Closed Loop NOx Control and Optimisation Using Neural Networks

Copyright @ IFAC Power Plants and Power Systems Control, Brussels, Belgium, 2000

CLOSED LOOP NO x CONTROL AND OPTIMISATION USING NEURAL NETWORKS.

Jack Gabor 1 Daniel Pakulski 2 Konrad Swirske Pawel D. Domanski4 •

I

Westinghouse Process Control, Inc., A Fisher-Rosemount Company 200 Beta Drive, Pittsburgh, Pennsylvania 15238, e-mail: [email protected] 2

Transition Technologies Sp.

Z 0.0.,

ul. Dzika 4,00-194 Warsaw, e-mail: [email protected] 3

Institute of Heat Engineering, Warsaw University of Technology,

ul. Nowowiejska 25, 00-650 Warsaw, e-mail: [email protected] 4

Institute of Control and Computation Engineering, Warsaw University of Technology,

ul. Nowowiejska 15//9, 00-665 Warsaw, Poland, e-mail: [email protected].

Abstract: This paper presents real industrial applications of Advanced Control for power plant systems and emission controls. Closed loop NOx control using artificial neural nets and boiler optirnisation was applied. Basis knowledge to build an advanced control tool called IVY is also briefly described. Obtained control performance is very high for all presented aspects of the combustion process. The advanced controller provided higher unit efficiency, while

simultaneously

maintaining environmental and technological constraints. Copyright lfi2000 IFAC

Keywords: neural networks, predictive control, adaptive control, optimisation, boilers.

noises INTRODUCTION

also degrade

system

performance.

The

dynamic properties of a controlled plant may not be very complex, even though its detailed structure and

The main issues to consider for the design of an

parameters are usually unknown.

industrial process control system are the negative

Soft computing methods are considered to be the

effects of non-linearities. Process and measurement

most appropriate in the presence of the imprecise

141

measurements and in the case of non-linear processes

hardware to implement control algorithms has been

with some unknown dynamics (Bartos 1997). This

improved significantly in recent years. Despite the

computing

difficulty in achieving high control quality, the fine-

methodologies that adhere to its guiding principle. At

tuning of the controller's parameters is a tedious task,

this juncture, the principal constituents of soft

requiring experts with knowledge both in control

computing are

theory and process dynamics. All of these call for the

approach

is

the

consortium

fuzzy

logic,

of

neural

computing,

evolutionary techniques, rough sets, probabilistic

development

of new

controllers.

The

modern

reasoning, etc...

advanced control techniques based on soft computing

At the same time, new emissions regulations form the

approach can become such a solution.

challenge for control systems to achieve the goals of

The most important methods

environmental

distinguish the systems are based on fuzzy logic

protection.

These

goals

consider

we can

use to

opacity, NOx, S02, and CO.

(Zadeh, 1965; Czogala, Pedrycz, 1982; Zimmermann

The combustion process and the creation of toxic

1991) and artificial neural networks (Hertz 1992).

particles are strongly connected. Because this process

Both of these methods can successfully deal with

is still the most popular energy source, we need to

non-linear or poorly defined problems.

think about some methods for reduction of these

Fuzzy

negative effects. We can distinguish two approaches:

linguistic or rule-based knowledge into a classical

logic

makes

it possible

to

incorporate

control scheme. This can be achieved in many ways, •

primary methods that use a better low

mainly by: Fuzzy Logic Controllers (FLC), Fuzzy

emission combustion process operation; •

Logic Supervision (FLS), Fuzzy Models, and Fuzzy

secondary methods that try to clean the

Expert Systems.

output

The potential of NNs (Neural Networks) for control

combustion

gases

from

toxic

applications lies in the following properties:

elements.

