Chemical Representation of Various Biomass Compounds

Chemical Representation of Various Biomass Compounds

Ana Isabel Batista Rita

Thesis to obtain the Master of Science Degree in

Chemical Engineering

Supervisors: Prof. Dr. Maria Amélia N. D. de Almeida Lemos (IST) Dr. Jan Verstraete (IFPEN)

Examination Committee

Chairperson: Prof. Dr. Sebastião Manuel T. da Silva Alves (IST) Supervisors: Dr. Jan Verstraete (IFPEN) Members of the Committee: Prof. Dr. João Carlos Moura Bordado (IST)

October 2014

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« Develop a passion for learning.

If you do, you will never cease to grow. »

Anthony J. D’Angelo

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Acknowledgements

I would like to start by thanking my supervisor from IFPEN, Dr. Jan Verstraete, with whom I

could learn so much. Thank you for the persistence and dedication when I lacked understanding on

what to do, for always having five minutes for a question, for the support. To Dr. Nadège Charon, my

co-supervisor from IFPEN, I would like to thank the availability and kindness, as well as providing the

necessary material for this project.

I would also like to express my gratitude to my supervisors from IST, Professor Francisco

Lemos and Professor Amélia Lemos, for their constant support and trust across the past six months. I

thank you for your interest and dedication to this project.

A special thank you to Professor Filipa Ribeiro, Dr. Tiago Sozinho and Dr. Joana Fernandes,

who worked hard to provide us, students, with the opportunity of a six months internship at a well

known institution, as IFPEN. I would like to show my appreciation to two colleagues that already had

this experience: Pedro Mendes, for his support and help with all the bureaucracies of traveling to a

foreign country, and Mafalda Lancinha, who always showed availability and patience when asked for

help.

To all my fellow colleagues at IFPEN I thank you for making my stay more pleasant. A special

thank you to the portuguese people that came with me, thank you for the friendship.

To my friends in Portugal, I would like to say thank you for everything, listening to me, making

me realize the important things to focus on and the constant friendship and support. I would like to

thank Renato, Rita and Duarte specially.

I would also like to thank my family for the patience and understanding. Their constant support

helped in decreasing my homesickness. Obrigada mãe, obrigada pai! Teresa, thank you for our

conversations and your jokes, they could not have helped me more.

And last but not least, Ricardo, thank you for your patience and your belief in me, thank you

for your dedication and for being my support in the past six months. I could have not done this without

you.

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Abstract

The objective of the present work is to develop a methodology that allows obtaining a correct

representation of the chemical structure of lignin and hydroconverted lignin. For this, a literature

review on molecular representation algorithms for asphaltenes was performed. Because there are no

molecular representation algorithms for lignin in the open literature, the first part on asphaltenes was

an important step to understand how to create an algorithm with this objective.

In the first part, eleven algorithms from the literature were validated with seven test molecules.

The results showed that some algorithms were better than others and possible reasons. From this

comparison, the considered most robust algorithm was the SAAH algorithm. After validation, the

eleven algorithms were applied to a sample of Buzurgan asphaltenes, and for the SAAH algorithm,

various molecular structure representations were compared. A blind test was made to verify the

robustness and credibility of the SAAH algorithm.

In the second part, the proposed algorithm provided a feasible chemical structure for lignin

molecules. It was validated with eight test molecules. After validation, the proposed algorithm was

applied to a Protobind 1000 lignin sample. The results show a molecular structure that closely agrees

with experimental data. After this, a sample of hydroconverted Protobind 1000 lignin was tested. To

represent hydroconverted lignins, different construction blocks were required and a modified algorithm

was proposed. It was validated with six test molecules. Its application to the hydroconverted lignin

provided an average molecular structure that approaches the experimental data but shows some

deviations.

Key Words

Lignin, Asphaltenes, Molecular Reconstruction, Algorithm, Renewable Energies, Biomass

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Resumo

O objectivo do presente trabalho é desenvolver uma metodologia que permita obter a

representação correcta da estrutura química da lenhina e da lenhina hidroconvertida. Para tal, uma

revisão bibliográfica sobre algoritmos de representação molecular de asfaltenos foi efectuada. Como

não existem algoritmos deste género para a lenhina, a primeira parte sobre os asfaltenos foi um

passo importante para compreender como criar um algoritmo de representação molecular.

