Softsensor for Estimation of Steam Quality in Riser Tubes of Boilers

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Moustafa Elshafei and Mohamed A. HabibSoftsensor for Estimation of Steam Quality in Riser Tubes of Boilers

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Original Article

Softsensor for estimation of steam qualityin riser tubes of boilers

Moustafa Elshafei1 and Mohamed A Habib2

Abstract

Steam fraction in riser tubes of boilers is a critical process variable which impacts the life of the tubes and could lead totube rupture, long boiler down time, and expensive repairs. Unfortunately this parameter is difficult to measure byhardware sensors. This article presents a new neural network softsensor for estimation and monitoring steam mass andvolume fractions in riser tubes. First, conventional data were collected from a target industrial boiler. The data are thenused to develop a detailed nonlinear simulation model for the two phase flow in the riser tubes and risers and down-comers water circulation. The model output is verified against the collected field data. Next, the boiler nonlinear modelis used to generate data covering a wide rage of operating conditions for training and testing the neural network. Theinput of the neural network includes the heating power, the steam flow rate, the water feed rate, the drum level, and thedrum pressure. The neural networks predict the mass steam quality and the steam volume fractions. The softsensorachieves a root mean square error on the test data less than 1.5%. The predicted steam quality is then compared with thecritical limits to guide the operators for safe and healthy operation of the boilers. The developed softsensor for esti-mation of the steam quality has simple structure and can be implemented easily at the operator stations or the appli-cation servers.

Keywords

Boiler dynamic modeling, boiler operation, boiler safety, softsensing, neural networks, nonlinear state estimation, boilersteam fraction, boiler steam quality

Date received: 4 May 2012; accepted: 23 November 2012

Introduction

Boiler operations face many challenges stemming

from various economic, regulatory, and safety

issues. Steam generation boilers are commonly used

in many industrial processes, and may experience

rapid and dynamic changes in the steam demand.

The rapid changes in steam demand result in fastchanges in drum pressure and water level in the

drum. The changes in steam demand are normally

met by controlling the feedwater flow rates and

firing rates. Consequently, heat flux along the riser

and downcomer tubes is increased and may result in

tube overheating. Tube overheating may cause tube

failure resulting in unscheduled boiler shutdown that

may interrupt plant operation. The problem impact is

not only due to the cost of replacing defective parts

but also due to the frequent need of system shutdown

and the possible imminent safety hazards. The heat

flux along the riser tube causes steam to start gener-

ation from a distance say x0, and the mass fraction of

steam, and its corresponding volume fraction,

continue to increase upward along the riser tubes.

The larger the volume fraction of steam, the smaller

the inner surface area of the tube in contact with the

water liquid phase, leading to substantial and fast

decrease in the effective heat transfer to the water in

the riser tube. As a result, the wall spots, which are

not in contact with the water liquid phase, will exhibit

localized higher temperatures, severe thermal stresses,and possibly rupture of the tubes. Accordingly, moni-

toring the steam quality and the natural circulation in

the riser/downcomers can provide valuable informa-

tion for the operator to ensure safe and reliable oper-

ation of the boilers. Unfortunately, measurement

of the steam quality in the riser tubes is not

1Systems Engineering Department, King Fahd University of Petroleum

and Minerals, Saudi Arabia2Mechanical Engineering Department, King Fahd University of

Petroleum and Minerals, Saudi Arabia

Corresponding author:Mohamed A Habib, Mechanical Engineering Department, King Fahd

University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

Email: [email protected]

Proc IMechE Part C:

J Mechanical Engineering Science

0(0) 1–11

! IMechE 2012

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possible using conventional instrumentation systems.

Monitoring the steam mass fraction, known as the

steam quality, in the riser tubes can be utilized to

avoid the problems of overheating of the riser tubes

and to control boiler firing in order to prevent tubes

overheating.

This study is aimed at investigating methods toprovide a softsensor to estimate the steam quality in

the riser tubes for boiler health monitoring and to

ensure safe operation of the boiler. Specifically, we

investigated the use of artificial neural networks

(ANNs) for estimating the steam quality in the riser

tubes. The estimator relies on the available conven-

tional measurement only such as fuel flow rate, pres-

sure, drum level, feedwater rate, and steam flow rate.

