CONTROL ENGINEERING LABORATORY

Monitoring of Small-Scale Biomass

Combustion Processes

Mika Ruusunen

Report A No 29, March 2006

University of Oulu

Control Engineering Laboratory

Report A No 29, March 2006

MONITORING OF SMALL-SCALE BIOMASS COMBUSTION PROCESSES

Mika Ruusunen

Control Engineering Laboratory,

Department of Process and Environmental Engineering,

University of Oulu

P.O.Box 4300, FIN-90014 University of Oulu, Finland

Abstract: This report presents a model-based monitoring framework for continuous small-scale

combustion processes. Proposed monitoring approach is an integration of principal component regression

(PCR) models and an operating condition depended approach. Both analytical and hardware redundancy

are utilised to achieve robustness against sensor drifts and failures. Adaptation to process changes is dealt

with recursive on-line adaptation of local PCR-models averages.

Principles of the monitoring method are discussed and demonstrated with a simulation example. The data

for simulations is from 300kW stoker-fired wood chip boiler measurements. Preliminary modelling results

indicate that maximising the information retrieval carried in temperature measurements, levels of the most

important process variables could be estimated. In addition, transferability and adaptation capabilities of the

method were notified. Thus, the model-based approach would greatly assist in implementation of new

automatic control methods to small-scale combustion units.

The research work was financially supported by Tekes, Veljekset Ala-Talkkari Oy, Masa-Tuote Ky and

ProDevice Oy. SÄÄTÖ-project, belonged to Tekes technology programme “Small-scale production and

use of wood fuels 2002-2006”.

Keywords: Process monitoring, modelling, carbon monoxide, identification.

ISBN 951-42-8027-X University of Oulu

ISSN 1238-9390 Department of Process and Environmental

Engineering

Control Engineering Laboratory

P.O.Box 4300, FIN-90014 University of Oulu

1 INTRODUCTION ...................................................................................................... 1

2 SMALL-SCALE COMBUSTION MONITORING ................................................... 2

2.1 Characteristics of stoker-fired boiler system ...................................................... 2

2.2 Current status of the measurement technology ................................................... 3

2.2.1 Semiconductor sensors and sensor arrays ................................................... 3

2.2.2 Soft sensors ................................................................................................. 5

2.2.3 Machine vision ............................................................................................ 6

2.2.4 Fuel moisture and calorific value monitoring ............................................. 7

3 STOKER COMBUSTION IDENTIFICATION....................................................... 10

3.1 Experimental setup............................................................................................ 10

3.2 Acquired data .................................................................................................... 11

3.3 Model identification and process simulations /28/ ........................................... 12

4 PROPOSED MONITORING FRAMEWORK ........................................................ 14

4.1 Motivation and concept outline ........................................................................ 14

4.2 Model structures and adaptation mechanisms .................................................. 15

5 RESULTS AND DISCUSSION ............................................................................... 17

5.1 Data analysis for model identification .............................................................. 17

5.2 Tests with developed models ............................................................................ 20

5.2.1 Fuel moisture estimation ........................................................................... 20

5.2.2 Effective heat output -model ..................................................................... 21

5.2.3 Oxygen in flue gas .................................................................................... 23

5.3 Sensor validation and process diagnostics ........................................................ 24

6 CONCLUSIONS....................................................................................................... 25

REFERENCES ................................................................................................................. 26

1

1 INTRODUCTION

Small-scale combustion units are typically operating under varying process conditions.

For example, continuously changing heating demand and fluctuating fuel quality

complicate the task to produce energy with high efficiency and low emissions. Unlike in

large-scale combustion, where sophisticated measurement and control systems are

available, lack of cost-effective sensors and automation sets hard limits to small-scale

control. This, in turn, usually prevents the real-time optimisation of the combustion

processes under 1 MW, together with non-optimal process development.

To overcome problems in monitoring of small-scale combustion processes, a soft sensor

–approach could be considered. In this methodology, the primary process variables to be

monitored are derived from the measurement of a single or multiple secondary variables.

Information from these inferential measurements is fused using mathematical models. As

a result, the estimated value of a primary variable can be formed. The motivation for this

type of monitoring framework is clear in a small-scale environment. Firstly, sensors can

be simpler making them more robust. Secondly, utilisation of fast response in-situ sensors

could assist to minimise time delays in control. Thirdly, the information about absolute

values of important process parameters can be of help in continuous emission and

condition monitoring, including real-time efficiency calculations. /1/

The purpose of this investigation is to examine and develop advanced process monitoring

methods for small-scale stoker fired boilers. The research is focused on combustion

processes using wood chips. The development of the monitoring framework is based on

the use of inferential measurements. Attention is paid to the reliability and adaptation

features of the monitoring concept. Absence of these properties is still one of the major

obstacles in deployment of soft sensors to industrial processes. Data-based mathematical

models utilising soft computing methods are identified with analysis techniques utilising

data from a real combustion process. Data collection is based on design of experiments –

procedure.

In the following sections, combustion process characteristics under consideration and

applicability of present measurement technologies are first reviewed. Next, the basis of

the proposed combustion monitoring framework is briefly discussed. Finally, some

results of the monitoring capability with the identified models are presented and

analysed.

