characterization tests, the ISA code and its implementation at Lambton. ..... -5.5. 1.1. 5. -12. 2.4. 5. UE, CC & SW. -30. 1.5 to 2. -2. 1. -10. 2.5. 4. -6.0. 1.2. 5. -16.
Sootblowing Optimization: Part II Soootblower Characterization and Implementation of an Intelligent Sootblowing Advisor1 Nenad Sarunac, Carlos E. Romero, Jiefeng Shan and Xiadong Bian, Energy Research Center, Lehigh University Bruce Clements and Richard Pomalis, CANMET Energy Technology Centre – Ottawa James Henrikson, Walenty Cylwa and Joseph Luk, Ontario Power Generation
ABSTRACT Furnace and convective pass slagging and fouling have a detrimental effect on boiler performance and emissions and represent the primary cause of reduced operating efficiency in fossil-fired boilers. Sootblowing is used to control the level of ash and slag deposits on the boiler heat transfer sections. For best boiler performance it is important to maintain an optimal balance between furnace and convective pass heat transfer. The Energy Research Center (ERC) has developed a practical sootblowing optimization and intelligent sootblowing approach for balancing furnace and convective pass heat transfer to improve boiler performance, reduce NOx emissions, and minimize disturbances caused by sootblower activation. The approach is implemented as an Intelligent Sootblowing Advisor (ISA), which depends on data and knowledge bases containing the effects of sootblower activation on cleanliness, boiler performance and emissions. Sootblower characterization tests are needed to develop a sootblower characterization database. This database is used to develop a knowledge base that is employed, along with live process data, to determine an optimal sootblower activation strategy. Details of the ERC approach are described in Part I of the paper. The ERC has teamed up with CANMET and OPG to develop and implement the ISA at Lambton Unit 3. CANMET has developed and implemented the Ash Monitoring System (AMS), which calculates cleanliness factors for boiler convective pass sections. These calculations are described in Part I of the paper. This paper describes results of sootblower characterization tests, the ISA code and its implementation at Lambton.
1
Prepared for presentation at Combustion Canada 2003, September 21-24, 2003, Vancouver, BC, Canada.
INTRODUCTION As described in Part I of the paper, the ERC has developed a practical sootblowing optimization and intelligent sootblowing approach, Refs. [1], [2], [3], for balancing furnace and convective pass heat transfer to improve boiler performance, reduce NOx emissions, and minimize disturbances caused by sootblower activation. The approach is based on a minimal data (volume and quality) and additional instrumentation requirements. The ERC approach depends heavily on a sootblower characterization database that describes the effects of activation of individual sootblower groups on cleanliness of boiler heat transfer sections and boiler performance and emission parameters. The data for this database are obtained by performing a series of sootblower tests. These tests are performed in the following fashion: • •
Plant sootblowers are divided into groups according to their physical location and potential impact on boiler cleanliness. The effect of the each sootblowing group on surface cleanliness, emissions and performance is determined in a series of sootblower characterization tests where one sootblowing group is activated at a time and unit response to the sootblowing event is recorded. The tests are performed at constant boiler operating conditions.
Boiler response to sootblower activation, that is, changes to steam temperatures, spray flow rate, emissions, opacity and other parameters are recorded as a time series. It has to be noted that slagging and sootblowing are transient processes and great care is needed in performing these tests to correctly and accurately capture boiler response to activation of individual sootblower groups. The steam temperature control system that includes desuperheating sprays and, for corner-fired boilers, burner tilts, interferes with the test and increases the complexity of the problem. Collected test data, including information on sootblower activation times are analyzed off-line and used to create a sootblower characterization database. This database is used to develop a knowledge base that is employed to determine an optimal sootblower activation strategy. The optimal sootblowing schedule, that satisfies the optimization objective(s) and operating constraints, is developed using the sootblower characterization data and knowledge bases. The resulting strategy can be time-driven or event-driven. The time-driven strategy involves development of a time-driven optimal schedule, which can be followed manually by the operator or can be programmed into the existing sootblower sequencing (control) system. In a timedriven sequence, sootblowers are activated based on a pre-determined time schedule. The eventdriven strategy is implemented as the ISA code. The code runs on-line in real time and, based on the live process data (process events), recommends an optimal sootblower activation sequence. The time-driven optimal sootblowing schedule is implemented and evaluated first; the ISA code is implemented as a second step. SOOTBLOWER CHARACTERIZATION TESTS Sootblower characterization tests were performed at OPG’s Lambton Generating Station Unit 3 under full load operating conditions. The test duration was two weeks. Cleanliness of lower
furnace was correlated with the furnace exit gas temperature (FEGT). Cleanliness factors for the convection pass sections were determined by CANMET’s AMS. Lambton Unit 3 is a subcritical, divided furnace, tangentially-fired unit, rated at 520 MWe. The furnace is cleaned by 54 wall blowers, arranged in three elevations (Fig. 1), while 18 retractable blowers, arranged in seven elevations, clean heat transfer surfaces in the convective pass of the boiler (Fig. 2).
