Agglomeration in fluidized beds: Detection and counteraction

Agglomeration in Fluidized Beds:

Detection and Counteraction

Agglomeration in Fluidized Beds:

Detection and Counteraction

Proefschrift

ter verkrijging van de graad van doctor

aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus prof. dr. ir. J.T. Fokkema

voorzitter van het College voor Promoties,

in het openbaar te verdedigen op

dinsdag 11 november 2008 om 10:00 uur

door

Malte BARTELS

Diplom-Ingenieur (FH), Fachhochschule Flensburg

Master of Science, Cleveland State University

Dit proefschrift is goedgekeurd door de promotor: Prof. dr. F. Kapteijn

Copromotor: Dr. ir. J.R. van Ommen

Samenstelling promotiecommissie: Rector Magnificus, voorzitter

Prof. dr. F. Kapteijn Technische Universiteit Delft, promotor

Dr. ir. J.R. van Ommen Technische Universiteit Delft, copromotor

Prof. dr. T. Pugsley University of Saskatchewan (Canada)

Prof. dr. W. Lin The Chinese Academy of Sciences (China),

Technical University of Denmark (Denmark)

Prof. ir. J. Grievink Technische Universiteit Delft

Prof. dr. R.F. Mudde Technische Universiteit Delft

Ir. J. Nijenhuis Technische Universiteit Delft

John Nijenhuis en Ruud van Ommen hebben als begeleiders in belangrijke mate aan de totstandkoming van het proefschrift bijgedragen.

Front cover: Agglomerate on three different scales (courtesy Energy Technology Group (3mE) TU Delft and ECN - Energy Research Centre of the Netherlands)

Back cover: “Agglomerate” of the summary of this thesis (word size related to occurrence, slight adapted). Created with and thanks to Wordle (http://wordle.net/).

Copyright © 2008 Malte Bartels Printed by Wöhrmann Print Service

All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without the written permission from the author.

A digital version of this dissertation is available at: http://www.library.tudelft.nl/ ISBN: 978-90-8570-311-2

For it is by grace you have been saved, through faith - and this not from yourselves, it is the gift of God - not by works, so that no one can boast. For we are God's workmanship, created in Christ Jesus to do good works, which God prepared in advance for us to do. Ephesians 2:8-10

Gas-solid fluidized beds are utilized for a variety of processes in the chemical industry, such as catalytic reactions, drying, coating, and energy conversion. The occurrence of unwanted agglomeration in fluidized bed processes can be a major problem in industrial practice. For energy conversion processes, typically combustion and gasification, the basic mechanism of agglomeration involves the simultaneous presence of alkali components from the fuel and silica from the bed material and the fuel. Together they can form alkali silicates having melting points below the typical operating temperatures of about 850°C. The presence of such a liquid phase results in increased particle stickiness and formation of agglomerates. If this effect is not counteracted it can eventually result in defluidization of the bed and subsequent shut-down of the installation. Therefore, an early warning system to reliably detect agglomeration is of high importance, enabling to take appropriate counteractions preventing further agglomeration and shut-down.

This work aims at identification and application of suitable methods for the early detection of and counteraction against agglomeration in fluidized beds. Agglomeration in energy conversion processes, specifically combustion and gasification of biomass, is the main focus. The monitoring method based on attractor comparison [1] has been developed in our group and successfully applied on the laboratory and industrial scale to bubbling fluidized beds for the purpose of agglomeration detection. The attractor comparison method compares high-frequency pressure measurements obtained during operation of the installation with reference data that was measured earlier, and expresses whether or not the behaviour has significantly changed.

In the present study, this method is investigated for its suitability to detect agglomeration and small changes in particle size in circulating fluidized beds (CFBs). This has been done by adapting the particle size in a lab-scale CFB during operation, by enforcing agglomeration during biomass gasification in a lab-scale CFB and by monitoring the occurring particle size changes in an industrial CFB during normal operation (chapter 3). Since more methods have been proposed in literature with more or less success, a screening methodology is developed that allows the efficient identification of methods that are sensitive for and selective to agglomeration (chapter 4). This methodology is applied to several case studies, using data from a pilot-scale

bubbling fluidized bed (BFB) exhibiting agglomeration and other isolated process

changes, from an industrial BFB biomass combustor during agglomeration and from a lab-scale CFB during agglomeration (chapter 5). The attractor comparison method is also investigated for its suitability to successfully prevent defluidization in combination with a suitable counteraction technique and the method is used to assess the benefit of different counteraction strategies. This has been done using a lab-scale and a pilot-scale BFB boiler during the combustion of biomass (chapter 6).

The attractor comparison method has shown to be sensitive to small, gradual changes in particle size in the order of 5%, based on a median size of 235 µm for a lab-scale circulating fluidized bed with an L-valve for the solids return. The method is also sensitive to the presence of model agglomerates (d50 = 1900 µm) in the order of 3

weight-%. The sensitivity of the method depends on the measurement location in the system and is clearly higher for the downcomer and horizontal return-leg than for the riser. This higher sensitivity is decreased when using a loop seal for the solids return and supports the hypothesis that the higher sensitivity for the downcomer with L-valve

from the riser and propagating into the downcomer. Importantly, the method is not sensitive to other process variables (bed mass, riser velocity and solids reflux) within certain limits for the L-valve and loop seal configuration. On an industrial scale the attractor comparison method has shown to be sensitive to small, gradual changes in particle size in the order of 8%, based on a median size of 240 µm and measurements in the bottom bed of the riser. For an agglomeration incident during biomass gasification on lab-scale the method has shown to detect the impending defluidization with an early warning time of ~30 min, based on measurements in lower riser and horizontal return-leg (Figure S-1).

A new methodology is proposed that consists of screening many different signal analysis methods in combination with pre-treatment (filtering) methods with the goal to find those combinations that are sensitive and selective to a specific cause for a change in the hydrodynamics. More specifically, agglomeration in fluidized beds is taken as the relevant cause for the hydrodynamics change here. Pressure fluctuation measurements form the basis for the analysis. The methodology itself is not limited to this application, but is more generally applicable for other multiphase reactor systems. The suitability of a combination is assessed by a selectivity index. This selectivity index is visualized in a result matrix by coloured tiles (Figure S-2) in which the colour is related to the selectivity index, so that one can quickly identify suitable methods and pre-treatment methods. -5 0 5 10 15 20 25 30 35 40 45 0:00 0:30 1:00 1:30

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Figure S-1: Successful agglomeration detection in a lab-scale CFB: The approaching defluidization is not anticipated using average pressure drop measurements, but it is correctly indicated by the S-value exceeding the critical value of 3 with an early warning time of about 30 minutes based on measurements from the lower riser and horizontal return-leg.

The screening approach was successfully applied to different case studies in pilot- and industrial scale bubbling bed installations and in a lab-scale circulating bed. For a combined (averaged) case of three different bubbling bed agglomeration incidents several suitable methods were found, among which the Kolmogorov-Smirnov test with a 15 Hz low-pass filter, the Kuiper test, the standard deviation analysis with a 10 Hz high-pass filter, the attractor comparison method, entropy analysis and average cycle time. Apart from this approach to average results from different installations, the screening methodology can of course also be applied to a single process and identify suitable methods for that specific process. Based on the cases studied and previous investigations a simple descriptive model to describe the development of the pressure fluctuations during different agglomeration incidents is proposed.