2

2 SMALL-SCALE COMBUSTION MONITORING

2.1 Characteristics of stoker-fired boiler systems

From the monitoring and control point of view, small stoker-fired boilers exhibit some

problematic properties in common. In this scale, the process environment usually differs

from the control environment (Figure 1), resulting in random changes of fuel quality and

load changes. In absence of real-time measurements this means that changes in fuel

calorific value and energy consumption have to be accounted for as unknown

disturbances.

Figure 1. Control and process environment of a small-scale stoker combustion system.

On the other hand, the small combustion units are volume products. This characteristic

makes the process environment differ considerably and individually in each case. Small-

scale of the process have also an influence on number of other factors, such as

availability of cost-effective:

materials and design for combustion chambers and grates,

energy storage units,

fuel feeding and dust removal systems,

sensors and gas analysers, and

control devices and actuators.

Typically in this scale, there are gradually drifting phenomena and non-linearity present

in the process, namely:

operating condition dependent dynamics, time-delays and interactions,

fouling of heat exchange surfaces and air feed as a function of time, and

strongly coupled system variables, i.e. a process input can affect several outputs.

Above-mentioned issues restrict the implementation of monitoring and control systems.

Following section reviews some of the potential and modern measurement solutions for

small-scale energy production.

3

2.2 Current status of the measurement technology

There are several types of measurements and monitoring technologies commercially

available or under intensive research to be considered for small-scale energy production

systems. This section focuses on semiconductor gas sensors /2/. The other topics covered

are soft sensors /1/ and machine-vision /3/. Also combinations of different technologies

have been proposed, and discussed here, for example integration of hardware sensors and

signal processing techniques in /4/, /5/, /6/ and /7/.

2.2.1 Semiconductor sensors and sensor arrays

This sensor family is one of the most widespread among the gas component

measurements. Typical applications include gas leak detectors, low-cost gas analysers

and lambda sensors. However, applications in combustion monitoring have been rare

because of the harsh nature of the flue gases. Semiconductor gas sensors are usually

produced applying a reactive metal oxide material that is sensitive to changes of gas

component concentrations. This is because of the changes in conductance between

electrodes accordingly, making it possible to measure different resistances over the same

semiconductor material layer /21/.

Often, doping some additional ingredient into a material enhances the properties of the

metal oxide. In this way, common characteristics of the sensor family like non-linear

response, cross-sensitivity and chemical material erosion – drifting in sensor response,

can be at least partly compensated. An example of a sensor construction is presented in

Figure 2. It shows the typical parts of a sensor application, namely need for temperature

control (typical operating temperature +200-400ºC) in the form of heating electrode.

Figure 2. A schematic of the thick-film air-to-fuel ratio gas sensor, sensing layer: Al2O3 -

doped zinc oxide /8/.

4

Recently, both research and commercialisation of applications consisting of multiple gas

sensors have been emerged. This kind of sensor arrays can be integrated into one sensor

(Figure 3), or combined from separate sensors. The target is to achieve the compensation

of the cross sensitivities for single sensors, and have more than one gas component

measured at the same time.

Figure 3. The basic idea of the gas sensor array with five gas sensitive dielectric layers

/11/.

In /9/, multiple Metal insulator silicon carbide field-effect transistor sensors

(MISiCFETs) and metal–oxide sensors were integrated as an electronic nose, and utilised

to measure flue gases on-line from a 100-MW pellets-fuelled boiler. All in all

information from 13 sensors were combined in the experiments using multivariate data

analysis techniques, in this case principal component analysis (PCA) and partial least

squares (PLS). As a result, the authors were able to roughly identify operating conditions

present in the boiler with the sensor array and data processing techniques. With the

method, also some quantified information from CO concentration were managed to

produce at the optimal operating regime of the boiler. Another conclusion was that the

training set did not cover all variations in the gas compositions of the flue gases. For this,

a more complete investigation of the important gas components in different operating

conditions at the boiler could be performed. Semiconductor sensors exhibit drifting

properties, so drift compensation algorithms should complement calibrations made in the

laboratory. Another potential problem according to authors is the possible fuel type

change during the measurement period, since that might give other gas compositions in

the flue gases.

Same kinds of results were demonstrated in /7/, where the possibility to use a low-cost

chemical sensor array to monitor a 200kW biomass boiler was studied. Using 16

semiconductor and 16 MISiCFET sensor elements together with multivariate data

modelling, promising results were obtained in on-line monitoring of O2, CO and HC. The

data modelling was based on blind source separation and multiple linear regression. As

with integration of data analysis and this kind of sensors, the validity checks of the data-

based models and sensor drift counteractions have to be accounted especially.

Semiconductor sensors are commercially available in large extent, for example integrated

CO/HC-sensors /12/ or gas sensors with a signal transducers /13/. Sensors can be applied

5

to measure also other phenomena, for example pressure, acceleration, temperature,

humidity and light /21/. Commercially available sensors are mainly designed for use in an

atmospheric environment currently. Still, probably the most common semiconductor gas

sensor concerning combustion systems is a lambda sensor for flue gas oxygen

measurements.