FEG T B
South
54 53
50
East
52
49
48
FEG T A
51
CAM ER AS
8
4
7
B
6
47
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3
5
A
39
2
40 W est
38
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44 N orth
35
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43
EL 690 ft
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32 31
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24 23
29 22
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EL 679 ft CO AL B U R N ER S
21 20 27
19
26
18
15 25
17
14 13
16
12 11
6
10
EL 633 ft
5 4 3
9 2
8 7
1
Figure 1. Location of Wall Blowers (IRs) in the Furnace
Figure 2. Arrangement of Heat Transfer Surfaces in Convective Pass of the Boiler and Locations of Retractable Blowers (IKs)
Typical information on the effect of sootblower activation on plant performance and emission parameters, collected during one day of sootblower characterization testing, is presented in Figs. 3 to 7. Time variation of FEGT, presented in Fig. 3, shows that FEGT decreases as furnace water walls and platen SHT are cleaned, and heat transfer to furnace walls is increased. Activation of other sootblowing groups does not affect FEGT, which increases monotonically as furnace water walls slag. Figure 4 shows corresponding changes in reheat steam temperature, which increases as retractable blowers IK 55, 56, 57, IK 58, 59, 60 and IK 61, 62, 63 are activated, RHT is cleaned, and RHT heat transfer is increased. Reheat steam temperature decreases as furnace waterwalls are cleaned and FEGT is reduced. Figure 2 shows the locations of IK groups: IK 55, 56, 57 group is located between the platen SHT and inlet RHT, IK group 58, 59, 60 is located within the RHT bundle, while IK group 61, 62, 63 is located between the inlet RHT and finishing HTSHT.
1,440
2 SBT 44 SBT 45 SBT 46 Platen HTSHT HTSHT Outlet RHT IK 55,56,57 IK 64,65,66 IK 67,68
1,420
SBT 52 Inlet RHT IK 59,60
1,400
1.5
SBT 49,50,51 Furnace
1,380 SBT 47 Inlet RHT & HTSHT IK 61,62,63
1,360 1,340 1,320
1 SBT 48 Outlet RHT & LTSHT IK 69,70
0.5
FEGT 1,300
SB Activation 1,280 8:00
9:00
10:00 11:00
12:00 13:00 14:00 15:00 16:00 17:00
0 18:00 19:00 20:00 21:00
Tim e
Figure 3. Effect of Sootblowing and Slagging on FEGT
565
2 SBT 44 Platen HTSHT IK 55,56,57
560 555 550
SBT 49,50,51 Furnace Elevations SBT 52 Inlet RHT IK 59,60
SBT 45 HTSHT IK 64,65,66 SBT 46 Outlet RHT IK 67,68
545
1.5
1
540
SBT 47 Inlet RHT & HTSHT IK 61,62,63
535
Trht
530
SBT 48 Outlet RHT & LTSHT IK 69,70
0.5
SB Activation 525 8:00
9:00
10:00 11:00
12:00 13:00 14:00
15:00 16:00 17:00
18:00 19:00
Tim e
Figure 4. Effect of Sootblowing and Slagging on FEGT
0 20:00 21:00
It is, therefore, expected that activation of these groups will affect cleanliness of the inlet RHT and increase the reheat steam temperature. The increase in reheat stream temperature is large, of the order of 15°C. Since the output of the IP turbine is affected by the reheat steam temperature, changes in this quantity affect gross unit output, Pg. The time variation of Pg (Fig. 5) shows that sootblowing and slagging have a significant effect on unit power output. At Lambton, cleaning of the inlet RHT increases power output by more than 5 MWe, while over-cleaning of furnace walls (activation of all furnace wall blowers) reduces power output by as much as 7 MWe. However, since furnace walls slag much faster than the inlet RHT, the negative effect of furnace cleaning on the power output is much shorter (about one hour) compared to the positive effect that is achieved by cleaning the RHT. 515