To the best of the authors’ knowledge, this is the first

technique ever for online estimation of the steam

quality in the riser tubes of boilers. Second, this

study is the first attempt to apply artificial intelligencetechniques, as ANN, for soft estimation of this par-

ameter. The ANN softsensor is a low-cost solution,

can easily be implemented on the existing boiler con-

trol systems, and can be integrated in the boiler con-

trol and management systems for better boiler

operation and safety. The study also investigates the

dynamic response of the steam quality at the exit of

the riser tubes in response to rapid variations in steam

demand.

Boiler dynamic modeling

Dynamic simulation models of boilers provide a verycost effective tool to study plant transient character-

istics with the aim to improve the design and control

strategies to meet stringent operational requirements.

In the present investigation, it is essential to be able to

analyze the dynamic response of the boiler system due

to changes in the input values, system parameters, and

operating conditions. Such a goal can be achieved via

numerical simulation of the boiler’s system dynamic

model with sufficient built-in details. Modeling of boi-

lers has been an on going effort for many years.

Dynamic models of boiler systems can be developed

on the basis of laws of conservation of mass, momen-tum, and energy as applied to the various system’s

components or modules. The model also necessitates

the use of several empirical formulae, e.g. to account

for friction effects, and heat transfer coefficients. Also,

the fluid properties must be accounted for as given by

the standard water-steam tables. In the literature,

there are several models of boiler systems built for

different objectives. Boilers are usually equipped

with many local controllers that need to be coordi-

nated to collectively reach a safe operation under

normal and abnormal system conditions. The efforts

have been mainly driven by control objectives. For

example; Astrom and Eklund,1 Unbehauen and

Kocaarslan,2 Kwatny and Berg,3 and Astrom and

Bell4 are among several other researchers.

A drum boiler model which runs in real time was

developed by Flynn and O’Malley,5 and validated

using dynamic data recorded on an actual plant. It

was indicated that the model can be used for dynamic

simulation studies in long time frames, greater than 30

s, in particular where assessment of deviations of

internal parameters, such as steam pressure, drumlevel, and steam temperature outside safety limits is

essential. A computer program for simulation of

boiler start-up behavior was provided by Dong and

Tingkuan.6 They indicated that, with the simulation

results, design and optimization of start-up proced-

ures for operation can be obtained. This can result

in minimum start-up losses by reducing the start-up

time as much as possible. Kim and Choi7 developed a

model for water level dynamics in the drum-riser-

downcomer loop of a natural circulation drum-type

boiler. The work provides an investigation of the

response of water level dynamics to changes insteam demand and/or heating rate. The results were

compared with those of Astrom and Bell.4

Thermal stresses problem

The problem of thermally induced stresses in boilers

has been a concern for many years. These stresses can

cause failures which can be abrupt, termed as ‘thermal

shock,’ or over a period of time, termed as fatigue fail-

ures. The latter are caused by repeated thermal over-

heating of the riser tubes. The impact of thermal stress

is amplified in the presence of sharp radius corners,

abrupt changes in thickness of metals, and corrosion.Wolf and Neill8 presented some protective measures

against thermally induced stress cycling. Among

these, the most important is the difference between

boiler supply-water temperature and the system

return-water temperature. Design of boilers against

failure due to thermal stresses went through a

number of improvements going from trial and error

type development to numerical and experimental ana-

lyses. Among the important experimental work is that

of Kudryavtsey et al.9 who developed a method for an

accelerated sample testing of boiler materials. In their

tests, the experimental conditions were made similar tothe operating conditions. The results from fatigue test-

ing of two different high strength boiler steels exposed

to different heats were used to compare the materials

with regard to their sensitivity to asymmetric loading.

The authors recommended that boiler manufacturers

should perform similar tests on the materials they

intend to use for building boilers and components.

Kruger et al.10 developed an optimal control for

fast boiler start-ups. Noting that the major limiting

factor relevant to power plant start-ups is the thermal

stress for thick-walled components, they have incor-

porated in their nonlinear model thermal stress evalu-

ation modules. They presented a start-up control

simulation in which drum and superheater maximum

thermal stresses are set as pre-defined constraints.