2.2.2 Soft sensors

Operating costs and lack of real-time performance may limit the usability of hardware

sensors and analysers in a continuous combustion monitoring of small-scale systems. For

these reasons, mathematical model-based software sensors are being increasingly probed

to avoid problems with traditional measurements. The basic idea of a soft sensor

approach is to model combustion products using inferential and more easily measurable

process measurements, first-principle models or mixture of them. /1/

If quantitative information, absolute values from combustion variables is needed, a

reference measurement device has to be available in order to calibrate the model. Typical

development of a model-based soft sensor then includes following stages:

identification of model structure, (inputs, outputs, delays)

experimental data collection and data pre-processing,

identification of model parameters w/wo data,

model testing, comparison to reference measurements,

model deployment and maintenance.

Much research have been performed on monitoring of combustion emissions, for

example applying neural networks (/14/, /15/), neuro-fuzzy modelling /16/, ARX-models

/17/, non-linear regression models /18/ and kinetic models /19/. Inferential approaches

have focused on large-scale energy production, where good results have been achieved

by using sophisticated model structures and informative sensors already existing in

processes. For this, however, careful selection and pre-processing of model identification

data with tuning expertise is essential.

Soft sensors could also be used as a replacement for hardware sensor (Figure 4) or to the

development of the appropriate controller directly from the generated model. /20/

Figure 4. Soft sensor in a control loop /20/.

6

Although there are commercially available services to implement and maintain predictive

emission monitoring systems (PEMS) /22/, the design and maintenance costs of model

based sensors and needed measurements still restrict their utilisation in small production

units. There are also partly open theoretical questions concerning the use of soft sensors

methodology in control and without reference measurements, namely reliability and

adaptation including parameter coupling issues in the control loop /23/.

2.2.3 Machine vision

The methods based on flame detection and monitoring have been used for decades in

combustion control. Still, not until recently the image processing techniques have been

started to exploit more extensively. Infrared, ultraviolet and visible light detectors

producing binary or continuous signals from the existence and intensity of the flames

have been utilised in small-scale boilers.

A comprehensive example of the vision based monitoring possibilities with cameras is

given in /4/, where a combustion monitoring system based on colour (RGB) images of

the flame was developed for an industrial steam boiler (Figure 5). Experimental results

demonstrated that the system was capable of predicting the performance of the boiler

system over a wide range of conditions.

The application utilised multivariate image analysis using multi-way principal component

analysis method. The rapidly time varying flame images were shown to give a very stable

score plot histograms in the principal component space. These score plots were very

stable for all flames imaged under constant process conditions, but changed in a

consistent way whenever the process conditions changed. Masking methods were used to

extract a number of features of the flames from the principal component score plots.

These features were shown to be highly related to the process fuel feed rates. Also NOx

and SO2 concentrations, and the energy content of the incoming waste liquid stream were

estimated from the images. The authors concluded that there is a tremendous amount of

information in flame images, which can be successfully used to monitor performance and

emissions of the combustion processes. /4/

Figure 5. An example schematic of the flame monitoring system /3/.

7

Another example of machine vision application is given in /24/, where infrared

thermographic camera system and image processing techniques were applied to monitor

the combustion dynamics within the primary kiln, in terms of both absolute temperatures

and characteristic times. In tests, the temperature profile of the waste fuel layer in the

combustion chamber was determined successfully and with a short delay. Also the flue

gas temperature was measured with the same system. From this information the authors

concluded that it could be possible to identify either hotspots or zones where the waste is

drying, gasifying, igniting, burning, and finally cooling. Consequently, the movement of

waste and flow rates of air could be regulated utilising the presented approach.

2.2.4 Fuel moisture and calorific value monitoring

As stated in section 2.1, fuel moisture and quality variations are among the main

disturbances in the combustion. For this reason, on-line information about fuel properties

in small boilers would favour the optimisation and stabilisation of the process.

Several techniques for determination of on-line wood chips moisture content have been

presented in /10/. Here, fuel moisture is measured either in a fuel flow or a fuel bulk

where the sample has to be specifically representative of the bulk. Six methods have been

reviewed: dual x-ray measurements, near infrared spectroscopy (NIR), indirect method

(based on flue gas water content measurement), microwaves, radio frequents (RF) and

nuclear magnetic resonance (NMR), (Table 1).

Table 1. The reviewed moisture measurement techniques for woodchips in /10/.

According to /10/, smallest standard error of performance (SEP, squared mean difference

between average error and current error) could be achieved in on-line use with nuclear

magnetic resonance or indirect method. For continuous fuel flow measurement, NIR-

method could be favourable with a multivariate calibration. However, invest costs and

practical implementation issues (inhomogeneous fuel flow and bed height, frozen fuel,

calibration needs) are strongly affecting the applicability of most these methods at least in

small-scale.

8

A method for on-line compensation of inhomogeneous fuel quality in fluidised bed

combustion has been reported for example in /25/. In this extensively studied strategy,

fluctuations in fuel calorific value are compensated in feedforward manner by estimating

the combustion power using total airflow and flue gas oxygen measurements.

Combustion power estimation is based on elementary analysis of fuel properties and on-

line calculation of oxygen consumption with above measurements (Figure 6). The main

assumption for applicability of the method is a fast burning fuel quality minimising the

estimation delay, which is not a case in a grate-fired boilers with slowly burning fuel bed.

