1. Operator Training Simulator for Power Plant Combustion
Optimization System Design
Don Labbe (Donald.Labbe@Invensys.com)
Marlina Lukman (Marlina.Lukman@Invensys.com)
Robert McHugh (Robert.Mchugh@Invensys.com)
Invensys Operations Management
Keywords: Operator Training Simulator, OTS, Optimization, Combustion Optimization
System, Once Through Boiler
ABSTRACT
An Operator Training Simulator (OTS) provides many valuable services in support of power plant
operation and design. The primary objective is operator training for start-up, shut downs, load
management, equipment malfunctions and special case scenarios. Such training provides substantial
operating cost savings by lowering unforced outage rates due to operator error and speeding startup
and shutdown rates for additional revenue and fuel savings. OTS systems have supported control
system upgrades and plant design enhancements by confirming design changes prior to installation in
the actual plant. Such capability has become increasingly important for cyber security compliance.
In this project a Combustion Optimization System (COS) was integrated with an OTS, comprised of
both a process model and a complete emulation of the DCS control design. The OTS process model
includes models of the air and fuel within the furnace and heat transfer models, allowing it to provide
detailed predictions of emissions values, steam temperatures, spray flows and other parameters
impacted by adjustments to fuel and air distribution. The complete emulation of the DCS control
design allowed the full integration of the COS interface logic and graphic displays. The integrated
system was applied to conduct parametric testing for COS model development. The faster than real
time feature coupled with the full time availability provided for rapid generation of modeling data. The
design of the COS was confirmed and potential benefits were quantified. Operator graphics and
operating scenarios were available to train operators in the details of the impact of COS on operations.
By applying the OTS, the interface logic and graphics were fully designed and checked out with
preliminary models prior to touching the actual plant. This greatly reduced any operational concerns to
the plant for the implementation of the COS and is yet another illustration of the value of an OTS.
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2. INTRODUCTION
The traditional role of an operator training simulator (OTS) has been to provide operator training for
start-up, shut downs, load management, equipment malfunctions and special case scenarios. High
fidelity OTS provides operator training with a close approximation of the specific operating
characteristics of the unit. Such training has demonstrated significant reduction in operator error,
speeds the operator response to undertake corrective action, and enhances startup and shutdown rates.
The net impact is substantial operating cost savings by lowering unforced outage rates due to operator
error, extending equipment life through stress mitigation, and fuel savings through faster start-ups and
shut-downs.
The role of high fidelity OTS has expanded beyond operator training to support a wide range of unit
related activities such as control logic modifications, control system upgrades and plant design
enhancements. The OTS can confirm design changes prior to installation in the actual plant. DCS logic
confirmation has become increasingly important for cyber security compliance.
However, applying OTS for unit heat rate and emission optimization lies beyond the typical
application domain. The required modeling demands a rigor beyond the typical high fidelity OTS
framework. A project was undertaken to explore the integration of a Combustion Optimization System
(COS) with an OTS and this paper describes the methods and results. The OTS featured sophisticated
air and fuel modeling within the furnace along with water/steam heat transfer models that provided the
framework for detailed predictions of emissions values, steam temperatures, spray flows and other
performance parameters. Through the OTS, adjustments in fuel and air distribution undertaken by
COS simulate the impact on emissions and thermal performance.
Other components of the OTS included a complete emulation of the DCS control design including
graphics. This provided the means to undertake a full integration of the COS interface logic and the
associated graphic displays.
The simulated unit is a pulverized coal fired supercritical once thru unit rated at approximately 800
MW gross featuring a split furnace design with 7 coal mills and 8 corners for fuel and air injection
with three levels of newly installed over-fire air. The simulator has been verified against an actual
power plant.
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3. OPERATOR TRAINING SIMULATOR (OTS) BACKGROUND
Figure 1: Overview of Operator Training Simulator
The figure above illustrates that there are two major software modules to an OTS: a dynamic
simulation module, that replicates the actual plant, and a control system module, that emulates the
control system. A simulation executive module coordinates the two modules, synchronizing time
control of the two modules. Time can be allowed to advance slower or faster than real time. The state
of the dynamic simulation module and the control system module can be saved as an initial condition,
which can be re-loaded to allow simulator operation to begin at a fixed condition.