2 SBT 44 Platen HTSHT IK 55,56,57
SBT 52 Inlet RHT IK 59,60
Pg SB Activation
510
1.5
SBT 48 Outlet RHT & LTSHT IK 69,70
505
500
SBT 45 HTSHT IK 64,65,66
495 8:00
9:00
10:00
1
SBT 47 Inlet RHT & HTSHT IK 61,62,63
0.5 SBT 49,50,51 Furnace
SBT 46 Outlet RHT IK 67,68
11:00 12:00 13:00 14:00
15:00 16:00 17:00 18:00
0 19:00 20:00 21:00
Tim e
Figure 5. Effect of Sotblowing and Slagging on Gross Unit Output
Figures 6 and 7 show the effect of sootblowing and slagging on the main steam temperature, Tmst and desuperheating spray (expressed as a percentage of the desuperheating valve opening). As long as the desuperheating spray valve is within the controllable range, the main steam temperature setpoint (538°C) is maintained. Activation of IK group 55, 56, 57 reduces the spray valve opening because, with the cleaner platen SHT and inlet RHT, less heat is available to the LTSHT. Cleaning of the furnace walls, with the resulting decrease in FEGT has the same effect. As the spray valve closes below approximately 15 percent, the setpoint cannot longer be maintained and Tmst decreases. As furnace waterwalls slag back up, Tmst increases since more heat is available to the convection pass. The desuperheating spray valve remains closed until Tmst approaches the setpoint value.
550
2 SBT 49,50,51 Furnace Elevations
545
SBT 52 Inlet RHT IK 59,60
540 SBT 45 HTSHT IK 64,65,66
SBT 44 Platen HTSHT IK 55,56,57
535
1 SBT 46 Outlet RHT IK 67,68
530
Tmst
525
SBT 47 Inlet RHT & HTSHT IK 61,62,63
SB Activation
SBT 48 Outlet RHT & LTSHT IK 69,70
0.5
520 8:00
9:00
1.5
10:00
11:00 12:00 13:00 14:00
15:00 16:00 17:00 18:00
0 19:00 20:00 21:00
Tim e
Figure 6. Effect of Sootblowing and Slagging on Main Steam Temperature 45
2 SBT 44 Platen HTSHT IK 55,56,57
40
SBT 49,50,51 Furnace Elevations
Valve Opening SB Activation
35
SBT 52 Inlet RHT IK 59,60
30
1.5
25 1 20 15 10 SBT 45 HTSHT IK 64,65,66
5 0 8:00
9:00
10:00
11:00
SBT 46 Outlet RHT IK 67,68
12:00
13:00
SBT 48 Outlet RHT & LTSHT IK 69,70
SBT 47 Inlet RHT & HTSHT IK 61,62,63 14:00
15:00
16:00
17:00
18:00
0.5
19:00
20:00
0 21:00
Tim e
Figure 7. Effect of Sootblowing and Slagging on Desuperheater Valve Opening Figures 8 and 9 show the effect of sooblowing and slagging on the fouling factor of the platen SHT and inlet RHT. Results show that activation of IK group 55, 56, 57 has a large effect on the cleanliness of the platen SHT, which increases as this surface is cleaned. The fouling factor decreases exponentially after cleaning is completed and the section slags again. As the results
show, recuperation time for the platen SHT fouling is about 6 hours. Figure 8 shows that other sootblower groups also affect cleanliness of the platen SHT. Figure 9 shows time variation in fouling factor (cleanliness) of the inlet RHT.