Figure 6. Simplified firing rate compensation structure for fluidised bed combustion. /25/

Another fuel quality compensation strategy in /26/ is based on oxygen measurement and

fuzzy logic. In this case, variations in calorific value are estimated monitoring the

changes in flue gas oxygen levels. A fuzzy rulebase was developed to produce a

correction term to the estimated and fixed heat value of the waste fuel. For example,

when the flue gas oxygen content increased, the output of rulebase decreased the

estimated waste fuel heat value. In the case of decreasing oxygen content, the heat value

was corrected upwards with fuzzy rules. Maximum correction range reported was ±15 %

change in the heat value. The corrected heat value of the fuel was then used as an input

for a main control concept.

Fuel quality monitoring concepts based on energy and mass balances are reported for

example in /5/ and /27/. These approaches, however, require a lot of measurements and

instrumentation including calculation power in order to be applicable in practice to small-

scale.

In /6/, the suggested estimation method of wood fuels calorific value was based on

temperature measurement inside the furnace, and fuzzy Tagagi-Sugeno modelling

approach /33/. The model structure was defined on the basis of combustion theory. It is

known that maximum theoretic flame temperature is strongly depending on excess air

and wet content of the wood fuel, see for example /29/. However, in a real combustion

process the determination of maximum temperature on-line can be impractical because of

non-adiabatic processes and strongly changing process variables.

9

For this reason, a model-based approach was considered that could enable a data-based

supervised identification of the model parameters. It was also argued that using the

model, it would be possible to filter out the main disturbing factors, namely different

process states and oxygen levels. Model inputs in premise part were chosen to identify

clearly the current operating conditions of a 5kW wood batch combustion process. For

consequent part of the model, a single combustion temperature was chosen with

calculated parameter indicating current operating point. The model was then trained with

the data including two different levels of fuel moisture. The known moisture percentages

were used as reference values for the model training. Model outputs were further utilised

in the calculation of on-line calorific value of the fuel. In the tests, a reasonable accuracy

was achieved in calorific value estimation, but only after a careful selection and

modification of utilised measurements, since the first moisture estimation results were

strongly affected by a measurement noise of a fuel mass measurement (a load cell

connected to the grate). /6/

10

3 STOKER COMBUSTION IDENTIFICATION

3.1 Experimental setup

For the combustion system identification, an experimental concept was set up. The

process consisted of a commercial 300kW stoker-fired boiler, fuel silo, fuel feeding

screw, heat exchanger, and instrumentation needed to monitor and control the system

(Figure 7).

Figure 7. Experimental setup.

Experimental design was made to collect representative data for the system identification.

It consisted of matrix-type design for determination of static relationships between

process variables and their importance in process control. In addition, a step response -

campaign for exploration of process dynamics was conducted. Detailed information on

the experimental procedure can be found in /28/. Some identification results are shown in

section 3.3.

Birch chips were used as a fuel at two moisture levels, 15% and 33%. Operating range of

the boiler was varied between 30%-100% of the nominal output during experiments. All

together there were 40 measurement points in the process. In addition to gas analyser

measurements (CO2, O2, CO, NOx, OGC), the boiler is equipped with its own lambda

sensor. Data from experiments was acquired continuously with sample rate of five

seconds. Two weeks period of data was collected for the subsequent data analysis.

11

3.2 Acquired data

In Figure 8, scaled values of carbon monoxide and heating power including oxygen

content in the flue gas are presented as an example data set. This data includes both step

response and matrix-design experiments.

Figure 8. Acquired CO (-), O2 (o) and effective heat output values (.), scaled to same

magnitude.

Data from four temperature sensors (K-type thermocouples) from different parts of the

boiler is shown in Figure 9. The lowest values are flue gas temperatures, whereas higher

temperature level indicates closeness to the furnace chamber.

Figure 9. Some of the combustion temperatures during experiments. Lowest: flue gas,

highest: furnace.

12

3.3 Model identification and process simulations /28/

Dynamic behaviour of the stoker combustion process was studied constructing a

mathematical model from it. Another motivation was to have a tool to test preliminary

control strategies using simulations instead of a real process experiments.

First order transfer functions were chosen for model structures, according to observed

behaviour of process responses (Figure 10). Since process dynamics was seen to depend

on operating point, namely fuel moisture and heat output, separate models were identified

for these conditions from the acquired data. Identification of model parameters was made

with MATLAB® System Identification Toolbox. Process steps downwards and upwards

seemed to have different responses and for this reason modelled also separately. Changes

in open loop process gains were accounted by fuzzifying them, resulting smoothly

changing values when moving for example to another heat output level. In addition,

effects of a moving grate were modelled.

(a) (b)

Figure 10. Example step responses of heat output, (a) fuel feed change from 200kW to

240kW, (b) fuel feed change from 240kW to 200kW, dry wood chips.

An example diagram of the developed model family and their interactions is presented in

Figure 11. Inputs for the models are fuel feed, air feed, grate movement and fuel

moisture. Outputs are oxygen level, carbon monoxide and effective heat output.