2 Proc IMechE Part C: J Mechanical Engineering Science 0(0)








They showed by simulation that their model will

result in drastic reduction of start-up time. In a later

work, these same authors Kruger et al.11 developed a

simple equation for estimating thermal stresses from

temperature during boiler start-ups. Emara-Shabaik

et al.12 developed a dynamic model, which enables

the prediction of risers’ tubes temperature under vari-ous operating conditions. The model is composed of

fluid dynamics model representing the fluid flow in the

drum downcomer-riser loop and a dynamic thermal

model of the riser’s temperature. Habib et al.13 inves-

tigated the problem of boiler’s tube overheating. A

thermal model for the prediction of possible tube

overheating was developed. The developed model

incorporates a nonlinear state space dynamic model

which captures the important physical interactions of

the main variables of steam generation in drum boi-

lers. The system under consideration includes the

drum, the riser, and downcomer as its major compo-nents. The dynamic response of the system’s state

variables due to rapid rises in steam flow rate was

investigated.

State estimation

Process monitoring and control requires estimation of

process variables, which are often not measurable dir-

ectly. A cost effective approach to monitor these vari-

ables in real time is to employ softsensing or model-

based state estimation techniques. Dynamic model

based state estimation is a rich and highly active

area of research and many novel approaches haveemerged over past few years. State estimation of

heat exchanger components has been investigated by

Wallace and Clarke,14 Lo et al.,15,16 and Lu and

Hogg.17 Lo et al.15,16 used least mean squares estima-

tion of a boiler static model. Wallace and Clarke14

and Lu and Hogg17 applied filter state estimation

using dynamic boiler models. These studies on state

estimation of dynamic boiler models relied on the

extended Kalman filter (EKF), which is an extension

of the Kalman filter for nonlinear systems.

The basic Kalman filter will provide optimal state

estimation for systems that can be modeled by linearsystem equations and Gaussian noise. The EKF

addresses the problem of state estimation of nonlinear

systems. EKF uses grid-based filters to approximate

the continuous state space as a set of discrete regions.

This necessitates the predefinition of these regions and

becomes prohibitively computationally expensive

when dealing with high-dimensional state space.

Another difficulty of EKF is that the state covariance

and the Jacobian matrix of the system model need to

be calculated. This is inconvenience since the algebraic

form of these equations may not be easy to derive.

The EKF is probably the most widely used estimation

algorithm for nonlinear systems. However, it is diffi-

cult to implement, difficult to tune, and only reliable

for systems that are almost linear.

In Julier and Uhlmann,18 to overcome the prob-

lems associated with EKF limitations, the unscented

transformation (UT) was developed as a method to

propagate mean and covariance information through

nonlinear transformations. It is more accurate, easier

to implement, and uses the same order of calculations

as linearization. UT can be tailored in many ways toaddress the subtleties of particular applications or

performance concerns, it is important to recognize

that the basic UT algorithm is conceptually very

simple and easy to apply. In this respect, the UT

has the same appeal as linearization for the EKF,

but unlike linearization the UT provides sufficient

accuracy to be applied in many highly nonlinear fil-

tering and control applications. Lo and Rathamarit19

investigated the problem of state estimation of a

300 MW drum-type boiler using an unscented

Kalman filter to improve estimation accuracy by pre-

serving the nonlinearities of the boiler equations. Theboiler is modeled by a system of differential state

equations for the dynamics of the circulation loop

and another set of partial differential equations for

the heat exchangers such as the super heaters, rehea-

ter, and economizer.

