POWER February 2014 - 46

PLANT AUTOMATION
Using Neural Network Combustion
Optimization for MATS Compliance
The U.S. Environmental Protection Agency adjusted the language of the final Mercury
and Air Toxics Standards (MATS) regulation to recognize the value of neural
network combustion optimization systems by extending the required " boiler tuneup "
frequency from 36 months to 48 months. Neural net systems have not only
demonstrated reduced boiler emissions and improved combustion efficiency, but
they also now can reduce the administrative costs of complying with MATS.
Peter Spinney
T
he Mercury and Air Toxics Standards
(MATS), promulgated by the Environmental
Protection Agency (EPA) on
Dec. 21, 2011, set maximum achievable control
technology (MACT) emission standards
for specific classes of hazardous air pollutants
(HAPs) found in the flue gases of coal- and
oil-fired utility boilers. The emission limits
vary based on the type of coal burned and
whether the units are new or already in operation
at time of publication of the final rule.
Specifically, MATS sets removal standards
for mercury (Hg), acid gases (such as hydrochloric
acid [HCl] and hydrofluoric acid),
toxic non-mercury metals (such as arsenic,
chromium, and nickel) and organic HAPs.
MATS also limits HCl emissions (a surrogate
for acid gases) and filterable particulate matter
(PM, a surrogate for non-mercury HAP
metals). Total non-mercury HAP metals and
individual non-mercury HAP metals can be
used as an alternative to the filterable PM
limits. Coal-fired electric utility steam generating
units (EGUs) equipped with flue gas
desulfurization (FGD) systems may use SO2
limits as an alternative to HCl limits. Complying
with these complicated and interrelated
standards will require boiler operators
to develop new operating and maintenance
practices. MATS identifies neural network
optimization software as a best combustion
practice for NOx
and CO reduction.
MATS Drives Work Practice
Standards
MATS also requires new work practice standards
to increase combustion efficiency, thus
decreasing CO, NOx
, and HAPs such as dioxin
and furan that cannot be measured by continuous
emissions monitoring systems. NOx
and
CO reduction tuning includes burners, overfire
air (OFA) controls, concentric firing system
improvements, control system calibrations, and
adjustment of combustion zone temperature
profiles. Selective catalytic reduction (SCR) and
46
selective noncatalytic reduction (SNCR) are included
in the NOx
tuning requirement.
Work practices include burner and combustion
control inspection and maintenance,
tuning combustion controls, maintaining records
of CO and NOx
emissions before and
after burner adjustments, and submitting
reports after each tune-up. The data must be
taken while operating at full load or the unit's
predominant operating mode.
The tune-up requires inspection of all burner
and combustion controls, and cleaning or replacement
of any components of the burner or
combustion controls as necessary upon initiation
of the work practice program and at least
once every required inspection period. The
inspections include operation such as damper
operation, cyclone and pulverizer coal feeder
loadings, or other pulverizer and coal mill performance
parameters. Also, air-fuel ratios must
be calibrated and functioning properly, including
calibration of excess O2
sensors, adjusting
OFA systems, changing optimization software
parameters, and calibrating associated actuators
and dampers to ensure that the systems
are optimally operated. Burner or combustion
control component parts needing replacement
that affect the ability to optimize NOx
and CO
must be installed within three calendar months
after the burner inspection.
The work practice and tune-up testing
reports must be submitted to the EPA every
three years-except for those units that employ
neural network optimization software.
For those units, the reports may be submitted
every four years, following MATS implementation
in 2015. Depending on the unit
specifics, this additional year may produce
significant costs savings.
The effect of the work rules on unit operations
is difficult to quantify at this time.
However, the cost of plant testing and outages
for repairs prior and subsequent to the testing
will likely be substantial. For example, a
one-day outage of a 500-MW coal-fired plant
www.powermag.com
will result in lost revenue of about $250,000,
assuming replacement generation is $20/
MWh more expensive.
Optimizing Combustion
Real-time combustion optimization systems
have demonstrated substantial value for reducing
NOx
emissions, controlling CO, and improving
heat rate for over a decade. In addition
to improved emissions performance, optimized
combustion can also reduce opacity, accelerate
unit load ramping and load following, reduce
tube leakage incidents by alleviating the reducing
conditions typically found inside the primary
furnace, and reduce slag agglomeration
through better management of the fuel gas exit
temperature. These problems are often cited as
the cause of most forced outages or reduced
unit availability and/or capacity.
Modern neural network-based combustion
optimization technologies have evolved
significantly since their introduction in the
mid-1990s. Early optimization systems were
manpower intensive to sustain targeted improvements,
causing some unit operators to
bypass the neural network. Today's more sophisticated
systems combine neural network-
based optimization and model predictive
control (MPC) to extract knowledge about
the combustion process, determine the optimal
balance of fuel and airflow in a furnace,
and quickly respond to changing conditions.
Neural networks are based on nonlinear,
multivariable steady-state models derived
from historical unit operating data that identify
the best combination of independent operating
variables that will produce the best
possible combustion efficiency and the lowest
possible emissions. MPC employs dynamic
models used to predict changes that will occur
during the next few minutes of operation
and anticipate the effects of disturbances.
Specifically, these optimization processes
directly adjust the unit's distributed control
system (DCS) or other control system to
POWER | February 2014
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POWER February 2014

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