Siemens PowerGen12 KCPL LaCygne

LOWERING COAL-FIRED NOx THROUGH ROBUST HYBRID
COMBUSTION OPTIMIZATION
Jim Stewart
LaCygne Station, Kansas City Power & Light Co., U.S.A
Dr. Sudha Thavamani
Siemens Energy, Instrumentation, Controls & Electrical, U.S.A
Till Spaeth
Siemens Energy, Instrumentation, Controls & Electrical, Germany
Abhijit Sarma
Siemens Energy, Instrumentation, Controls & Electrical, U.S.A
Bruce Kelly
Sega Inc., U.S.A
Keywords:
Combustion Optimization, Low Emissions, Wall Fired Boiler, Siemens Power Plant Automation
SPPA-P3000, Laser based Measurement, Non-linear Optimization based on Neural Networks
ABSTRACT
In the current market conditions, steam power plants must always be run at the most profitable
operating point. This primarily demands low emissions and the most cost efficient way to
minimize emissions is to optimize the combustion process. The Siemens SPPA-P3000 process
optimization solutions allow plant operators to achieve these objectives.
This paper describes an online robust Combustion Optimization methodology used at LaCygne
Unit 2 power plant of Kansas City Power and Light (KCPL) to significantly reduce NOx
emissions on a 30-plus-year-old 720 MW B&W wall-fired boiler firing 100 percent powder river
basin coal. The Siemens SPPA-P3000 Combustion Optimization solutions were employed on the
boiler without over-fired air by utilizing the existing combustion equipment, existing combustion
controls system, and a new in-furnace laser based combustion monitoring system.
The Combustion Optimizer provides closed-loop optimization of fuel and air combination by
maneuvering the appropriate fuel and air flows in the furnace. This paper elaborates the hybrid of
model based controls with neural network optimization technology, its implementation and
operational results.
INTRODUCTION
The ever- rising demand for cost-efficient power generation and tough environmental regulation
has motivated implementation of process optimization strategies in coal-fired power generation
during the recent few years. Coal is a vital constituent of the energy source in the United States,
process optimization for stack emission reductions and efficiency improvements in coal-fired
boilers and plays a significant function in minimizing operational and maintenance (O&M) costs,
and maximizing performance and unit availability. One area receiving major interest is
optimization of the combustion process for nitrogen oxides (NOx) emission.
The investigations of the characteristics of NOx formation have facilitated scientists and
engineers to develop control methodologies for reducing NOx emissions in fossil-fuel power
plants. The preliminary research has concentrated principally on controlling NOx at the source as
NOx is formed during the combustion process. This is referred to as combustion control or infurnace NOx reduction techniques. NOx formation is promoted by rapid fuel-air mixing resulting
from high peak flame temperatures and excess oxygen.
Combustion modifications offer the most cost-effective step in controlling a number of pollutants
in addition to NOx. Advancement of guiding principles and premium practices, data from fullscale demonstrations, and evaluation of flourishing technologies present crucial information to
formulate knowledgeable decisions. Value to members may comprise of the capability to take
advantage of the anticipated NOx credit market and avoidance of higher-cost emissions controls.
Siemens SPPA – P3000 Combustion Optimization is a technique proven for reducing NOx
emissions from coal-fired boilers for enabling industrial compliance with today’s regulatory
requirements. The Combustion Optimizer provides closed-loop optimization of fuel and air
mixing by manipulating fuel and air levels to balance combustion in the furnace.
The paper describes in detail the emission improvements achieved by using the Combustion
Optimizer at LaCygne Unit 2 of Kansas City Power and Light (KCPL). The combustion
optimizer is a hybrid of model based controls with neural network optimization technology. The
control approach together with the operational results is presented.
LACYGNE POWER PLANT UNIT 2 BOILER DESCRIPTION
LaCygne Unit 2 Power Plant of Kansas City Power & Lighting (KCPL) is located in LaCygne,
KS. It a 30-plus-year-old 720 MW B&W wall-fired boiler firing 100 percent powder river basin
coal.
La Cygne Unit 2 is a balance draft, Carolina type radiant B&W wall fired unit. It has 7 MPS-89
pulverizers and 56 B&W 2nd generation dual register type low Nox burners. It does not have an
OFA system. There are seven coal feeders and mills. At each level there are eight burners. There
are 14 independently controllable air flows.