Figure 11. Developed simulation model in MATLAB® Simulink

®.

3

Out3

2

Out2

1

Out1

In1Out1

Subsystem7

In1Out1

Subsystem6

In1Out1

Subsystem5

In1Out1

Subsystem4

In1Out1

Subsystem3

In1Out1

Subsystem2

In1Out1

Subsystem1

In1Out1

Subsystem

3

In3

2

In2

1

In1

13

Some examples of simulation runs with identified models are presented in Figure 12. It

can be seen that accuracy of the models is satisfactory. More important thing for control

purposes is that process delays and dynamic behaviour in general are almost fully

accounted by the models in this case.

(a) (b) (c)

Figure 12. Some simulated and measured process responses. (a) Heat output, (b) oxygen

concentration in dry flue gas, (c) resulted CO-spike after grate movement.

In addition to process model development, some descriptive information was identified

from measured data. One example typically utilised in combustion control is a function

describing relation of optimal air feed and fuel power (Figure 13). The picture shows the

relationship between oxygen concentration in flue gas (y-axis) and fuel power (x-axis).

Figure 13. Optimal flue gas oxygen level (y-axis) dependence on fuel power (x-axis)

during the tests.

The above process knowledge was utilised in further development of several control

methods including decoupled PID, a Smith-predictor and control characteristics-based

strategies. Methods were implemented to the same simulation environment and compared

with each other applying constant control (input) sequences. Detailed description and

results of these simulations are presented in /28/.

14

4 PROPOSED MONITORING FRAMEWORK

4.1 Motivation and concept outline

According to the literature review and results from /28/, importance for achieving real-

time information from combustion processes is clear. For continuous monitoring and

control, also absolute values of the most important variables such as heating power,

oxygen and combustible gases in the flue gas would be favourable. In many cases, the

observation of these quality measures is in major role when new control methods are to

be implemented in future. Thus, a small-scale process environment requires transferable

low-cost methods for monitoring and control. Any approach then faces a scientific

question of how to reliably extract maximum information from the minimum amount of

available sensors.

Based on these demands, a model-based approach for combustion monitoring task is

suggested (Figure 14). Mathematical models can be considered as a replacement for

sophisticated hardware measurements in this case. On the other hand, boiler temperature

data could be utilised as real-time inputs to models to accomplish the important criteria of

cost effectiveness and robustness. The other motivation is the fast response time of

temperature sensors. Variables selected for monitoring were oxygen, heat output, unburnt

gas components and fuel moisture, according the defined control strategy in /28/.

Figure 14. Suggested combustion monitoring approach for small-scale units.

For the reference sensor to model calibrations, a lambda sensor and gas analysers are

considered. The reference is needed if models are thought to produce quantitative

information in the form of absolute values.

A more detailed view of modelled variables and their interactions is presented in Figure

15. The idea is based on knowledge that the behaviour of a stoker combustion process

may change according to fuel moisture and heat output levels, /28/.

15

Figure 15. Model interactions and monitoring of process state is based on information

from fuel moisture and heat power -models.

4.2 Model structures and adaptation mechanisms

The presented monitoring approach strongly relies on the temperature measurements and

data-based models. Therefore, reliability of the measurements and models is crucial for

robust process monitoring and control. Also adaptation of the methods is needed as the

combustion unit or process environment can change significantly from case to case.

In this study, a modified version of self-validating inferential sensor approach /32/, is

presented. As in the reference, for this case principal component analysis (PCA) /31/,

could be applied for utilising the redundancy of correlating temperature measurements.

Strong correlation among model inputs could lead to biased parameter estimates and

deteriorate the performance of a model. On the other hand, when the sensors are linearly

correlating, PCA is an optimal tool for the task. PCA makes also the straightforward

validation and signal reconstruction of failed or drifting sensor possible, as long as

linearity assumptions hold. The procedure can also give indications on extrapolation of

models, meaning exceeding of the operating range of the identified model. Similarly to

the reference, in the presented approach the principal components are model inputs

(linear principal component regression, PCR). Finally, by using different combinations of

temperature sensors for each model, the unwanted dependency between the models could

be remarkably reduced.

As an additional feature, linearity and proper functioning with partly vague estimations

(moisture and kW-models) could be ensured using the fuzzified information on

combustion conditions. Then, the current operating point could be fused with multiple

local PCR-models as presented similarly in /37/. This way, the operating range of the

models can be limited to local, maybe linear, process conditions. Here the procedure

resembles Tagagi-Sugeno modelling approach. Model switching surface at range

boundaries can be then continuous, because more than one model is valid at the same

time in a transient point. The principles of model calibration, sensor validation and

process condition monitoring are presented in Figure 16.

Since the modelling is focused on local operating regimes with PCR, recursive updating

of the moving averages of model inputs is a logical choice for adaptation purposes. This

w.-% -model

kW-model

Tem

per

atu

re s

enso

rs O2 -model

Op

erat

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nd

itio

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on

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CO/unburnt -model

w.-% -model

kW-model

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CO/unburnt -model

16

is true especially, when measurement values having the same scale and variation are

utilised. The sensor validation procedure could be performed actually with the local

principal components, simplifying the method.