• In this study the primary methods will be addressed,

they could be used to approximate any continuous mapping;

mainly in connection with measurable emissions. Because these processes are very complex, and

•

they achieve this approximation through learning;

highly non-linear, the suggested way for control is using the soft computing approach. •

The paper consists of the presentation of applied technologies. These methodologies are illustrated with followed industrial applications: •

parallel processing and fault tolerance are easy to be accomplished with NNs;

•

they make it very easy to implement control structures with internal non-linear

NOx reduction at Polaniec Power Plant, unit

modeling, for example in the predictive

#4, Polaniec, Poland,

or adaptive structures. •

Valley Power Plant, unit #4, Milwaukee, Wisconsin 2.1

2

ADVANCEDCONTROLTECHNOLOGffiS

Model Predictive Control - MPC

Model Predictive Control algorithms form the wide range of predictive structures that compute the

Contemporary industrial process control systems

control signal on the basis of the process model (Qin,

dominantly rely on Pill-type controllers, though the

Badgwell, 1996). While the proper model provides

142

support to the controller with the process prediction,

multi-regional

a method is needed for calculating the control signal

1994).

with respect to the process. In this step we

•

incorporate into the controller some non-linear

MODEL

controllers

(Qin,

REFERENCE

Borders

ADAPTIVE

CONTROL (MRAC) algorithm based on a

optimisation methods. The signals must be filtered,

controlled plant model in the form of

conditioned and validated. There also needs be

ARMAX structure.

boundaries placed around the process input and output signals.

•

SELF-TUNING

(STR)

REGULATOR

The control action (in each sample interval) is as

algorithm is quite similar to the MRAC, but

follows:

we do not use ARMAX model explicitly to

•

set control horizon for

compare with the plant.

which the control

scenario sequences will be checked; •

2.3

The process model is the main part of a model

set the prediction horizon (longer than control

predictive controller. This model should be accurate

horizon) for which the system performance will

over a wide range of plant operation, and thus it is

be checked; •

Non-linear Process Modeling

desirable to use the non-linear model for non-linear

run the optimisation algorithm which will be

processes. Non-linear identification using equation-

looking for optimum control actions with respect

based

to the defined performance criteria.

consuming. Many alternate approaches can be found

modeling

can

be

expensive

and

time

This procedure is repeated in each step. Since the

in the literature, such as fuzzy logic, neural networks,

control derivation process is time consuming, an

evolutionary computation, linguistic reasoning, rough

efficient computer system is required.

sets, etc.

Adaptive Control

2.2

3

IMPLEMENTED SOFfWARE: IVY ADVANCED CONTROL TOOL

In most real world control problems, the plant under consideration has its known response characteristics

IVY is an advanced control software tool. It enables

and unknown response characteristics. The primary

designing and tuning of a model predictive controller

purpose of an adaptive control scheme is to

with a receding horizon for base multidimensional

manipulate the controller to cope with the unknown

control and adaptive structures based on the fuzzy

and changing dynamics in the plant environment. In

approach to the gain scheduling algorithm.

addition to that, however, adaptive controllers have

IVY software consists of two parts:

also been advocated as an alternative to prior •

Run-Time Controller

means to relieve the user from modeling uncertainty

•

TT Design - Modeling Design Tool

(black box philosophy). We can enumerate three

MPC

basic

The model is based on fuzzy neural network

analysis: "automatic adaptation" is then seen as a

adaptive

control

structures

(Astrom,

Wittenmark 1994): •

technologies. A fuzzy model can be viewed as a fuzzy

GAIN SCHEDULING - Nowadays this

non-linear

NARMAX

(Non-linear

Auto

Regressive Moving Average with auXiliary input)

approach can be met with a new version, if

model (Horikawa, et al. 1991). This notion comes

we consider fuzzy Takagi-Sugeno based

from piecewise linear systems.

143

Gain Scheduling

wall fired boiler, 650 klbh steam, Foster-Wheeler

To perform the function of Gain Scheduling, an

boiler.

adaptive algorithm or a hybrid PID-IMC (Internal

The main goal for the NOx Reduction System

Model Control) multi-regional control structure can

implemented at the Valley Power Station is to reduce

be used (Zacho, et al. 1993; Domanski, et al. 1997).

