POWER February 2014 - 47

PLANT AUTOMATION
more consistently position dampers, burner
tilts, OFA, and other controllable parameters
to continuously minimize NOx
and CO. The
process of determining the optimal biases
and adjusting them accordingly is continuous
and occurs in closed loop, without the need
for operator action.
Strong Return on Investment
There are several important ways in which
combustion optimization provides economic
and operational value, even if the system is
focused on just the optimization of NOx
and
CO required by the pending MATS work
practices. There are inherent boiler efficiency
improvements that will be achieved when optimizing
NOx
and CO as required by the rule.
Figure 1 shows an example of the boiler NOx
reduction achieved with neural net combustion
optimization on a 600-MW coal-fired
unit. Note that the average NOx
is approximately
19% lower during the 30-day test and
there were fewer excursions when the neural
net system was engaged.
Units with post-combustion NOx
control
(SCR and SNCR) experience additional cost
savings from neural net combustion optimization
in two significant ways. First, combustion
optimization enables boiler controls to more
closely match boiler temperatures and NOx
profiles to catalyst effectiveness and reagent
distribution as each changes over time. Second,
combustion optimization will reduce reagent
usage by 10% to 20%. Other beneficial side
benefits include reduced ammonia slip and
minimizing sulfur trioxide conversion (Figure
2). Combined with the typical fuel savings
achieved with combustion optimization, the investment
in combustion optimization produces
a very attractive financial return. Table 1 shows
fuel savings and reagent cost savings for two illustrative
coal-fired units, one a large 600-MW
unit with an SCR, and the second a mediumsized
350-MW unit with an SNCR.
Integrated Optimization
Boiler optimization, once synonymous with
furnace fuel and air mixing, now refers to the
integrated optimization of the combustion and
sootblowing processes, including the furnace
and backpass regions of the boiler. An example
of the holistic nature of boiler optimization is
seen in the sootblower system. The typical sootblowing
system determines the specific boiler
zones to be cleaned and activates the needed
blowers and lances through the DCS system.
However, boiler cleanliness significantly impacts
combustion efficiency, and furnace temperatures
affect ash build up, fouling, and slag
formation. The complexity of these interrelated
processes must also be considered when the
best possible operating economics is the goal.
February 2014 | POWER
1. Demonstrated advantage. The figure illustrates measured NOx produced (horizontal
scale) under the same operating conditions when the neural network is engaged (right) and
when removed from service (left). The data shown was taken at equivalent load, coal quality,
and ambient conditions. The vertical scale is percentage of time the boiler operates at the prescribed
concentration of NOx. Courtesy: NeuCo Inc.
2. Reduced operating costs. Ammonia flow required to meet a 90% NOx removal setpoint
(vertical axis) over one year is illustrated with the neural net combustion optimizer enabled
(right) and secured (left). The horizontal axis is unit load. Source: NeuCo Inc.
Resolving these complex relationships is
the strength of neural net combustion optimization
systems. Heuristic models, representing
knowledge in the form of situation-action rules,
are the cornerstone of the neural net combustion
system. Instead of attempting to fully express
every possible operating alternative in a
physical control system (an impossible task),
heuristic models use expert knowledge gleaned
from the plant data historian and plant operator
experience to represent situations in which
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plant operating experts know how best to react.
These rules can be systematically applied by an
inference engine, which automatically ranks a
set of possible actions described in the form of
situation-action rules and selects the optimal
action. In the sootblower example, the rule set
would determine when and where to perform
the next sootblowing operation so the overall
unit operation is cost-optimized. Expert rules
can also be used to address challenging discrete
changes in plant operations, such as determin47

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POWER February 2014

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