Hybrid System for fouling control in biomass...

Engineering Applications of Artificial Intelligence 19 (2006) 915–925

Hybrid System for fouling control in biomass boilers

Luis M. Romeo!, Raquel Gareta

Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior,Marıa de Luna, 3, Zaragoza 50018, Spain

Received 30 May 2005; received in revised form 26 January 2006; accepted 29 January 2006Available online 23 March 2006

Abstract

Renewable energy sources are essential paths towards sustainable development and CO2 emission reduction. For example, theEuropean Union has set the target of achieving 22% of electricity generation from renewable sources by 2010. However, the extensive useof this energy source is being avoided by some technical problems as fouling and slagging in the surfaces of boiler heat exchangers.Although these phenomena were extensively studied in the last decades in order to optimize the behaviour of large coal power boilers,

a simple, general and effective method for fouling control has not been developed. For biomass boilers, the feedstock variability and thepresence of new components in ash chemistry increase the fouling influence in boiler performance. In particular, heat transfer is widelyaffected and the boiler capacity becomes dramatically reduced. Unfortunately, the classical approach of regular sootblowing cyclesbecomes clearly insufficient for them.Artificial Intelligence (AI) provides new means to undertake this problem. This paper illustrates a methodology based on Neural

Networks (NNs) and Fuzzy-Logic Expert Systems to select the moment for activating sootblowing in an industrial biomass boiler. Themain aim is to minimize the boiler energy and efficiency losses with a proper sootblowing activation. Although the NN type used in thiswork is well-known and the Hybrid Systems had been extensively used in the last decade, the excellent results obtained in the use of AI inindustrial biomass boilers control with regard to previous approaches makes this work a novelty.r 2006 Elsevier Ltd. All rights reserved.

Keywords: Biomass; Boiler fouling; Hybrid system

1. Introduction

The European Union has the aim to increase thecontribution of renewable energy sources up to 22% ofelectricity generation from renewable sources by 2010.Intensive research is being carried out in order to takeadvantage of biomass potential as a renewable energysource.

The behaviour of the medium-sized biomass boilersstarted up recently shows a dramatically tendency to arapid fouling of the superheater surfaces that reduce theboiler capacity (Zevenhoven-Onderwater et al., 2000).Therefore, the development of large biomass boilers andthe wide use of the biomass to produce electricity is beingquestioned. Due to the inherent complexity of the biomass

fouling, this problem has not been completely solved.Biomass fouling not only influences the steam production,but also produces other important effects on boilerperformance. A boiler efficiency reduction, an increase incarbon dioxide emissions, or a deteriorated combustionbehaviour with higher combustion temperatures and anextra formation of nitrogen oxides and carbon monoxidehave been reported.The mechanism of fouling formation is concerned with

the high formation of incombustible volatiles belonging toseveral biomass components (Sami et al., 2000). Thecondensation of these vapours over the superheaters tubescreates a stick film for the solid soot particles. Theincreasing layer reduces heat transfer and evidently steamproduction, also boiler efficiency decreases dramatically(Heinzel et al., 1998; Peltola et al., 1999; Skrifvars et al.,1998; Dare, 2002). Although the principles and mechan-isms that cause these phenomena have been clearly

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!Corresponding author. Tel.: +34976 762570; fax: +34 976 732078.E-mail address: [email protected] (L.M. Romeo).

established, the inherent complexity of fouling and theuncertain evolution of mineral constituents have compli-cated to solve the problem.

During the last decades, the fouling formation mechan-isms in coal power plants has been widely analysed(Anthony et al., 2001; Fan et al., 2001; Lee et al., 2002;Wang and Harb, 1997; Bergeles et al., 1997). This includesdifferent point of views: chemical fuel analysis, complexCFD simulations and experiments in pilot-sized boilers.Traditional methods for coal boilers to reduce boilerfouling (i.e. sootblowing) are not suitable without a properboiler evaluation. In this case research has been focussed inboiler monitoring by means of special measuring instru-ments and on-line calculations. Monitoring could beaccomplished by means of standard and special powerplant instrumentation or a combination of both techni-ques. In any case external software is required forsimulation and calculations. Market has some examplesof boiler monitoring, especially for fouling phenomena(Electric Power Research Institute, 2000; Energy ResearchCenter, 2005; BMS International Ltd., 2005), but there is alack of information about the internal behaviour of theseapplications.