COMBUSTION OPTIMIZATION PROJECT EXECUTION
The combustion optimization project was executed between 2011-2012 timeframe with four
major steps as shown in the Figure 1.
1. The first step was the installation of laser measurement grids inside the Boiler.
2. The second step was to carry out parametric testing based on the conditions of the boiler.
This was followed by the deductions and analysis of the spatial distributions.
3. Based on observations and inferences of step 2 above, decision on the required controlled
variables for the closed loop controls engineering was made, which was the third major
step of the project.
4. The fourth step was to integrate and commission the SPPA-P3000 Combustion
Optimization controls in the existing I&C system.
Figure 1 Combustion Optimization Project Execution at LaCygne Unit 2 Power Plant
SIEMENS SPPA – P3000 COMBUSTION OPTIMIZER DESCRIPTION
The Siemens Combustion Optimizer is software based solutions and does not require any
modifications to mechanical equipment. The solution comprises of Combustion Optimizer
modules for laser-based measuring technology, distribution calculation based on Computer-Aided
Tomography (CAT) procedure, a standard imaging method from the field of medical technology,
and Combustion Optimization Controls as shown in Figure 2. At LaCygne Unit 2, the combustion
optimize was completely integrated with the included spectroscopy-based in-furnace
measurements system.
Figure 2 Combustion Optimizer Module Interactions
The laser-based measurement system maps the concentration of in-furnace CO, O 2 , H 2 O and
temperature simultaneously in real time and directly in the furnace as shown in Figure 3. Laser
transmitters and receivers are arranged outside the boiler resulting in a grid of laser beams crisscrossing the furnace. Each path measures an average value for temperature, O 2 , H 2 O, and CO
simultaneously, and together, the paths are used to create a tomographic image of this plane in the
boiler, which is also displayed to the operators directly in the control room.
Figure 3 Laser –based Measurement System
The temperature and concentration distributions are calculated from the measured path averages
with the aid of the CAT procedure. This calculation is performed on the thin client as shown in
Figure 4. CAT algorithm is also used for calculating certain characteristic distribution data, e.g.
values at different grid points, averages, minimum and maximum values for different paths in the
distribution, skewness etc
Figure 4 Distribution Calculation Based on CAT Procedure
A snapshot of the grid information portraying the complete map of the combustion at LaCygne
unit 2 boiler is shown in Figure 5.
Figure 5 Concentrations and Temperatures from the grid in the Boiler
COMBUSTION OPTIMIZER HARDWARE AND SOFTWARE
The functions of the Combustion Optimizer modules ran on a SPPA-T3000 I&C system.
The system comprised of the following components:
Siemens ftServer, e.g. for archiving and optimization
Thin Client with software for CAT display and for Neural networks (PM/SQP)
Standard interface to existing DCS
The Siemens ftServer contains general SPPA-T3000 applications such as the web server, central
project containers and the archive. Applications can be accessed via the user interfaces using a
standard web browser. The Microsoft Windows server operating system installed on the Siemens
server offers users the convenience of a Windows-based application environment.
The hardware configuration of SPPA-P3000 Optimizer at LaCygne Unit 2 Power Plant’s unit 2 is
shown in Figure 6.
The Laser Measurement System is connected to the Siemens ftServer through OPC (object
linking and embedding for process control) via TCP/IP. There is also a thin client connected to
the ftServer. The Siemens ftServer is connected to the Bailey Infi90 DCS via OPC through a
Rovisys application server. This permits access to measured data from the existing DCS, such as
coal and air flows or CO and NOx in the flue gas, by the combustion optimization process. It
also facilitates sending optimization process signals back to the DCS.
Figure 6 Optimizer Hardware Configuration
The Optimizer control functions are implemented on the Siemens Server in standard T3000
control logic format. Function plans can be generated or engineered in configuration mode.
Operation mode is for interaction with the Optimizer and observation of trends and displays
including dynamic presentation of actual and historic values. Displays of function plans in
operation mode show the actual values of all signal connections between function blocks.
SIGNAL TRANSFER BETWEEN SIEMENS OPTIMIZER AND BAILEY INFI90 DCS
The basic block diagram for signal transfer between the Combustion Optimizer and Bailey Infi90
DCS is shown in Figure 7.
There is a watchdog signal available for the communication verification between the optimizer
and the DCS. The process measurements from the DCS along with the laser measurements data
from the boiler are used to calculate the optimized values in SPPA-P3000 Optimizer. These
values are provided as the additive biases to the existing setpoints in the DCS which are
transferred to the basic underlying controls and to the field.