DYNAMIC SIMULATION MODULE
This module undertakes the mathematical modeling of the plant using comprehensive, rigorous, field
proven dynamic process simulation software. The model of this power plant features the primary
components of the plant, including the water and steam systems, the fuel, air and flue gas systems, and
the auxiliary systems. The water and steam systems include detailed models of the steam turbine,
condenser, deaerator, feedwater heaters, pumps, steam and water piping systems, and the heat transfer
surfaces comprising the boiler. The fuel, air and flue gas systems include detailed models of the
pulverizers, fans, ducting, dampers, furnace enclosure, and air heater. The auxiliary systems include
models of the cooling water, lubrication, and electrohydraulic system. The models are dynamic,
predicting transient behavior by using equations based on conservation of mass and energy and
appropriate rate equations.
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4. The furnace model divides the furnace into several discrete volumes, including discrete volumes for
each elevation where fuel and air are introduced into the furnace and accounting for the side to side
variations in fuel and air flows. Calculations for each volume account for the mass transfer, heat
transfer, and chemical reactions taking place in the volume. Mass transfer accounts for the vertical and
horizontal movement of the gas. Heat transfer accounts for various modes of heat transfer from the
volume to the boiler heat transfer surfaces, including flame and gas radiation and convection. The
chemical reaction models produce a time-varying heat release, gas composition, and temperature
profile throughout the furnace.
The main steam temperature leaving the boiler is measured along the four paths providing steam to the
main steam header and the heat transfer surfaces are modeled to account for the possibility of different
heat absorptions along each path. The reheat steam temperature leaving the boiler is measured along
the two paths providing steam to the hot reheat header and the reheat heat transfer surfaces are
modeled to account for the possibility of different heat absorptions along each path.
This level of detail allows the simulator to describe the behavior of the plant over a wide range of
conditions, For example, the model predicts the changes in heat absorption by the various in-furnace
heat exchangers due to changing the distribution of air between primary and secondary levels or
among the secondary air dampers.
Models of CO and NOx production are also included in the OTS model. These models are empirical,
rather than rigorously derived from first principles. The emissions models predict the CO level based
on oxygen concentrations at different furnace elevations and secondary over fire air damper positions.
The emissions models predict the NOx level based on oxygen concentrations at different furnace
elevations and burner tilt positions (Reference 1).
CONTROL SYSTEM MODULE
This module is where the actual DCS is being emulated. In this case the plant control algorithms are
matched one to one using emulation software. The emulation is, in essence, identical to the DCS
software; providing a virtual controller instead of actual controller hardware by executing the
controller software on the simulation workstation rather than a real-time control processor. The
control emulation was based on exactly the same computer software (source code) as the actual DCS.
This enables the load of actual configuration files from a project directly into the simulator without any
need for translation. In addition, since it behaves just like a normal DCS system, any peer-to-peer
stations on the control network will connect to the control emulation without any need for re-
configuration. This includes graphic workstations, engineering stations, historian packages, off-
platform network connections, etc. And it guarantees that the behavior of the control system and
connected graphics screens function on the OTS just like they do on the main control system.
For this simulator, the control configuration and graphics are identical to the actual DCS as the OTS is
configured from the same files as the actual DCS and uses actual DCS operator console hardware. This
integrated simulator then provides an environment for operators to practice a wide range of activities,
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5. including start-up, shut-down, raising or lowering load, and responding to emergency situations such
as equipment failures.
In this project the simulator also provides an environment for operators to observe and understand the
consequences of various actions on heat-rate and emissions, for example, how increasing excess air
may decrease heat-rate but also decrease the amount of CO production and increase NOx production.
PROJECT OBJECTIVE
The intent of this project was to demonstrate that COS could be integrated with the OTS plant controls
and that COS could drive the unit to an optimum condition of reduced emissions and improved thermal
performance. The selected optimization control variables were furnace NOx, CO, O2 and steam
temperatures, since these are the dominant emissions and thermal performance variables in once
through coal fired application.