Ultimately, any agglomeration detection method should serve as the basis to actually prevent defluidization and shut-down. Here, it was investigated whether the attractor comparison method can provide a sufficiently early alarm to be able to take counteractions avoiding defluidization. Tests have been carried out on lab-scale (~1 kWth) and pilot-scale (~1 MWth).

In general, the observed agglomeration cases were not well reproducible, considering small variations in the process conditions to be unavoidable in practice. For the pilot-scale setup, rapid agglomeration can occur especially during transition regions (start-up, changes in load/temperature). In such cases a moving reference, i.e. the reference is moved together with the current evaluation window at a constant offset, for the attractor comparison is clearly advantageous. For both rapid and more gradual agglomeration incidents, attractor comparison was able to detect the approaching defluidization early enough to avoid defluidization. A decrease in temperature was successfully applied as a temporary measure to ensure continued operation and to avoid

Figure S-2: Result matrix with coloured tiles illustrating the selectivity indices for all possible combinations of data pre-treatment and signal analysis methods (only lower left corner of the full matrix is shown for illustration purposes here). The colour and intensity is related to the value of the selectivity index, increasing from black to red, yellow, and white.

with fresh bed material has also shown to work as a permanent solution to ensure continuous operation. Neither a temporary increase in gas velocity nor using an additive were successful in this case.

The research objectives for this work have been successfully accomplished. The results clearly show that the attractor comparison method is indeed suitable for the detection of agglomeration and small particle size changes in circulating fluidized beds. The presented screening methodology has revealed several specific methods that are potentially suitable as an early agglomeration warning tool and it can be readily applied to find suitable methods tailored to a specific application. The attractor comparison method also indicated agglomeration in an early enough stage to successfully avoid defluidization of the bed if combined with a suitable counteraction strategy.

The application of the attractor comparison method - and potentially other monitoring methods - is considered capable of increasing the reliability of operation and the efficiency of industrial solid fuel energy conversion processes. The presented successful application of the method in circulating fluidized beds is especially important in light of the wide-spread application of circulating beds in the energy sector. As a result, this technology can play a prominent role in the process of further promoting the acceptance of biomass as renewable energy source, particularly at the producers’ side. References

[1] van Ommen JR, Coppens MO, van den Bleek CM, Schouten JC. Early Warning of agglomeration in Fluidized Beds by Attractor Comparison. AIChE Journal

2000;46:2183-2197 600 650 700 750 800 850 900 09:15 09:45 10:15 10:45 11:15 11:45 12:15 Time [hh:mm] T e m p e ra tu re [ °C ] -5 0 5 10 15 20 25 30 S -v a lu e [ -]

Figure S-3: Successful agglomeration counteraction: after the S-value indicated agglomeration (S>3), a temperature decrease to below 750°C (typical first melting points of alkali silicates) was applied and resulted in stable operation at the lower temperature level. A moving reference with 30 min time difference between reference time window and evaluation time window was used, which is responsible for the second peak.

Gas-gefluïdiseerde bedden worden voor veel verschillende processen toegepast, o.a. voor katalytische reacties, drogen, coaten en energieconversie. Het optreden van ongewenste agglomeratie in fluïde bedden is in de industriële praktijk soms een groot probleem. Bij de conversie van energie via verbranding of vergassing wordt agglomeratie veroorzaakt door de gelijktijdige aanwezigheid van alkali-componenten uit de brandstof en siliciumoxide (kwarts) uit het bedmateriaal en/of brandstof. Samen kunnen deze alkalisilicaten vormen, waarvan het smeltpunt lager ligt dan typische operatiecondities met temperaturen rond de 850°C. De aanwezigheid van een vloeibare fase resulteert in een verhoogde kleverigheid van de deeltjes en het ontstaan van agglomeraten. Gaat dit agglomeratieproces onopgemerkt door en wordt het niet tegengewerkt, dan kan dit leiden tot defluïdisatie van het bed en vervolgens een tijdelijke buitenbedrijfstelling van de installatie. Het is daarom noodzakelijk om een detectiesysteem te hebben dat agglomeratie vroegtijdig en op een betrouwbare manier kan detecteren, om vervolgens maatregelen te nemen om verdere agglomeratie en buitenbedrijfstelling te voorkomen.

Dit onderzoek richt zich op de identificatie en toepassing van geschikte methoden voor de vroegtijdige detectie en tegenwerking van agglomeratie in fluïde bedden. De nadruk ligt hierbij op agglomeratie tijdens energieconversie, specifiek de verbranding en vergassing van biomassa. De monitoringmethode gebaseerd op attractorvergelijking [1] is in onze groep ontwikkeld en is met succes toegepast voor de detectie van agglomeratie in bubbelende fluïde bedden op labschaal en industriële schaal. De methode vergelijkt hoogfrequente drukmetingen verkregen tijdens operatie met eerder gemeten referentiedata en geeft aan of de hydrodynamica van het bed significant veranderd is.

In dit werk wordt onderzocht of de methode geschikt is om agglomeratie en kleine verschillen in deeltjesgrootte in circulerende fluïde bedden (CFB) te detecteren. Dit is gedaan door middel van veranderingen in de deeltjesgrootte tijdens het opereren, geforceerde agglomeratie tijdens de vergassing van biomassa in een lab-schaal CFB en het monitoren van de deeltjesgrootte in een industriële CFB tijdens operatie (hoofdstuk 3). Aangezien er in de literatuur verschillende methoden met wisselend succes voorgesteld zijn, wordt hier een methodologie voorgesteld voor de efficiënte identificatie van methoden die gevoelig en selectief voor agglomeratie zijn (hoofdstuk 4). Deze methodologie is toegepast op meerdere casestudy’s (voorbeelden) met data van een pilot-schaal bubbelend fluïde bed (BFB) waarin agglomeratie en andere geïsoleerde procesveranderingen opgelegd zijn, data van een industriële BFB biomassa-verbrandingsinstallatie tijdens agglomeratie en data van een lab-schaal CFB tijdens agglomeratie (hoofdstuk 5). Verder is onderzocht of de attractorvergelijkingsmethode in combinatie met een geschikte tegenwerkingsstrategie geschikt is om defluïdisatie te voorkomen en de methode wordt gebruikt om het succes van verschillende tegenwerkingstrategieën te evalueren. Dit is gedaan in een lab-schaal CFB en in een pilot-schaal BFB tijdens de verbranding van biomassa (hoofdstuk 6).