Safety measures are followed in sequence for incoming setpoint biases before they are used in
control and boiler processes. The first safety measure is the minimum and maximum limitation
setpoint biases from the Optimizer should be within. Next there is a ramp up and ramp down rate
for changes of the incoming setpoint biases so the values do not change dramatically. The next
safety measure before transferring the setpoint biases from the SPPA-P3000 Optimizer to the
DCS is a bumpless transfer switch. The Operator can switch ON/OFF the SPPA-P3000
Optimizer controls smoothly using this switch. If the Optimizer loses connection to the DCS, the
control interface design automatically transfers control back to control room operators.
Figure 7 Signal Transfer between Combustion Optimizer and DCS
Operator has to place a specific function in optimization mode to enable the optimizer biases.
There are enable switches for the compartment secondary airflow optimizer biases, the secondary
air damper north/south optimizer biases and the individual mill optimizer feeder bias.
COMBUSTION OPTIMIZATION CONTROLS CONCEPT
Siemens Combustion Optimizer targeted on reducing the emissions under stringent plant
operational constraints. The plant engineer has insight into precisely what the optimizer has been
acquainted with and how the process is responding. The controls concept involved ways to
minimize emissions by applying primary measures based on optimized combustion adjustments.
Combustion optimizer technology was designed to give operators a better understanding of the
plant’s data and how to prioritize it to reduce emissions.
The Combustion Optimizer reduces uncertainties surrounding the actual combustion process
allowing operators more power to overcome the considerable variations normally occurring to
fuel properties, fuel flow rate imbalances, load range, and air flow disturbances.
o Hybrid Structure of Closed Loop Controls
The combustion optimizer is a hybrid of model based controls with neural network optimization
technology. As shown in Figure 8, it provide closed-loop optimization of fuel and air mixing by
manipulating fuel and air levels to balance combustion in the furnace. Different models to
represent the boiler behavior were built for the combustion optimization based on classical
control theories and based on neural networks. A model based on physical equations requires a
large number of parameters, some of which are unknown. For these situations it is viable to use a
neural network for modeling the boiler behavior in addition to the model based controllers.
Figure 8 Hybrid Structure of Combustion Optimizer
Figure 9 Combustion Optimizer Controls Overview
The Figure 9 shows an overview of the SPPA-P3000 Combustion Optimization controls.
The main controls for emission reduction are

CO distribution balancing
o using secondary air north/south damper trim
o using compartment airflow setpoint

Staged Combustion
o Fuel staging using Feeder bias
o Air staging using compartment airflow setpoint

O 2 Reduction

Neural network optimization
The Optimizer is impacted by certain constraints. If the constraints are not met then the optimizer
biases are either ramped up/down to zero or are maintained at the same value (frozen) till the
constraints are valid. These constraints are also portrayed in Figure 9.
CO distribution balancing using Secondary Air Biases:
The objective behind the CO balancing is to uniformly spread the CO distribution in the boiler.
Distributing the air properly is a critical step in combustion optimization.
The CO distribution in the boiler, determined from the laser measurements through CAT
algorithm, was used as control inputs for balancing. Two models were developed one each for the
front side and rear side of the boiler. Each of these models has the weighted average of CO
measurements calculated for north and south across the boiler. These weighted average act as the
control deviation for the PI controllers. As shown in Figure 10, a vertical distribution model was
developed for every level in the front and rear of the boiler which determined the amount of
secondary air bias for the corresponding level proportional to the distribution of secondary air
north/south position setpoint for level A through level G in the boiler.
Figure 10 CO Distribution Balancing using Secondary Air North/South Damper Trim
CO distribution balancing using Compartment Air Setpoint Biases:
The CO distribution balancing using the compartment air setpoint biases was performed by
balancing the compartment air between the front and rear sections of the boiler. The CO
distributions in the boiler, determined from the laser measurements through CAT algorithm, were
used as control inputs. A model was built based on weighted average for CO measurements for
the front and rear sections of the boiler. This model, as shown in Figure 11, determined the
compartment air setpoint bias proportional to the distribution of secondary air flow setpoint for
level A through level G.
