politecnico di milano · ai miei nonni “nothing in life is to be feared, it is only to be...
TRANSCRIPT
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Politecnico di Milano SCUOLA DI INGEGNERIA INDUSTRIALE E DELL’INFORMAZIONE
Corso di Laurea Magistrale in Ingegneria Chimica
TESI DI LAUREA MAGISTRALE
Reactor Network Model of biomass combustion in fluidized beds
Anno Accademico 2016-2017
Candidato:
Matteo MENSI
Matricola 858912
Relatore:
Prof. Tiziano FARAVELLI
Correlatori:
Dr. Giancarlo GENTILE
Prof. Alberto CUOCI
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Ai miei nonni
“Nothing in life is to be feared,
it is only to be understood.
Now is the time to understand more,
so that we may fear less.”
Marie Curie
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Ringraziamenti
Vorrei ringraziare il prof. Tiziano Faravelli per l’opportunità fornitami di
misurarmi con un problema stimolante ed in grado di mettermi alla prova,
accompagnandomi con discussioni e consigli. Ringrazio il prof. Alberto
Cuoci per avere avuto la pazienza tra i suoi mille impegni di aiutarmi a fare
i primi passi nel mondo della programmazione in C++. E ringrazio
Giancarlo per avermi seguito e supportato con attenzione e interesse,
spingendomi a mettere in dubbio tutto e a prendermi in giro per essere più
vecchio di mio padre. Con loro, ringrazio tutti il resto del gruppo CRECK
Modeling, i prof. Alessio Frassoldati ed Eliseo Ranzi per l’immenso aiuto
che hanno saputo fornire sui vari aspetti di questa tesi, partecipando a
discussioni e chiarendo i miei dubbi, nonché Alessandro e Matteo.
Ringrazio tutte le persone che ho incontrato durante i miei studi al
politecnico, partendo da Nicolò, Luca, Claudia, Chiara e Davide. Grazie per
le belle serate e per i momenti di studio. Ringrazio Francesco, Daniele,
Andrea, Claudia e Paola per le infinite pause caffè, sempre troppo lunghe.
Un grazie va sicuramente a tutti i ragazzi dell’aula tesisti: Paolo, Erica,
Fabio e Francesco, “compagni” di gruppo, nonché Giulio, Matteo, Luca e
tutti gli altri.
Menzione d’onore per Ayoub e Francesca. Tempo di qualità passato
assieme. Gne.
Ringrazio i ladri in cucina Francesco, Luca, Manu e tutti gli altri che sono
sparsi nei ringraziamenti e citarli due volte fa tanto favoritismo e non mi
piace. Un giorno capirò cosa era quella salsa strana giapponese.
Ringrazio il resto della sagra della salsiccia, Niccolò, Marco e Alberto. Si
spera che Niccolò sia ancora vivo. Assieme a Niccolò ringrazio Mattia, per
il brillante fatturato ottenuto l’anno scorso.
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Uscendo dall’ambito universitario, mi sarebbe impossibile non ringraziare
i miei amici di sempre: Matteo, Nicolò P & S, Marco, Federico, Paolo A & A
& I, Daniele, Fabio V e anche Gabriele, Fabio F, Simone e Marco. Senza
dimenticarsi di Ilaria, Giulia e Paola. Una grossa fetta di chi sono ora (e
considerando la mole una grossa fetta è grossa grossa) è grazie a voi. Grazie
di tutto, vi voglio un mondo di bene ragazzi.
Potrei non aver ringraziato tutti. Non sono propriamente bravo e ordinato
in queste cose. Se meriteresti un grazie e non l’ho messo, allora questo è
per te: grazie.
Ora è forse il caso di passare alla famiglia di sangue. Ringrazio tutti, dal
primo all’ultimo, specialmente i miei nonni Virginio e Franca. Sono
estremamente fortunato ad avere due nonni come voi. Ringrazio mia
madre per la (poca) pazienza e per avermi insegnato la disciplina. Di mio
padre ammiro la curiosità infinita e la determinazione. Ad entrambi devo
una quantità di affetto che forse non sono in grado di dimostrare, ma ci
provo. D’altro canto, è solo con il loro supporto che ho potuto fare questo
percorso.
Per ultima, ringrazio Federica, la mia ragazza. Grazie per questi anni
assieme, per il supporto, il conforto, l’incoraggiamento e tutti i momenti
speciali. Soprattutto, grazie per sopportarmi.
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Table of contents Table of contents .......................................................................................... 7
List of Figures ............................................................................................. 11
List of Tables .............................................................................................. 15
Sommario ................................................................................................... 17
Abstract ...................................................................................................... 19
1. Introduction ................................................................................... 21
1.1 Lignocellulosic biomasses ................................................................ 23
1.1.1 Conversion routes for lignocellulosic biomass to liquid fuels 24
1.1.2 Thermochemical mechanism of biomass conversion ............. 26
1.2 Biomass gasification ......................................................................... 28
1.2.1 Fluidized bed gasifier ................................................................ 29
1.2.2 Challenges in predictive modeling............................................ 30
1.3 Aim and structure of this thesis ...................................................... 31
2. State of the Art ............................................................................... 33
2.1 Biomass kinetics ............................................................................... 34
2.1.1 Biomass composition ................................................................. 35
2.1.2 Biomass devolatilization and pyrolysis kinetic scheme .......... 41
2.2 Gasification units ............................................................................. 43
2.2.1 Types of gasifier ......................................................................... 43
2.2.2 Energy and cost management ................................................... 46
2.3 FBBG Modeling ................................................................................. 47
2.3.1 Conversion process .................................................................... 48
2.3.2 Classification of models ............................................................. 49
3. NetSMOKE ....................................................................................... 57
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3.1 Mathematical formulation ............................................................... 58
3.1.1 A reactor network according to NetSMOKE ............................. 59
3.1.2 Mathematical formulation of the units .................................... 59
3.1.3 Formulation of the reactor network problem .......................... 62
3.1.4 Dealing with multi-phase systems ............................................ 65
3.2 Numerical methods .......................................................................... 66
3.3 Premise to NetSMOKE ...................................................................... 70
3.3.1 Why C++ ..................................................................................... 70
3.3.2 Libraries used and compatibility .............................................. 71
3.3.3 NetSMOKE vs KppSMOKE ......................................................... 72
3.3.4 NetSMOKE and CFD simulations ............................................... 72
3.4 NetSMOKE structure ........................................................................ 73
3.4.1 Solver .......................................................................................... 74
3.4.2 Data structures ........................................................................... 75
3.4.3 Input reader system ................................................................... 77
3.4.4 Units ............................................................................................ 78
3.4.5 Reactor network class ................................................................ 81
3.5 NetSMOKE algorithm ....................................................................... 82
3.5.1 Reading input and creating a reactor network ........................ 82
3.5.2 Preliminary operations performed by the network class ....... 85
3.5.3 Solving the fully-coupled system .............................................. 88
3.6 Validation ......................................................................................... 89
3.6.1 Tests ............................................................................................ 90
3.6.2 Conclusions ................................................................................ 99
4. Applications and Case Studies ..................................................... 101
4.1 RTD analysis ................................................................................... 102
4.1.1 Residence time distribution of ideal reactors ........................ 103
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4.1.2 RTD analysis in NetSMOKE ..................................................... 105
4.1.3 Tests and result ........................................................................ 105
4.2 Van Paasen reactor ........................................................................ 109
4.2.1 Simulation setup ...................................................................... 111
4.2.2 Results ...................................................................................... 114
4.3 NREL 4FBR Reactor ........................................................................ 116
4.3.1 Simulation setup ...................................................................... 118
4.3.2 Results ...................................................................................... 121
4.4 Higher-complexity ERN.................................................................. 123
4.4.1 Results ...................................................................................... 126
5. Conclusions ................................................................................... 131
5.1 Future work .................................................................................... 133
5.1.1 Future work on biomass kinetics ............................................ 133
5.1.2 Expanding NetSMOKE ............................................................. 133
6. 135
7. 135
8. 135
9. References .................................................................................... 135
Nomenclature ..................................................................................... 143
Appendix A .......................................................................................... 145
Appendix B .......................................................................................... 149
Appendix C .......................................................................................... 153
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List of Figures Figure 1.1: Data from EIA on energy consumption from different renewable
sources in the US, up to the last months of 2017. It is possible to see how biomasses
grew in recent years. ................................................................................................ 22
Figure 1.2: Different perspectives of biomass modeling. ....................................... 24
Figure 1.3. Biomass conversion pathways .............................................................. 25
Figure 1.4. Thermochemical conversion of biomass in a gasifier. Adapted from
Stark et al. [9] ............................................................................................................ 27
Figure 1.5. Overall process scheme for gasification to drop-in liquid fuels ......... 28
Figure 1.6. a) Bubbling bed reactor, b) Circulating bed reactor ............................ 29
Figure 2.1: Multiscale problem. Adapted from Ranzi et al. [12] ............................ 34
Figure 2.2: Comparison of atomic ratios of different classes of biomasses and coals.