De attractorvergelijkingsmethode blijkt gevoelig voor kleine, geleidelijke veranderingen in deeltjesgrootte, in de orde van grootte van 5% op basis van een gemiddelde deeltjesgrootte van 235 µm, gemeten in een lab-schaal circulerend fluïde bed met een “L-valve” voor de terugvoer van materiaal naar de riser. De methode is ook gevoelig voor de aanwezigheid van modelagglomeraten (d50 = 1900 µm) in de orde van grootte

systeem en is duidelijk hoger in de “downcomer” en het gedeelte van de horizontale materiaalterugvoer naar de riser dan in de riser zelf. Deze hogere gevoeligheid wordt door de toepassing van een “loop seal” in plaats van een “L-valve” verminderd. Dit effect ondersteunt de hypothese dat de hogere gevoeligheid voor de configuratie met “L-valve” hoofdzakelijk het gevolg is van een gemodificeerd drukfluctuatiesignaal dat zich vanuit de riser naar de downcomer voortplant. Tegelijkertijd is de methode ongevoelig voor veranderingen in andere procesvariabelen (bedmassa, riser gassnelheid, massarecirculatie) binnen bepaalde grenzen voor de “L-valve” en de loop-seal- configuratie. Voor een agglomeratie-incident tijdens biomassavergassing in een lab-schaal opstelling heeft de methode de vervolgens optredende defluïdisatie rond 30 minuten eerder voorspeld dan de gemiddelde drukval (Figuur N-1).

Daarnaast wordt een nieuwe methodologie geïntroduceerd, waarmee verschillende signaalanalysemethoden in combinatie met methoden voor de voorbewerking (filteren) gescreend worden, om combinaties te identificeren die gevoelig en selectief voor een specifieke procesverandering zijn. Agglomeratie in fluïde bedden wordt hierbij als voorbeeld van een relevante procesverandering gebruikt. Drukfluctuatiemetingen vormen de basis voor de analyse. De methodologie is echter niet alleen voor deze specifieke toepassing in te zetten, maar ook voor andere meerfasesystemen toepasbaar. De geschiktheid van een combinatie van datavoorbewerking en signaalanalysemethode wordt geëvalueerd door middel van een selectiviteitindex. Deze selectiviteitindex wordt door gekleurde velden in een resultaatmatrix weergegeven, waardoor men geschikte methoden snel kan identificeren (Figuur N-2).

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Figuur N-1: Succesvolle agglomeratiedetectie in een lab-schaal CFB: De naderende defluïdisatie wordt niet voorspeld door gemiddelde drukvalmetingen over het bed, maar wordt wel correct voorspelt door de S-waarde, welke ongeveer 30 min voor defluïdisatie van het bed boven de kritische waarde van 3 uitkomt (gebaseerd op metingen in het dichte bed onderin de riser en het gedeelte van de horizontale materiaalterugvoer naar de riser).

Deze screeningmethodologie is met succes toegepast op verschillende casestudy’s data van bubbelende bedden op pilot-schaal en industriële schaal, en data van een circulerend bed op lab-schaal. Voor een gecombineerde casus, bestaande uit drie verschillende agglomeratie-incidenten in bubbelende bedden, zijn verschillende geschikte methoden geïdentificeerd, waaronder de Kolmogorov-Smirnov test met 15 Hz low-pass filter, de Kuipertest, de standaarddeviatie met 10 Hz high-pass filter, attractorvergelijking, entropie en gemiddelde cyclustijd. Naast deze benadering van een gecombineerde casus is het uiteraard ook mogelijk om de methodologie op een enkel proces toe te passen om geschikte methoden voor dit specifieke proces te identificeren. Vanuit deze casestudy’s en eerder onderzoek wordt een eenvoudig, beschrijvend model voor de ontwikkeling van drukfluctuaties tijdens agglomeratie voorgesteld.

Uiteindelijk moet een detectiemethode voor agglomeratie dienen als basis voor het voorkomen van defluïdisatie en tijdelijke buitenbedrijfstelling. Het is onderzocht of de attractorvergelijkingsmethode vroegtijdig genoeg reageert om met tegenmaatregelen de defluïdisatie van het bed te voorkomen. Het onderzoek hiervoor is uitgevoerd op lab-schaal (~1 kWth) en op pilot-schaal (~1 MWth).

Over het algemeen zijn de geobserveerde gevallen van agglomeratie niet goed reproduceerbaar, ervan uitgaande dat kleine variaties in de procescondities in praktijk niet te voorkomen zijn. Voor de pilot-schaal opstelling kan er vooral tijdens overgangen (zoals opstarten en veranderingen in last of temperatuur) snel agglomeratie optreden. In zulke gevallen is het gebruik van een “moving referentie”, i.e. de referentie wordt achter de momentele evaluatie met een constante offset meegevoerd, voor de attractorvergelijkingsmethode duidelijk van voordeel. Voor zowel snelle als ook geleidelijke agglomeratiegevallen was de attractorvergelijkingsmethode in staat om agglomeratie vroeg genoeg te voorspellen om defluïdisatie te voorkomen. Daarnaast zijn verschillende strategieën om agglomeratie tegen te werken geëvalueerd. Verlaging

combinaties van data voorbewerking en signaalanalysemethoden illustreren (alleen een deel van de hele matrix is hier te zien). De kleur en intensiteit van de velden is gerelateerd aan de waarde van de selectiviteitindex, stijgend van zwart naar rood, geel en wit.

operatie mogelijk te maken en defluïdisatie te voorkomen (Figuur N-3). Op lab-schaal bleek dat het gedeeltelijke vervangen van bedmateriaal met vers bedmateriaal een permanente oplossing voor continue operatie is. De tijdelijke verhoging in gassnelheid en gebruik van een additief waren niet succesvol in dit geval.

Samenvattend kan worden geconcludeerd dat de doelstellingen van dit onderzoek zijn bereikt. De resultaten laten duidelijk zien dat de attractorvergelijkingsmethode gevoelig is voor agglomeratie en kleine veranderingen in deeltjesgrootte in circulerende fluïde bedden. De voorgestelde screeningmethodolgie heeft meerdere specifieke combinaties van datavoorbewerking en signaalanalysemethoden geïdentificeerd die potentieel geschikt zijn voor de detectie van agglomeratie en de methodologie kan direct toegepast worden om geschikte methoden voor een specifieke toepassing te identificeren. De attractorvergelijkingsmethode heeft agglomeratie in een vroeg stadium aangetoond, zodat defluïdisatie in combinatie met een geschikte tegenwerkingstrategie kon worden voorkomen.

Toepassing van de attractorvergelijkingsmethode – en potentieel andere monitoring-methoden – zal de effectiviteit en de betrouwbaarheid van de operatie van industriële energieconversieprocessen verhogen. De getoonde succesvolle toepassing van de methode in circulerende fluïde bedden (CFB) is van groot belang omdat dit type fluïde bed veel wordt gebruikt in de energiesector. De monitoring-technologie kan daarom een prominente rol spelen bij de acceptatie van biomassa als hernieuwbare energiebron, vooral bij energieleveranciers.

Referenties

[1] van Ommen JR, Coppens MO, van den Bleek CM, Schouten JC. Early Warning of agglomeration in Fluidized Beds by Attractor Comparison. AIChE Journal

600 650 700 750 800 850 900 09:15 09:45 10:15 10:45 11:15 11:45 12:15 Tijd [hh:mm] T e m p e ra tu u r [° C ] -5 0 5 10 15 20 25 30 S -w a a rd e [ -]

Figuur N-3: Succesvol geval van agglomeratietegenwerking: nadat de S-waarde agglomeratie aantoont (S>3) wordt de temperatuur verlaagd tot onder 750°C (typisch gebied van smeltpunten van alkalisilicaten), wat resulteert in stabiele operatie op een laag temperatuurniveau. Hier is en “moving referentie” met 30 minuten tijdsverschil tussen referentie en evalutie is gebruikt, deze veroorzaakt de tweede peak.