Biomass fouling approaches use experimental correla-tions, based on biomass chemical analysis to estimatefouling probability (Jenkins et al., 1998; Seggiani, 1999).However, these correlations could establish the tendency tofouling of a selected biomass, but neither solve theproblem, nor develop a methodology to operate the boilerminimizing the fouling effects.

The introduction of the Neural Networks (NNs) to solveother problems concerned to power plants, power systemsmodelling, control and forecasting in energy systems hasbeen progressively reported elsewhere (Olofsson et al.,1998; Lu and Hogg, 2000; Mihalakakou et al., 2000;Bechtler et al., 2001; Gareta et al., 2006). The applicationof the Artificial Intelligence (AI) techniques to renewableenergies (Kalogirou, 2001, 2003) and to the evaluation offouling deposits evolution inside the boilers (Afgan et al.,1996; Lalot and Lecoeuche, 2003) also have been reported.However, an integrated control methodology that evalu-ates actual and future boiler performance depending onsootblowing (and fouling) and decide to clean heat transfersurfaces has not been already developed. The present workis conceived as a contribution to this question.

The objective of this paper is to present the applicationof a Hybrid System that combines NNs advantages withFuzzy-Logic Expert System (FLES) to control boilerfouling and optimize boiler performance, minimizing theeffect of fouling. This Hybrid System uses several sets ofNN with different objectives: boiler monitoring, foulingforecasting, prediction of boiler behaviour and the cleaningeffect if a sootblowing cycle was activated. The applicationis completed by the development of a FLES that takes thesootblowing decision. Validation shows important energysaving between Hybrid System outputs and real dataobtained from a biomass boiler.

2. Problem description

Fouling and slagging cause a severe reduction of theboiler heat transfer, these phenomena were firstly observedin coal boilers, specially in those using brown coals. But themedium-sized biomass boilers started up recently report aperformance strongly affected by fouling/slagging problem,as pointed out in Fig. 1. A design load of 74.6 ton/h isreduced, at the end of the operating period (hour 4200),approximately 30%. The boiler uses biomass with amoving inclined grate. It produces a steam output of74.6 ton/h with clean surfaces. The actual fuel is a mix ofdifferent types of biomass (bark and wood of indeterminatespecies) with 48.5% of moisture, 3% of ash and an averageheating value of 8.5MJ/kg. The boiler includes furnace,two superheaters, two convective evaporators and twoeconomizers. The steam output temperature from the firstsuperheater is 276 1C and the steam output temperaturefrom the final superheater is 475 1C.Eight sootblowers on the superheater area and six

sootblowers in the convection area are installed in theboiler, as shown in Fig. 2. Two automatic sootblowingcycles are possible. In short cycles, only the sootblowers inthe superheater area are operated, but superheater andconvective area sootblowers become activated in longcycles. One sootblowing cycle is usually performed perworking shift; therefore, total steam consumption forsootblowing is 9.7 ton/day if three long cycles of sootblow-ing were performed. Despite this steam consumption, thefouling effect increases with operational time as is shown inFig. 1.

3. Hybrid System design

The introduction of the NN and AI techniques toanalyse energy systems as previously commented hasstimulated the development of an Hybrid System tominimize fouling influence. It includes a combination ofNNs and a FLES to simulate, predict and control thebiomass boiler fouling.

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L.M. Romeo, R. Gareta / Engineering Applications of Artificial Intelligence 19 (2006) 915–925916

A fundamental step in the design of the foulingpredicting and control system has been the developmentof an accurate thermal boiler simulation. The use of NNinstead of other thermodynamic-based simulation hasintroduced important advantages as a better knowledgeof the boiler performance based on the operation data andsimple and accurate model to be used in on-line applica-tions.

Based on this simulation a design of different set of NNhas been developed as shown in Fig. 3:

! Thermal monitoring system. An NN monitoring systembased on thermal boiler simulation is needed in order tomanage historic and on-line data, calculate boilerfouling and fouling indexes, select the objective variablesand prepare the data to train following NN.

! Boiler fouling level evolution. Based on previous NN,historical data and boiler simulation, fouling evolutionindexes with and without sootblowers operation arepredicted.