Scaled Fouling Factor for Platen HTSHT
0.55
SBT 44 Platen HTSHT IK 57,56,55
0.50
2 SBT 49, 50, 51 Furnace Elevations
SBT 47 Inlet RHT/HTSHT IK 61,62,63
SBT 45 HTSHT IK 64,65,66 SBT 46 Outlet RHT IK 67,68
1.5 SBT 48 Outlet RHT & LTSHT IK 69,70
0.45
1 0.40
0.5 0.35
SBT 52 Inlet RHT IK 59,60
FF Platen SHT SB Activation 5 per. Mov. Avg. (FF Platen SHT)
0.30 8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
0 21:00
Time
Figure 8. Effect of Sootblowing and Slagging on Cleanliness of Platen HTSHT 0.60
2 SBT 44 Platen HTSHT IK 57,56,55
0.55
SBT 45 HTSHT IK 64,65,66
SBT 46 Outlet RHT IK 67,68
0.50
SBT 47 Inlet RHT/HTSHT IK 61,62,63
SBT 48 Outlet RHT & SBT 49,50,51 LTSHT Furnace IK 69,70 SBT 52
1.5
Inlet RHT IK 59,60
0.45
1
0.40 0.5 0.35
FF Inlet RHT SB Activation 5 per. Mov. Avg. (FF Inlet RHT)
0.30 8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
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0 21:00
T im e
Figure 9. Effect of Sootblowing and Slagging on Cleanliness of the Inlet RHT
Similar data were collected for other sootblower groups. Replicate tests were conducted for consistency and accuracy. Results show that some sootblower groups affect more than one heat transfer surface, and some are more efficient than the others in restoring section cleanliness. Some sootblowers (for example, IK 64, 65, 66) cause no measurable change in surface cleanliness, steam temperatures and other parameters. These sootblower groups were put into the scheduled group category. In addition, test results showed that cleaning effectiveness of some sootblowers is too high and results in very large reheat steam temperature excursions. It was recommended that the pressure of the soootblowing steam for these sootblowers be reduced to provide an optimal level of cleaning. Test results were also used to divide sootblowers into three groups: must have, nice to have, and not important. This classification helped plant maintenance personnel to concentrate on the most effective sootblowers and, thus, prioritize their maintenance. DATABASE The off-line analysis of sootblower characterization test data is performed after tests are finished and test data are collected. Figures 10 and 11 summarize the effect of different furnace sootblower groups on furnace cleanliness (expressed as FEGT) and on the reheat steam temperature. Results show that both quantities are reduced as furnace walls are cleaned; the magnitude of the effect depends on the location of individual sootblowers (furnace elevation, hot or cold corner). Figures 12 and 13 show the effect of the retractable group IK 55, 56, 57 on reheat steam temperature and desuperheating valve opening. Results show that activation of this IK group, on average, increases reheat steam temperature by 14°C and also reduces spray valve opening by 14 percent. The effect of this IK group on cleanliness of the platen SHT and inlet RHT is presented in Figs. 14 and 15. Results show that, although this IK group affects both heat transfer sections, its effect on the platen SHT is much larger compared to the inlet RHT. Similar analysis is performed for all sootblower groups and other parameters of interest. SB C haracterization:Furnace W allblowers
42
5
1
13
22
26
32
38
50
57
63
65
69
14
23
33
39
51
61
51
61
0 Avg = -13 C -20
Avg = -32 C
-40
-60
Avg = -20 C
Cold Corners
All Corners Hot Corners
Cold Corners & Side Walls
-80
-100
All Corners Upper and Middle Elevations
Upper Furnace Elevation