Chapter 1 Introduction ...17

1.1. Fluidization & fluidized beds...18

1.2. Circulating fluidized beds (CFB)...20

1.3. Fluidized bed processes ...21

1.4. Agglomeration & fluidized bed monitoring...22

1.5. Measurement techniques...24

1.6. Signal analysis methods...26

1.7. Research objectives...27

1.8. Structure of the thesis ...28

1.9. References ...29

Chapter 2 Agglomeration in fluidized beds at high temperatures: Mechanisms, Detection and Prevention ...31

2.1. Abstract...32

2.2 Introduction ...32

2.3. Mechanisms of agglomeration in a fluidized bed at high temperature...36

2.3.1. Hydrodynamics...36

2.3.2. Mechanisms of chemical reactions ...38

2.3.3. Mechanisms of interactions between ash and bed materials ...39

2.3.4. Molecular Cramming...41

2.4. Agglomeration tendency determination and detection methods ...42

2.4.1. Fuel ash measurements ...42

2.4.2. Analysis methods based on process measurements ...49

2.4.2.1. Pressure...49

2.4.2.2. Acoustic emission ...64

2.4.2.3. Temperature...67

2.4.2.4. Detection of gas-phase alkali components ...71

2.4.2.5. Other measurement sources ...72

2.5. Agglomeration counteraction strategies and technologies ...73

2.5.1. Methods for implementation in existing processes ...73

2.5.1.1. Operational ...73

2.5.1.2. Additives...77

2.5.1.3. Alternative bed materials ...82

2.5.2. Improved reactor design ...86

2.6. Conclusions ...88

2.7. Future Developments...90

2.8. References ...91

Chapter 3 Detection of agglomeration and gradual particle size changes in circulating fluidized beds...103

3.1. Abstract...104

3.2. Introduction ...104

3.3. Experimental...106

3.4. Results & Discussion ...111

3.5. Conclusions ...134

3.6. Recommendations for Implementation ...135

3.7. Acknowledgements...136

Chapter 4 Methodology for the screening of signal analysis methods for selective

detection of hydrodynamic changes in multiphase reactors ...137

4.1. Abstract...138 4.2. Introduction ...138 4.3. Screening methodology ...141 4.4. Illustrating results ...148 4.5. Conclusions ...151 4.6. References ...151 4.7. Appendix ...153

Chapter 5 Case studies for selective agglomeration detection in fluidized beds: Application of a new screening methodology ...157

5.1. Abstract...158

5.2. Introduction ...158

5.3. Methodology...159

5.4. Experimental...161

5.5. Results & Discussion ...163

5.6. Conclusions ...193

5.7. Acknowledgements...194

5.8. References ...194

5.9. Appendix ...195

Chapter 6 Detecting and counteracting agglomeration in fluidized bed biomass combustion ...197

6.1. Abstract...198

6.2. Introduction ...198

6.3. Experimental...200

6.4. Results & Discussion ...204

6.4.1. WOB setup (lab-scale)...204

6.4.1.1. Reference cases...204

6.4.1.2. Replacement of bed material ...207

6.4.1.3. Temporary increase in fluidization velocity ...210

6.4.1.4. Temporary temperature decrease ...211

6.4.2. Crone boiler setup (small commercial scale) ...213

6.4.2.1. Reference cases...213

6.4.2.2. Temporary temperature decrease ...218

6.4.2.3. Addition of kaolin...224

6.5. Conclusions ...226

6.6. Acknowledgements...226

6.7. References ...227

Chapter 7 Conclusions & Recommendations ...229

7.1. Conclusions ...231

7.2. Recommendations...234

7.3. References ...235

List of publications ...237

Acknowledgements ...239

Chapter 1

Introduction

In the chemical and process industry various different reactor concepts are applied that facilitate chemical reactions and mixing of solid, liquid and/or gas to obtain various products. Reactors are applied for single-phase systems, but more often to multi-phase systems that are characterized by the presence of at least two phases. Selection procedures for a suitable multi-phase reactor system for a specific process have been discussed by e.g. Krishna and Sie [1]. Multi-phase reactors for gas-solid systems can be distinguished in fixed beds and fluidized beds. Major advantages of fluidized beds above other reactor concepts are the good mixing characteristics and therefore high heat- and mass transfer rates. This makes them a suitable choice for highly exothermal reactions and cases where easy solid handling is needed. Some drawbacks are the generally lower conversion and yield (compared to a fixed bed) due to gas back-mixing, erosion, particle agglomeration and elutriation. An overview of some major applications for fluidized beds in shown in Table 1-1.

Table 1-1: Overview of some major applications of fluidized beds, distinguished into chemical and physical processes.

Principle Processes

Chemical • Roasting of sulfide ores

• Catalytic synthesis reactions (e.g. chlorination of metal oxides, fluid catalytic cracking (FCC), liquid fuel production via fischer-tropsch synthesis, various gas-phase synthesis reactions [2])

• Catalytic polymerization of olefins

• Energy conversion of solid fuels (combustion, gasification, pyrolysis)

Physical • Heat exchange

• Granulation • Drying

• Particle coating • Adsorption

In the following, an introduction on fluidization and fluidized beds will be presented, together with some examples for processes and the relation to the focus of this thesis – agglomeration in fluidized beds.

1.1. Fluidization & fluidized beds

A fluidized bed can be designated as a certain amount of solid particles contained in a vessel and suspended by an upstream gas flow. For low gas velocities, the gas is flowing through the inter-particle spaces without causing any considerable motion of the particles, i.e. a fixed bed (Figure 1-a). When increasing the gas velocity further, the drag force from the upward flowing gas will balance the gravitational force on the particles. As a consequence, the bed gets expanded, i.e. increases in height. The point where this phenomenon occurs is called minimum fluidization (Figure 1-b), leading to a liquid-like behavior of the solids. A further increase in gas velocity can either lead to homogeneous fluidization for relatively small particles and low gas velocities (type Geldart A), or to the formation of gas voids for larger particles (type Geldart B) for

which the upward flowing gas cannot flow through the inter-particle spaces in a homogeneous manner (Figure 1-c).

Figure 1-1: Illustration of the different regimes in a fluidized bed: Fixed bed with low gas flow (a), expanded bed at minimum fluidization (b), bubbling fluidization (c) and turbulent fluidization (d). (Adapted from [2])

These gas voids are also often called “bubbles” and have a similar shape as bubbles that gas forms in a liquid, but they should be distinguished from a liquid system by the absence of surface tension. The gas voids rise upwards and can grow in volume through coalescence with other voids. When increasing the gas velocity even further, no distinct round voids are present anymore but a turbulent motion of solid clusters and voids of different size and shape is observed (Figure 1-d). In addition, there is no more distinct upper bed surface and particles start getting entrained from the bed. A further increase in gas velocity will lead to pneumatic transport of particles.