The heating value increases with increasing H:C ratio and decreasing O:C. Adapted
from Jenkins et al. [13] ............................................................................................. 35
Figure 2.3: Cellulose unit. ......................................................................................... 36
Figure 2.4: Cellulose polymeric structure. .............................................................. 37
Figure 2.5: Monomers of hemicellulosic polysaccharides. .................................... 37
Figure 2.6: Hydroxycinnamyl alcohol polymers which lignins are composed of. 38
Figure 2.7: Diagram used for biomass characterization. ....................................... 39
Figure 2.8: Reference species used in the CRECK mechanism. .............................. 40
Figure 2.9: Types of gasifier ..................................................................................... 43
Figure 2.10: Updraft gasifier. The bed temperature profile is valid for a downdraft
gasifier as well. ......................................................................................................... 44
Figure 2.11: a) FBBG b) FCBG ................................................................................... 46
Figure 2.12: a) Three ways for indirect gasification, by external heat or internal
heat (gas and char). b) Indirect internal gasifier with char burning. ................... 47
Figure 2.13: Processes occurring in a FBBG. Adapted from Stark et al. [9] .......... 49
Figure 2.14: Multiscale modeling paradigms. Adapted from Bates [12] ............... 50
Figure 2.15. Kinetic post-processing method .......................................................... 53
Figure 3.1: General, top-level structure of NetSMOKE ........................................... 73
Figure 3.2: NetSMOKE splash screen in the terminal window. ............................. 74
Figure 3.3: Struct usage in C++. ................................................................................ 75
Figure 3.4: Data structures used in NetSMOKE. ..................................................... 76
Figure 3.5: Structure of the input reader system.................................................... 77
Figure 3.6: Structure of the unit class and all its ramifications. ............................ 78
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Figure 3.7: Example of usage of polymorphism. .................................................... 80
Figure 3.8: Overview of the ReactorNetwork class. ............................................... 81
Figure 3.9: Preparing the ReactorNetwork class at runtime. ................................ 83
Figure 3.10: From raw unit data to a vector of objects. ......................................... 85
Figure 3.11: Preliminary operations of the network class in NetSMOKE. ............ 87
Figure 3.12: NetSMOKE approach for fully-coupled system solution. .................. 88
Figure 4.1: Typical CRTD and RTD function profiles for a step injection experiment.
................................................................................................................................. 104
Figure 4.2: Results from series of reactor RTD. Blue figure is one 1.0 s CSTR. Red
figure are two 1.0 s CSTRs. ..................................................................................... 106
Figure 4.3: Recycle cases configuration. ............................................................... 107
Figure 4.4: Residence time distribution for recycle cases. Blue lines for single
reactor, red lines for two CSTR in series with a recycle end-to-start. ................. 107
Figure 4.5: Configuration for a recycle with intermediate reactor and relative
CRTD curve. ............................................................................................................. 108
Figure 4.6: Configurations adopted for the bypass test case. .............................. 108
Figure 4.7: CRTD for a CSTR with bypass and two CSTR in parallel. ................... 109
Figure 4.8: Experimental setup for FBBG, taken from Van Paasen et al. [46] .... 109
Figure 4.9: Dimensions of the FBBG reactor, provided by ECN and taken from Stark
et Al. [9] ................................................................................................................... 110
Figure 4.10: From reactor to ERN model, taken from Gomez and Leckner [27] 112
Figure 4.11: Network model in NetSMOKE. .......................................................... 113
Figure 4.12: Plots of the main outlet species as a function of temperature. Dots are
experimental data from Van Paasen, red lines are with CRECK WGS and blue lines
are with Macak and Malecha [51] WGS. ............................................................... 115
Figure 4.13: Experimental set up used. Taken from by Bates et al. [39] ............. 117
Figure 4.14: Network morphology for the first modelling of the 4FBR NREL
reactor. .................................................................................................................... 120
Figure 4.15: Comparison between NetSMOKE results and Bates et al. [39]
measurements, red column is experiment and blue column prediction. ........... 122
Figure 4.16: ERN for the 4FBR reactor with a PFR representing bubbles. .......... 123
Figure 4.17: Three bubble expansion of the scheme in Figure 4.16. ................... 126
Figure 4.18: Outlet gas composition according to the three different ERN setups.
Red, blue and black bars are respectively zero, 1 and 3 bubbles morphologies.127
Figure 4.19: Total tars variation with the ERN complexity. Red, blue and black are
respectively 0, 1 and 3 bubble PFRs. ..................................................................... 128
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Figure 4.20: Distribution of tars with temperature for the three ERN morphologies.
Red, blue and black stand for zero, 1 and 3 bubble reactors. .............................. 130
Figure C.1: Scheme of the FBBG model. ................................................................ 154
Figure C.2: Processes occuring on each element of volume of the PFR. ............. 155
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List of Tables Table 2.1: CRECK Biomass kinetic scheme .............................................................. 42
Table 2.2: Different modelling approaches for fluidized beds. Adapted from
Gomez et al. [27] ....................................................................................................... 55
Table 3.1: System dimensions according to case in exam. ..................................... 64
Table 3.2: Comparison between OpenSMOKE and NetSMOKE, Isothermal CSTR.90
Table 3.3: Comparison between OpenSMOKE and NetSMOKE, Isothermal PFR. . 91
Table 3.4: Comparison between OpenSMOKE and NetSMOKE, Adiabatic CSTR. . 92
Table 3.5: Comparison between OpenSMOKE and NetSMOKE, Heat-exchange
CSTR. .......................................................................................................................... 93
Table 3.6: Comparison between OpenSMOKE and NetSMOKE, Adiabatic PFR. ... 94
Table 3.7: Comparison between OpenSMOKE and NetSMOKE, Heat-exchange PFR.
................................................................................................................................... 95
Table 3.8: Comparison OpenSMOKE++ vs NetSMOKE, series of reactors. ............ 96
Table 3.9: CSTR with a recycle. ................................................................................ 97
Table 3.10: PFR with recycle. ................................................................................... 98
Table 3.11: Trend of the norm of residuals of a PFR vs an approximating series of
CSTRs. ........................................................................................................................ 99
Table 4.1: Char gasification reactions from Ranzi et al. [12]. .............................. 102
Table 4.2: Dry ash free elemental composition of beech wood used in Van Paasen
et al. [46] .................................................................................................................. 110
Table 4.3: Inlet biomass composition used in NetSMOKE for reproducing Van
Paasen data. ............................................................................................................ 111
Table 4.4: General parameters for the network model used for Van Paseen et al.
[46] ........................................................................................................................... 113
Table 4.5: Reactor parameters for the network model used for Van Paseen et al.
[46] ........................................................................................................................... 113
Table 4.6: Values for WGS reaction. ...................................................................... 114
Table 4.7: dry composition of the oak used in Bates et al. [39] ............................ 116
Table 4.8: 4FBR reactor geometries from Gaston et al. [47] ................................. 117
Table 4.9: Biomass composition of oak chips used in the experiments. ............. 118
Table 4.10: General parameters for the network model used for Bates et al. [39]
................................................................................................................................. 121
Table 4.11: Reactor parameters for the network model used of Bates et al. [39]121
Table 4.12: Fluidization parameters calculated for the 4FBR reactor ................. 124
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Table 4.13: Parameters calculated for the ERN with a single PFR to model the
presence of bubbles. ............................................................................................... 125
Table 4.14: Parameters calculated for the ERN with a series of PFR to model bubble
growth. .................................................................................................................... 125
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Sommario Con il crescente interesse verso la gassificazione e la combustione di
biomasse, predire gli inquinanti nei differenti processi è diventato
fondamentale. La sfida in questo viene dalla forte interazione tra
fluidodinamica e chimica in reattori a letto fluidizzato, principali reattori
usati per la valorizzazione delle biomasse. Meccanismi cinetici dettagliati
sono obbligatori per investigare la formazione di tar, PAH e soot, ma se
accoppiati ad una simulazione CFD presentano tempi computazionali
proibitivi. Questo problema può essere risolto usando modelli di rete di
reattori equivalente, dove il campo di moto è fissato usando reattori ideali
interconnessi in vari modi sui quali è possibile applicare meccanismi
cinetici dettagliati.