Figure 10. Effect of Upper Elevation Wall Blowers on FEGT
71
SB Characterization, Lambton Unit 3 Upper Furnace Elevation 42
5
1
13
22
26
32
38
-6
-6
-6
50
57
63
65
69
14
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33
-5
-5
39
51
61
51
61
71
Change in Reheat Steam Temperature [oC]
0
-5 -7
-8
-7
-6
-7 -8
-9
-10 -10
-10
-10 -11
-11
-12
-15
-12
All Corners
-14
Hot Corners
Cold Corners
-16 All Corners Upper and Middle Elevations
Cold Corners & Side Walls
-20 SB Characterization Test
Figure 11. Effect of Upper Elevation Wall Blowers on Reheat Steam Temperature
SB Characterization: Convective Pass 55, 56, 57 (56A Locked)
25
Change in A-Side Trht [oC]
20
18
18 17 o
Average = 14 C
15
14 13
10 9
10
5
3
0 2
12
27
40
C
44
53
59
SB Characterization Test
Figure 12. Effect of IKs 55, 56, and 57 on Reheat Steam Temperature
67
SB Characterization, Lambton Unit 3 55, 56, 57 (56A Locked)
60
Before SB After SB Difference
Desuperheating Valve Opening [%]
50 40 30 20 10
3
0 -4
-10
-6
-7 Average = -14 %
-20
-10
-12
-24
-30 2
12
27
40
C
44
53
59
67
SB Characterization Test
Figure 13. Effect of IKs 55, 45 and 57 on Desuperheating Spray Valve Opening
SB Characterization, Lambton Unit 3 Platen HTSHT: IK 55, 56,57 (IK 56A Locked)
Scaled Fouling Factor for Platen HTSHT
0.60 Before SB After SB Change in FF
0.50
0.40
0.30
0.20
0.15 0.12
0.11
0.11
0.13 0.10
0.10 0.05 0.00 2
12
27
40
C
44
53
59
67
SB Characterization Test
Figure 14. Effect of IKs 55, 56, and 57 on Cleanliness of Platen HTSHT
SB Characterization, Lambton Unit 3 Inlet RHT: IK 55, 56,57 (IK 56A Locked)
Scaled Fouling Factor for Inlet RHT
0.60 Before SB After SB Change in FF
0.50
0.40
0.30
0.20 0.12 0.08
0.10
0.06
0.06
0.05
0.03 0.00 2
12
27
40
C
44
53
59
67
SB Characterization Test
Figure 15. Effect of IKs 55, 56 and 57 on Cleanliness of Inlet RHT Table 1. A-side Sootblower Characterization Database SB Groups Furnace UE, HC UE, CC & SW ME, HC ME, CC & SW LE, All IRs Convective Pass IK 55 IK 56 IK 57 IK 58 (= IK60) IK 59 IK 60 IK 61 IK 62 IK 63 IK 64 IK 65 IK 66 IK 67 IK 68 IK 69, 70 IK 71 IK 72 IK 55, 56, 57 IK 58, 59, 60 IK 61, 62, 63 IK 64, 65, 66 IK 67, 68 IK 69, 70 IK 71, 72
∆FEGT Deg. C -30 -30 -30 -30 -30
τR hrs 1.5 to 2 1.5 to 2 0.5 to 1 0.5 to 1 0.25
∆NOx ppm -2 -2 -2 -1 0
τR hrs 1 1 0.25 0.25 0
TRHT Deg. C -9 -10 -5 -5 -6
τR hrs 2.3 2.5 0.6 0.6 0.4
∆TRHT/∆τ /∆τ ∆TMST Deg C/hr Deg. C -5.5 4 -6.0 4 -4.0 8 -4.0 8 -5.5 16
τR hrs 1.1 1.2 0.4 0.4 0.6
-11 -11 -11 -20 -20 -20 0 0 0 0 0 0 0 0 0 0 0
0.67 0.67 0.67 0.5 to 1 0.5 to 1 0.5 to 1 0 0 0 0 0 0 0 0 0 0 0
-1.7 -1.7 -1.7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0.17 0.17 0.17 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4.7 4.7 4.7 13 16 13 4.3 4.3 4.3 0 0 0 0 0 0 0 0
3.1 3.1 3.1 8.7 10.7 8.7 2.9 2.9 2.9 0 0 0 0 0 0 0 0
-1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5
-1.7 -1.7 -1.7 -8 -8 -8 -1 -1 -1 0 0 0 0 0 0 0 0
0.42 0.42 0.42 2.0 2.0 2.0 0.25 0.25 0.25 0 0 0 0 0 0 0 0
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
-33 -20 0 0 0 0 0
2 0.5 to 1 0 0 0 0 0
-5 0 0 0 0 0 0
0.5 0 0 0 0 0 0
14 14.5 13 0 0 0 0
9.3 9.7 8.7 0 0 0 0
-1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5
-5 -8 -3 0 0 0 0
1.25 2.0 0.75 0 0 0 0
4 4 4 4 4 4 4
τR hrs 2.4 3.2 1.2 1.0 1.2
∆V.Lift /∆τ %/hr 5 5 5 5 5
-4.7 -4.7 -4.7 -7.0 -7.0 -7.0 -1.0 -1.0 -1.0 0.3 0.3 0.3 0.0 0.0 2.0 2.0 2.0
0.93 0.93 0.93 1.4 1.4 1.4 0.2 0.2 0.2 0.07 0.07 0.07 0 0 0.4 0.4 0.4
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