Different types of particles are used in fluidized beds, depending on the specific application. Geldart [3] has categorized them into four different groups depending on their size and density. For the same density and increasing particle size the groups are named C, A, B and D. Group C particles are of the smallest size and are strongly cohesive; the strong inter-particle forces make them very difficult to fluidize. Group A particles normally fluidize well, showing homogeneous bed expansion (i.e. no voids) at low gas velocities, small bubbles even at higher gas velocities and a maximum in the bubble size beyond a certain height in the bed. Group B particles, also called “sand-like”, fluidize well with a vigorous bubbling behaviour and no maximum in bubble size along bed height. Group D particles are rather large and are characterized by large exploding bubbles and so-called “spouting” behaviour during fluidization. For most industrial processes group A and B particles are used as they are well suitable for fluidization and handling.

1.2. Circulating fluidized beds (CFB)

Beyond the turbulent fluidization regime significant entrainment of particles out of the bed takes place. For a high particle reflux a dense suspension upflow regime develops, whereas for a low particle reflux a dilute phase

transport regime develops. For

intermediate particle refluxes a fast fluidization regime develops, which is characterized by a dense region at the bottom and a lean region further up in the so-called riser. In all of those cases one has to ensure that the entrained particles are being separated from the gas and re-circulated into the bottom of

the bed. Such a fluidized bed

configuration with gas-solid separation and solids return system is then called “circulating fluidized bed” (CFB), a schematic is shown in Figure 1-2. Both gas-solid separation and the solids return can be carried out externally, as illustrated here, or also internally, where both are situated within the riser.

For the separation step typically cyclones are used. For more efficient separation of fines two subsequent cyclones can be applied. For larger systems, requiring more gas to be processed, several parallel cyclones are typically used. The solids return system can be designed in different ways; an extensive overview of the different designs is given by Knowlton in [4]. For both laboratory and industrial practice non-mechanical solids return systems are preferably used as they have no moving parts. In this case the solids return system enables the solids flow by

the geometric shape of the return system in combination with some aeration (recirculation) gas flow. A selection of the most important solids return system is shown in Figure 1-3. An L-valve (Figure 1-3a) consists of an L-shaped pipe, directly connecting the solids removal from the cyclone with the riser. This volume is filled with a bed of solids that are transported back into the riser with the help of some aeration, resulting in a moving bed. The aeration is typically applied on the side of the lower

Figure 1-2: Schematic of a circulating fluidized bed (CFB) with external solids recirculation. (Adapted from [2])

the amount of aeration gas. A seal pot configuration (Figure 1-3b) connects a straight dipleg from the cyclone with a small fluidized bed from which the bed material is transported back to the riser via an overflow. The loop seal (Figure 1-3c) is a variation of the seal pot. It contains the same elements as a seal pot, but here the solids from the cyclone enter the fluidized bed from the side rather than from the top and two separate gas streams are used. In large industrial CFBs, e.g. for energy conversion processes, seal pots or loop seals are mainly used. The bubbling bed section can be rather large and e.g. facilitate a heat exchanger.

Figure 1-3: Typical solids return systems for circulating fluidized beds: L-valve, Seal Pot and Loop Seal. (Adapted from [4])

1.3. Fluidized bed processes

Gas-solid fluidized beds are utilized for a variety of processes in the chemical industry, such as catalytic reactions, drying, coating and energy conversion (e.g. [2]), as summarized in Table 1-1; a few examples for different processes are given in the following.

Fluidized bed conversion processes of solid fuels are thermo-chemical in nature and can be distinguished into combustion, gasification and pyrolysis (e.g. [5]). The evolving heat and gases are used to generate steam, electricity and/or syngas. As feedstock various different carbonaceous material can be used, e.g. coal, biomass and waste. Before conversion, the solid fuel is first processed by grinding or pelletizing to obtain a specific particle size, important for its conversion properties and for handling. The fuel is added to the fluidized bed of inert solid material, which acts as a heat reservoir. Silica sand is most commonly used as bed material. The actual amount of the fuel itself in the bed as compared to the inert bed material is relatively low, in the order of a few percent. The fluidized bed ensures good mixing of the continuously fed fuel and a good distribution of the produced heat. Through heat exchange tubes within or above the bed this heat can be removed efficiently. The resulting homogeneous temperature

distribution is one of the important advantages of fluidized beds over other reactor designs. Moreover, fluidized beds in energy conversion have the advantage of being flexible for a variety of different fuels.

The process of gas-phase polymerization of olefins is widely applied for production of both high density and low density polymers, typically polyethylene (PE) and polypropylene (PP) [6]. As compared to other processes, the gas-phase polymerization in fluidized beds has the advantages of generally lower operating costs and versatility of the process for quickly switching production between different grades. For this process, typically solid Ziegler-Natta catalyst type particles of < 100 µm are injected into the fluidized bed. Polymerization of ethylene or propylene from the gas phase takes place at the interface between the solid catalyst and the developing polymer matrix. The developing polymer particles typically grow up to a size range of a few millimetres while the catalyst is broken up and still present in the polymer. These particles and are continuously removed from the bottom bed.

Fluidized bed granulation and drying is widely applied in the areas of pharmaceutics and food. For pharmaceutical applications the powders are typically first processed by a wet granulation process step, in which a liquid is sprayed onto a powder containing the active ingredient and filler. For food applications, e.g. production of milk powder, a liquid solution is heated and atomized in a spray chamber, leading to evaporation of the water. For all cases a subsequent drying phase is applied to further reduce the moisture content by evaporation in a fluidized bed. It is desired to obtain an optimal particle size distribution and residual moisture content during this process. In case of pharmaceutics this is important for the mechanical stability of tablets produced from the resulting powder. For food applications the taste and further processing properties are important, e.g. the solubility of milk powder.

1.4. Agglomeration & fluidized bed monitoring

The occurrence of unwanted agglomeration in fluidized beds can be a major problem in industrial practice. It has been reported in some the earliest work on this topic, e.g. for reduction of ore powder [7] and coal gasification [8]. In the area of energy conversion, typically combustion and gasification of coal, biomass and waste, reports of agglomeration are numerous, e.g. [9,10,11]. Subsequent defluidization of the bed often results in a lengthy and expensive shut-down of the installation. This can be avoided with the help of a monitoring system that gives the process operator an early warning so that he can take the appropriate measures to prevent shutdown. The on-line monitoring of fluidized bed hydrodynamics therefore is an important area of research. Besides applications for early agglomeration detection, on-line monitoring is also useful for other processes where the particle properties influence the bed hydrodynamics and therefore the process efficiency. Some examples for industrially important processes are given in the following.

In case of energy conversion agglomeration is related to the simultaneous presence of alkali components from the fuel and silica from the bed material and/or fuel. Alkali silicates can be formed, having melting points starting around 750°C, lower than the

typical operating temperatures of about 850°C. Note that operating temperatures are only average bed temperatures; locally temperatures may be even much higher around burning fuel particles (e.g. [12]). The presence of a liquid phase results in an increased particle stickiness and formation of agglomerates upon particle collision (e.g. [10]); an example is shown in Figure 1-4.