Lavori precedenti sui modelli a rete di reattori riguardavano
principalmente sistemi omogenei gassosi. Quando più fasi erano coinvolte,
si tendeva a segregare il sistema e studiare i fenomeni disaccoppiati. Il
problema della segregazione è che si perdono informazioni sulle
interrelazioni tra i processi, nonché i problemi numerici dovuti ad una
lenta convergenza e alta probabilità di divergere nonostante il minor costo
computazionale dei metodi segregati.
Questo lavoro ambisce a gettare le fondamenta per modelli a rete di
reattori eterogenei “fully-coupled”. Un approccio di questo tipo consente di
includere meglio la competizione e le relazioni tra i vari processi. A questo
scopo, è stato sviluppato un tool numerico chiamato NetSMOKE, un
framework modulare che estende le librerie OpenSMOKE++ con la capacità
di trattare reti di reattori. La formulazione matematica del problema e la
struttura del software sono discusse. NetSMOKE è stato poi validato con
casi test e applicato nella riproduzione di dati sperimentali da reattori a
letto fluido pilota. I risultati ottenuti sono stati positivi.
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Abstract With the rise of biomass gasification and combustion, predicting pollutants
in the different processes became fundamental. The challenges in this
derive from the strong relationships between fluid dynamics and
chemistry in fluidized beds, which are the main type of reactors used for
biomass valorization. Detailed kinetic mechanisms are mandatory to
investigate tar, PAH and soot formation, but their application in CFD
simulations presents prohibitive computational times. This issue can be
solved using equivalent reactor network (ERN) models, which allow to fix
the motion field using ideal reactors interconnected in various ways where
detailed kinetics can be applied.
Past work on these models involves gas-phase homogeneous cases. When
more than one phase is involved, segregation has been applied to study all
the phenomena in a disjointed fashion. The issue here is that pursuing
mathematical segregation reduces the capability to study interrelations
between processes and, from a purely numerical standpoint, while it
involves a lower computational cost in terms of raw CPU power
convergence is slow and solution can easily diverge.
This thesis aims to set the foundation for fully-coupled heterogeneous
reactor network models. A fully-coupled approach can encompass better
how each phenomenon influences the others. To this end, a numerical tool
was developed called NetSMOKE. It is a modular framework that extends
the OpenSMOKE++ libraries with capabilities to interpret and solve reactor
networks. Mathematical formulation of the problem is discussed. This tool
was then validated with test cases, and then used to reproduce
experimental data of pilot-scale fluidized beds reactors. Good agreement
was found.
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Chapter 1
1. Introduction
Since the 1970s oil crisis there has been great effort to find viable
alternatives to fossil fuels. But that was due to economic reasons.
Nowadays, it is a matter of the survival of humanity.
World population is ever-growing. In 2016, the annual growth has been of
1.09% (which is a slower growth than 2015 and 2014 where it was of 1.14
and 1.12% respectively) which translates in roughly 80 million more
people, considering that currently on planet earth there are about 7.6
billion individuals [1]. Just two hundred years ago, world population was
estimated to be 1 billion.
This swelling in population was facilitated by the industrial revolution,
during which technologies to rapidly extract, transport, and convert energy
from fossil fuels were invented and widely adopted. In the same timespan
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Chapter 1 ♦ Introduction
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that took humanity to grow this large, emissions from fuel combustion have
raised from nearly zero to 8.76 GTc/year, IEA1 reports [2].
More than 60% of anthropogenic greenhouse gas emission is due to
combustions, and it is widely known that its accumulation in the
atmosphere is extremely likely to cause and have caused global average
surface temperature to rise.
Our dependence on fossil energy is simply unsustainable, and if business
as usual continues, the risks of catastrophic climate change will be even
worse than previously believed.
In this scenario, renewable biofuels play a critical role. They remain the
only sustainable energy source which can integrate into existing road and
aviation transport fuel infrastructure, and are indeed the most consumed
renewable energy source in the US (Figure 1.1).
Figure 1.1: Data from EIA on energy consumption from different renewable sources in the US, up to the last months of 2017. It is possible to see how biomasses grew in recent years.
1 International Energy Agency, founded in 1974 by the Organisation for Economic Co-operation and
Development (OECD), following the oil crisis.
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Chapter 1 ♦ Introduction
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In fact, IEA forecasts utilization of biofuels to quadruple by 2035 and to
supply a significant fraction (about 8%) of the global road transport fuel
demand. The issue with biofuels right now is that the current global
production of oil-equivalent products is almost entirely ethanol derived
from sugarcane or corn [3]: this raised issues on food competition and land
usage. As such, a technical limitation adds up, since most of the current
vehicle fleet and fuel distribution networks are unable to operate with
ethanol/gasoline fuel blends greater than 10% in ethanol volume.
All of this heads to a international regulatory direction towards the
development of second-generation biofuels. The 2009 Renewable Energy
Directive, issued by the EU commissions, requires biofuels to constitute
10% of transportation fuels by 2020. Recent proposals followed this
directive to focus on limiting the extent to which food-derived biofuels
count towards this mandate [4], [5].
Second generation biofuels differ from their predecessors because they
utilize lignocellulosic biomass – the most abundant and affordable form of
biomass – and include a variety of inedible feedstocks like
agricultural/forest residues, organic wastes and dedicated energy crops
(like willow or switchgrass). This reduces production costs and prevent
feedstock conflict between energy and food industries.
To understand how lignocellulosic biomasses can be converted into various
form of fuels or energy, an overview on the state of art biomass kinetic
modeling is following.
1.1 Lignocellulosic biomasses Kinetic modeling of lignocellulosic biomasses is out of the scope of this
thesis. Yet, as biomass application are a clear target of this work, as well as
providing the case study for this thesis, it is worth to spend some time
reviewing the available knowledge on biomass thermochemical
conversion modeling.
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Chapter 1 ♦ Introduction
▪24▪
Figure 1.2: Different perspectives of biomass modeling.
Studying and understanding biomass kinetics is not an easy task. There are
several processes involved (Figure 1.2), happening in a multitude of scales,
and various possible thermochemical conversion pathways varying with
conditions. Being the matter solid, not only one has to face the pure
chemical part of biomass conversion, but even the fluid dynamics
consisting of mixing, intra and interparticle diffusion, thermal conductivity
and so on.
1.1.1 Conversion routes for lignocellulosic biomass to liquid fuels
Four main pathways for lignocellulose conversion to liquid fuels are shown
in Figure 1.3 and begin with either thermochemical or hydrolysis steps.
We can distinguish three different temperature levels, each favouring
specific conversion routes, plus an extra biotech processes category.
I. High temperature: When the temperature is higher than 700
°C, biomass is gasified into syngas from which it can be
catalytically synthesized into alkanes or alkenes via Fisher-
Tropsch.
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Chapter 1 ♦ Introduction
▪25▪
II. Medium temperature: Around 500 °C, biomass is pyrolyzed
into bio-oil, which is a corrosive, unstable mixture of water, tars,
aromatics and char [1]. These bio-oils can be gasified to syngas or
upgraded to vehicle-ready fuels by deoxygenation under
hydrogen or other catalytic routes.
III. Mild temperatures: at under 200°C, acid hydrolysis occurs.
Dilute mineral acids at these conditions decompose the
lignocellulose to intermediate aqueous products which are then
separated and upgraded to value-added fuels and chemicals
through catalytic processes [6].
IV. Enzymatic promotion: Enzymatic hydrolysis of lignocellulosic
biomass results in sugar monomers and lignin. Sugar monomers
like glucose can be fermented by customized bacteria to produce
ethanol, while the by-product of lignin can be burned [7].