-14 -7 -3 1 0 2 4
2.8 1.4 0.6 0.2 0.0 0.4 0.8
5 5 5 5 5 5 5
∆TMST/∆τ ∆V.Lift Deg C/hr % -12 5 -16 5 -6 10 -5 10 -6 10
These results were used to create a sootblower characterization database. This database includes information on the magnitude and duration of the effect of each sootblowing group on cleanliness of boiler heat transfer sections, NOx emission rate, steam temperatures, desuperheating sprays, opacity, flue gas temperature at boiler exit, and other parameters of interest (Table 1). KNOWLEDGE BASE AND OPTIMAL SCHEDULE The sootblower characterization database is used to develop a knowledge base that is employed to determine an optimal sootblower activation strategy. The knowledge base contains explicit knowledge in forms of rules or statements. Some of the knowledge is generic in nature, such as: how activation of furnace wall blowers affects the FEGT, NOx, reheat steam temperature and main steam desuperheating spray (if the main steam temperature is controlled by the spray). The site-specific part of the knowledge depends on the arrangement of heat transfer surfaces and location of sootblowers relative to the surfaces. For example, sootblower group A affects heat transfer sections 1 and 2, while sootblower group B affects only heat transfer section 3. The optimal sootblowing schedule, that satisfies the optimization objective(s) and operating constrains, is developed using the sootblower characterization data and knowledge bases. Optimization goals can include maintaining the FEGT below a prescribed limit for slagging control, maintaining steam temperature or flue gas temperature (for SCR applications) setpoint, lowest emission level, lowest emission level with acceptable heat rate penalty, lowest emission level with target FEGT value, etc. Typical constraints might include the FEGT limit (to control furnace slagging), steam temperature limits, opacity limits, and so on. It has to be mentioned that the FEGT can be used as the optimization goal (or a part of the goal) or as a constraint. ISA CODE The resulting optimal sootblowing strategy can be time-driven or event-driven. In a time-driven strategy, sootblowers are activated based on a pre-determined time schedule. The time-driven strategy can be followed manually by the operator or can be programmed into the existing sootblower sequencing system. The event-driven strategy is driven by the process events, such as exceeded deviation from the setpoint for target parameters, exceeded cleanliness limit, and exceeded time between sootblowing activations. The event-driven strategy is implemented as the ISA code. The ISA employs a knowledge based-expert system, sootblower characterization database, live process data, and information on cleanliness of boiler heat transfer sections to make decisions on optimal sootblower activation, that is, it selects sootblower groups to be activated when and where needed to satisfy optimization goal and operating constraints. The ISA also requires information about the elapsed time since last sootblowing activation (time since sootblowing, TSS) and recuperation time. Information about TSS is needed on two levels: for individual sootblower groups and for the heat transfer sections. Recuperation time is defined as a time, elapsed since sootblower activation that is required for a specific parameter (FEGT, steam temperature, cleanliness, etc.) to return to the initial value (value before the sootblowing event was initiated).
