(84e) Improved Load Following of a Boiler with Advanced Process Control

Authors: 
Hedengren, J. D., Brigham Young University
Jensen, K., Brigham Young University


Improved Load Following of a Boiler with Advanced Process Control

Kevin R. Jensen and John D. Hedengren

Brigham Young University

Provo, UT 84602

Load following in power generation is a recent opportunity as
time of day pricing and co-generation are adopted in refining, chemical, and power
plants.  Also, traditional sources
of power generation are increasingly mixed with a growing fraction of emerging
energy sources such as wind and solar. 
Wind and solar have the characteristic of being intermittently available
in the case of cloud cover or as weather patterns move through an area.  As of July 2011, 89% of base-load power
generation in the U.S. came from coal, gas, oil, and nuclear plants (EIA, 2011).  As non-traditional sources of energy
integrate into the base-load, there is an opportunity to improve load-following to allow full utilization of the intermittent
sources on a smart grid.  The
contribution of this study is to investigate the application of advanced
process control for improved load following in traditional power generation,
and in this case, boiler constraint control. Also, a new trajectory formulation
is introduced for controlled variables in Nonlinear Model Predictive Control
(NMPC). This new formulation allows rejection of process noise within a dead
band while only introducing linear contributions to the objective function.

Base plant process control is typically deemed important from
a safety and quality viewpoint. While these are two vitally important factors,
many times, there are other aspects that greatly affect the success of a
process unit. Controls are often developed from a working knowledge of a
process or preconceived limitations. Although they have been very successful in
the last 40 years since they were introduced, in many instances they "lack a
systematic stability analysis and controller design" (Feng,
2006). It is based on operator knowledge, and adaptive algorithms (Hagan, Demuth,
De Jesus, 2002). While operating a system in this manner may allow a process to
proceed safely and meet quotas or quality assurance, there may be ways in which
other elements of a process can be optimized (i.e. emissions, economics, and
process unit life). Coal fired furnaces and boilers are an example of this.
They are constrained by certain physical limits, such as rate of temperature
change on tubes, allowing only restricted power cycling. Furthermore, energy
from renewable resources has become increasingly popular. The National
Renewable Energy Laboratory indicates that the US Department of Energy has a
vision wherein wind energy contributions to total electricity production in the
future is projected to increase. It is their aim that over the next 2 decades,
wind will comprise 20% of US electricity production (Thresher Robinson, and
Veers, 2008). However, coal-fired power plant cycling to accommodate renewable
resources can actually increase wear and tear costs on coal-fired boilers. The
longer the boilers sit idle, the greater the damage done to the boiler as it is
ramped up after the idle time. This will decrease plant life and increase costs
(Lew, Brinkman, Lefton, Piwko,
2011). It may seem as though these two factors cannot be optimized
simultaneously. However, by using a robust controller, it may be possible to
optimize multiple facets of a process and comply with multiple constraints.

In this study, we investigate the use of model-based control
and PID controls in a coal fired furnace. By generating a differential
algebraic model (DAE) of a coal-fired boiler, the constraints and parameters are
explicitly modeled and controlled. Responses to process disturbances requiring
power cycling are also optimized to increase profitability and process unit
life.

The first step was to generate a
model that could represent the cycling time and temperature changes of a
coal-fired furnace. The model was created using first principles based on material
and energy balances. The energy balance was built around the boiler, with
appropriate heat-transfer terms for exchange between the bed, tubes, and high
temperature water.  A heat transfer
term was incorporated into the model to represent the time delay of heating up
and cooling down. The heat transfer was based on irradiative heating, as this
is the dominant form of heating certain types of coal-fired boilers (Basu, Kefa, Jestin,
2000). The use of a lag variable allowed for the approximation of apparent dead
time that is observed in a coal furnace. Dead time is often derived using step
tests (Schnelle, Laungphairojana, Debelak, 2006). However, we used observations from operators and
individuals knowledgeable about these systems. Load cycles were then simulated
with control of the system through the APMonitor
software. Control of the system was accomplished using trajectory tracking and constraints
in Nonlinear Model Predictive Control (NMPC). For comparison the use of a PID
controller on this system was explored. The results of the two control
simulations were then compared.