Figure 16. A modelling principle (b), validity of each local principal regression model is

determined by current fuzzy operating condition estimate. Sensor validation,

conditional signal reconstruction (a) and adaptation mechanisms (c) of the system.

For a long term process monitoring, a multi-way PCA (MPCA),/34/, is proposed. It has

been applied mostly on batch process monitoring, because its ability to take into account

the evolution of batches by having three-dimensional input space (batch runs, variables

and time). The cubic matrix is projected into a two-dimensional array, and normal PCA is

then applied to this newly arranged data.

For the continuous combustion monitoring a modified multi-way PCA is introduced by

using the same procedure of operation condition-based analysis as depicted in Figure 16.

Instead of using measured values of temperatures, the correlation matrix can be formed

for local data spaces defined by an operating point. The main motivation is that by

monitoring the correlations between the variables, many of the problems connected to

adaptation and scaling could be avoided. Also, local operating ranges could favour the

linearity criteria.

This research also focuses on systemisation of model training stage. Therefore, the

Shannon’s information theory, /35/, is utilised in the form of entropic analysis to search

representative data segments at the identification stage. Details of the entropic analysis

can be found in /36/. The basic idea is to search for signals that carry maximum

information about the modelled variables. Several entropy measures can be calculated

and utilised. These include, for example, conditional entropy, maximum entropy and

mutual information. When applied to a short period time series signals, which is the case

if models are rapidly deployed to monitoring task, the results may not be reliable. This is

mainly because the entropic analysis is based on the estimation of the joint probability

distributions. If available data is not a fully representing sample of the population, it may

lead to wrong conclusions. Anyway, some useful information could be achieved for the

determination of signal-to-noise ratios and informative data segments.

Temperature

sensors

Local PCR / MPCA

Model output

Fuzzy operating

condition mon.

Gas analyser,

Ref. sensor

Parameters /

Moving average

Sensor

validation

Signal

reconstruction

(a) (b) (c)

Initial calibration

Temperature

sensors

Local PCR / MPCA

Model output

Fuzzy operating

condition mon.

Gas analyser,

Ref. sensor

Parameters /

Moving average

Sensor

validation

Signal

reconstruction

(a) (b) (c)

Initial calibration

17

5 RESULTS AND DISCUSSION

5.1 Data analysis for model identification

To test first the practical suitability of the temperature measurements and extracted

features as a primary information source for monitoring, a comprehensive model-based

analysis was programmed and conducted with MATLAB® software. The capability of the

temperatures as model inputs was studied with the method similar to procedure presented

for example in /30/. The target here is to find out the most important variables and

features connected to the monitored process variables applying the data sets for multiple

model identification in short, overlapping time windows. Structure (inputs, delays) of the

best performing models can then indicate usability of their inputs for final modelling.

Part of the available matrix design data and part of the dynamic response data were

utilised for the analysis. The other parts of data were reserved for testing the models with

simulations.

At his stage also entropic analysis (Section 4.2) was applied in the form of sliding

window –based procedure. There, the analysis is made sequentially in each of the data

windows that are partially overlapping.

The data analysis showed that at least three temperature measurements were found to

carry useful information of the modelled variables, namely data from two furnace

chamber sensors and the flue gas outlet sensor. The result is similar to /1/.

It was also found in the training data set analysis that the best preliminary modelling

results (with linear models) were achieved using only a minor part of the whole training

data set. These data areas were indicated with both entropic and model-based analyses.

An optimal training data length was usually around 200 measurement values for each

case, whereas the data containing a representative sample of process conditions was

10000 points. Typically, model identification is based on this kind of large sized data.

However, initial results here and in /1/ indicate the opposite at least for small combustion

processes with the local linear model approach.

Based on the monitoring principle (Figure 15), i.e. the need to observe changing process

dynamics for monitoring and control algorithms, the following meta-rules were formed to

estimation of momentary process state:

IF moisture-model output BIG and kW-model output BIG THEN big power with wet fuel

IF moisture-model output SMALL and kW-model output BIG THEN big power with dry fuel

IF moisture-model output SMALL and kW-model output SMALL THEN small power with dry fuel

IF moisture-model output BIG and kW-model output SMALL THEN small power with wet fuel,

where premise part (IF-part) consists of kW- and moisture-models outputs. Consequent

THEN-part is a fuzzy degree i.e. truth-value of the premise – a number between zero and

one after implication. SMALL and BIG are half-trapezoidal membership functions

18

defining membership degree of model output in fuzzy sets small and big. Ranges of the

functions can be defined from 20% to 60% for BIG moisture and 0% to 40% for

SMALL-moisture, functions are 50% overlapping. Respectively, for heating power,

SMALL is 0-200kW and BIG from 100kW to 300kW. Final process state estimate is rule

with the biggest truth-value. It can be seen that for stoker-fired process, the rulebase

needs necessarily no tuning since relative comparison between rules is used as a criteria

for selecting the estimate. Of course, if none of the rules is fired, it can be regarded for

example as an input to system diagnostics meaning failure, system shutdown or

adaptation need of the models.

The least squares method was applied to parameter estimation in final modelling. No data

pre-processing was needed because of good quality of acquired data. It was also noticed,

that the local average values of model inputs were changing within experiments,

therefore adaptation mechanism described before was considered important.