Sootblower groups in the furnace and convective pass are divided into two categories: controlled and uncontrolled (scheduled). The controlled groups are those that affect section cleanliness and boiler performance and emission parameters, and are employed to achieve and maintain the optimization goal. The uncontrolled groups are those that have no measurable effect on section cleanliness and/or boiler performance and emissions parameters but have to be periodically activated (once per day, for example) to prevent formation of deposits that could be difficult to remove later on. Since slagging and fouling intensity and sootblowing effectiveness are affected by changes in fuel quality, operating conditions, and maintenance status of the sootblowing equipment, the ISA needs to account for these changes in order to determine the optimal sootblowing strategy under changing operating and maintenance conditions. This is accomplished on three adaptability levels. Level 1: Since the ISA creates an event-driven sootblowing schedule, changes in slagging and fouling rates will be reflected in more or less frequent activations of sootblowers that are required to satisfy the optimization goal. Level 2: Fuzzy logic and membership function concepts are used to determine section cleanliness status from the information on the FEGT, section cleanliness factor and other process data. Membership functions are developed that correspond to the range of conditions: very dirty, dirty, normal, clean and very clean conditions. Sootblower characterization test data are used to make initial determination of what represents dirty, clean and other surface conditions. However, these definitions are not rigid and are allowed to change as process conditions change. This, auto-calibration feature, makes the process of determining section cleanliness status adaptive to changes in process conditions and eliminates the need for periodic calibrations. Figure 16 illustrates changes in definitions of dirty and clean furnace conditions as fuel quality changes. Initially, when a fuel producing hard-to-remove slag is fired, sootblower effectiveness is reduced, resulting in very high FEGT values. With such a coal, in this example, FEGT values of approximately 2,860°F correspond to dirty furnace conditions, while FEGT values of approximately 2,800°F correspond to clean furnace conditions (that is, after all furnace blowers were activated). When a different fuel, producing slag that is easier to remove is fired later on, sootblower effectiveness in removing slag from water walls is increased, resulting in significantly lower FEGT values. For the second fuel, FEGT values of approximately 2,780°F correspond to dirty furnace conditions, while FEGT values of approximately 2,740°F correspond to clean furnace conditions. Without the ability to adapt to process changes, section cleanliness status would be determined incorrectly and frequent system recalibrations would be necessary. Level 3: The ISA analyzes plant response to sootblowing and updates sootblower characterization database to account for process changes such as changes in coal quality, maintenance condition of sootblowing or firing equipment, etc. The ISA code is an on-line, real-time application that runs on a personal or plant computer supporting Microsoft Windows XP, 2000, NT, 98, and 95 applications. The code is designed as a system of modules (Fig. 17). Each module performs specific functions: range check on input
parameters, buffering and time-averaging of live process data, maintaining the sootblower characterization database, determining and displaying the cleanliness status of boiler heat transfer sections (furnace and convective pass), and determining an optimal sootblowing strategy. The following ISA modules are available: Boiler Parameters Module, Sootblowers Module, Furnace Cleanliness Module, Convective Pass Cleanliness Module, Sootblowing Advisor and Simulator Module. 2,880
1.0 DIRTY
2,860
FEGT 0.8
FEGT [oF]
2,820 0.6 CLEAN
2,800
DIRTY
0.4
2,780 2,760
SB Activation Indication
SB Activation Indication 2,840
0.2 2,740 CLEAN
2,720
0.0
0
100
200
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400
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600
700
800
900
1,000
Data Sample Number
Figure 16. Effect of Fuel Quality on Furnace Cleanliness Status Boiler Parameters Module: Performs a range check on parameters that are used to determine furnace cleanliness status, performs buffering and time-averaging of instantaneous process data and provides time-averaged data to the other ISA modules. Sootblowers Module: Maintains the sootblower characterization database and information on individual sootblower groups and calculates elapsed time since sootblowing (TSS) and the availability factor (AF) for each sootblower group. The AF parameter represents the number of sootblowers that are available for activation (that is, that are not locked or tagged out) in a particular sootblower group as a fraction of a total number of sootblowers in that group. Furnace Cleanliness Module: Determines cleanliness status of the lower furnace using information on the FEGT and other process data, such as excess O2 level, unit load, burner tilt angle, mill loading, etc. Fuzzy logic and the FEGT are used to determine the furnace cleanliness status. Convective Pass Cleanliness Modules: Determine cleanliness status of boiler convective pass sections using fuzzy logic and information on section cleanliness factor and other process data.
