steep load ramps and

usually follow operation with temperatures below setpoint. Load ramps with mill changes result inover-firing scenarios that require precise control of superheat sprays from zero to very high values. Thepeak temperatures were 1028/1021F with standard deviations of 16.5/16.6F. The superheattemperature is above the threshold of 1010F approximately 0.51% of the period.


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Since superheat temperature translates into reheat temperature in a very short time, the reheat controlloop also has a difficult task. The peak temperatures were 1028/1027F with standard deviations of35.8/34.3F. The reheat temperature is above the threshold of 1010F about 5.4% of the period.

DYNAMIC OPTIMIZATION METHODOLOGY

The optimization solution applies a neural net/model predictive control combo which is described insome detail in Reference 1. This system combines model predictive control with its dynamic processmodels and neural nets with its quasi steady state gain derivations to provide both control andoptimization for a dispatching unit.

Model predictive control models are well suited for processes like steam temperature control and O2control where the variables are related by thermodynamic, chemical or control relationships. Forexample, final steam temperature is related to superheat spray with a particular gain based on steamproperties and a time response based on metal mass and steam flow.

Neural networks are well suited for processes like NOxwhere the relationship to dampers is dependenton other dampers. The air dampers form a parallel network and are interactive suggesting a neuralnetwork approach.

Neural networks can be designed to be dynamic, but this increases the computational overheaddramatically resulting in a compromise to the size of the solution or the execution interval. Also, neuralnets pose no advantage to processes with known physical relationships and can in fact reduce theaccuracy of response for such systems. This arises from the statistical characteristics of neural netlearning from noisy plant data.

The approach with this system is to combine the two methods and apply each to its strengths: modelpredictive control to fast dynamic manipulation and neural nets to variables like NOx.

FIG. 3- NEURAL NET/MODEL PREDICTIVE CONTROL COMBO

Figure 3 illustrates the merging features of the neural net and model predictive models (Reference 2).The control variables (1, 2, 3, 4 and 5) have model predictive relationships with manipulated variables(A, B, C and D). There are neural net models between a subset of these variables and anothermanipulated variable (E). The Combo features models from both the model predictive control and theneural net. This example illustrates that control variables can have a combination of model predictive

A B C D A B E1

2

3

4

5

2

3

4

Neural NetModel Predictive

A B C D E1

2

3

4

5

Combo

+ =>


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and neural net models. In this application the NOxtype control variable featured neural net models andsteam temperature type control variables applied model predictive relationships.

RESULTS

Figure 4 presents a trend of the superheat and reheat temperatures over a 20 day period of dispatchoperation with large load changes. Tan horizontal lines are placed at the 1010F superheat and reheattemperature constraint thresholds. Again, an objective of the optimization system was to limit thenumber of occasions and the duration of steam temperatures exceeding this value.

FIG. 4- STEAM TEMPERATURE CONTROL PERFORMANCE

The peak superheat A/B temperatures during the period were 1015/1016F exceeding 1010F less than10 times in the 20 days. The standard deviations were 10.6/11.4F. This represents a 10F reduction inpeak superheat temperature and 33% reduction in standard deviation. The superheat temperature isabove the threshold of 1010F approximately 0.29% of the period, a reduction from 0.51%.


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The peak reheat A/B temperatures during the period were 1011/1011F exceeding 1010F twice in the20 days. The standard deviations were 19.9/22.4F. This represents a 15F reduction in peak reheattemperature and 40% reduction in standard deviation. The reheat temperature is above the threshold of1010F approximately 0.01% of the period, a reduction from 5.4%.

The response to dispatch requirements over a shorter period is illustrated in figure 5. The unit provideddispatch service in the low load range, then ramped up to near maximum load followed by a briefperiod of high load operation prior to returning to low load operation.

FIG. 5- TYPICAL OPTIMIZED STEAM TEMPERATURE CONTROL

The steam temperature control performance illustrates the ability to tightly regulate temperaturesduring much of the load profile. However, at high loads the shift in energy from the superheat sectionis indicated by the dropping superheat steam temperatures as discussed previously.

EXCESS AIR

Another aspect of dynamic optimization impacting both heat rate and NOxformation is the control ofexcess air. The challenges to lowering excess air are increased dramatically by dispatch operation. Fueland air swings due to load changes and mill starts/stops result in large variability of the furnace exit


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O2. Excessively low O2is of great concern to operations. The combination of the concern for low O2and the large variability in the O2typically results in a significant operational margin in the O2control.

By applying a model predictive approach to the control of O2, the variability below the low constraintis reduced through aggressive constraint control. With less variability the O2operating margin can be

reduced with no increase in operational alarms. This results in a lower net O2for improved heat rateand lower NOx.