NOx emission. The secondary goal is to maintain all

Optimization algorithm

other boiler parameters. It was identified during the

The problem of choosing manipulated variables for a

tests that LOI is strongly connected with NOx

given prediction

concentration, and NOx reduction can make LOI

horizon

is formulated

as

an

optimisation problem. The IVY controller can use

worse. Due to this fact the LOI measurement has

different optimisation algorithms,

been used as a second control goal. Both of these

since the calculated manipulated variables must

signals will be called CVs - Controlled Variables.

satisfy constraints on values and the speed of

The control structure was implemented on a WDPF

changing, a penalty function is added to the

system. The implementation is built around the IVY

performance index.

controller (Model Predictive Controller with receding

Graphical User Interface - GUI

horizon for multidimensional control) with nc2w2

The User can access and tune the controller using

software for the interface to the DCS.

GUI's (Graphical User Interface) written in JAVA.

The base

The interface consists of seven

main graphic

smooth and bumpless co-ordination with the neural

control structures have been updated for

windows that enable the monitoring of the controller,

controller. A specialised graphical screen has been

tuning and setting of its parameters.

prepared, and existing screens have been updated.

System requirements

The system is running on a dedicated WeStation. The

Graphical User Interface was written in Java.

system assures bumpless and safe operation while the

Therefore, an installation of Java on the system is

base control can still work in normal MANUAL or

necessary to execute the interface. The controller can

AUTO mode. To introduce the System, the new

run under Windows NT, Solaris UNIX 2.5 or LINUX

control

operating system.

(Supervisory). When the controls are in the SPV

Applications

mode it means that the NOx Reduction System is

mode

has

been

added,

called

SPV

running. •

NOx emission control (Arabas, et al. 1998)

•

Opacity optimisation (Domanski, et al. 2000)

•

Boiler optimisation

When controls are in AUTO, the WDPF will track the above biases from the NOx Reduction System, when the system is turned ON it starts from actual

(Neelakantan, et al.

boiler

1998)

parameters.

Simultaneously

the

initial

operator's parameters are stored, so that after turning it off, the system goes back to the original parameters.

4

The results of the 48 hour performance test are

APPLICATIONS OF THE DYNAMIC

shown in the table 1. The average steam flow, NOx

EMISSION BOll...ER CONTROL

level, O 2 , and desuperheater spray flow are indicated.

4. J

NOx control

An average of 15 % reduction in NOx emissions was achieved. Figure 1 and 2 presents comparison of the

The project was implemented on boiler #4, Valley

emission NOx without NOx Reduction System

Power Plant, Wisconsin Electric Power Company,

(figure 1) and with it (figure 2) in approximately the

Milwaukee, Wisconsin, USA, a pulverised coal front

same steam flow.

144

Table 1 NOx commissioning test results

•

6 mills system (l mill system for 1 burner row)

Date

NN

Steam

NOx

O2

Despht.

Status

Flow

[#/mmb

[%]

Spray

[kpph]

tu]

Flow

•

Burner angles control ability

•

air flow control: primary air; secondary air,

[kpph] 12/15/99

OFF

416.3

0.468

5.24

27.82

12/16/99

ON

429.7

0.399

4.29

22.37

3.2%

% change

OFA. The aims of the project are: •

NOx emission reduction

•

Increase in boiler efficiency factor

•

Flue gas temperature symmetrization

-14.790 -18.1% -19.6%

Fig. 1. Test with controller not running

The control structure was implemented on the WDPF

I
PPM

system

E

675

using

Aspen

Target-model

predictive

controller with nc2w2 software as the interface to the DCS. To obtain better combustion control, a Burner Management System was added. The process model

600

was implemented using neutral network technology. The model's inputs are:

525 •

manipulated variables: flue and air fan drive positions; O2 setpoint; secondary air dampers positions; mill feeders;

•

disturbances:

O2

concentration,

drum

pressure; total air flow; pressure differential

Fig. 2. Test with controller running

and temperature in combustion chamber; flue

NOx

10 SECOND AVERAGE

285

675

PPM

KLBH

gas fan motor power; unit capacity

The model's outputs are NOx emission level, CO

600

emission level, outlet flue gases temperature, and steam temperatures.