COS – OTS INTEGRATION METHODOLOGY
The COS interface to a DCS involves a degree of complexity and security, since COS exports control
signals to an active control system. These exported signals automatically modulate key manipulated
variables and drive the plant to optimum conditions for emissions and thermal performance. By first
implementing within the OTS environment, the control interface design is confirmed prior to
commissioning on the DCS.
A staged approach was undertaken to establish the COS – OTS Integration:
• Link the COS computer and software to the OTS control system module and demonstrate COS
data collection
• Apply COS software to conduct Pseudo Random Binary Sequencing (PRBS) testing of the
dynamic simulation module in support of the generation of COS controller models
• Configure the COS controller models and optimization strategy
• Configure the COS control interface to the OTS for closed loop optimizing control
• Operate the OTS and enable COS to simulate emissions and thermal performance changes
• Assess the optimization performance
The OTS control system module software was compliant with current NERC Critical Infrastructure
Protection (CIP) requirements. The COS computer and software were linked to the OTS consistent
with these requirements. Software communications were established applying a proprietary protocol
and COS data collection was established. The integrated system was applied to conduct parametric
testing using PRBS testing for model development. The faster than real time feature coupled with the
full time availability of the simulator provided for rapid generation of modeling data.
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6. The control variables (CVs) are the process variables that the COS will optimize. For this work the
selected CVs included: NOx, CO, O2, superheat temperature, reheat steam temperatures, and other
select variables. These are representative of the crucial control variables necessary to achieve
emissions and performance benefits.
The manipulated variables (MVs) are the input signals generated by the COS and sent to the DCS. For
this work the selection of MVs was based on prior experience with similar boilers and the desire to
simplify the demonstration yet retain the dominant variables. The following MVs are included: O2
controller setpoint bias, windbox controller setpoint bias, and a number of damper bias signals.
The COS interface to the DCS includes a secure supervisory control system which employs watch
dogs and other techniques within both the DCS domain and the COS domain to ensure continuous and
secure communication. Should a loss of communication be detected, each manipulated variable falls
back automatically according to the specific individual configuration. All transfers to and from normal
control and COS optimization are bumpless.
The emissions models for NOx, CO and O2 distribution within the OTS are empirical and not intended
to be rigorous. When COS is applied to the actual unit, the emissions models within COS would be
finalized based on the emissions data from the actual unit. An on-line model adaptation technique can
be applied to finalize the COS emissions models on an operating unit (Reference 2).
RESULTS
The Delta Heat Rate Methodology (Reference 3) was applied to quantify the performance benefits.
Figure 2 presents a trend chart of 16 pens in groups of four and illustrates:
• The delta efficiency, delta NOx, furnace stoichiometry and furnace O2
• #1, 2, 3, and 4 finish superheat steam temperatures
• NOx, CO, A & B side reheat steam temperatures
• A, C, E and G level furnace stoichiometry
This trend graphic illustrates the results for the 1st
hour after enabling the optimizer. The key
performance variables impacting efficiency (heat rate) are furnace excess air (O2) and superheat and
reheat steam temperatures. A significant reduction in furnace excess air is achieved without a
significant increase in CO, providing an efficiency improvement of 0.31% within the hour. The
furnace stoichiometry is adjusted to a more favorable range and achieves a NOx reduction of 9.7%.
Superheat and reheat steam temperatures are maintained near setpoint throughout.
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7. Figure 2: COS Performance Trends
Figure 3: COS Trend: NOx, CO, O2, Reheat, Air Heater and Superheat Temperatures
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Optimizer Enabled
Baseline CO increased,
prior to enabling

8. Figure 3 presents the emissions and key thermal performance parameters following the enabling of the
optimizer. This COS trend charts below present more detail of the unit response and optimizer action
and displays the following variables:
• NOx and O2 corrected NOx in ppm
• CO in ppm
• Furnace O2 controller measurement and setpoint
• Reheat A/B side steam temperatures
• The air heater A/B side gas outlet temperatures
• The four superheat outlet temperatures
Just prior to enabling the optimizer, the baseline CO level used in the simulator was adjusted to a
higher and more typical value. Since CO is a key feedback to combustion optimization, a higher
baseline would increase the CO sensitivity and challenge the optimizer to achieve improved air
distribution and lower the O2.