Figure 11 CO Distribution Balancing using compartment Airflow Setpoint Bias
Staged Combustion
In wall-fired unit, the countermeasures involved in lowering NOx include air staging and fuel
staging. At LaCygne Unit 2, as shown in Figure 12, staged combustion technique for NOx
control involved adding a portion of the secondary air (staged air) from the main combustion
zone to the top levels and reducing the amount of secondary air at the bottom levels. The
compartment air adjustments are made to move air to air-starved regions in the furnace and to
improve air and fuel mixing within regions.
In addition to this, the fuel staging is performed by adding supplementary fuel on the bottom
levels and reducing the same amount of fuel on top level of the boiler which sums up the total
feeder bias to zero. This methodology reduces stoichiometry during combustion and thus
minimizes formation of both fuel and thermal NOx. Hence the staged combustion helps in better
completion of the combustion process in the boiler.
Figure 12 LaCygne Unit 2 Staged Combustion
The Feeder Optimizer biases constraints were based on a maximum allowable stack CO,
maximum allowable mill dP, maximum allowable mill Amps and minimum mill outlet
temperature. The block diagram for the feeder bias controls is shown in Figure 13.
Max. Allowable CO
DCS
Stack CO
CONST
Min DmprPos
Tn, kp-up, kp-down
Max
Max. Allowable Amps
CONST
Pulv A Motor Amps
Max
SP
Max. Allowable dP
CONST
Pulv A Mill dP
I
Max
MinMax
UL
Min. Mill Temp
CONST
Pulv A Outlet Temp
Min
SP
LL
Max. Allowable Amps
CONST
Pulv C Motor Amps
Max. Allowable dP
MinMax
I
Max
CONST
Min
UL
Min. Mill Temp
Pulv C Outlet Temp
CONST
SP
Max
CONST
Pulv C Mill dP
MinMax
SP
MinMax
CONST
LL
Max. Allowable Amps
SP
CONST
Pulv E Motor Amps
Max. Allowable dP
I
CONST
Pulv E Mill dP
Max
Min. Mill Temp
CONST
Pulv E Outlet Temp
Min
I
Max. Allowable dP
Max
I
CONST
Pulv F Mill dP
Max
Min. Mill Temp
CONST
Min
CONST
Max
CONST
Max
CONST
Min
Pulv F Outlet Temp
Max. Allowable Amps
Pulv G Motor Amps
Max. Allowable dP
MinMax
Level G
Comp.
Master
Bias
SP
CONST
Pulv G Mill dP
UL
DCS Watchdog
Switch ON
Logic
MAX
MIN
RAMP
Ramp
Rate
Ramp
Rate
&
CONST
0
SWITCH
Watchdog
Mill in Auto
Unit Load
Ramp Rate
Compartment
Master
Optimizer
Biases
.
Econ Avg Temp
Comp Master
Opt Bias ON
BUMPLESS
.
.
Min. Mill Temp
Pulv G Outlet Temp
SWITCH
DCS
DCS
.
Track to 0
Max. Allowable Amps
Pulv F Motor Amps
RAMP
Level D
Comp.
Master
Bias
Level F
Comp.
Master
Bias
UL
MAX
MIN
Level C
Comp.
Master
Bias
MinMax
UL
CONST
Level B
Comp.
Master
Bias
Level E
Comp.
Master
Bias
SP
CONST
Level A
Comp.
Master
Bias
MinMax
Max
Ramp Rate
Min, Max
CONST
AND
Load
Range OK
Figure 13 Block Diagram for the Fuel Staging Logic
SWITCH
BUMPLESS
ON
o Excess O 2 Reduction
The minimization of excess air is, in fact, one of the most direct and effective primary measures
(combustion regulation adjustments) for optimizing performance and NOx emissions in any type
of boiler. However, boiler operators are extremely reluctant to use this type of adjustment, due to
the possible creation of sub-stoichiometric areas in the furnace which may cause high levels of
unburnt fuel or even a plant shutdown. Therefore, relatively high base levels of excess air are
habitually used, in spite of its negative effect on heat rate and on the generation of NOx, with
priority being given to considerations of operational safety. However, this “critical” parameter is
usually calculated as the average of measurements taken at only 4 to 8 points in the boiler outlet,
whose representativeness, with regard to the combustion conditions near each burner, is very
limited. With the knowledge of the real-time O2 laser measurement values in the boiler, the
Combustion Optimizer has better control intelligence for providing the O2 setpoint bias.