Figure 1.3. Biomass conversion pathways
Currently all four pathways constitute active fields of research. Biomass
gasification is currently being widely tested for commercial applications
due to its ability to utilize existing technologies (as coal gasifiers can be
revamped for a biomass application) and offering drop-in compatible fuels
for the current transportation infrastructure. Moreover, synthetic fuels are
SYNGASGASIFICATION
BIO-OILS
AQUEOUS PRODUCTS
SUGAR MONOMERS
PYROLISIS
ACID HYDROLYSIS
ENZYMATIC HYDROLYSIS
FERMENTATION
CATALYST
CATALYST
FISCHERTROPSCH
CELLULOSICBIOMASS
ALKANES &METHANOL
LIQUIDFUELS
FUELS &CHEMICALS
ETHANOL
TE
MP
ER
AT
UR
E
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Chapter 1 ♦ Introduction
▪26▪
attractive because they demonstrate superior characteristics compared to
conventional diesels including a high cetane number (>70), low sulphur
and aromatics content [8], thus anything that aims to get these as products
is deemed interesting. The additional appeal of using biomasses is due to a
substantial carbon dioxide emission reduction from transportation
vehicles traffic, compared to traditional fossil fuels, thanks to biomass
being a “CO2-Neutral” feedstock.
1.1.2 Thermochemical mechanism of biomass conversion
Thermochemical conversion of biomass (and solid fuels in general) is a
continuous process defined by the interplay of a number of complex
processes (heat and mass transfer, primary and secondary pyrolysis,
heterogeneous chemistry and oxidation chemistry of gas products) which
cannot be thought as discrete or subsequent.
Nevertheless, it is possible to distinguish four processes during the
thermochemical conversion [9]:
I. Drying: Characterized by processes occurring at temperatures
around 100 °C in which moisture is liberated by evaporation.
II. Devolatilization: Chemical conversion from raw biomass to
gases and char. This happens between 200 °C and 600 °C, and
product distribution is a function of temperature and biomass
composition.
III. Secondary pyrolysis: The intermediate devolatilization
products undergo further pyrolytic reactions, heterogeneous
reaction with the char, tar cracking and oxidation and PAH
growth reactions in the gas phase.
IV. Char consumption: After the devolatilization process is
complete, solid residue undergoes slower oxidation reaction. In
the case of fluidized bed reactors, it is possible to have loss from
elutriation or due to ashes disposal.
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Chapter 1 ♦ Introduction
▪27▪
All of these processes, resumed in Figure 1.4, occur simultaneously under
fluidized bed gasification conditions, which, as aforementioned, is the most
attractive application for commercial use at the moment.
Many works in the field of chemical conversion of biomass have been
aimed at understanding the reaction pathways of pure components of
biomass (lignin, hemicellulose and cellulose), while limited rigorous
particle models have been worked on, which capture anisotropic
characteristics of biomass considering fluid dynamics and competing
reaction pathways. These computational-cost demanding models come do
not allow for sufficiently extensive chemistry model to represent variations
in tar components.
Most recent publications have been focused on improvement of sub-
models and secondary gas phase mechanism.
Figure 1.4. Thermochemical conversion of biomass in a gasifier. Adapted from Stark et al. [9]
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Chapter 1 ♦ Introduction
▪28▪
Debiagi et al. [10], [11] have been working heavily on biomass mechanisms.
The latest mechanism includes 32 reactions and 28 species which, coupled
with the detailed gas kinetic mechanism of more than 20000 reactions and
500 species, makes up for the full mechanism.
1.2 Biomass gasification During the gasification pathway, elevated operating temperatures (800-
1400 °C) combined with an oxidant (usually steam or oxygen)
thermochemically convert carbonaceous feedstocks into a mixture of
carbon monoxide and hydrogen known as syngas, which can be
subsequently be converted into various products (Figure 1.5).
Ideally, syngas mixture produced should be close to a 2:1 ratio in H2/CO
terms, making it suitable for synthetic fuels production through F-T
process.
Figure 1.5. Overall process scheme for gasification to drop-in liquid fuels
Depending on the feedstock used, the process is referred as coal-to-liquids
(CTL), biomass-to-liquids (BTL), or gas-to-liquids (GTL). Generally, the
process that stems from carbonaceous feedstocks to provide liquid fuels
can be called XTL. Compared to reforming solutions adopted for natural
gas, where the biggest engineering challenge is about controlling the
reactions that leads from methane to the desired products (which can be
Biomass& Waste
Coal
Natural Gas
SyngasCO + H2
GasificationReforming
T = 800 – °C
FTSynthesis
T = 200 – °C
AlkenesCnH2n
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Chapter 1 ♦ Introduction
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achieved in various ways, even autothermic processes), coal and biomass
gasification introduce the complication of solid materials and diffusional
resistances in the system.
There are different gasifier configurations for solid particles: fixed bed,
fluidized bed and entrained flow.
Fixed bed designs are unattractive for CTL/BTL applications due to their
scale limitations. Entrained flow gasifiers have scale advantages, but raw
biomass is not an optimal feedstock: it would require pre-treatment
processes like torrefaction and fine grinding.
Fluidized bed gasifiers overcome both the scaling issues associated with
fixed beds and the dry fine particle requirement of entrained flow
configurations.
1.2.1 Fluidized bed gasifier
The base concept of this configuration is to take advantage of fluidization.
By using the oxidizer gas in combination with other inerts like nitrogen,
and pellets of inert substances like silica or alumina, the solid bed can be
fluidized in the sense of making it behave like a fluid in motion: smooth
fluidization utilises chaotic mixing in the bed (induced by the fluidization
agent) to ensure excellent gas-solid heat and mass transfer characteristics.
Figure 1.6. a) Bubbling bed reactor, b) Circulating bed reactor
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Chapter 1 ♦ Introduction
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Two possible configurations are used: bubbling beds, where lower gas
velocities limit the bed expansion in the lower portion of the reactor, and
circulating beds, where gas velocity is higher for uniform expansion and
solid residues are circulated back into the bed after separation from gas
products using a cyclone. These two configurations are shown in Figure 1.6.
Currently, fluidized bed are the ones of most interest. An in-depth
comparison with the alternatives is carried on in the next chapter.
1.2.2 Challenges in predictive modeling
A fluidized bed gasifier is a system largely affected by fluid dynamics: it is
a chaotic system composed of emulsion (pseudo-monophasic system),
bubbles and discrete particles. According to the velocities involved, the
dimension of the pellets and other conditions, there can be different flow
regimes that affect thermochemical conversion due to different heat and
mass transfer involved.
Due to the duality in dependence from both chemical kinetics and fluid
dynamics, studying fluidized bed gasifier can be challenging: CFD
simulations are already computationally intensive, making a coupled
system of CFD and detailed kinetic mechanisms prohibitive due to the
computational times required.
Nevertheless, large detailed kinetic schemes are a necessity when pollutant
formation has to be investigated, as the reactions that lead to PAH, tars and
soot formation are in extreme non-equilibrium conditions and can include
thousands of pathways and species.
Since biomass is now looked at as the most possible alternative to fossil
fuels for the foreseeable future, its application are going to rise in number
and scale, making pollutant predictions mandatory.
Recently, renovated interested has been given to equivalent reactor
network approaches to tackle this issue.
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Chapter 1 ♦ Introduction
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1.3 Aim and structure of this thesis As interests towards biomass continuously grows, a numerical tool to allow
researcher to work easily is needed. Equivalent reactor network models
are an appropriate approach to these issues: besides allowing to use
detailed kinetic mechanisms, they can also help in the jump from lab-scale
reactors to real-scale reactors.
The problem with most reactor network models and associated tools is that
they tend to either work only on homogeneous cases or utilize segregation
when used for heterogeneous systems. The problem with segregation of
the processes involved is that, while computationally convenient in terms
of raw power, convergence is slow and often never reached as methods like
sequential substitution easily diverge.
As such, the aim of this thesis work was to develop a tool to solve
heterogeneous reactor network models in a fully-coupled fashion.
This thesis is structured as following:
I. Chapter 1: Introduction, with historical background and
presentation of both the problem dealt with and the aim of the
work.
II. Chapter 2: State of art of the issue investigated. Here, biomass
kinetics, fluidized bed gasifiers and different modeling
approaches are discussed.