After enabling the optimizer the O2 setpoint bias and other MVs transitioned towards their optimum
targets, a solution representing optimum performance. The steam and gas temperatures responded to
the MV moves, but settled soon after at near optimum values. The calculated benefits are displayed in
the following trend.
Figure 4: COS Trend: Δ NOx and ΔEfficiency including gross MW, Fuel and Btu/kw-hr
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Gross Efficiency

9. Figure 4 presents the delta efficiency values (based on Delta Heat Rate Methodology) and the
calculated gross unit heat rate displaying the following variables:
• Percentage NOx reduction
• Percentage efficiency improvement total and by variable: superheat, reheat and air heater exit
gas temperatures and O2
• Gross unit efficiency, Btu/kw-hr
• Gross MW
• Total fuel, tons/hr
The Delta Heat Rate Methodology approximates a total heat rate improvement of 0.332%, which
compares favorably to the 0.255% heat rate improvement based on a comparison of the before and
after ratio of total fuel consumption to gross MW generation, shown in Figure 4. Assuming 70%
capacity factor and $50/ton the projected annual fuel savings for 0.332% is ~$380,000.
To further investigate the performance of the optimizer an additional constraint was placed on the
optimizer to balance the four superheat steam temperatures, thereby increasing the average steam
temperature. The trends below illustrate the results of a change to the superheat temperature constraint.
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10. Figure 5: Sensitivity results of Superheat Steam Temperature Constraint
The initial conditions show differences between the four finish superheater outlets, which is quite
typical for large boilers. The #2 finish superheat steam temperature was operating approximately 10°F
cooler than setpoint. Since balanced steam temperatures can improve turbine cycle performance, the
side to side superheat temperature difference constraint was tightened from 5°F to 0.5°F. The response
illustrates an increase in the #2 superheat steam temperature and impacts emissions performance.
The response is not representative of final tuning, but this example demonstrates the “give and take”
characteristics of a multivariable optimizer. Action is undertaken by the MVs to address CV
constraints with the ultimate response dependent on assigned tuning weights and optimizer importance
factors. For example, the CO increases slightly due to the action undertaken to balance superheat steam
temperature which increases the O2 setpoint.
CONCLUSIONS
The COS was successfully integrated to an OTS featuring current NERC CIP compliance. The OTS
served as an effective platform to conduct parametric testing, design the COS interface control logic,
build operator graphics, commission the COS and evaluate COS performance.
The integrated optimizer/simulator demonstrates key principles of the COS operation potentially
providing operators and engineers with insight to the incremental performance improvements available
to the unit. The system illustrates how both heat rate and emissions objectives can be addressed
through multivariable optimization. With such a tool the operators and engineers can widen their
optimization experience and further the achieved benefits.
The design of the COS was confirmed and potential annual fuel saving benefits of $380,000 were
quantified. Operator graphics and operating scenarios were available to train operators in the details of
the impact of COS on operations. By applying the OTS, the interface logic and graphics were fully
designed and checked out with preliminary models prior to touching the actual plant. This not only
greatly reduces any operational concerns to the plant for the implementation of the COS, but addresses
NERC CIP requirements for control validation prior to installation. The COS-OTS integration is yet
another illustration of the value of an OTS.
REFERENCES
1. Influence of Process Parameters on Nitrogen Oxide Formation in Pulverized Coal Burners, R. P.
van der Lans, P. Glarborg, K. Dam-Johansen, Prog. Energy Combust. Sci. Vol. 23, pp. 349, 1997.
2. Smart Firing Control System, by C. Houn, B. Begley of Wisconsin Public Service and D. Labbe, T.
Kinney, A. Morrow, and A. Speziale of Invensys Operations Management, presented at the 55th
ISA POWID Controls & Instrumentation Symposium, June 3-8, 2012, Austin, TX.
3. Entergy Independence NOx/Heat Rate Optimization and Steam Temperature Control with
Neural Net/Model Predictive Control Combo, by D. Labbe, A. Speziale and S. Coker, presented
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11. at the 15th Annual Joint ISA POWID/EPRI Controls and Instrumentation Conference, 48th
Annual ISA POWID Symposium, 5-10 June 2005, Nashville, TN.
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