On the other hand, inferential sensing techniques

have been gaining momentum recently as viable low-

cost alternatives to hardware sensors in various situ-

ations, as well as for nonlinear state estimation. The

core of inferential sensing is built on process models

and estimation techniques. Inferential techniques, also

known in the literature as softsensors, range from

statistical methods to modern heuristics techniques.Application of softsensors has been recently increas-

ing in many fields including prediction of pollutants

from different combustion operations. By the middle

of 1997, hundreds of applications of softsensors had

been reported in the process industries alone, Keeler

and Ferguson20 and Martin.21 For example, softsen-

sors were successfully applied by Martin21 to infer

kerosene flash point, distillate flash point, and other

important parameters in a refinery crude tower. It has

been applied as well to estimate the bottom compos-

ition in distillation columns.22 Application of ANN

for emission monitoring has been recently proposedas well. Traver et al.23 demonstrated successful appli-

cation of ANN to predict emission of a 300 HP Diesel

Engine. A multi-layer feedforward ANN of 10

inputs, 42 hidden neurons, and 3 outputs was devel-

oped to predict NOx, CO, and opacity. Elshafei

et al.24 developed an inferential softsensor for emis-

sion monitoring of NOx and O2 from a water-tube

boiler using polynomial function network. Data

were obtained from a simulated computational fluid

dynamic model developed by Elshafei et al.,25 Shakil

et al.,26 who developed a dynamic NN for softsensing

of NOx emission from an industrial boiler. Recent

work27–29 indicates the need for models that should

consider the performance of boilers under unsteady

state applications.

Elshafei and Habib 3








This article provides a development and an appli-

cation of ANN for prediction of steam quality in boi-

lers, instead of using nonlinear state estimation

methods. In the following section we present the

boiler model. Next, a brief introduction to ANN is

provided. Although computational fluid dynamics

methods are available but they include many modeledterms that lack validation and, therefore, lack accur-

acy. The developed ANN softsensor will then be pre-

sented in ‘Artificial NNs.’ The simulation results are

shown in ‘Results and discussion’ and contain a thor-

ough evaluation of the proposed softsensor.

Boiler model

The boiler under consideration is of the water-tube

natural circulation type. The main components of

the boiler are the steam drum, mud drum, the down-

comer tubes, and the riser tubes, which represent thecomplete water circulation loop as schematically illu-

strated by Figure 1. The subcooled feedwater into the

steam drum flows in the downcomers where it is

heated by absorbing sensible energy from hot gases

in the return chamber. The saturated steam from the

mud drum, then, is heated and partially evaporated in

the riser tubes before entering the steam drum.

In Figure 1, Q:

ðMWÞ is the heat applied on the riser

tubes, which boils the water in the drum. The applied

heat causes saturated steam to rise in riser-drum-

downcomer loop. Feedwater, _m fwðkg=sÞ, is the flow

rate of water being supplied to the boiler. Saturated

steam, _msðkg=sÞ, is the flow rate of the steam which istaken from the drum to the superheaters and the tur-

bine. A full simulation model was built for the boiler

and tuned to match the collected operating measure-

ments including the three element control of the

boiler. The simulation model is built using the

Astrom and Bell4 model. The set of nonlinear differ-

ential equations representing the time dependence of

the state variables can be presented in a matrix form

as follows

a11 a12 0 0

a21 a22 0 0

a31 0 a33 0

a41 0 a43 a44

26666664

37777775

dP=dt

dV wt=dt

dr=dt

dV sd =dt

26666664

37777775

¼

_m fw _ms

Q þ _m fwh fw _mshs

Q rhs _mdc

s

T d

ðV 0sd V sd Þ þh fw hw

hs

_m fw

266666664

377777775

ð1Þ

The model derived parameters are given by

a12 ¼ w s ð2Þ

a11 ¼ V wt

@w

@ p þ V st

@s

@ p ð3Þ

a22 ¼ whw shs ð4Þ

a21 ¼ V wt hw@w

@ p þ w

@hw

@ p

þ V st hs

@s

@ p þ s

@hs

@ p

V t þ mtC p@ts

@ p ð5Þ

a31 ¼ w

@hw

@ p rhc

@w

@ p

ð1 vÞV r

þ 1 rð Þhc

@s

@ p þ s

@hs

@ p

vV r

þ ðs þ ðw sÞrÞhcV r@v

@ p V r þ mrC p

@ts

@ p

ð6Þ

a33 ¼ ðð1 rÞs þ rwÞhcV r@v

@r

ð7Þ

a41 ¼ V sd

@s

@ p

þ1

hc

sV sd

@h

@ pþwV wd

@hw

@ p V sd V wd þ md C p

@ts

@ p

þrð1 þÞV r v

@s

p þ ð1 vÞ

@w

@ p þ ðs wÞ

@v

@ p

ð8Þ

e43 ¼ rð1 þ Þðs wÞV r@v

@r

ð9Þ

Figure 1. Schematic picture of the boiler.