III. Chapter 3: The numerical tool developed, NetSMOKE, is
presented in his mathematical formulation, its features and its
structure. The program was validated with the methods
illustrated.
IV. Chapter 4: Various experimental fluidized bed gasifiers data
were reproduced using NetSMOKE. The results obtained are
presented and discussed, as well as the approach taken to setup
the simulations.
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Chapter 1 ♦ Introduction
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V. Chapter 5: Summarizes the work done with a recap of the
obtained information to give an outlook of potential future work.
Finally, this thesis is correlated by three appendixes, referenced
throughout the manuscript, that contain mathematical sub-models
developed and correlations adopted.
In Appendix A, a simplified model for an heterogeneous perfectly stirred
reactor is developed and presented.
Appendix B collects the fluidization correlations used for this work, with
the reference to the publications where they were presented.
Appendix C illustrates a in-development model for fluidized bubbling bed
reactors.
-
▪33▪
Chapter 2
2. State of the Art
In the introduction chapter a quick overview of all the topics part of this
thesis was given. Before undergoing the central part of this work, it is
appropriate to review the “state of the art” – the currently best practices –
of all the topics touched or studied.
This allows to get a solid foundation on the problem and how it is currently
tackled. It also enables the reader to fully understand how and why
something was implemented in the code or done in a simulation.
Thus, three main themes will be discussed: the current state of chemical
kinetic modeling for biomasses (with a rapid look at gas phase mechanisms
as well), different models at various level of detail and scale for gasification
of solids, and the CRECK Modeling in-house KppSMOKE, the current
numerical tool to perform automatic ERN-based kinetic post-processing.
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Chapter 2 ♦ State of the Art
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2.1 Biomass kinetics As previously mentioned in the first introduction chapter, mathematical
modeling of thermal degradation of biomass is a challenging problem
because its complexity occurs at several levels:
I. Multicomponent problem: Biomass is a complex feed of many
different substances, with high variability based on its origin, and
it requires a proper characterization.
II. Multiphase problem: Biomass reacts in a condensed phase
forming a solid, a liquid and a gas phase. Biochar is later involved
in heterogeneous gas-solid reactions, while in the gas phase
released gases and bio-oil go through an homogeneous
mechanism.
III. Multiscale problem: Coupling of kinetic and transport processes
is needed both at a particle and at a reactor scale.
Figure 2.1: Multiscale problem. Adapted from Ranzi et al. [12]
Multiscale nature of the problem, as seen in Figure 2.1, is evident both at a
dimension and time scale: we can compare the angstroms of the molecular
scale to reactor dimensions in meters, as well as fast propagating radicals
with a short lifespan and the several minutes required to heat and
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Chapter 2 ♦ State of the Art
▪35▪
devolatilize thick biomass particles or char gasification which occurs much
slower than the release of volatiles.
2.1.1 Biomass composition
Defining the chemical composition of complex biomass materials is a first
key and necessary capability for modeling of thermochemical processes of
biomass conversion to fuels and chemicals. A database of more than 600
samples is available for research purposes [11].
Compared to coal, biomass has a lower density and a lower heating value
(see Figure 2.2). This is because of the oxygen content, which is around 35-
40% on dry basis weight.
Figure 2.2: Comparison of atomic ratios of different classes of biomasses and coals. The heating value increases with increasing H:C ratio and decreasing O:C. Adapted from
Jenkins et al. [13]
Cellulose, hemicellulose and lignin are the major building blocks of woody
biomasses, with some extractives (respectively, 30-55, 13-35, 14-36 and
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Chapter 2 ♦ State of the Art
▪36▪
the overall set of samples. Proximate, ultimate and structural analyses are
used to obtain information on biomass composition: this is not a trivial task
since biomass samples are heterogeneous and demand reliable
preparation methods. Institutions such as the National Renewable Energy
Laboratory (NREL) and the American Society for Testing and Materials
(ASTM) are seeking solutions to standardize preparation procedures and
analysis methodology [14], [15].
Molecular structure
Lignocellulosic biomass materials are mainly constituted by a combination
of polysaccharides, which can be grouped into holocellulose (cellulose and
hemicellulose) and lignin species. Other components such as moisture,
minerals, extractives and acetyl groups are also present.
Structurally, it is a porous structure where cellulose microfibril represents
the important element surrounded by hemicellulose and pectin, which act
as ligand and embed lignin materials.
Cellulose
It is the most abundant structural polysaccharide in cell walls and accounts
for 15-50% of the dry weight of plant biomass.
Figure 2.3: Cellulose unit.
It is composed of β-D-glucopyranose units (Figure 2.3) linked by β-1,4
glycosidic bonds, which can be summarized as (C6H10O5)x, so that mass
elemental composition is C = 44.4%, H = 6.2%, and O = 49.4%. An example
polymeric structure is reported in Figure 2.4.
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Chapter 2 ♦ State of the Art
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Figure 2.4: Cellulose polymeric structure.
The degree of polymerization (DP) is of approximately 10-15 thousand
chair configuration units: DP is a structural property with high impact on
mechanical properties, solubility and hydrolysis of the biomass. Due to
several strong H-bonds, it has resilience towards hydrolysis and enzymatic
activity.
Hemicellulose
Accounts for 25-50% of the dry weight. It is a heterogeneous polysaccharide
made of hexose and pentose monosaccharide units and classified in three
major classes (xylans, mannans and xyloglucans) according to the
branching and richness in the particular monomers shown in Figure 2.5.
Figure 2.5: Monomers of hemicellulosic polysaccharides.
OH
O
O H
OHO H
OH
OH
O
O H
OHO H
OH
O H
O H
O HOH
O
O
OH
O H
OH
O H
Mannose Galactose Xylose Arabinose
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Chapter 2 ♦ State of the Art
▪38▪
Polymerization degree is relatively small at 70-200, with larger molecules
found in hardwoods and smaller ones found in softwoods.
Lignin
Lignins are aromatic racemic polymers resulting from oxidative coupling
of 4-hydroxy-phenyl-propanoid units and, among the fundamentals
components, they are the ones most influenced by the nature of the
biomass. This component of biomasses can provide a high heating value
due to the high H/C ratio. They contribute to rigidity of walls of secondarily
thickened cells.
Figure 2.6: Hydroxycinnamyl alcohol polymers which lignins are composed of.
Lignin polymers are derived from the three hydroxycinnamyl alcohol
monomers in Figure 2.6 (p-Coumaryl, coniferyl and sinapyl alcohols) that
differ in their methoxylation degree and that produce the main monomers
of lignin.
Other components
Biomasses are usually characterized by varying degree of extractives,
moisture (water) and the ash content: ashes are mainly inorganic
compounds (like metals) residual after complete combustion.
Characterization procedure
Proximate analysis is performed as a thermogravimetric analysis (TGA) in
accordance with the ASTM procedure. It allows the determination of
moisture, volatile matter, fixed carbon, and ash.
O H
OH
O H
OH
OCH3
OC H3
O H
OH
OCH3
p-Coumaryl Alcohol Sinapyl Alcohol Coniferyl Alcohol
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Chapter 2 ♦ State of the Art
▪39▪
Ultimate or elemental analysis decomposes the sample via high-
temperature oxidation to determine the composition of the main atoms C,
H, N and S through measurements of the corresponding oxidized gases.
Oxygen is calculated by difference. Mass spectrometry allows to determine
the elemental composition from electron ionization [16].
Structural or biochemical analysis measures the main components of
cellulose, hemicellulose, lignin and sometimes extractives and proteins.
This analysis is very valuable if the interest is to analyze the successive
biomass decomposition. The problem is that current methods (wet
chemicals) are time-consuming, labor-intensive and can induce some
degree of decomposition rendering the results inaccurate. Promising
progress is shown by Fourier transform infrared spectroscopy, but in
general these detailed analyses are not available [15].
Figure 2.7: Diagram used for biomass characterization.
When direct information from a structural analysis is not available, this
composition can be derived from elemental analysis: the H/C/O atomic
balances allow to evaluate a suitable combination of a few reference
species in cellulose, hemicellulose, lignin and extractives as these constitute
the largest portion. In the CRECK group approach [17] lignin is accounted
as three species according to richness in oxygen, hydrogen or carbon.
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Chapter 2 ♦ State of the Art
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Figure 2.8: Reference species used in the CRECK mechanism.