Fuel wet content was monitored with similar model-based procedure described in Section

2.2.4, /6/. With data analysis the features for continuous moisture level estimation were

extracted from the differences of two combustion temperatures (Figure 17).

Figure 17. Feature extracted from combustion temperatures for fuel moisture –model,

training data set. Solid line – temperatures during wet fuel combustion. Circle – dry

fuel.

As can be seen from the figure, difference between fuel moisture levels, namely 15% and

33%, is not fully separable straight with the proposed features. However, the best

available inputs were chosen to test capacity of the model.

For kW-model, inputs were determined by searching measurements mostly resembling

the measured heating power with above-mentioned methods. It was found that flue gas

temperature correlated mainly linearly with heat output, but the variation of dependency

was high and range of temperatures narrow compared to heat output scale. However, it

was tested if an indication even of rough heat output level could be utilised with the

model. Difference between two combustion gas measurements was chosen for another

model input.

19

Based on the data analysis and combustion theory, many process factors can have an

influence on the amount of oxygen in flue gas. For prediction of oxygen concentration in

this case, the available temperature measurements together with knowledge on current

operating conditions (rulebase previously discussed) were tested as model inputs. Figure

18 shows that the two temperatures alone can only partly describe the behaviour of

oxygen in flue gas. On the other hand, there are also some operating point depended areas

were the correlation is clearer.

Figure 18. Oxygen vs. two combustion temperature measurements.

During the data analysis it was concluded that carbon monoxide is hard to estimate based

solely on temperature measurements. In Figure 19, the dependency of these variables is

presented. Still, it was seen with reference measurements (kW, O2, CO2, measured fuel

moisture) that interactions could be found. This however required a comprehensive

analysis of the interactions between CO and other reference measurements.

Figure 19. Normalised carbon monoxide versus carbon dioxide values in the

experiments.

The model-based analysis was first made to identify linear or near linear correlations

between CO and other measured variables. Discriminant analysis (see for example /38/)

was then applied to the rough determination of important predictor variables for CO

20

using the local data set separated with the preceding analysis. In this type analysis the

target is to find out, which variables discriminate between groups present in data. In this

view, discriminant analysis can be compared to the analysis of variances (ANOVA).

Typically, multiple regression modelling and canonical correlation analysis are used to

find the most informative variables able to discriminate between data groups. Results of

this analysis are depicted in Figure 20. The figure shows that in this case CO vs. CO2 -

data groups are mainly separated according to increase or decrease of heating power, fuel

moisture and carbon dioxide. Interestingly, most of the lines identifying classified data

groups seem to intersect in the one particular point. In this case the reference point is

residing around CO: 0.05vol.-%, CO2: 8.5vol.-% (large white circles).

Figure 20. Results of the model-based and discriminant analysis. Separated data groups

are illustrated with black solid lines. Large white circles describe the intersection

point for all lines (black circle in the middle).

The reference point is here named as the “anchor point”. Moreover, a connection between

process state of the anchor point and the optimal training data set was noticed. In general,

connection between these points could remarkably ease the transferability of the

monitoring and control systems. It can be concluded that at least in this case different

levels of CO are possible to identify if kW, CO2 (O2) and fuel moisture values are

available in real-time.

5.2 Tests with developed models

5.2.1 Fuel moisture estimation

The model of fuel moisture level was tested with data left out from the analysis stage.

The results are presented in Figure 21. During the tests, fuel moisture was analysed

periodically from the bulk samples. The 6-sigma values of the measured fuel moisture are

presented with dotted lines in the figure.

21

Figure 21. Comparison of modelled and measured moisture values. Solid line – model

outputs. Dotted boxes – moisture variation (±3%) of measured bulk fuel samples

taken from the silo during test period.

Compared to Figure 17, the separation of two moisture levels is quite clear. Model

outputs seem to stay mostly within the bulk sample moisture levels. The model thus

filters out the process disturbances (changes of operating points) affecting the raw

measurements.

Model output values have been scaled to measured moisture levels based on information

from the training data set. The difference between model output levels could be an

important criterion in control, for example in determination of primary - secondary air

ratio. For real-time fuel quality compensation this approach could be applicable together

with dynamic predictive models presented in section 3.3 and in /28/.

5.2.2 Effective heat output -model

Before implementation of adaptation mechanism, the model seems to perform adequately

only around the operating point defined by the training data set (Figure 22). Clearly, at

least updating of input averages is needed. The time lag of reference measurements

(calculated with water temperatures outside the boiler and heat exchanger pump power)

was reduced from 150 s to 75 s when using the model in simulation. This property could

enable to control the fuel feed in a more robust way. In fact, the model-based approach

helped to identify the real lag time (from the fuel feed to heat release) for this boiler type.

This is because the response time of temperature sensors is fast enabling practically

undelayed kW-model estimates.

0 500 1000 1500 2000 2500

10

15

20

25

30

35

40

w.-%

MeasuredModelled

33

15

0 500 1000 1500 2000 2500

10

15

20

25

30

35

40

w.-%

MeasuredModelled

33

15

22

Figure 22. Heat output model performance without recursive model adaptation

mechanism.