The CO distribution balancing and the staged combustion controls helped to allow the reduction
of the O 2 excess for reducing the emissions. This additional logic using CO concentration was
implemented to determine the O 2 setpoint correction dependant on the actual combustion
situation in the boiler. The CO values in the boiler from the laser measurements and the stack CO
from the DCS were used to determine the rated CO concentration. The CO setpoint to the integral
control was determined as a function of the unit load from the DCS. The optimization controller
also has the lower limit for O 2 reduction. The Constraints related to O2 SP Optimizer Bias were
based on the maximum allowable stack CO. As shown in Figure 14, during the NOx/CO
calibration phase and during load ramps, the O2 SP bias is maintained at the same value (frozen).
Figure 14 Block Diagram for O 2 Setpoint Reduction Logic
Neural Network Model Overview
Neural network technology can be viewed as a multivariate nonlinear nonparametric estimation
tool. It shares a descriptive term from biology in that they are represented as networks of simple
neuron-like processors. This highly adaptive technology uses a unique combination of neural
network and chaotic systems algorithms to learn the complex interactions of process variables
from historical data.
The manipulated variables for the neural model used at LaCygne Unit 2 were secondary air
north/south damper positions, compartment airflow setpoint and feeder speeds. Some of the input
variables comprise of laser measurement readings of temperature, O2 and CO profiles,
unspecified basic conditions, such as load. The output variables include the stack CO
measurement. The target value to be optimized was the Stack NOx.
The neural boiler model was trained, as shown in Figure 15, during the commissioning phase
based on real process data for these variables. Sensitivity analysis were performed based on this
model so as to find out which manipulated variables have the greatest impact on output or target
values. At the end of this sensitivity analysis, a neural boiler model is produced simulating
essential impacts of controlled variables on target values.
Figure 15 Neural Network Training
The model created during the commissioning phase replicates the behavior of the boiler in this
phase. Slagging and mill wear sometimes have a significant effect on boiler behavior, however.
These gradual changes are detected by means of online adaptation of the boiler model, as
represented in Figure 16. For this purpose the calculated output and target values of the model are
compared with real process data.
With the help of the neural network, the optimum manipulated variables for the specified target
of NOx reduction are determined with due consideration of constraints. The constraints were
related to maximum allowable stack CO and Opacity. The other modeling constraints were to
keep the total air and the sum of feeder speeds constant.
An interior-point method with a track record of fast convergence for large-scale optimization
problems with constraints was used to solve this optimization problem. The optimization method
varies the input variables of the neural network until the target values have been minimized. This
iteration produces optimum setpoints for manipulated variables, such as the different secondary
air flows. The iteration process is portrayed in Figure 17.
Figure 16 Neural Network Online Adaptation
Figure 17 Neural Network Iterative Optimization
An example of neural network training during the situations when there are no load ramps and
iterative optimization of stack NOx is shown in Figure 18. The red trend represents the actual
NOx values and blue trend represents the neural network predicted values.
Figure 18 Neural Network Training & Prediction
BOILER OPERATION IMPROVEMENTS WITH COMBUSTION OPTIMIZER
Figure 19 and Figure 20 illustrates snapshots of the long term emission trends with and without
the Combustion Optimizer in operation on two different days. The magenta color trend represents
the unit load, the black color trend corresponds to excess oxygen and CO is shown in blue color
trend. The NOx is shown as red color trend with the flooded green characterizing Combustion
Optimizer ON/OFF.
As can be seen in Figure 19 and Figure 20, more than 20% reduction in NOx was achieved when
the optimizer was switched ON.
Figure 19 NOx Improvement Trend
Figure 20 NOx Reduction Trend
CONCLUSION
The Combustion Optimizer was tested for different plant conditions including different unit
loads, different coal, etc.
The principle benefits of using Combustion Optimizer at LaCygne Unit 2 Power Plant were:
Enhanced and balanced combustion
Better CO distribution in boiler
Supplementary O 2 reduction based on the balanced combustion
Better centralization of the fireball
Staged Combustion achieved emission reductions
Overall the Combustion Optimizer helped in achieving the objective of reducing the NOx
emission rate from an annual average of about 0.31 lb/mmbtu to 0.25 lb/mmbtu.
The emission improvements attributed largely to the combustion balancing controls, fuel/air
staging controls and O2 reduction controls. Coal and air flow staging control solutions for
controlling the distribution of air and pulverized coal flow to individual boiler level resulted in
lower emissions. O2 setpoint reductions proved to boost the benefits of staged combustion
producing even lower NOx emissions.