Figure 1-4: Scanning Electron Microscope (SEM) photograph of a small agglomerate comprised of several individual grains covered and connected by a hardened melt-phase (a). The particle coverage is further illustrated in (b), showing an individual grain broken off from an agglomerate. The neck formation in-between particles is clearly visible. (Courtesy “Energy Technology” group at 3mE / TU Delft)

Continued agglomeration can result in defluidization of the bed, i.e. the loss of fluidization and formation of a fixed bed. Biomass fuels, especially the economically interesting fast-growing grassy biomasses (e.g. straw, miscanthus), may contain relatively large amounts of alkali and are often responsible for agglomeration problems (e.g. [13]). Reliable early warning systems are therefore expected to be capable of stimulating the acceptance and use of biomass in energy conversion in the future. Besides agglomeration, also the particle size distribution of the bed material can be an important process parameter, especially in the riser section of circulating fluidized beds where the size distribution determines the efficiency of the heat transfer to heat exchangers. Here, a monitoring system could help to optimize the bed composition by addition or replacement of bed material without mechanical bed sampling and analysis. In gas-phase polymerization of olefins, producing polyethylene (PE) and polypropylene (PP), using solid catalyst particles (e.g. [6]), the polymer properties are strongly influenced by the catalyst properties and operating conditions, such as temperature. For

sub-optimal conditions, the growing polymer particles can become sticky and form agglomerates in the bed or sheets on the reactor walls (e.g. [14,15]). In this process also electrostatic forces can play an important role. Monitoring is not only important for detecting agglomeration events here, but potentially also for controlling the process to ensure optimal particle size and properties.

In fluidized bed drying of powders, e.g. milk powder, agglomerated moist particles break up into smaller entities, which can be seen as “reverse agglomeration”. The moisture content and particle size distribution are important parameters that define the product quality. Overheating of the product due to the hot drying gas should also be avoided. Here, monitoring is necessary for accurately determining the optimal point to stop the drying process (e.g. [16]).

1.5. Measurement techniques

Detection methods for agglomeration and changes in particle size can be based on different types of measurements that characterize the hydrodynamics of the bed. A distinction should be made between lab-scale fluidized beds and industrial installations. For an industrial application the measurements should be reliable, cheap and simple to implement. For industrial fluidized beds the measurement of pressure and temperature are the most suitable options [17].

In this work, absolute pressure fluctuation measurements are primarily used. Pressure fluctuations do not directly express a distinct property of the bed (e.g. void fraction, density) but contain information about the global hydrodynamics of the bed comprised of bubble phenomena (formation, coalescence, break-up) and bed oscillations. The design of the measurement probes connecting the fluidized bed with the pressure transducer requires some attention to avoid the distortion of the pressure fluctuations [18]. The measurements carried out in this work are designed according to those guidelines. It is also necessary to prevent bed particles penetrating and blocking the measurement probe. In some cases this can be done with a wire gauze, but often, especially under hot conditions, it is necessary to apply a gas purge stream that enters the bed through the probe [19]. To resolve the pressure fluctuations sufficiently well for the subsequent analysis methods it is necessary to apply significantly higher sampling frequencies than usual for sampling of process variables. Typical sampling frequencies should be in the range of ten to a few hundred Hz. The necessary minimum sampling frequency depends on the frequency characteristics of the specific bed and can be estimated from a power spectrum of the measurements. The power density reduces with higher frequencies and remains roughly constant beyond a certain point; any higher frequency components are considered to not contain any relevant information on the hydrodynamics of the bed and are therefore obsolete for the analysis. The pressure transducer itself needs to be capable of sufficiently resolving the pressure fluctuations. Here, pressure transducers of type Kistler 7261 with a resolution of 1.5 Pa are used (note that this resolution is not considered a minimum requirement). Subsequently the pressure signal is amplified, further modified and finally sampled and stored by a data acquisition system. For the analysis only the fluctuations around the current average pressure are of interest. The signal is therefore first high-pass filtered. The high-pass

average from the signal but at the same time does not remove valuable hydrodynamic information. To avoid aliasing the signal is then low-pass filtered at a cut-off frequency of half the sampling frequency according to the Nyquist criterion. Finally, the signal is digitized (A/D conversion) and stored. A schematic for the measurements and acquisition of the pressure fluctuation measurements in shown in Figure 1-5.

Figure 1-5: Schematic of the pressure fluctuation measurements, signal conditioning and storage

Acoustic emissions also have been used in fluidized bed monitoring (e.g. [20]) and can in essence be considered pressure measurements with much higher frequencies. Despite generally being considered a promising measurement technique, it is important to take care that any surrounding noise not originating from the bed is either avoided or identified and filtered out.

Temperature measurements are generally carried out with thermocouples in fluidized beds. As compared to pressure, temperature measurements are much more localized. This can be a disadvantage for monitoring purposes, as only a limited volume of the bed is actually monitored. Normally, temperature gradients are very low in fluidized beds, but especially during agglomeration they can get larger. Conductive temperature transducers, such as thermocouples, also have worse, i.e. more dampened, response characteristics for higher frequency components. The measured signal will therefore contain less information about the hydrodynamics of the bed as compared to pressure measurements.

Besides pressure and temperature also different other techniques have been proposed and applied, such as optical measurements using local light transmittance by optimal probes or photo/video measurements (e.g. for determining solids volume faction [21]), capacitance measurements (e.g. [22]), heat transfer measurements (e.g. [17]), x-ray/γ -ray tomography (e.g. [23]) and different measurements for sampling and characterizing gas and small solid particles from the gas-phase (e.g. [24]). Although in principle often feasible, these measurements are generally not very suitable for an industrial environment in terms of robustness and cost; in addition they typically only yield information about a small part of the bed.

1.6. Signal analysis methods

Many methods for agglomeration detection have been proposed. Relatively simple methods are often based on existing average process measurements at the available sampling frequencies, usually below 1 Hz, and therefore do not analyze the higher frequency bubble phenomena. The average value of the pressure drop over the bed has been proposed (e.g. [25]). For higher-frequency data the standard deviation and variance (e.g. [26,27]) as well as principal component analysis based on pressure drop (e.g. [28]) have been proposed. These methods generally do detect particle size changes or agglomeration, but only in a relatively late stage of the change. Moreover, their sensitivity for other process changes (cross-sensitivity) has either not been investigated or has been shown to be rather large, e.g. for changes in the fluidizing gas flow [29]. A review of methods is presented in chapter 2.

The method of “attractor comparison” [30] is especially considered in this work because it has shown to be a suitable early agglomeration warning method for lab-scale and industrial scale bubbling beds [19,30]. The principle of this method consists of reconstructing and comparing so-called attractors. An attractor is the projection of a time series into a multi-dimensional state space, in this case a pressure fluctuation time series. One first has to construct a reference attractor that is representing a desired (e.g. well-fluidized) state of the system. Subsequently, the attractor of the current operating state is reconstructed and compared to the reference attractor. This comparison is based on a statistical test developed by Diks et al. [31], which evaluates the dimensionless squared distance S between two attractors. The S-value is defined as:

( ) with:

: Estimator of the squared distance

between the two smoothed distributions of the two attractors ( ) : Variance of c c Q S V Q Q V Q Q ∧ ∧ ∧ ∧ ∧ =

For attractors being generated by the same mechanism, S has an expectation of 0 and a standard deviation of 1. An S-value larger than 3 indicates with at least 95% confidence that the two attractors have been generated by a different system, i.e. the hydrodynamics have significantly changed. For bubbling fluidized beds the method has shown to be sensitive to agglomeration and insensitive to changes in bed mass and gas velocity up to about 10%.