NOX SETPOINT

Obtained results:

2,5 hours

The controller enables the maintenance of the environmental limits as its shown on table 2,

4.2

symmetries, and the flue gas temperature at the same

NOx Control and Boiler Optimisation

time. An average increase of 0.4-0.8 % in the boiler efficiency was obtained. Simultaneously, there was

The project was implemented on unit # 4, Polaniec

no over or under heating of the steam. Nor was an

Power Plant, a pulverised coal fired boiler OP-650.

•

increase of CO emission or more slugging observed.

24 low NOx burners placed on sides in 6 rows

145

Table 2 NOx commissioning test results

stability analysis, European Control Conference '97 CD-ROM Proc., Brussels.

Controler

NOx

status

[mgiNm3]

ON

430

OFF

470

Domanski P.D., Swirski K., Williams J.J (01/2000)

Application of Advanced Control Technologies to the

Emission

Control

and

Optimisation,

Conference on Power Plant Emission Control and Monitoring Technologies, London, UK. Hertz J., Krogh R., Palmer A. (1992) Introduction to

the theory of neural computing, Addison Wesley. 5

Horikawa S. et al. (1991) A study on Fuu:y Modelling

CONCLUSIONS AND FURTHER

using Fuu:y Neural Networks, IFES'91, pp. 562-

IMPLEMENTAnONS

572.

Neelakantan R., Domanski P.D., Swirski K (12/1998)

This paper presents real industrial applications of power plant systems and

Hybrid Neural Network Model Based Control of

emission controls. Obtained control performance is

a Coal Fired Boiler, PowerGen International '98,

very high for all presented aspects of the combustion

Orlando, USA.

Advanced Control for

Qin SJ., Badgwell TJ. (01/1996) An overview of

process. The advanced controller provided higher unit efficiency, while

industrial model predictive control technology,

simultaneously maintaining

CPC-V, Tahoe.

environmental and technological constraints. The

Qin SJ., Borders G. A (02/1994) Multiregion Fuzzy

plant staff, both operators and engineers, have accepted the implementations.

Logic Controller for Non-linear Process Control.

The next step is directed toward the supervision of

IEEE Trans. on Fuzzy Systems, Vol.2, No.I,

the second part of the power energy unit - the

pp.74-81. Zadeh L. (1965) Fuzzy Sets and Systems, Proc.

turbine. The first results are expected by the end of

Symp. Syst. Theory, Polytech. Inst. Brooklyn,

this year.

pp.29-37. Zhao Z.Y., M. Tomizuka, S. Isaka (1993) Fuu:y Gain

Scheduling of PID Controllers. IEEE Trans.

REFERENCES

Systems, Man and Cybernetics, vo1.23, No.5, pp.I392-1398.

Arabas J., Bialobrzeski L., Domanski P.D., Swirski

Zimmermann H.-J (1991) Fuu:y Set Theory and Its

K. (1998) Pulverised Boiler Optimization and

NO x

Reduction

by

Neural

Application.

Networks,

Publishers.

Pragoregula'98, pp.61-69, Praha. Astrom

K.J.,

Wittenmark

B.

(1994)

Adaptive

Control, Addison Wesley, Reading, MA. Bartos FJ., Artificial Intelligence (1997) Smart

Thinking

for

Complex

Control,

Control

Engineering, July 1997. Czogala E, Pedrycz W (1982): Control problems in

fuzzy systems, Fuzzy Set and Systems 7, pp.257274,. Domanski P.D., Brdys M., Tatjewski P (1997) Fuzzy

logic multi-regional controllers - design and

146

Boston,

Kluwer

Academic