Na primeira parte, onze algoritmos da literatura foram validados com sete moléculas teste. Os

resultados indicaram algoritmos melhores que outros e possíveis razões. Desta comparação, o

algoritmo considerado mais robusto foi o algoritmo SAAH. Após validação, os onze algoritmos foram

aplicados a uma amostra de asfaltenos Buzurgan, e para o algoritmo SAAH, diversas representações

moleculares foram comparadas. Um « teste cego » foi efectuado para verificar a robustez e a

credibilidade do algoritmo SAAH.

Na segunda parte, o algoritmo proposto devolveu uma estrutura química viável para

moléculas de lenhina. Foi validado com oito moléculas teste. Após validação, o algoritmo proposto foi

aplicado a uma amostra de lenhina Protobind 1000. Os resultados mostram uma estrutura molecular

que se aproxima dos dados experimentais. Em seguida, uma amostra de lenhina Protobind 1000

hidroconvertida foi testada. Para representar lenhinas hidroconvertidas, foram necessários diferentes

blocos de construção e um algoritmo modificado foi proposto. Este foi validado com seis moléculas

teste. A sua aplicação à lenhina hidroconvertida devolveu uma estrutura molecular média que se

aproximou dos dados experimentais mas com alguns desvios.

Palavras Chave

Lenhina, Asfaltenos, Reconstrução Molecular, Algoritmo, Energias Renováveis, Biomassa

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Contents

Acknowledgements ..................................................................................................................................v

Abstract................................................................................................................................................... vii

Resumo ................................................................................................................................................... ix

Contents .................................................................................................................................................. xi

List of Tables .......................................................................................................................................... xv

List of Figures ........................................................................................................................................ xix

Nomenclature ........................................................................................................................................ xxi

1. Introduction ....................................................................................................................................... 1

Renewable Energies ............................................................................................................... 1

Biomass ................................................................................................................................... 1

2. Motivation ......................................................................................................................................... 3

Biofuels Production .................................................................................................................. 3

2.1.1. First Generation Biofuels ................................................................................................. 3

2.1.2. Second Generation Biofuels ............................................................................................ 4

2.1.3. Third Generation Biofuels ................................................................................................ 4

Biorefineries ............................................................................................................................. 5

Biomass Conversion ................................................................................................................ 5

2.3.1. Biochemical Conversion Paths ........................................................................................ 6

2.3.2. Thermochemical Conversion ........................................................................................... 7

2.3.3. Electrochemical Conversion Paths .................................................................................. 9

Conclusion ............................................................................................................................. 10

3. Thesis Outline ................................................................................................................................ 10

4. Literature Review ........................................................................................................................... 10

Lignocellulosic Feedstock ...................................................................................................... 10

4.1.1. Cellulose ........................................................................................................................ 11

4.1.2. Hemicellulose ................................................................................................................ 11

4.1.3. Lignin ............................................................................................................................. 12

Pretreatments of Lignocellulosic Feedstock .......................................................................... 13

xii

4.2.1. Physical Pretreatments .................................................................................................. 13

4.2.2. Biological Pretreatments ................................................................................................ 13

4.2.3. Chemical Pretreatments ................................................................................................ 14

4.2.4. Physicochemical Pretreatments .................................................................................... 14

Technical Lignins ................................................................................................................... 14

Chemistry of Lignin ................................................................................................................ 15

Straw Lignin ........................................................................................................................... 18

5. Objectives of the Work ................................................................................................................... 19

6. Molecular Reconstruction ............................................................................................................... 19

7. Heavy Petroleum Fractions ............................................................................................................ 20

Asphaltenes ........................................................................................................................... 21

7.1.1. Experimental Data ......................................................................................................... 22

Determination of average structural parameters of asphaltenes .......................................... 22

7.2.1. Algorithm of Sato ........................................................................................................... 22

7.2.2. Algorithm of Speight ...................................................................................................... 23

7.2.3. Algorithm of Montgomery and Boyd .............................................................................. 24

7.2.4. Algorithm of Hirsch and Altgelt ...................................................................................... 24

7.2.5. Brown-Ladner modified Algorithm ................................................................................. 25

7.2.6. Algorithm of Williams ..................................................................................................... 25

7.2.7. Algorithm of Knight ........................................................................................................ 26

7.2.8. Algorithm of Cantor ........................................................................................................ 26