In general, signal analysis methods can be distinguished into two groups: Methods that characterize a specific aspect of the pressure fluctuation signal, such as cycle time between subsequent crossing of the signal with its average or amplitude, and methods that signify a statistically significant change as compared to a certain reference state, such as attractor comparison or the Kolmogorov-Smirnov test. The latter group needs by definition a reference state to evaluate the relative changes. A comparison of relative changes can also be done for the first group of methods, but one has to define a measure

for the significance of such a change; this can be done e.g. with the standard deviation during the chosen reference.

In addition to the high frequency pressure fluctuation data often also “average” process data, obtained with a low sampling frequency, are used for the interpretation of the results of the high-frequency data analysis. Typical examples are temperatures, gas flow rates, gas composition and mass refluxes.

Besides the actual signal analysis method one can also apply pre-treatment methods, i.e. modifications of the signal before the actual analysis. This pre-treatment can consist of the application of a frequency filter or also reducing the dimensionality of the data. The purpose of such a pre-treatment step is to potentially increase sensitivity and selectivity to a specific hydrodynamic phenomenon, e.g. like agglomeration, in the subsequent analysis.

1.7. Research objectives

This thesis is based on the following main research objectives:

1. Investigation whether the attractor comparison method [30] based on high-frequency pressure measurements, previously successfully applied to bubbling fluidized beds, can also successfully applied in circulating fluidized beds for the early detection of agglomeration as well as small uni-modal changes in particle size. The application in circulating fluidized beds is of importance due to their widespread industrial use, particularly for energy conversion. For the applicability of attractor comparison method, it should be investigated how to optimally implement the method in the process with respect to the pressure fluctuation measurement location(s), tuning of the method and electronic pre-treatment (filtering) of the signal before the actual analysis.

2. Investigation on which signal analysis methods are suitable to serve as a suitable early warning method for agglomeration in fluidized bed processes, both on lab-scale and industrial lab-scale. A method is suitable if it is sensitive, selective and early enough to reliably detect agglomeration; here, the sensitivity and selectivity are investigated. The application of (electronic) pre-treatment of the pressure fluctuation data before the actual analysis is also incorporated in this investigation to see if this can improve sensitivity and/or selectivity of the subsequent analysis method.

3. Investigation of the suitability of attractor comparison to successfully prevent defluidization in an energy conversion process in combination with a suitable strategy to counteract agglomeration. This investigation is carried out on lab-scale and small commercial scale to investigate the applicability and implementation of the method on different scales. In addition, the suitability of different counteraction methods is investigated in light of successfully preventing further agglomeration and defluidization.

The focus of all these research objectives is on the process of energy conversion of solid fuels, in particular combustion and gasification of biomass. Related to the proposed research objectives is the identification of the agglomeration mechanisms and suitable detection and counteractions methods proposed in the literature.

1.8. Structure of the thesis

For each chapter of this thesis the general scope is shortly addressed below.

Chapter 2 reviews agglomeration mechanisms, detection methods, and counteraction strategies in fluidized beds, focusing on high-temperature processes. For the complex phenomenon of agglomeration in fluidized beds at high temperatures various aspects are distinguished: hydrodynamics, chemical reaction mechanisms, particle interaction mechanisms, and molecular cramming. Special emphasis is given to the detection of agglomeration. Detection methods are either based on fuel ash analysis methods to predict potential agglomeration on (on-line) process measurements. Finally, different methods to counteract agglomeration phenomena are presented: operational measures, utilization of additives, alternative bed materials and improved reactor design.

Chapter 3 discusses the application of the attractor comparison method in circulating fluidized beds. The two applications considered here are the detection of small, gradual changes in particle size and the early detection of agglomeration. The analysis is based on high-frequency pressure measurements, obtained from different locations (riser, downcomer and return-system) in each of the four investigated setups. A lab-scale cold-flow setup with two alternative solids return systems, L-valve and loop seal, was used to investigate the influence of small particle size changes and the presence of model agglomerates on the method. In a hot lab-scale setup an agglomeration case during biomass gasification was investigated. On industrial scale the method was applied for the detection of small particle size changes. Optimal performance in terms of measurement locations and filtering are discussed.

Chapter 4 presents a new methodology used for efficient screening of many different signal analysis methods in combination with different signal pre-treatment methods. The goal to identify those combinations that are selective towards a specific process change. Background for the development of this methodology is that there are many different analysis techniques available, but only sufficiently sensitive and selective methods will be suitable for the unambiguous detection of a specific process change. For the assessment a selectivity index is defined and visualized as coloured elements in a matrix form. The methodology is illustrated with fluidized bed agglomeration data, demonstrating the validity and the benefit of this approach.

Chapter 5 presents several case studies based on the methodology introduced in Chapter 4. Data from a pilot-scale cold-flow bubbling fluidized bed with isolated process changes were used to investigate the selectivity to agglomeration compared to changes in gas flow and bed mass. For an agglomeration case in an industrial bubbling bed combustor and in a lab-scale circulating gasifier, agglomeration is compared to common fluctuations in the process instead of isolated process changes. Several methods that visually were identified to perform well, i.e. having a high sensitivity and selectivity to agglomeration, are presented and discussed.

Chapter 6 establishes a link between agglomeration detection and counteraction strategies on both lab-scale and small commercial scale. Attractor comparison is

counteractions with the goal to avoid defluidization of the bed. Temporary temperature decrease, gas velocity increase, bed material replacement and introduction of an additive were investigated; their suitability to successfully counteract agglomeration is discussed.

Chapter 7 concludes the main findings of this thesis and gives recommendations for further research.

1.9. References

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[2] Kunii D, Levenspiel O. “Fluidization Engineering”, Butterworth-Heinemann Ltd, USA, 1991

[3] Geldart D. Types of Gas Fluidization. Powder Technology 1973;7(5):285-292

[4] Grace J, Avidan A, Knowlton T (Editors). Circulating Fluidized Beds, Blackie Academic & Professional, New York, 1997

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[6] Tuyu Xie, Kim B. McAuley, James C. C. Hsu, and David W. Bacon. Gas Phase Ethylene Polymerization: Production Processes, Polymer Properties, and Reactor Modeling. Ind Eng Chem Res 1994;33(3):449-479

[7] Gluckman, M.J., Yerushalmi, J. and Squires, A.M., Defluidization characteristics of sticky materials on agglomerating bed, Fluidization Technology, Vol. 2, (ed. Keairns, D.L.), 1976, p. 395 – 422

[8] Yerushalmi J, Kolodney M, Graff RA, Squires AM. Agglomeration of Ash in Fluidized-Beds Gasifying Coal - Godel Phenomenon. Science 1975;187(4177):646-648

[9] Ergüdenler A, Ghaly AE. Agglomeration of silica sand in a fluidized bed gasifier operating on wheat straw. Biomass and Bioenergy 1993;4(2):135-147

[10] Öhman M, Nordin A, Skrifvars B-J, Backman R, Hupa M. Bed Agglomeration Characteristics during Fluidized Bed Combustion of Biomass Fuels. Energy & Fuels 2000;14(1):169-178

[11] Werther J, Saenger M, Hartge E.-U., Ogada T, Siagi Z. Combustion of agricultural residues. Progress in Energy and Combustion Science 2000;26(1):1-27

