Controlling emissions with a nonlinear model-predictive dynamic controller

11/10/15, 11:40 AMControlling emissions with a nonlinear model-predictive dynamic controller

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Controlling emissions with a nonlinear model-predictive dynamicControlling emissions with a nonlinear model-predictive dynamiccontrollercontrollerBy Dr. Russell F. Brown, Pavilion Technologies

Table of ContentsEfficient NOx control beyond human capabilitiesNOx control on coal-fired boilers make prime candidates for MPCApplying MPC to a 900 MW coal-fired utility boilerResultsConclusions

Efficient NOx control beyond human capabilitiesNOx control of a coal-fired boiler is complicated, especially a boiler with hardware NOx controls in place such as a Level 3 LNCFS (LowNOx Concentric Firing System). It is difficult for a human to constantly manipulate six or more elevations overfire air dampers, O2,windbox to furnace differential pressure, and many other variables to minimize NOx subject to other constraints such as reheattemperature, CO, and auxiliary air damper position.

To automate NOx control on a 900 MW supercritical coal-fired boiler with Level 3 LNCFS, the plant owner installed a multivariablemodel predictive dynamic controller. The owner wanted to control or minimize NOx subject to CO constraints, while simultaneouslycontrolling reheat steam temperature and minimizing dry gas loss.

With the multivariable model predictive dynamic controller, the plant achieved excellent control. NOx dropped by 0.049 lb/mmBtuversus the previously established baseline in NOx control mode. And, NOx dropped by 0.059 lb/mmBtu in NOx minimization modewhen a 5°F sag in reheat temperature was allowed. In both cases, the plant maintained CO below the 400 PPM constraint imposed by theplant. (Return to Table of Contents)

NOx control on coal-fired boilers make prime candidates for MPCNOx control on a coal-fired boiler makes a prime candidate for neural-network based model-predictive control (MPC). Neural networksare universal approximators , which means a neural net can represent any physical model. Neural networks allow models to be builtquickly and easily from test data, historical data, or a combination of the two.

MPC is necessary because boilers often have many (five to 15) variables that can be used to control NOx. These variables also affect otheroutputs, such as CO, LOI, and steam temperature. A multivariable optimizing controller allows operators to trade off the use of thesevariables to achieve the objectives of minimizing or controlling NOx, while simultaneously maintaining steam temperature and honoringCO or LOI constraints.

For NOx control, Pavilion Technologies used a tool that combines optimization and dynamic control. This dynamic controller uses anonlinear neural network model to predict the steady state gains, and a first- or second-order dynamic model to estimate the plantdynamics. This particular nonlinear MPC application has been used successfully in many applications , and it is re-ported to be the mostwidely used nonlinear MPC used in the process industries .

The nonlinear MPC consists of the following parts: a steady-state neural network model, a steady-state optimizer, a dynamic model, acontrol model, and a dynamic optimizer. The steady-state neural network predicts the steady-state gains and the steady-state prediction,while the steady-state optimizer determines the target values sent on to the dynamic optimizer.

The dynamic model predicts the dynamic response of the plant. The control model combines the steady-state neural network and thedynamic model. The control model is constructed on the fly by using a two-point gain-scheduling method . This simplified nonlinearcontrol model allows the dynamic optimizer to very quickly predict and optimize the control horizon over the next several steps.

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A very important feature of the tool used to develop the MPC is knowledge guided training. Empirical models in general do notextrapolate well. By using knowledge guided training, Pavilion Technologies can enforce known relationships, while training the neuralnetwork model. For example, we can enforce a positive gain for NOx with respect to excess O2. (Return to Table of Contents)

Applying MPC to a 900 MW coal-fired utility boilerThe nonlinear MPC application was applied to a 900 MW coal-fired utility boiler. The boiler is a twin furnace, supercritical CE tangentialfired unit with eight elevations of burners. The unit was retrofitted with a level three, low NOx, concentric firing system, and has twoelevations of close-coupled overfire air dampers (CCOFA) and four elevations of separated overfire air (SOFA).

The plant operates in an ozone transport region, and can buy or sell NOx credits. The plant wanted to reduce NOx without increasingheat rate. To achieve these objectives, the controller was designed to:

Control or minimize NOx emissions.Maintain CO below a maximum constraint for each furnace.Control reheat temperature.Maintain the reheat temperature difference between side A and B to less than 25°F.Minimize dry gas loss.Honor a minimum constraint on the auxiliary air damper setting.

The control matrix constructed to meet these objectives consists of fourteen MVs (manipulated variables, for example O2), six DVs(disturbance variables, such as ambient air temperature) and seven CVs (controlled variables, such as NOx rate).

The models were built based on test data taken in two stages. In the first stage, the plant executed a fractional factorial design ofexperiments (DOE). The DOE was designed to get the required amount of information from the least number of tests. The DOE test datawere preprocessed to remove dynamics (usually the first 30 to 60 minutes of each test), non-test periods, and disturbances such as sootblowing events. Pavilion Technologies then trained the neural network steady-state models on these data, using knowledge guidedtraining to enforce known relationships.

Next, the plant conducted a series of one-variable-at-a-time step tests to determine plant dynamics. Settling times (dead time plus threetimes the first order lag) ranged from three minutes to 47 minutes for the various MV-CV pairs. Long settling times, significant dead time(greater than 50% of the time constant), and a wide variation of settling times indicates that a dynamic controller is preferred over asteady-state optimizer.

Using the techniques of carefully constructing a test-plan, preprocessing the data to exclude dynamics and disturbances like soot blowing,and separately developing the dynamic models, a very robust model is achieved. In practice, these models (which use feedback fromonline monitors and calculations to adjust model bias) don't need to be retrained unless there is a major equipment change in the boiler,like addition of overfire air.