Extractives are included via two lumped species for hydrophobic (TGL) and
hydrophilic (TANN) extractives. This adds up to a total of seven reference
species, all reported in , from which each biomass can be represented.
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Chapter 2 ♦ State of the Art
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Via a the van Krevelen [18] diagram reported in Figure 2.7, which relates
atomic H/C and O/C ratios, it is possible to produce a composition in terms
of reference species as a linear combination respecting the H/C/O balances.
To reduce the number of degrees of freedom, three reference mixtures are
defined as combinations of the seven reference species. RM-1 represents
holocellulose, while RM-2 and 3 are mixtures of lignins with some content
of extractives. Their compositions are defined from experimental findings
and can be easily modified.
From the input H/C/O composition, the linear system of balance equations
gives the mass composition in terms of the three reference mixtures. From
these values and the respective internal composition of each RM, the
composition in terms of the seven reference species is calculated.
Linear combinations of these reference species are able to describe most of
the biomasses contained in the databases [19]–[22].
2.1.2 Biomass devolatilization and pyrolysis kinetic scheme
Table 2.1 reports the most recent biomass kinetic scheme proposed by the
CRECK group. It comprises 29 reactions and 28 species. For secondary
reactions, the detailed gas phase mechanism for hydrocarbons and
oxygenated fuels with more than 500 species and 20000 reactions is
coupled. According to the predictive focus of the work in analysis, the
group is capable of providing skeletal and reduced kinetic mechanisms for
specific fuel cases, to ease the computational task with no significant loss
of quality in the prediction [23].
Kinetics and molecular scale modeling evolution
In 2008 Ranzi et al. [24] proposed the first iteration of the pyrolysis model,
which has been expanded in Debiagi et al. [11] with the addition of
extractive species. The most recent version of the CRECK biomass scheme
was presented in 2017 in Ranzi et al. [17].
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Chapter 2 ♦ State of the Art
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Particle scale modeling
A detailed moving-mesh model for particle scale CFD-applications, embed
in OpenFOAM® and called BioSMOKE, was developed by Gentile et al. [25].
Table 2.1: CRECK Biomass kinetic scheme
Pyrolysis Reactions Kinetic Parameters A (s−1), Eact
Cellulose
1 CELL → CELLA 1.5 × 1014 × exp(−47000/RT)
2 CELLA →
0.4 HAA + 0.05 GLYOX + 0.15 CH3CHO + 0.25 HMFU + 0.35
ALD3 + 0.15 CH3OH + 0.3 CH2O + 0.61 CO + 0.36 CO2 + 0.05 H2
+ 0.93 H2O + 0.02 HCOOH + 0.05 C3H6O2 + 0.05 G{CH4}+
2.5 × 106× exp(−19100/RT)
3 CELLA → LVG 3.3 × T × exp(−10000/RT)
4 CELL → 5 H2O + 6 CHAR 6 × 107 × exp(−31000/RT)
Hemicellulose
5 GMSW → 0.70 HCE1 + 0.30 HCE2 1 × 1010 × exp(−31000/RT)
6 XYHW → 0.35 HCE1 + 0.65 HCE2 1 × 1010 × exp(−28500/RT)
7 HCE1 → 0.6 XYLAN + 0.2 C3H6O2 + 0.12 GLYOX + 0.2 FURF + 0.4 H2O +
0.08 G{H2} + 0.16 CO
3 × T × exp(−11000/RT)
8 HCE1 →
0.4 H2O + 0.79 CO2 + 0.05 HCOOH + 0.69 CO + 0.01 G{CO} +
0.01 G{CO2} + 0.35 G{H2} + 0.3 CH2O + 0.9 G{COH2} + 0.625
G{CH4} + 0.375 G{C2H4} + 0.875 CHAR
1.8 × 10−3 × T × exp(−3000/RT)
9 HCE2 → 0.2 H2O + 0.275 CO + 0.275 CO2 + 0.4 CH2O + 0.1 C2H5OH +
0.05 HAA + 0.35ACAC + 0.025 HCOOH + 0.25 G{CH4} + 0.3
G{CH3OH} + 0.225 G{C2H4} + 0.4 G{CO2} + 0.725 G{COH2}+
5 × 109 × exp(−31500/RT)
Lignins
10 LIGC → 0.35 LIGCC + 0.1 COUMARYL + 0.08 PHENOL + 0.41 C2H4 + 1.0
H2O + 0.7 G{COH2} + 0.3 CH2O + 0.32 CO + 0.495 G{CH4}+
1 × 1011 × exp(−37200/RT)
11 LIGH → LIGOH + 0.5 ALD3 + 0.5 C2H4 + 0.2 HAA + 0.1 CO + 0.1 G{H2} 6.7 × 1012 × exp(−37500/RT)
12 LIGO → LIGOH + CO2 3.3 × 108 × exp(−25500/RT)
13 LIGCC → 0.3 COUMARYL + 0.2 PHENOL + 0.35 HAA + 0.7 H2O + 0.65 CH4 +
0.6 C2H4 + H2 + 1.4 CO + 0.4 G{CO} + 6.75 CHAR
1 × 104 × exp(−24800/RT)
14 LIGOH → 0.9 LIG + H2O + 0.1 CH4 + 0.6 CH3OH + 0.05 G{H2} + 0.3
G{CH3OH} + 0.05 CO2 + 0.65 CO + 0.6 G{CO} + 0.05 HCOOH +
0.85 G{COH2} + 0.35 G{CH4} + 0.2 G{C2H4} + 4.25 CHAR+
1 × 108 × exp(−30000/RT)
15 LIG → 0.7 FE2MACR + 0.3 ANISOLE + 0.3 CO + 0.3 G{CO} + 0.3
CH3CHO 4 × T × exp(−12000/RT)
16 LIG → 0.6 H2O + 0.4 CO + 0.2 CH4 + 0.4 CH2O + 0.2 G{CO} + 0.4 G{CH4} +
0.5 G{C2H4} + 0.4 G{CH3OH} + 2 G{COH2} + 6 CHAR
8.3 × 10−2 × T × exp(−8000/RT)
17 LIG → 0.6 H2O + 2.6 CO + 1.1 CH4 + 0.4 CH2O + C2H4 + 0.4 CH3OH+ 1 × 107 × exp(−24300/RT
Extractives
18 TGL → ACROL + 3 FFA 7 × 1012 × exp(−45700/RT)
19 TANN → 0.85 FENOL + 0.15 G{PHENOL} + G{CO} + H2O + ITANN 2 × 101 × exp(−10000/RT)
20 ITANN → 5 CHAR + 2 CO + H2O + G{COH2} 1 × 103 × exp(−25000/RT)
Metaplastic
21 G{CO2} → CO2 1 × 106 × exp(−24000/RT)
22 G{CO} → CO 5 × 1012 × exp(−50000/RT)
23 G{COH2} → CO + H2 1.5 × 1012 × exp(−71000/RT)
24 G{H2} → H2 5 × 1011 × exp(−75000/RT)
25 G{CH4} → CH4 5 × 1012 × exp(−71500/RT)
26 G{CH3OH} → CH3OH 2 × 1012 × exp(−50000/RT)
27 G{C2H4} → C2H4 5 × 1012 × exp(−71500/RT)
28 G{PHENOL} → PHENOL 1.5 × 1012 × exp(−71000/RT)
H2O Evap.
29 ACQUA → H2O 1 × T × exp(−8000/RT
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Chapter 2 ♦ State of the Art
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2.2 Gasification units Following, an overview of the main type of gasifiers and approaches to coal
and biomass gasification is presented. While it might stem from the focus
of this work, it is important to have a clear idea of the alternatives, since it
allows to understand why fluidized bed applications are currently the most
attractive. Later on, this will be useful to understand how NetSMOKE can
be used to represent particular flow conditions of each of these gasification
approaches.
2.2.1 Types of gasifier
As mentioned previously, there are three types of gasifier: fixed or moving
bed, fluidized bed and entrained flow and they are quickly reported in
Figure 2.9.
Figure 2.9: Types of gasifier
Among these designs there are variations such as spouted bed, draught
tube, internally circulating fluidized beds, etc.
Fixed and moving bed designs
There are two possible flow configurations for fixed bed gasifiers: updraft
(counter-current) or downdraft (co-current). In updraft configuration the
gas flows from bottom to top. In this situation the gas leaves the near the
pyrolysis zone thus having a high content of tars, as it picks up moisture
and organic compounds from the cooler zones of the bed (see Figure 2.10).