! Evaluation of boiler thermal response with and withoutsootblowing. Once fouling evolution with and without

sootblowers operation is known, another NN set basedon boiler simulations forecasts the energy responses inboth scenarios to know the hourly energy improve-ments.

! Sootblowing decision maker. Previous information isused by Fuzzy-Logic System that decide the right timeand type of sootblowing cycle.

This novel architecture is entirely conceived to optimizesootblower activation strategy taking into account the NNand Fuzzy-Logic advantages. This design has been devel-oped to avoid the classical approach that estimate foulingevolution through a time exponential decay equation(Kern and Seaton, 1959; Allmon et al., 1991) with severalcoefficients calculated with complicated NN architectures.Moreover, the problem has been reinterpreted in order touse a wide operation plant data collection and to develop astrong and efficient prediction system that could be used inon-line applications.The developed NN architecture is a basic multilayer

feedforward NN. It is a simple and well-known type of NNbut it was chosen because it fits perfectly with the problem

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LIVED STEAM

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Fig. 2. Sootblowers and heat transfer equipment in biomass boiler.

L.M. Romeo, R. Gareta / Engineering Applications of Artificial Intelligence 19 (2006) 915–925 917

to solve. Feedforward NN shows a great process capacitywith a robust performance specially suitable to dealingwith non-filtered and on-line real process data (Olofssonet al., 1998; Bechtler et al., 2001). Moreover, non-linearmodelling of time series and forecasting utilization(Olofsson et al., 1998; Lu and Hogg, 2000; Mihalakakouet al., 2000) are another important NN characteristics thatare appropriate for this problem. The general idea of thesystem is not to design a strong protocol to manage datafor developing a ‘‘white box’’ (where the connectionbetween inputs and outputs could be expressed usingequations), but allow NN to deal with on-line real processdata by means of a ‘‘grey box’’ model (Ikonen et al., 2000).In ‘‘grey box’’ models theoretical information is allowed tobe introduced by the way of selecting an architecture of theglobal NN where the relationship between variables wasimplicit. Moreover, since irrelevant inputs have beenreported of causing overfitting in the NN, special attentionhas been paid in inputs selection (Ikonen et al., 2000;Lertpalangsunti et al., 1999; Prieto et al., 2001).

4. Biomass boiler monitoring. Fouling calculation by NN

Thermal monitoring is the basis of the knowledge ofboiler performance and the design of the sootblowing

control system. An NN simulation module has beendeveloped in order to simplify the control design andmake stronger the boiler simulation, taking advantage ofthe great qualities of the NN.A set of simple NN was developed in three levels

corresponding with thermal simulation strategy: combus-tion gases composition calculated from stack and fuelanalysis, heat transferred in each exchanger andfouling index, as shown in Fig. 4. NN has the samestructure and input variables as a thermal simulation andas much physical concepts as possible have been intro-duced in its design. Each NN has three layers of neuronsfollowing a feedforward architecture. A sigmoid activ-ation function in the neuron hidden-layer and a linear onein the neuron output layer have been selected. Each NNhas been trained independently following an iterativescheme. Non-relevant inputs have been identified andnon-considered, 3000 data selected randomly have beenused in the training process. To validate each NN,resubstitution test (with the 3000 training data) andresistance test (with a reserved group of 1455 data) havebeen accomplished in order to verify the accuracy withoutoverfitting.Special attention has been paid in the input selection.

Since a negative influence of an excessive researcher’s

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FUZZY LOG ICEXPERT SYSTEM

RED NEURONAL -2 RED NEURONAL -2RED NEURONAL -2-2 RED NEURONAL -2-2

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POWER BOILERINSTRUMENTATION

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Fig. 3. Biomass boiler control system design.


subjectivity should be avoided, the selection of the inputswas made using only tools concerned with NN. In the firsttraining of each NN, all the available inputs (23 variables)

were used and the mean square error (MSE) obtained wasregistered. After that, a variable was eliminated in everynew training executed and the MSE obtained was also

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Fig. 4. Relation between variables of the three monitoring NN sets.


registered and compared with the value obtained in the firsttraining. The higher the influence of the absent input, themore the increase in the MSE value, and therefore theeliminated input variable was more relevant (Sung, 1998).This analysis of the input’s influence is an iterative task andtime-consuming, but the highly logical input variablesobtained compared with thermo-physical relations indicatea strong and coherent NN input variable selection. As anexample, to calculate the exhaust gases composition, inputvariables related with exit gases composition in the stackand the moisture in the boiler have been selected, but thoserelated with temperature and pressure signals have beenrejected.