After applying the recursive adaptation, kW-models succeed to predict measured heat

output more accurately (Figure 23). Thus, it can be argued that the adaptation feature

considerably helps to minimise the initial amount of training data. On the other hand,

PCR models already within a small range of data captured the general interactions

between inputs and the modelled variable.

In addition, the method also succeeded to compensate occasional measurement failures in

heat output. This is another advantage of using the modelling approach as the diagnostics

and validation of sensors becomes available.

Figure 23. The recursive adaptation in use. Model output (circle), measured heating

power (solid line).

23

5.2.3 Oxygen in flue gas

Results of O2 -model simulations are presented in Figure 24. The change in fuel moisture

has been taken into account by the model. With this testing data, the calculated root mean

squared error of the model predictions was 1.6 [O2 vol.-%]. The same error for the

lambda sensor calculated from the same data range was 1.8 [O2 vol.-%]. Variation of the

lambda sensor measurement errors was also remarkably higher compared to modelling

error.

Figure 24. Flue gas oxygen -modelled (dotted line), measured oxygen concentration

(solid line), testing data.

In Figure 25, gas analyser failure compensation with the model is demonstrated. The gas

analyser had an automatic calibration procedure, executed on-line during the tests.

Figure 25. Short calibration spike of the gas analyser during the flue gas oxygen

measurements (solid line). Simulated model outputs at the same time (circle).

24

5.3 Sensor validation and process diagnostics

The reliability of the modelling approach has to be guaranteed in a continuous use. Since

the model is based on measurements, the first task is to validate sensors real-time. The

approach presented in Section 4.2 was tested with the collected data from the

experiments. Sensor validation procedure was based on PCA. As the sensors were highly

correlating, the redundancy changes between them were observed. The validation is then

based on the analysis of what sensor/sensors break the redundancy. For this, two common

tools used in PCA were applied: the squared prediction error (SPE) and contribution plot

analyses. The former refers to error between measured and its PCA-constructed estimate

with all variables, the latter to the contribution of single variable to SPE.

Data analysis with PCA-based measurement validation procedure was able to reveal all

the sensor failures (movement of a sensor from its initial position). This was done

monitoring the SPE, particularly its passing points over the calculated confidence limit.

The limit was determined with training data errors. In addition, from the contribution

plot it was possible to see what sensor contributed to the error. In Figure 26, SPE plot is

depicted, where 13 sensor or process conditions have been identified. These include both

sensor failures and process deviations from its normal behaviour. No sensor or process

drifting was observed due to short time period of combustion tests.

Figure 26. Observed process incidents from data. Spikes above confidence limits (CL)

are either mark from movements of temperature sensors, air leaks or fire-failure

within an experiment.

This sensor and process diagnostics tool managed to identify process situations present in

data. However, careful selection of data is needed for PCA parameter determination. If

data is not representing normal process behaviour, a proper functioning may not be

guaranteed for monitoring purposes.

25

6 CONCLUSIONS

In this work a general monitoring framework is proposed small-scale stoker combustion

processes. Details of the monitoring system structure are discussed and principles are

demonstrated with a simulation example. In the presented framework, the main idea is to

extract maximum information included in multiple identical temperature sensor

measurements and use it for realtime process monitoring.

Utilisation of model-based approach and data-analysis techniques is therefore chosen as

process identification tools. Local recursive principal component regression PCR is

applied utilising partly inexact information from process conditions in the form of fuzzy

rulebase that defines local operating conditions. Orthogonal property in PCR-models

makes it possible to handle redundant temperature sensors. At the same time the

important requirement of system reliability can be fulfilled with the integrated principal

component analysis utilised for sensor and process validation. Adaptation of monitoring

system is studied using moving averages of model inputs to tune them continuously.

Based on analysis and simulation results with data collected from a 300kW stoker-fired

boiler, the following conclusions can be drawn:

Temperature measurements carry significant amount of information for real-time

wood chip combustion monitoring, especially of O2, kW, CO and fuel moisture.

Entropic analysis can be a potential tool in validation of representative training

data sets for modelling.

The presented combination of model-based monitoring methods can help to avoid

time delays and compensate sensor failures in control.

In the tested cases, adaptation of input mean values in modelling produced

satisfactory results.

Identical multiple simple sensors offer both analytical and hardware redundancy

to be utilised with orthogonal algorithms like PCA in small-scale combustion

processes.

An anchor point was found that largely characterised system behaviour, making it

a possible point for referencing typical behaviour of carbon monoxide also in

other combustion units.

The disadvantage of model-based monitoring system is the need for an accurate model,

and usually a more complex design procedure. Deeper insight and understanding of the

process is also required as well as representative data for model identification. Future

work will therefore focus on further development of self-configuration and adaptation

issues of multiple soft sensors.

26

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ISBN 951-42-8027-X

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Control Engineering Laboratory – Series A

Editor: Leena Yliniemi 1. Yliniemi L, Alaimo L & Koskinen J, Development and tuning of a fuzzy controller

for a rotary dryer. December 1995. ISBN 951-42-4324-2.

2. Leiviskä K, Simulation in pulp and paper industry. February 1996. ISBN 951-42-

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