e44 ¼ s ð10Þ

q2dc ¼

2wAdcðw sÞ g vV r

k ð11Þ

v ¼ w

w s

1 s

ðw sÞr

ln 1 þw s

s

r ð12Þ

The model is highly nonlinear, and the available state-

dependant measurements are basically the drum level

and the drum pressure. With only these two process

variable outputs, the application of conventional state

estimation can be very difficult and computationally

expensive for online estimation. Although the drum

temperature is also available, it is directly related to

the drum pressure.

Artificial NNs

ANNs are composed of signal processing elements

called neurons. The neurons are interconnected and

the strength of the interconnections is denoted by

parameters called synaptic weights. The model of a

neuron is as shown in Figure 2, where x1, x2, . . . , x p

are the input signals, wk1, wk2, . . . , wkp the synaptic

weights of neuron k, wk0 the bias, vk the linear com-

biner output, f (.) an activation function, and yk the

output signal of the neuron.

In mathematical terms, the kth neuron isdescribed as30

vk ¼X p

j ¼1

wkj x j þ wk0

yk ¼ f ðvkÞ

ð13Þ

The activation function, f (.), defines the output of a

neuron in terms of the activity level at its input.

There are several classes of ANNs structures. The

most common structure of ANN is known as feedfor-

ward neural networks (FFNNs). FFNNs are com-posed of layers of interconnected neurons. Usually,

an input layer, a number of hidden layers, and an

output layer are used, as shown in Figure 3.30 input

layer is essentially a direct link to the inputs of the first

w k0

w k1

w k2

w kp

∑ f (.)

V k y k

x 1

x 2

x p

1

Biase

Summing

Junction

Activation

function

Synaptic

weights

Neuron

Figure 2. Model of a neuron.

S

S

S

f

f

f

S

S

S

f

f

f

S

S

S

f

f

f

y1

y2

y3

u1

u2

Input

Layer First Hidden

Layer

Second Hidden

Layer

Output

Layer

Figure 3. Multi-layer FFNN.

FFNN: feedforward neural network.









hidden layer. The output of each neuron may be con-

nected to the inputs of all the neurons in the next

layer. Signals are unidirectional, i.e. they flow only

from input to output.

The potential of FFNN as a basis for the modeling,

classification, and statistical estimation stems from

the following characteristics. Each neuron (processingunit) has a nonlinear activation function that enables

the modeling of any underlying nonlinearity or com-

plex relations in physical processes. Due to the feed-

forward structure, the training of the network is

simple and can be made to adapt to varying condi-

tions. In a multi-layer NN, each neuron in a particular

layer is connected to a large number of source neurons

in the previous layer. This form of global interconnec-

tivity has the potential to be fault-tolerant. If a neuron

or its synaptic links are damaged, the quality of pre-

dicting the output behavior will not be seriously

degraded. For a sufficient number of hidden units,FFNNs can approximate any continuous static

input–output mapping to any desired degree of

approximation.30,31 The back propagation (BP) algo-

rithm is usually used for (FFNN) training. The most

popular version of BP algorithms is Levenberg

Marquardt (LM).32 The LM algorithm outperforms

simple gradient descent and other conjugate gradient

methods in a wide variety of problems. LM has

become very popular due to its relative ease of imple-

mentation and its ability to converge promptly from a

wide range of initial guesses.

Results and discussion

Boiler dynamic results

The industrial boiler used in the present investigation

has the data and operating conditions, as presented in

Table 1. These data are at the maximum continuous

rating (MCR). The boiler (Figure 1) was connected

with other boilers to a common steam header along

with the steam coming from the cogeneration unit.