Main results include new variables not collected in powerplant data acquisition system as heat transferred from thecombustion gases to the steam or water in each section,intermediate exhaust gas temperatures, steam mass flows inthe heat exchangers and variables that include the foulinginfluence in heat transfer (fouling index).

Every NN has been individually validated under twostrategies in order to reject NN overfittings. Firstly, 3000training output data were compared with the NNresults (resubstitution test) concluding a good agreementwith training data. Secondly, the comparison was madewith 1495 data reserved from the data collection and non-used in the training stage. A resistance test was made in thisway pointing out clearly any possible overfitting(Reich and Barai, 2000). The validation with the resub-stitution–resistance methodology is achieved when theerrors obtained with this analysis are similar, as shown inTable 1. Despite the low number of neurons, results clearlyshow a non-overfitting NN. Only a slightly morerelevant deviation was pointed out in the convectiveevaporator fouling index, because of the lack of arepresentative data set.

Since the output data obtained from NN were notincluded in the power plant data system variables,the validation cannot follow other traditional NN valida-tion rules. In this case a final verification was madecomparing the sensitivity analysis results of the calculatedvariables obtained from historical data. SH1 fouling indexshows a deviation of 7.5% taking into account theuncertainty of the measure instruments and, indeed,the convective evaporator fouling index could reach till9.8%. Results in Table 1 show deviation clearly belowthese limits.

5. Fouling and boiler thermal response forecasting

The classical approach to estimate fouling evolutiontried to adjust heat transfer with a time exponential decayequation (Kern and Seaton, 1959). In consequence, thegreat number of physical mechanisms involved in thedeposit growing avoids a good agreement between realdata and the calculation of heat transfer coefficientexponential decay constants (Allmon et al., 1991).It is proposed to analyse the fouling tendency by

forecasting a fouling index (ratio between real heat transfercoefficient compared with clean conditions) hourly afterthe sootblowing. In opposition to the classical approach, aset of NN was developed in order to calculate two timeseries of fouling indexes (with and without sootblowing)for 12 h just after the moment of study, Fig. 3. Theavailable data to develop the NN come from 2 years ofboiler operation. Five hundred and seventy-three soot-blowings were located during this period and 161 wereselected as effective and with enough data to train an NNwith a prediction of 12 h. Approximately 100 sootblowingseries have been used to train, that means a 60% of thedata, and the rest was reserved for the validation stage.Developed NN contains three layers of NN with three

layers of neurons, every one following a feedforwardarchitecture. A sigmoid activation function has beenselected for the neuron hidden-layer and a linear one forthe neuron output layer. The training stage has followedthe same principles exposed in the monitoring module: anNN input selection as previously described, a trainingbased in resubstitution test and a final validation followinga resistance test. This methodology allows to point outcuriosities as showed in Fig. 5, where the NN architecturedesign evidences the reduction of the influence of actualdata, while the influence of the previous fouling forecastedvalues increases. The trend provided by the foulingforecasting NN is highly coherent with physical conceptsand can be asserted as faithful in long-term analysis.Tables 2 and 3 display the number of neurons anddeviations in reconstitution and resistance validations.Results are also excellent in superheaters (in Figs. 6and 7 the results for superheaters 1 and 2 h aftersootblowing are shown); also in convective evaporatorsresults show a coherent evolution.The following step lies in the evaluation of the fouling

influence in boiler performance. Two NNs have been

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Table 1Training and validation error values of the fouling index monitoring NN

Variable Neurons number Resubstitution average error (%) Resistance average error (%)

Furnace fouling index 3 1.03 1.01SH 2 fouling index 4 1.60 1.54SH 1 fouling index 3 1.08 1.09Convective evaporator fouling index 4 2.35 2.36Economizer fouling index 6 0.86 0.88


carried out in order to simulate boiler performance andcompare the energy recovered with or without sootblow-ing, Fig. 3. Results are calculated as energy improvementsfor 4, 6 and 8 h after evaluation. This final NN uses asinput data obtained from the fouling forecasting NN (heatexchanger fouling level values). Three thousand data havebeen used to train and 1550 data have been used to validatethis model. This NN has the same structure as thoseexplained in the monitoring and in the forecasting module.Table 3 displays the number of neurons used in this NN setand the average resubstitution and resistance errors. NNresults for training and validation are shown in Fig. 8. Agood agreement is observed between calculated and realboiler heat absorption, the key variable to analyse boiler

fouling influence. Although a slightly higher dispersion inthe outputs than in previous results is noticed, it isexplained by the reduced number of inputs required tothis set of NN, compared for instance, to those needed fora thermodynamic model.