Figure 17. ISA Code Modules Cleanliness status results are presented in a graphical fashion, using dynamic bars. The length and color of the bar changes as the cleanliness status changes. The confidence level is highest when the dynamic bar is in a middle of the specific cleanliness interval. For example, when the dynamic bar is in the middle of the normal cleanliness interval, the confidence level approaches 100 percent. When the dynamic bar is on the boundary between two cleanliness intervals, the confidence level is lowest, about 50 percent. Sootblowing Advisor Module: Employs a knowledge-based expert system to make decisions on sootblower activations that satisfy the optimization goal and operating constraints. The sootblowing advice is presented in the advice window and includes recommendations on which sootblowing groups to activate. Separate advice is given for the controlled and scheduled sootblowing groups. Slagging/Sootblowing Simulator Module: This module is used to test ISA advice and resulting optimal sootblowing strategy, or as a training tool for the operators. The simulator uses slagging/fouling and recuperation rates and the sootblower characterization database to simulate boiler slagging/fouling and sootblowing processes in a boiler. The user can control the simulation speed, which makes it possible to quickly simulate long periods of plant operation.
IMPLEMENTATION AT LAMBTON Objectives and goals of the sootblowing optimization program at Lambton are to reduce the sootblowing frequency and erosion damage to boiler tubes, prioritize sootblower maintenance, and maintain steam temperatures close to their setpoint values. Currently, the operators have very little information on the cleanliness status of boiler heat transfer sections and sootblowers are deployed on a time schedule and at the operators’ discretion. This frequently results in steam temperatures that are too low. The ISA implementation at Unit 3 was accomplished using the OPC (OLE for Process Control) Server/Client approach. An OPC server, providing access to the distributed control system (DCS) data, was installed by ABB. The convective pass cleanliness calculations (CANMET’s AMS) and ISA were implemented as OPC clients. Schematic implementation diagram is presented in Fig. 18. OPC clients were configured to receive live DCS process data via the OPC server that was connected to the plant DCS via Ethernet. Windows NT Computer
Graphics Package
Base Sootblower Option
Display
Controller
Interpreter
Logic
OPC Clients CANMET AMS
Unit Sootblowers
Lehigh ISA
OPC Server
Data I/O
On-line Advice (ISA)
On-line Control (ISC)
Plant Network Figure 18. Schematic Representation of ISA and AMS Implementation at Lambton G.S. Unit 3 DCS screens were developed for the operators to display ISA and AMS results. The overview screen, presented in Fig. 19, summarizes the cleanliness status and TSS for all boiler heat transfer sections and displays the ISA advice. The operator can click on particular boiler heat transfer surfaces to obtain more information. Detail screens are available for each boiler heat transfer surface and include graphical representation of a section cleanliness status and time trend. The ISA was implemented at Lambton G.S. Unit 3, in February 2003, just before a scheduled unit outage. Field evaluation of ISA will continue after the outage.
Figure 19. Sample Operators’ Overview Screen with ISA and AMS Results CONCLUSIONS The challenge in developing an optimal sootblowing strategy is to determine which portions of the boiler to clean and on what schedule, considering the trade-offs between performance and emissions and other factors such as tube life, sootblower steam or air consumption and maintenance cost. The ERC and CANMET have developed and implemented an intelligent sootblowing optimization and advisory system for Lambton G.S. Unit 3. The system consists of the CANMET’s Ash Monitoring System, which uses on-line process data (steam flow, temperature and pressure) to determine cleanliness factors (fouling factors) of boiler heat transfer sections, and ERC’s Intelligent Sootblowing Advisor, which uses on-line process data, historical data from program databases, and information on cleanliness factors, supplied by the AMS code to determine optimal sootblower activation. The system issues advice to the operator on which sootblowers to activate and when. Parts of the system were in operation since early 2002, with AMS code installed and calibrated in the fall of 2002. Installation of the system was completed in February 2003, just before the unit outage for an SCR retrofit. At the time of writing this paper, evaluation of sootblower optimization benefits at Lambton was in progress. REFERENCES 1. Sarunac, N. and Romero, C., “Sootblowing Optimization and Intelligent Sootblowing,” Presented at 4th Intelligent Sootblowing Workshop, Houston, Texas, March 2002. 2. Sarunac, N., Romero, C. E. and Bilirgen, H., “Optimization of Sootblowing in Utility Boilers,” EPRI Heat Rate Conference, Birmingham, Alabama, January 28-30, 2003. 3. Romero, C.E., Sarunac, N., and Levy, E. K., “Combined Optimization for NOx Emissions, Unit Heat Rate and Slagging Control with Coal-Fired Boilers,” 28th International technical Conference on Coal Utilization and Fuel Systems, March 9-14, Clearwater, Florida.