Figure 6 illustrates the results of model predictive O2control during the load dispatch operation. TheO2 exhibits the classic changes associated with transitions between low and high load. The operatoralarm of 2% is presented on the trend. The intent of the controls is to minimize the occurrences of lowalarms while holding O2 levels to low values. The low constraint of the O2 control was 2.3%. Theresults indicate that the O2levels did not exceed the alarm limit over the entire span of data. Combinedwith the improved steam temperature control response, the unit now has the capability of dispatchingcontinuously at 20 MW/min.

FIG. 6- EXCESS AIR (O2) CONTROL


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SMART SOOT BLOW POTENTIAL

One way to address this energy distribution issue is to apply soot blow sequencing in a more effectivemanner. However, due to budget constraints, the installation of a smart soot blow system was delayedand completed this summer. Through effective soot blow techniques, a more favorable energy

distribution provides increased superheat temperature, lower reheat spray flow and lower air heater exitgas temperature with preliminary estimates of 0.1 to 0.2% heat rate improvement at the high loadcondition.

The following trend illustrates the performance at another Entergy unit of the same design and capacitywhere smart soot blow was included in the optimization system (Reference 1). The trend illustrateshigher superheat temperature performance at the high load conditions. The contrast of the unitsprovides an indication of the effectiveness of more effective soot blowing on maintaining highersuperheat temperatures and improved temperature side to side balance with the accompanyingimprovements in thermal performance.

FIG. 7- STEAM TEMPERATURE CONTROL WITH SMART SOOT BLOW

This data is from Entergy Independence Unit 1 with a nearly identical boiler and turbine. It features asimilar dynamic optimization system, but includes the smart soot blow system. The average superheat


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temperature increase was 8F over its baseline condition. Comparison with high load data from WhiteBluff, post optimization, indicates a 7F higher average superheat temperature at Independence andcomparable average reheat temperatures, suggesting that the smart soot blow system is primarilyresponsible for the superheat temperature increase and its associated heat rate benefit. A side benefitwas reduced bottom ash maintenance and greatly reduced erosion damage of soot blowing to boiler

tubes.

TOTAL BENEFITS

These improvements in steam temperature and O2control along with air damper optimization result inNOxand heat rate performance improvements. The Delta Heat Rate methodology (Reference 1) wasapplied to provide an on-line assessment of the benefits. The components include those controlledvariables that are controlled or influenced by the optimization system. These include:

NOx

Excess air (O2)

Superheat and reheat temperature Superheat and reheat spray flow

Air heater exit gas temperature (AHEGT)

The trend presents the data from the 20 day dispatch period. The baseline values were derived fromperiods of steady load operation prior to the installation of the optimization system. Since thesecomparisons are occurring dynamically during dispatch operation there are transient periods whenbenefits are negative. For example, during load increases when the unit is firing up, superheat andreheat spray flows increase sharply over steady load values. However, these are offset by other periodsof operation. The key assessment is to determine the average or mean benefit over extended periods.


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FIG. 8- DELTA HEAT RATE ON-LINE BENEFITS

The statistics for the 20 day period are presented in Table 1. Although the unit was operating indispatch mode through a wide range of loads, the NOxlevels were reduced an average of 18% and the

heat rate was improved by 0.98%.

Table 1- DELTA HEAT RATE ON-LINE BENEFITS STATISTICS


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CONCLUSIONS

A dynamic optimization system combining Model Predictive Control and Neural Nets operating athigh execution rates was integrated with the DCS. The system provides tighter regulation of the criticalload ramping variables thus allowing the reduction of operator margin for heat rate improvements

approaching 1% with an estimated annual value of $1M and NOx reductions of more than 15%.Through the dynamic multi variable control structure these improvements are maintained duringdispatch operation. The system provides the added benefit of lower peak steam temperatures, thusenhancing ramp rate capability while improving heat rate. The target dispatch rate of 20 MW/min ismaintained over the load range.

The comparison of results with a prior project illustrates the contributions of a smart soot blow systemto heat rate performance. Such a system was added to the overall optimization system this summer andachieved an additional 0.1 to 0.2% in heat rate benefits.

REFERENCES

1. Labbe, D., Coker, S. and Speziale, A. Entergy Independence NOx/Heat Rate Optimization andSteam Temperature Control with Neural Net/Model Predictive Control Combo, 15th Annual JointISA POWID/EPRI Controls and Instrumentation Conference, Nashville, TN, June 2005, Vol. 49.

2. Speziale, A., An Innovative Approach to Non-Linear Control with Neural Nets, Society ofPlastics Engineers Polyolefin 2004 Conference, Houston, TX, Feb. 22-25, 2004

isa expo 06 dynamicnoxheatrateoptimization entergywb oct2006

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