7.2.9. Algorithm of Dickinson ................................................................................................... 26

7.2.10. Algorithm of Qian, Zhang and Li 1983 ........................................................................... 27

7.2.11. Algorithm of Qian, Zhang and Li 1984 ........................................................................... 27

7.2.12. Other Algorithms ............................................................................................................ 27

Validation ............................................................................................................................... 28

Comparison between Algorithms........................................................................................... 28

Structure Proposal ................................................................................................................. 33

Blind Test ............................................................................................................................... 36

Conclusion ............................................................................................................................. 38

xiii

8. Lignocellulosic Feedstock .............................................................................................................. 39

Lignin ..................................................................................................................................... 39


8.1.2. Proposed Algorithm ....................................................................................................... 41

8.1.3. Validation ....................................................................................................................... 54

8.1.4. Application of the Algorithm to Protobind 1000 Lignin .................................................. 58

Hydroconverted Lignin ........................................................................................................... 60


8.2.2. Proposed Algorithm ....................................................................................................... 61

8.2.3. Validation ....................................................................................................................... 66

8.2.4. Application of the Algorithm to Hydroconverted Lignin ................................................. 67

Conclusion ............................................................................................................................. 69

9. Conclusions and Future Perspectives ............................................................................................ 70

10. References ................................................................................................................................ 72

A. Appendix .................................................................................................................................... 77

A.1. Experimental Data for Asphaltenes ....................................................................................... 77

A.1.1. Buzurgan asphaltenes at 85 wt% conversion ............................................................... 77

A.1.2. Test molecules ............................................................................................................... 77

A.1.3. Blind Test ....................................................................................................................... 79

A.2. Algorithms for the Reconstruction of Asphaltenes ................................................................ 81

A.2.1. Algorithm of Sato ........................................................................................................... 81

A.2.2. Algorithm of Speight ...................................................................................................... 83

A.2.3. Algorithm of Montgomery and Boyd .............................................................................. 84

A.2.4. Algorithm of Hirsch and Altgelt ...................................................................................... 85

A.2.5. Brown-Ladner modified algorithm.................................................................................. 87

A.2.6. Algorithm of Williams ..................................................................................................... 88

A.2.7. Algorithm of Knight ........................................................................................................ 88

A.2.8. Algorithm of Cantor ........................................................................................................ 89

A.2.9. Algorithm of Dickinson ................................................................................................... 90

A.2.10. Algorithm of Qian, Zhang and Li (1983) ........................................................................ 90

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A.2.11. Algorithm of Qian, Zhang and Li (1984) ........................................................................ 91

A.3. Experimental Data for Lignins ............................................................................................... 91

A.4. Results for the Reconstruction of Lignins .............................................................................. 95

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List of Tables

Table 1-1 - Different types of biomass (Basu, 2010) ............................................................................... 1

Table 1-2 – Different sources of harvested biomass (Lorne, 2007) ........................................................ 2

Table 7-1 – Analytical techniques used as input in each algorithm ...................................................... 28

Table 7-2 - Most important structural variables for test molecule 4 ...................................................... 29

Table 7-3 - Most important structural variables for test molecule 7 ...................................................... 29

Table 7-4 - Most important structural variables for Buzurgan asphaltene sample ................................ 29

Table 7-5 - Comparison between previous works and present work about Buzurgan asphaltene

sample at 85% of residue conversion ................................................................................................... 35

Table 7-6 - Results of the algorithm and the real values of a proposed structure for component 1 ..... 37

Table 7-7 - Results of the algorithm and the real values of a proposed structure for component 2 ..... 37

Table 8-1 - Structural representation of the six construction blocks ..................................................... 42

Table 8-2 - Structural representation of an extra internal CB and the two terminal CB ........................ 42

Table 8-3 - Characteristics and composition of the various construction blocks .................................. 43

Table 8-4 - Test molecules based on the proposed construction blocks .............................................. 54

Table 8-5 - Obtained results after applying the algorithm to the six test molecules based on the

proposed construction blocks ................................................................................................................ 54

Table 8-6 - Test molecule with 2 construction blocks of type k2 and 1 construction block of type k4 ... 55

Table 8-7 - Obtained results for test molecule t7 ................................................................................... 55

Table 8-8 - Real solution for test molecule t7 ........................................................................................ 55