[12] Chirone R, Salatino P, Scala F. The Relevance of Attrition on the Fate of Ashes during Fluidized-bed Combustion of a Biomass. Proceedings of the Combustion Institute 2000;28:2279-2286

[13] Skrifvars BJ, Öhman M, Nordin A, Hupa M. Predicting Bed Agglomeration Tendencies for Biomass Fuels Fired in FBC Boilers: A Comparison of Three Different Prediction Methods. Energy & Fuels 1999;13:359-363

[14] Hatzantonis H, Goulas A, Kiparissides C. A comprehensive model for the prediction of particle-size distribution in catalyzed olefin polymerization fluidized-bed reactors. Chemical Engineering Science 1998;53(18):3251-3267

[15] Hendrickson G. Electrostatics and gas phase fluidized bed polymerization reactor wall sheeting. Chemical Engineering Science 2006;61(4):1041-1064

[16] Chaplin G, Pugsley T. Application of electrical capacitance tomography to the fluidized bed drying of pharmaceutical granule. Chemical Engineering Science 2005;60(24):7022-7033

[17] Werther J. Measurement Techniques in Fluidized Beds. Powder Technology 1999;102:15-36

[18] J. Ruud van Ommen, Jaap C. Schouten, Michel L. M. van der Stappen, Cor M. van den Bleek. Response characteristics of probe–transducer systems for pressure measurements in gas–solid fluidized beds: how to prevent pitfalls in dynamic pressure measurements. Powder Technology 1999;106(3):199-218 +Erratum

[19] Nijenhuis J, Korbee R, Lensselink J, Kiel JHA, van Ommen JR. A method for agglomeration detection and control in full-scale biomass fired fluidized beds. Chemical Engineering Science 2007;62(1-2):644-54

[20] śukowski W. The pressure pulses generated by the combustion of natural gas in bubbling fluidized beds. Combustion and Flame 2002;30(1-2):15-26

[21] Magnusson A, Rundqvist R, Almstedt AE, Johnsson F. Dual fibre optical probe measurements of solids volume fraction in a circulating fluidized bed. Powder Technology, Volume 151, Issues 1-3, 1 March 2005, Pages 19-26

[22] Sutton, CR, Chen JC. A local transient approach to monitoring fluidization quality in freely bubbling beds. Powder Technology 2008:180(3):296-306

[23] Martin MP, Turlier P, Bernard JR, Wild G. Gas and solid behavior in cracking circulating fluidized beds. Powder Technology 1992;70(3):249-258

[24] Häyrinen V, Hernberg R, Aho M. Demonstration of plasma excited atomic resonance line spectroscopy for on-line measurement of alkali metals in a 20 kW bubbling fluidized bed. Fuel 2004;83(7-8):791-797

[25] Rehmat AG, Patel JG; Inst. Gas Technology (IGTE); “Controlling and maintaining fluidised beds - under non-steady state conditions in ash agglomerating fluidised beds” Patent US4544375-A (1985)

[26] Davies CE, Fenton K. Pressure fluctuations in a fluidized bed: A potential route to the continuous estimation of particle size. IPENZ Transactions 1997;24(1):12-20 [27] Chirone R, Miccio F, Scala F. Mechanism and prediction of bed agglomeration

during fluidized bed combustion of a biomass fuel: Effect of the reactor scale. Chemical Engineering Journal 2006;123(3):71-80

[28] Fuller TA, Flynn TJ, Daw CS, Halow JS. Interpretation of Pilot-Scale, Fluidized-Bed Behavior Using Chaotic Time Series Analysis. Proceedings of the 12th International FBC Conference 1993, Edited by L.N. Rubow, Vol.1, 141-155

[29] van Ommen JR, Schouten JC, van den Bleek CM. An early-warning-method for detecting bed agglomeration in fluidized bed combustors. In: Reuther RB, editor. Proceedings of the 15th international conference on fluidized bed combustion, Paper no. FBC99-0150. New York: ASME; 1999.

[30] van Ommen JR, Coppens MO, van den Bleek CM, Schouten JC. Early Warning of agglomeration in Fluidized Beds by Attractor Comparison. AIChE Journal 2000;46:2183-2197

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Chapter 2

Agglomeration in fluidized beds at high temperatures:

Mechanisms, Detection and Prevention

This chapter gives an extended overview of the open literature on mechanisms of the agglomeration process, methods to detect agglomeration and strategies to prevent and counteract agglomeration in fluidized beds. The focus lies on agglomeration at high temperatures, mainly in the area of solid energy conversion, but also relevant literature outside of this area is included.

This chapter has been published as:

Malte Bartels, Weigang Lin, John Nijenhuis, Freek Kapteijn, J Ruud van Ommen. Agglomeration in fluidized beds at high temperatures: Mechanisms, Detection and Prevention. Progress in Energy and Combustion Science 2008;34(5):633-666

2.1. Abstract

Fluidized-bed conversion of solid fuels is a well-established and widely used technology. Yet, operational problems are encountered in industrial practice. One of the most important problems is the occurrence of agglomeration at high temperature, meaning that bed particles adhere to each other to form larger entities (agglomerates). This process is often not recognized until sudden defluidization and often leads to a costly shutdown of the whole installation. In particular, the thermal conversion of certain biomass fuels, which is becoming increasingly popular, increases the risk of agglomeration.

This paper critically reviews the current research status of this topic in terms of agglomeration mechanisms, detection and counteraction strategies.

To understand the complex phenomenon of agglomeration in fluidized beds at high temperatures different areas are distinguished viz. hydrodynamics, chemical reaction mechanisms, particle interaction mechanisms and molecular cramming. Special emphasis is given to the detection of agglomeration. The range of detection methods is comprised of fuel ash analysis methods to predict potential agglomeration as well as analysis methods based on (on-line) process measurements, such as pressure and temperature. Finally, different methods to counteract agglomeration phenomena are presented; they comprise operational measures, utilization of additives, alternative bed materials and improved reactor design.

2.2 Introduction

Fluidized bed conversion of solid fuels

Fluidized beds are used for a variety of applications in the process industry, such as fluidized catalytic cracking (FCC) including catalyst generation and other strongly exothermal processes, drying, solid fuel conversion and gas-phase polymer production [1]. This review focuses on high-temperature processes, especially thermo-chemical solid fuel conversion carried out in fluidized beds. For an overview of the different thermo-chemical conversion routes (combustion, gasification, pyrolysis, liquefaction) as well as bio-chemical conversion routes (fermentation, anaerobic digestion) see e.g. [2]. Fluidized bed conversion of carbonaceous solid material (e.g. coal, biomass, etc.) at high temperatures is industrial practice to generate steam, electricity and hydrogen. The solid fuel is added to a fluidized bed of inert solid material, which acts as a heat reservoir. Silica sand is most commonly used as bed material. The actual amount of the fuel itself in the bed as compared to the inert bed material is relatively low, in the order of a few percent. The fluidized bed material ensures good mixing of the introduced fuel as well as the evolving heat. The homogeneous temperature distribution is one of the important advantages of fluidized beds over other reactor concepts. Depending on the process, heat can either be directly utilized, e.g. combustion combined with heat exchangers or a gas turbine (in case of a pressurized system), or the evolving gases can be processed, e.g. gasification combined with Fischer-Tropsch synthesis to produce liquid fuels.