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Chapter 2 ♦ State of the Art
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The solid fuel is almost completely converted into gas and tars. Advantages
are relative insensitivity to pellet size and they can use wet fuels.
Figure 2.10: Updraft gasifier. The bed temperature profile is valid for a downdraft gasifier as well.
With a downdraft the gas enters top and exit from bottom and thus leaves
from the hottest zone, granting lower organic compounds concentration
but some particulate.
If the bed is moving, these configurations are still valid granted that fuel
bed moves from the top of the reactor to the bottom.
Fixed and moving bed applications are limited to few MW2 fuel power due
to difficulties in maintaining a regular conversion front – radially wise –
when the reactors is wide.
Entrained flow gasifiers
These systems are attractive for large-scale implementations (>400 MW):
fine coal or biomass are co-fed with the oxidizing agent to the main
chamber, and entrained ash can be removed with a liquid system. Thanks
to extremely turbulent flow and high temperatures, the produced syngas
or tail gas is clean and free of tars.
Given the high temperatures, coal and biomass ashes are melted into an
inert slag. This slag can be processed and utilize in cements blend to
valorize it as a by-product. With coal, this can be prohibitive since coal ash
2 Megawatts
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Chapter 2 ♦ State of the Art
▪45▪
has a high melting point, thus an higher amount of oxygen is needed for
slug operation. In the case of biomasses, ashes melt easily, and the oxygen
demand is kept low. However, molten slag from biomasses is corrosive and
aggressive to the reactor lining.
The other main drawback is the difficulty in pre-treating biomass for
drying and particle size reduction by grinding, which is expensive
compared to coal.
These reasons, together with the fact that currently the quantity of biomass
that can be delivered to a plant is limited, prevented introduction of
entrained-flow gasification in biomass applications. In the short terms, all
points out to fluidized bed being the status quo.
Fluidized bed gasifier
Fluidized bed gasifiers have a number of advantages over fixed beds,
especially with regards to mixing, reaction rates, and scalability. It is
possible to distinguish two different types of fluidized bed gasifiers:
bubbling (FBBG3) and circulating (FCBG4). Both are represented in Figure
2.11.
An FBBG operates at low gas superficial velocities, in the range of 0.5 to 2
m/s, in a way that most of the fuel remains in the lower part of the reactor,
and fluidization makes it bubble much like in a cauldron or pot. We have
then a bottom bed and the rest of the structure acts as a freeboard for the
gas.
FCBGs work with velocities from 2 to 5 m/s. Thus, bed expansion takes place
in the whole reactor with partial entraining: tail gas passes through a
cyclone or other solid-gas separation device to recover and recycle the fuel.
This favors solid contact time.
3 Fluidized bubbling bed reactor. 4 Fluidized circulating bed reactor.
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Chapter 2 ♦ State of the Art
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Figure 2.11: a) FBBG b) FCBG
2.2.2 Energy and cost management
Depending on the way the heat necessary for the process is provided to the
gasifier, it is possible to distinguish two approaches: autothermal and
allothermal gasification (Figure 2.12).
Direct gasification
In autothermal (or direct) gasification, the heat is released during the
partial oxidation of the fuel in the gasifier itself. According to the use of air
or oxygen (thus acting on the equivalence ratio), it is possible to obtain
different heating value gases.
Indirect gasification
Allothermal or indirect gasification uses steam as the fluidizing agent while
heat is provided not by oxidation. This yields medium heating value gases
at the outlet and allows to save on the oxygen. Indirect gasification can be
further investigated according to the heat sources being external or
internal.
An example of an external heat source can be solar gasification or heat
exchangers. An indirect internal gasifier can work with the internal gas or
internal char: in the char-indirect configuration, a secondary fluidized bed
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Chapter 2 ♦ State of the Art
▪47▪
combustor is coupled to the main FB to burn the residual char. Heat is
exchanged by circulation of the bed between the two reactors.
The alternative is to recycle hot gases leaving the reactor. This usually is
not enough for realizing an autothermal process, but it is often used to
maintain bed temperature when the pellets are rich in moisture.
Figure 2.12: a) Three ways for indirect gasification, by external heat or internal heat (gas and char). b) Indirect internal gasifier with char burning.
Commercially, internal indirect gasifiers are more interesting and
currently offered. External indirect gasifiers have not been successful due
to the costs involved and technical problems related to heat transfer tubes
erosion.
2.3 FBBG Modeling There is a great amount of published works and reviews on modeling of
biomass and coal conversion: most of the available literature focuses on
the particle scale. Only a fraction of the reviews have been devoted to
reactor scale modeling of fluidized bed gasification, and mostly of coal.
Despite the different chemical and physical properties, no conceptual
differences subsist between biomasses and coal with respect to the model
structures and mathematical description of the process. This allows to
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Chapter 2 ♦ State of the Art
▪48▪
conserve most of the existing modeling elements for fluidized bed coal
combustors, but caution is necessary as there are differences such as in the
mode of conversion of char particles and in the amount of heat transferred
to surfaces. This means that once the model is structurally defined, there
are a number of processes and parameters that need to be adjusted
according to the fuel,
The choice of a model – and this is valid in a broader sense – depends on
the objectives and the experimental information available. Advanced
models are far more useful due to a wider information obtained from them
but are bound to reasonable and realistic input data. Simple models are
appropriate for many situations, i.e. preliminary predictions, as long as the
user is aware of their limitations.
2.3.1 Conversion process
As aforementioned, a biomass particle undergoes a series of processes:
initial drying and devolatilization, oxidation of volatiles and char and char
gasification by carbon dioxide and steam. The particles are affected by
shrinkage, porosity increase and primary fragmentation (caused by
thermal and internal pressure stresses of volatile release). Secondary and
percolative fragmentation and attrition of char particles take place
together with char conversion. Fluidization establish a (ideally) pseud-
homogeneous medium where these processes take place.
It is possible to distinguish two main detail levels in the particle scale and
reactor scale (Figure 2.13).
At the particle scale, volatile release and surface mechanism of char
conversion happen. The fuel particle will lower in density and dimensions
due to devolatilization and surface reactions. Particles communicate with
the reactor by heat and mass transfer, which rates are determined by the
fuel reactivity but also by the fluid dynamics of the bed. Its description
should include particle size, density and thermal conductivity. Detailed
models tend to include elutriation into account by means of semi-empirical
rates.
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Chapter 2 ♦ State of the Art
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Figure 2.13: Processes occurring in a FBBG. Adapted from Stark et al. [9]
Reactor level focuses on the macro-scale of the bed, and such on the gas-
phase reactivity as well as the hydrodynamics. Thus, it is important to
consider a multi-phase region capable of representing both the emulsion
and the bubbles formed by fast gas entering the reactor. These bubbles can
grow along the height of the bed and exchange mass and heat with it and
influence mixing. Here, factors like residence times, fuel feed points and
rates, velocities must be considered. A meso-level, sort of a layer to link the
different scales of study through appropriate boundary conditions for heat,
mass and species transport, was proven important [26].
2.3.2 Classification of models
Fluidized bed gasifiers are a challenge, as they present a high degree of
complexity in both the chemistry and the fluid dynamics, which are strictly
interrelated. This poses difficulties, as, technically speaking, no model
-
Chapter 2 ♦ State of the Art
▪50▪
implemented is close to ideal: coupling both detailed CFDs and complex
kinetic schemes is prohibitive as of now. Simulations with simplified
kinetics can take weeks even with great resources at disposal.
Figure 2.14: Multiscale modeling paradigms. Adapted from Bates [12]
This naturally stems into multiple kinds of model available reported in
Figure 2.14. Roughly, they can be distinguished in three categories:
Computational fluid-dynamics models (CFDM), fluidization models (FM)
and black-box models (BBM) [27]. A summary of the following section can
be found in Table 2.2.
Black box models
These models do not resolve the processes occurring within the reactor and
apply generalized heat and mass balances around the reactor or to zones
within the reactor. They lack kinetics and make use of assumptions
regarding chemical equilibrium or pseudo-equilibrium, or empirical
relationships.
While their predictive capability is severely limited, they can be useful for
larger-scale studies at, for example, plant-scale or supply chain level, where
anything slightly detailed can become an issue [28].