Due to an upset in the gas turbine steam cogeneration

unit, the header pressure dropped. The boiler control

system reacts to meet the demand in steam, as

reflected in the plot of Figure 4(a) which presents

the time variations in the steam flow rate of the

boiler. The increase in the steam load causes pressure

drop in the boiler drum and increase in the feedwater,

which results in decrease in the delivery of steam,

causing the exhibited oscillatory response.The boiler model and the parameters of the control

system were fitted to data obtained from an actual

boiler of 208 MW at MCR.33 The model was vali-

dated by comparing the response of the model and

the actual boiler during normal operation and

during a short upset period shown in Figure 4. The

top plot (Figure 4(a)) presents the actual steam load

during the upset period, while the following plots

show the actual boiler response versus the fitted

model response. The results of the feedwater flow

rate (Figure 4(b)) and the heating power (Figure

4(c)) show that the model provides good agreementwith the actual boiler. The difference between the

actual heating power and simulated power in the

steady-state region is due to the heat absorbed in

the economizer, the energy loss in the water blow-

down, and radiation.

Simulation results

The simulation model described in ‘Artificial NNs’ is

used to assess the performance of the boiler under

various operating conditions. In particular, the

model is used to generate operating conditions for

building a simplified NN based softsensor for estimat-ing the riser tube steam quality. The inputs for the

NN are presented in Table 2. The normal boiler

operating conditions considered in the present simu-

lation are _ms ¼ 45kg/s, steam power ¼ 92 MW,

pressure ¼ 4990 kPa, V sd ¼ 4.14 m3, and

V wt ¼ 57.73 m3. The steam load profiles for training

and testing of the NN are shown in Figure 5 and

cover a wide range of operations and rate of change.

Several NN structures were tested with various

numbers of neurons in the hidden layer, as presented

in Table 3. The number of neurons was taken to be 10,

15, and 20. All cases were tested using 100 epochs. AllNNs have ‘tansig’ functions in the hidden layer and

‘logsig’ function in the output layer. The performance

of the tested NNs is summarized also in Table 3. Table

3 clearly shows that networks 2 and 3 gave the best

performance on the test data. However, network 3

was selected because it has less number of weights.

The performance of this network, based on the test

data, is shown in Figure 6 and indicates close agree-

ment between the simulated and predicted results for

the steam volume fraction. The correlation coefficient

is better than 0.998. The corresponding simulated and

predicted results of the steam mass quality and the

water flow rate in the downcomers are shown in

Figure 7. Agreement between simulated and predicted

results of these two parameters is indicated.

Table 1. Boiler data and typical operating conditions.

Maximum continuous steam power (MCR) 208 MW

Drum saturation pressure 4996.6 kPa

Drum saturation temperature 263.9 C

Header super steam pressure 4490 kPa

Super heater temperature 411 C

Energy loss to stack 18.5%

Energy absorption in superheater 12.7%

Energy absorption in economizer 7.8%

Natural gas fuel LHV 39.8 MJ/kg

The energy absorbed by the systemof the risers, downcomers, and drum

61%

MCR: maximum continuous rating.











0 10 20 30 400.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55(a) (b)

Time in minutes

S t e a m v o l u m e q

u a l i t y

Prediction of steam volume ratio

Predicted

Simulated

0.2 0.3 0.4 0.5 0.60.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

Targets

N N o u t p u t

Testing Performance of the Neural Network

Figure 6. (a) Steam volume quality; blue (dotted line): simulated; red (solid line): predicted and (b) predicted versus simulated values.

(b)(a)Steam load for testing

0 20 40 60 80 1000

10

20

30

40

5060

70

80

90

100

S t e a m D

e m a n d

i n k g / s e c

Time (minutes)

Steam load for training the ANN

0 10 20 30 4010

20

30

40

50

60

70

80

90

Time in minutes

S t e a m f l o w r a t e i n k g / s e c

Figure 5. Steam flow rate for training (a) and testing (b) of the NN.

NN: neural network.

Table 3. Summary of the NNs performance.