6. Sootblowing evaluation

To complete the application and obtain a Hybrid Systemto for fouling control in on-line applications, a decisionmaker based on fuzzy-logic rules has been developed. Theaim of the decision maker is to evaluate from the resultsprovided by previous NN modules the activation of thesootblowing cycle. Therefore, the decision maker module

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Fig. 5. Relation between variables of the fouling forecasting NN sets.

Table 2Training and validation error values of the fouling index forecasting NN


Superheater 1Hour 0 fouling index 5 2.79 4.97Hour 1 fouling index 6 2.04 6.79Hour 2 fouling index 3 2.01 2.80Fouling index in the next hour 3 3.54 3.98

Convective evaporatorsHour 0 fouling index 3 3.80 9.57Hour 1 fouling index 7 4.21 11.17Hour 2 fouling index 3 2.66 8.80Fouling index in the next hour 3 5.32 7.22

Table 3Training and validation error values in the boiler performance evaluation after fouling forecasting


Total heat 20 2.59 3.12Furnace heat 20 2.44 3.38Superheater 2 heat 20 3.97 4.75Superheater 1 heat 20 2.81 3.27Convective evaporator heat 20 2.33 2.69Economizer heat 20 2.41 2.94Steam flow 20 2.65 3.18


should be developed attending to the experience implicit inthe historical data available and the decision thresholds areextracted from a detailed study of previous results, Fig. 3.A FLES was selected because of the ability of arriving toconclusions dealing with non-precise data inputs (Franttiand Mahonen, 2001; Chen et al., 2003). The approach is

based on previous experience and do not require a physicallaw to calculate the system performance, becoming anadequate tool for the control of fouling in a biomass boiler(Fig. 9).The outputs of the Hybrid System decision maker were

selected as: no activation of the sootblowers, activation of

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Validation data (real vs. NN)SH1 Fouling levelTwo hours after sootblowing

Fig. 7. Training and validation results of the SH1 fouling level 2 h after the sootblowing cycle.

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Fig. 8. Boiler heat absorption. Training (left) and validation (right).


a short cycle or activation of a long cycle (depending on thefouling influence in the boiler). The inputs of the decisionmaker were exclusively the results obtained from the NNmodules and the thresholds of activation were fixed inorder to guarantee energy savings. With all these require-ments the membership functions and pre- and post-processing of the data were designed.

The inputs selected from previous NN set are thoserelated to the energy improvement caused by the soot-blowing cycle, specially the prediction of the accumulatedenergy improvement due to the sootblowing after 4, 6 and8 h. FLES module has been designed to emphasize theimprovements in the first hours in order to recover thesootblowing energy as soon as possible, taking intoaccount that the fouling evolution could not be as regularas predicted. The first calculation made in the FLESmodule assigns a numeric value to the prediction achievedby the NN modules. After that, this number is comparedwith the threshold values and a final decision is emitted.There are two threshold values: the first establishes thedifference between not advising the activation of thesootblowers or advising a short cycle, the second has beencalculated in order to advise a long cycle if the energy savedis enough.

Forced by the availability of historical data, theadjustment and validation of the FLES module have beenmade with them. Results show that approximately 1020sootblowing cycles would be activated in an operationperiod (6700 h considered), which represent approximately200 more sootblowing cycles than traditional operatingmanoeuvres. From these 1020 sootblowing cycles, theFLES module recommends almost 800, but the rest will beautomatically carried out by the system in a security limitestablished in 12 h. From the 800 sootblowing cycles

recommended by the FLES module, 670 of them are longcycle type. Although it is known that the convective sectionis not a critical area, this also means a good level ofcleanliness in it.Finally, it is really worth comparing the power produc-

tion by the biomass boiler performing with historicalsootblowing strategy and with the new strategy, Table 4.Previous strategy caused a decrease in energy saving of7556.4MWh due the sootblowing activation when theboiler was nearly clean or too fouled to be cleaned bysteam, as Fig. 1 shows. The NN–FLES strategy shows anincrease of energy saving up to 12509.0MWh in anoperational period. This means a power productionaugmentation of approximately 3.5%, from an average ofheat produced as steam of 54.1MW to a new average of56.0MW.