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Chapter 2 ♦ State of the Art
▪51▪
Computational fluid dynamics models
CFDMs have the advantage to fully resolve hydrodynamics, but due to the
associated computational cost, they are currently limited in terms of
geometric sizes and associated kinetics. 2D and 3D reactive simulations are
limited to lab scale geometries and low times, except Eulerian-Eulerian
methods which can be applied to industrial scales as long as a reduced
kinetic scheme is used. Application of CFDMs to biomass gasification is by
any means promising.
Further classification can be applied in this field, distinguishing
beforehand two approaches to fluid dynamics: one can study motion by
tracking single particles (Lagrangian models) or by studying the phase as a
continuum with the Navier-Stoke equation (Eulerian models).
Lagrangian-Lagrangian models like the lattice-Boltzmann models (LBM)
treat both solid and gas phases as particles, and coupling is done via elastic
collisions. This is a direct numerical simulation approach (DNS) for
multiphase systems and has been proven useful for deriving drag laws. It
has the highest computational expense; thus, it is not suitable for reactive
simulations.
Lagrangian-Eulerian models (LEM) treat solids as particles and gas phase
as a continuum. LEMs can be further separated into discrete element
models (DEM) and discrete particle models (DPM), according to the
Eulerian grid size implemented. In DEM, the grid size is much larger than
the particles and requires empirical closures (drag laws) for gas-solids
momentum transfer. In DPM, the grid is smaller than the particles by an
order of magnitude, so the flow on the surface is directly computed. Here,
it is possible to apply simplified kinetics, but due to the scaling with the
number of particles (for a large scale bed it is around 1012 [29]) it is limited
to small reactors. LEM has been recently applied to biomass gasifiers in
Pepiot & Desjardins [30].
In Eulerian-Eulerian models (EEM), often referred to as two-fluid models,
all phases are described as inter-penetrating continuum. Closures for solid
-
Chapter 2 ♦ State of the Art
▪52▪
phase viscosity and normal stress are required and the kinetic theory of
granular flows (KTGF) is a popular method for this [31]. A recent
application of the Eulerian-Eulerian approach to fluidized beds is
presented in Bakshi et al.[32].
Discrete particle models (DPM) treat bubbles as lagrangian elements to
track velocity and size at each time step but applying incompressible quasi-
steady state assumption to the governing equations, greatly simplifying the
solution for both phase motions. This enables commercial scale beds to be
modelled, provided care is taken in accounting for wall effects and bubble
interactions and to the empirical relations used to describe bubble
phenomena, as the method is highly sensitive in this regard. Yang et al. [33]
recently used DPM to validate a proposed two-fluid models.
Fluidization models
Fluidization models (FM) directly assume that the bed is composed of
various phases (usually two) or regions with a predefined topology,
allowing transport of mass and heat between them. They do not solve
momentum equations but use semi-empirical correlations for fluid-
dynamic pattern and bubble dynamics.
It is worth noting that here the term phase discerns from the merely
thermodynamic definitions, meaning instead a region with a predefined
configuration (for particle mixing, flow pattern etc.).
Usually FM are 1D models, but 3D is achievable [34]. Early models treated
gas and solids as completely mixed, but over time the need of a multiphase
approach process was recognized. The two-phase theory of fluidization [35]
was the start for making this possible, as it allowed to distinguish between
bubble and emulsion phases. Now, with Davidson model [36] and empirical
correlations to estimate the division of gas between the phases, bubble
fraction and such, it is possible to obtain transport rates and degrees of
mixing. Over the years, different models showed up with different
simplifications or added detail, often taking the names of the authors or
their fundamental differences among the rest, but they are all based on the
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Chapter 2 ♦ State of the Art
▪53▪
same concept of estimating essential fluid dynamics information via
similar semi-empirical relationships.
In Table 2.2 the most important modeling approaches are summarized.
Reactor Network Models – Kinetic post processing
A sub-category of FM is reactor network models (RNM). They employ an
arrangement of chemical reactors in which the gas phase mixing behavior
is idealized, using 0D PSRs5 or 1D PFR6s. Ehrhardt et Al. [37] were the first
to propose a kinetic post-processing (KPP) technique to predict NOx
reburning in a pilot scale furnace.
Figure 2.15. Kinetic post-processing method
First, volumes and fluxes between the reactors (which reproduce the
hydrodynamic of the system) are calculated separately or pre-specified,
allowing for a reproducible approach with versatile input data (CFD
simulations for kinetic post processing (Ehrhardt et al. [37], empirical
correlations) and use cases. Secondly, by decoupling solution of motion
field and chemical kinetics, much more comprehensive and larger gas
5 Perfectly stirred reactor 6 Plug flow reactor
CFD Simulation
Simplified kinetic
mechanism
Steady state network solution
Detailed kinetic
mechanism
ERN Generation
TemperaturesFlow fields
Pollutants prediction
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Chapter 2 ♦ State of the Art
▪54▪
phase mechanisms can be tractably solved. This is schematically
summarized in Figure 2.15.
Being able to apply detailed kinetic mechanisms to complex fluid-dynamic
systems is fundamental for pollutant prediction. Reduced kinetic
mechanisms are often sufficient to predict the major species, but the
pathways for growth of tar, soot and PAH components are extremely
complex, with thousands of different reactions and a theoretical infinite
number of species, to the point that even for experimental measurements
there is the need to classify them into “bins”, or categories, based on factors
like molecular weight or number of carbon atoms in the molecule. They are
also present in traces, thus to be able to model them great level of detail is
needed. Recent application of reactor network models to biomass
combustors is found in Stark et al. [9], [38] and Bates et al. [39].
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Chapter 2 ♦ State of the Art
▪55▪
Table 2.2: Different modelling approaches for fluidized beds. Adapted from Gomez et al. [27]
NA
ME
&
AB
BR
EV
IAT
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C
ON
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ES
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DP
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lag
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Flu
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KL
M:
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Th
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ilib
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mu
lti
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ne
mo
de
l
-
▪57▪
Chapter 3
3. NetSMOKE
Up to now, almost all the attempts at fluidization modeling through reactor
network have been applied to heterogeneous gas phase cases. Only recent
works have been applied to heterogeneous combustions due to the rise in
interest towards biomasses.
In these cases, most tried to de-couple biomass devolatilization, gas phase
conversion and char gasification into three different processes. This
approach, while yielding solid results, is not computationally efficient due
to segregation, which is the term that describes the mathematical
discretization into different sequential process rather than a single
concurrent one. Sequential substitution, often utilized to solve the global
system made of discretized processes, while computationally less
expensive in terms of raw power compares to more refined solution
procedures, is usually slow to converge and, by lacking estimation
-
Chapter 3 ♦ NetSMOKE
▪58▪
correctors and gradient methods to decide a solving path, might not reach
convergence at all.
Approaches called fully-coupled are usually preferable due to a much
closer resemblance to reality and the ability to implement elaborate solving
methods. Computationally speaking, they are not as efficient in terms of
raw data processing due to the need to compute a numerical jacobian
matrix for the system. On the other hand, fully-coupled methods are much
more robust than sequential substitution and reach convergence faster.
Chemical engineering problems often present large systems of stiff
equations, thus the ability to scale into large problems while retaining
robustness is vital.
The goal of this work was to set foundation for a fully-coupled reactor
network approach to heterogeneous systems, without the need to solve
different subsystems in different moments but rather study them
concurrently.
The product of this thesis is NetSMOKE, a C++ framework based on the
OpenSMOKE++ libraries which is highly modulable which can be used to
solve both homogeneous and heterogeneous reactor network models in a
fully coupled manner.
3.1 Mathematical formulation From a mathematical standpoint, solving a reactor network is nothing
conceptually complicated. On the other hand, this kind of problem can be
a numerical challenge, due to the fact that non-linear system can have
convergence problems if the first guess solution is not sufficiently close to
the real one. Also, the problem can scale easily into large number of
reactors.
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Chapter 3 ♦ NetSMOKE
▪59▪
3.1.1 A reactor network according to NetSMOKE
NetSMOKE implements a very simple view of a reactor network. The
network is composed of reactors and various auxiliary units.
All the reactors are blocks where reactions take place. They are
characterized by residence time and energy condition (isothermal,
adiabatic or