The dynamic controller resides on a PC with a CPU running at 450 MHz. Communication with the DCS occurs over an Ethernet throughOPC. It runs once every 15 seconds, and takes about half of the CPU resources. (Return to Table of Contents)

ResultsThe plant used two stages to implement the controller. In the first stage, the controller operated as a steady-state optimizer only with theoperators periodically entering the recommended setpoints of the MVs. Later in the 1999 ozone season, the controller converted todynamic control in closed-loop (or automatic) mode with the setpoints being written directly to the DCS once every 15 seconds.

In dynamic control mode, the controller was tested in NOx control mode in which a NOx setpoint was entered and in NOx minimizationmode, in which the objective was to minimize NOx. In both instances, the controller had a constraint on CO and a setpoint on reheattemperature. The controller also was tuned so it would sacrifice a few degrees of reheat temperature if necessary to maintain the NOxsetpoint.

The results presented in Table 1 and Figure 1 through Figure 8 have NOx rate presented as a delta from the mean of the 1998 ozoneseason. The plant considers their NOx control strategy to be confidential. CO data are presented in PPM, reheat temperature is in °F, andboiler efficiency is reported as a delta from the mean of the 1998 ozone season data.

Table 1. Performance test results for steady-state and dynamic control





Both the steady-state and the dynamic modes of control demonstrated dramatic improvements over the 1998 ozone season operation,which was already a low baseline compared to the industry standard.

In the 1998 ozone season, the plant operated with a wide range of NOx rates, and the standard deviation was 10.9% of the mean (Figure 1and Table 1). The mean of CO was 200 PPM, but the CO regularly exceeded 400 PPM, and occasionally exceeded 600 PPM (Figure 2).The reheat temperature setpoint was 1005°F, and the mean of the temperature was 1003°F with a deviation of 0.5%. Reheat temperaturesags occasionally as a result of soot blowing.



In the 1999 ozone season through Aug. 3, the boiler was on NOx control using a steady-state optimizer only in advisory mode. NOxcontrol greatly improved (Figure 3 and Table 1). The mean of NOx rate was 0.046 lb/mmBtu below the 1998 ozone season, and deviationwas only 3.6% of the mean.

The actual NOx setpoint corresponds to -0.049 on the delta scale, so control was very good. CO control was somewhat better than in1998. The optimizer had an upper CO constraint of 400 PPM, and this was achieved slightly better than in 1998 (Figure 4). Reheattemperature was controlled by the DCS, and not the optimizer. Reheat setpoint for 1999 was usually 1000°F.



Aug. 16, the dynamic controller was commissioned in closed loop mode for NOx control. NOx control was excellent, with the mean of theNOx rate being 0.049 lb/mmBtu below 1998 ozone season level, and only 1.0% deviation (Figure 5 and Table 1).

The NOx setpoint was also -0.049, so the target was achieved. CO and reheat temperature control were also greatly improved. CO wasmaintained below the 400 PPM limit, and reheat was maintained near the 1000°F setpoint (Figure 6). In this case, the dynamiccontroller controlled reheat temperature. The controller responded very quickly to disturbances such as soot blowing, allowing lesstemperature sag than when under DCS control.



A second test was performed with the controller in NOx minimization mode. In this case, the reheat temperature was allowed to sagabout 5°F to allow lower NOx operation. NOx decreased to 0.059 lb/mmBtu below the 1998 ozone season level, while still maintainingCO below the 400 PPM limit (Figures 7 and 8, and Table 1).

In addition, for each test the calculated boiler efficiency showed no statistically significant change, although the mean for all tests showedslightly higher efficiency than in 1998. (Return to Table of Contents)

ConclusionsDynamic control of a coal-fired boiler demonstrated improvement in NOx control over a steady-state optimization alone, which in turnprovided significant NOx reduction versus no multivariable control.




The controller and optimizer combination allows one to trade-off reheat temperature for additional NOx reduction when desired, and tostay within constraints such as CO and auxiliary air damper position. In addition, the much tighter control of NOx results in less time inwhich the plant is operated at lower than desired NOx, which results in few symptoms of low NOx operation like water-wall waste andlower efficiency. (Return to Table of Contents)

About the author: Dr. Russell F. Brown serves as manager of power systems engineering for Pavilion Technologies in Austin, TX. Formore information, visit www.pavtech.com.

References

1. Hartman, E. J., Keeler, J. D., and Kowalski, J. M., "Layered Neural networks With Gaussian Hidden Units as Universal Approximations", Neural Computation, 1990,

Vol. 2, pp. 210-215.

2. Martin, G., "Neural Network Applications for Prediction, Control, and Optimization", ISA/95 Advances in Instrumentation and Control, Vol. 50, Part 2, pp. 433-441.

3. Qin, S. J., and Badgwell, T. A., "An Overview of Industrial Model Predictive Control Technoogy", J. Kantor, C. Garcia, and B. Carnahan (eds.), Chemical Process

Control – AIChE Sympsium Series, 1997, pp. 232-256.

4. Piche, S., Keeler, J., Martin, G., Boe, G., Johnson, D., and Gerules, M., "Neural Network Based Model Predictive Control", To appear in Proc. Of Neural Information

Processing Systems Conf., June 2000.

5. Piche, S., Sayyar-Rodsari, B., Johnson, D., and Gerules, M., "Nonlinear Model Predictive Con-trol Using Neural Networks", to appear in IEEE Control Magazine,

June 2000.


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Controlling emissions with a nonlinear model-predictive dynamic controller