Number of neurons

in the hidden layer Training MSE (106) Testing MSE (104)

Maximum absolute

error (testing)

1 5–10–1 4.6062 31 0.1414

2 10–10–1 4.27 0.3766 0.0207

3 5–15–1 4.0132 0.4324 0.0155

4 10–15–1 3.7889 355 0.6424

5 5–20–1 2.4283 683 0.75016 10–20–1 3.7 11 0.2018

NNs: neural networks; MSE: mean squared error.









impingement of high temperature gases. A volume

steam quality of 78% is considered critical to avoid

thermal stress. Figure 8 can be used as a guide for the

operator to insure safe operation of the boiler. The

dotted line of Figure 8 indicates the predicted condi-

tion of the steam volume fraction of the tubes having

the highest heat flux intensity. The horizontal line rep-

resents a critical level that should be avoided to pre-

vent thermal stresses in the riser pipes, and possible

damage of pipes. The figure indicates that the highest

steam quality occurs between 20 and 27 min and cor-

responds to that of the steam flow rate of Figure 5(b).

In this region, the predicted steam volume quality

exhibits the worst situation where it gets closer to

the maximum value of 78%. Although other alterna-

tive NNs could also be considered for estimating

steam quality, e.g. support vector machines, radial

basis functions, and adaptive neuro-fuzzy inference

system, the basic FFNN used in this study was suffi-

cient to prove the concept and provided satisfactory

results at low complexity. Future study may investi-

gate the use of other NNs and compare them from

computational cost, generalization capability, and

effectiveness in steam quality softsensing.

0 10 20 30 400.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time in minutes

P r e d i c t e d S t e a

m v

o l u m e q u a l i t y

Critical limit of Steam volume quality

Average heat flux

Highest heat flux

Unsafe Limit

Figure 8. Critical steam volume fractions.

0 10 20 30 400.005

0.01

0.015

0.02

0.025

0.03

0.035(a) (b)

Time in minutes

S t e a m M a s s Q

u a l i t y

Prediction of steam mass ratio

Predicted

Simulated

0 5 10 15 20 25 30 35 402000

2200

2400

2600

2800

3000

3200

3400

Time in minutes

W a t e r F l o w r a t e q d c ( m 3 / s e c )

Down Comer water flow rate qdc

PredictedSimulated

Figure 7. (a) Prediction of steam mass quality and (b) prediction of the downcomer flow rate.









Conclusions

The article proposed an ANN softsensor for estimat-

ing the riser tubes steam quality. The model was vali-

dated with the results of a nonlinear dynamic model.

ANNs are easy to use for online monitoring of boiler

performance instead of using a full nonlinear model.NNs are also used to estimate the water circulation in

the boiler instead of using nonlinear state estimator.

The critical regions of possible tube overheating were

reasonably predicted by the present model. The avail-

ability of predicted steam quality improves boiler

operation and safety, and reduces boilers downtime

and maintenance cost.

Funding

The authors acknowledge the support provided by King

Abdulaziz City for Science and Technology through the

Science and Technology Unit at King Fahd University of Petroleum and Minerals (KFUPM) for funding this study

through project NSTIP# 04-ENV59-08, as part of the

National Science, Technology and Innovation Plan. They

also thank KFUPM for its support of this study.

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Appendix

Notation

A steam quality

C p specific heat of metalh fg specific condensation enthalpy

h fg ¼ h g h f h fw specific enthalpy of feedwater

hs specific enthalpy of steam leaving the

boiler

hw specific enthalpy of saturated water

H w specific enthalpy of saturated liquid

water

Ls level variation caused by the steam in

the drum

Lw level variations caused by changes of

the amount of water in the drum

_mcd the condensation flow in the drum

_mdc the downcomer mass flow rate

_m fw mass flow rate of feedwater supplied to

the drum

_mr the flow rate out of the risers

_ms mass flow rate of steam exiting the

boiler

_msd steam flow rate through the liquid sur-face in the drum

P drum pressure (kPa)

Q:

heat flow rate to the risers

V d drum volume

V dc downcomer volume

V r volume of riser tubes

V sd volume of steam under the liquid level

in the drum

V 0sd the volume of steam in the drum in the

hypothetical situation when there is no

condensation of steam in the drum

V st total volume of steam in the systemV t the total volume of the drum, downco-

mer, and risers; V t ¼ V st þ V wtV wd volume of water under the liquid level

V wt total volume of water in the system

average volume fraction of steam in the

riser

v steam volume fraction in the riser tubes

exit

g density of saturated steam

s saturated steam density

w saturated water density


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Softsensor for Estimation of Steam Quality in Riser Tubes of Boilers