NMPC was able to predict appropriate
controller outputs in order to achieve the correct ramping rate and cycling.
The model-based control was superior for several reasons. The physical
constraints of the system may be set and the controller keeps the system explicitly
within those bounds. For example, typical practice for changing furnace load is
to increase heating output at a rate of about 1%/min. This rate constraint can
be controlled explicitly in the multivariable controller along with trajectory
load following. NMPC uses predictive values based on current measurements in
order to achieve the set point within the dictated constraints. It was demonstrated
over the entire range of operation, including transient and steady state
conditions. It optimized load changes and achieved set points within the reliability
constraints.

One of the major benefits of the PID
is its simplicity. In simulations, it was able to achieve and maintain set
point and reject disturbances. However, the controller had several
shortcomings. In order to achieve the desired output temperature, the
controller frequently saturated. The PID also had several challenges with
start-up and ramping cycles. Typically, load changes are performed at a slower
rate. The PID controller was unable to accommodate these constraints. In
certain instances, especially those in which large disturbances were
introduced, the PID controller violated the constraints on temperature gradient
for the boiler temperature tube integrity. This situation can greatly affect
safety, performance, and economic success, especially over the lifetime of the
furnace. The PID controller was however very useful at steady state or for
small disturbances. It was able to keep a set point, but not effective enough
to be used in cases where large disturbances were encountered. It was not as robust
as would be desirable in certain circumstances, especially those in which large
disturbances or changing conditions are frequently observed.

Process data for this study was
obtained from an operating coal-fired boiler facility.  With current controls on the boiler, load
changes can be performed at about 1%/min. A model-based controller challenges
this restriction by driving to actual process constraints. Load changes can be
performed faster if the constraints are better understood, or perhaps, in order
to prolong boiler life, they should be done slower.  The model-based approach enables an environment where
constraints can be explicitly targeted in moving the boiler to the new power
generation load. As a final statement, there are also some concerns that
certain process units may be too complex to be accurately modeled. In these
cases it may be beneficial to use a combination of empirical and first
principles methods to obtain the best results.  Future extensions to this work may also include forecasting
of energy availability and load, time-of-day pricing, and anticipated peak
power demands.  This will enable feed
forward information for improved load following optimization.

References

[1] EIA, U.S. Energy Information
Administration (2011), Electric Power Monthly, URL:
http://205.254.135.24/electricity/monthly/, Retrieved: 14 October 2011.

[2] Feng,
G. (2006). A Survey on Analysis and Design of Model-Based
Fuzzy Control Systems. IEEE
Transactions on Fuzzy Systems
14(5) 676 – 697.

[3] Hagan,
M., Demuth, H., and O. De Jesus (2002) An Introduction to the Use of Neural
Networks in Control Systems, International Journal of Robust and Nonlinear
Control
, 12(11) 
959-985.

[4] Thresher, R., Robinson, M., and
P. Veers (2008). The Future of Wind Energy Technology in the
United States. URL: http://www.nrel.gov/docs/fy09osti/43412.pdf. Retrieved: 14 October 14, 2011.

[5] Lew, D., Brinkman, G., Lefton, S., and D. Piwko (2011).
How Does Wind Affect Coal? Cycling, Emissions, and Costs, URL: http://www.nrel.gov/docs/fy11osti/51579.pdf. Retrieved: 14 October 2011.

[6] Basu,
P., Kefa, C., and L. Jestin
(2000). Boilers and
Burners.
New York, NY: Springer

[7] Schnelle,
K., Laugphairojana, A., and K. Debelak
(2006). Emission reduction of NOx and CO by Optimization
of the Automatic Control System in a Coal-Fired Stoker Boiler. Environmental Progress 25(2),
129-140.

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