7. Conclusions

There are some difficulties for extensive biomass utiliza-tion. In particular, biomass combustion produces fouling

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NONO

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YES YESLONG

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Energy recovery in thenext hours when

Fig. 9. Fuzzy-Logic Expert System design.

Table 4Comparison of energy values in the application of NN–FL methodologyto control boiler sootblowing cycles

Energy values for the biomass boiler

Average heat output produced in the boiler 54.1MWEnergy recovered in historical sootblowing strategy "7556MWhEnergy recovered in NN-FL sootblowing strategy +4953MWhEnergy savings from historical to optimized strategy +12,509MWhPercentage of energy savings +3.45%Average heat output produced in the boiler 56.0MW


in boiler heat transfer equipment, causing a reduction ofsteam output and boiler efficiency. The traditionalapproach uses experimental correlations but the problemhas not been solved due to the inherent complexity offouling and it is necessary to develop new techniques andstrategies to minimize its consequences. The application ofa Hybrid System combining NNs and a FLES has allowedto control boiler fouling and optimize its performance,although it has obligated to a new approach to the problemdescription.

Different sets of NN have been used to monitor theboiler, to forecast fouling and the consequent future boilerperformance. A set of fuzzy-logic rules based on real datathat takes as inputs the results from previous NN hasoptimized boiler cleaning cycles and minimized foulingevolution. A procedure that compares the energy cost of asootblowing cycle with the thermal improvements has beendeveloped and it decides the sootblowing activation. NNand FLES system has been designed and trained obtainingencouraging results. Especially considering that anysootblowers control based on the state of the boiler hasnot been reported previously. A relative comparisonbetween the old routine of sootblowing strategy and thenew strategy programmed in the NN–FLES shows thatapproximately 3.5% of power production improvementcan be obtained. The relevance of these results shows thattechniques of AI as those proposed in this paper(NN–FLES) could help to reorient the study of complexphenomena, providing new point of views and tools todevelop strong and easy-to-use control systems.

Acknowledgements

The work described in this paper was supported by theFifth Framework Programme of the European Union,FP5–Energy, Environment and Sustainable Development,NNE5-2001-00128, Intelligent Process Control System forBiomass Fuelled industrial Power Plants. The authors aresolely responsible for this article and it does not representthe opinion of the European Community. We gratefullyacknowledge the inputs, collaboration and patience of themembers of the INTCON project, TPS TerminskaProcesser AB, Tecnatom and CINAR.

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Luis M. Romeo (Ph.D. Eng.) is Project Manager of the Centre ofResearch for Energy Resources and Consumption. He also teaches HeatTransfer and Thermodynamics in the Department of MechanicalEngineering, University of Zaragoza. He gained his professionalexperiences developing and managing several projects in differentSpanish power plants such as the Pressurised Fluidised Bed CombustionPower Plant at Escatron, Pulverized Coal Teruel Power Plant, CombinedCycles and Biomass Boilers. He works on R&D projects and in activitiesrelated with efficiency improvements, power plant modelisation and CO2

capture.

Ms. Raquel Gareta (Ph.D. Eng.) was a CIRCE researcher. She studiedMechanical Engineering at the University of Zaragoza with majorspeciality in Heat Transfer and Fluids Mechanics. She has worked onR&D projects since August 1999 in activities related with Gas Turbineperformance optimisation, Neural Networks development, Fouling andSlagging influence minimization and Biomass Boilers improvement. HerThesis ‘‘INTELLIGENT MODEL BASED SUPERVISION SYSTEMTO BIOMASS BOILER SOOTBLOWING CYCLES MANAGE-MENT’’ has been one of the winners of the European Talent Award forInnovative Energy Systems 2005 (European Foundation for PowerEngineering).

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