Table 8-9 - Test molecule t8 with analytical data closer to the Protobind 1000 experimental data ....... 57

Table 8-10 - Three possible solutions for the test molecule t8 .............................................................. 58

Table 8-11 - Obtained results from the application of the proposed algorithm to the experimental data

............................................................................................................................................................... 58

Table 8-12 - Proposed structure for Protobind 1000 lignin ................................................................... 59

Table 8-13 - Comparison of general results between the experimental data for Protobind 1000 and the

proposed structure p1 ............................................................................................................................ 60

Table 8-14 - Comparison of structural results between the experimental data for Protobind 1000 and

the proposed structure p1 ...................................................................................................................... 60

Table 8-15 - Structural representation of the five hydroconverted construction blocks ........................ 62

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Table 8-16 - Structural representation of the new propyl terminal construction block that replaces the

previous g2 group................................................................................................................................... 62

Table 8-17 - Characteristics and composition of the various construction blocks ................................ 63

Table 8-18 - Structural representation of the test molecules based on the proposed construction

blocks ..................................................................................................................................................... 66

Table 8-19 - Obtained results after applying the algorithm to the six test molecules based on the

hydroconverted CB ................................................................................................................................ 66

Table 8-20 - Structural representation of a hydroconverted fragment that resulted from test molecule t8

............................................................................................................................................................... 67

Table 8-21 - Results of the application of the modified algorithm to test molecule t14 .......................... 67


for hydroconverted lignin ....................................................................................................................... 67

Table 8-23 - Structural representation of the proposed structure for hydroconverted lignin ................. 68

Table 8-24 - Comparison of general results between the experimental data and the proposed structure

............................................................................................................................................................... 69

Table 8-25 - Comparison of structural results between the experimental data and the proposed

structure ................................................................................................................................................. 69

Table A-1 - Experimental data for the Buzurgan asphaltene sample ................................................... 77

Table A-2 - Name and respective structure of each test molecule (http://webbook.nist.gov/chemistry/)

............................................................................................................................................................... 78

Table A-3 - Analytical data for the seven test molecules (http://webbook.nist.gov/chemistry/) ............ 79

Table A-4 – 1H NMR spectrum for component 1 ................................................................................... 79

Table A-5 – 13C NMR spectrum for component 1 ................................................................................. 80

Table A-6 – 1H NMR spectrum for component 2 ................................................................................... 80

Table A-7 - 13C NMR spectrum for component 2 .................................................................................. 81

Table A-8 - Analytical data as input for the algorithm of Sato (1997) ................................................... 81

Table A-9 - Optimized values for the parameters according to Least Square Method for Buzurgan

asphaltene sample with Sato (1997)’s algorithm .................................................................................. 82

Table A-10 - Structural variables calculated for Buzurgan asphaltene sample with Sato (1997)'s

algorithm ................................................................................................................................................ 83

Table A-11 - Structural variables calculated for Buzurgan asphaltene sample with Speight (1970)'s

algorithm ................................................................................................................................................ 84

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Table A-12 - Simultaneous resolution of the three carbon balances and the two correlations of the

Montgomery and Boyd’s algorithm ........................................................................................................ 84

Table A-13 - Calculated values for the five parameters of the Montgomery and Boyd’s algorithm ...... 84

Table A-14 - Reduction of the "average" molecule to a pure hydrocarbon ........................................... 86

Table A-15 - Simultaneous resolution of three non-linear equations F1, F2 and F3 to estimate the

three unknown variables CI, CPN and n ................................................................................................. 86

Table A-16 - Obtained values for the five parameters of the algorithm, this step is only possible after

the non-linear resolution ........................................................................................................................ 87

Table A-17 - Calculated values for the structural variables ................................................................... 87

Table A-18 - Calculated values for the structural parameters of Brown-Ladner modified method ....... 87

Table A-19 - Calculated values for the structural variables of Williams's algorithm .............................. 88

Table A-20 - Calculated values for the structural variables of Knight's algorithm ................................. 89

Table A-21 - Calculated values for structural variables of Cantor's algorithm ...................................... 89

Table A-22 - Calculated values for the structural variables of Dickinson's algorithm ........................... 90

Table A-23 - Calculated values for the structural variables of Qian, Zhang and Li's algorithm ............ 91

Table A-24 - Calculated values for the structural variables of Qian, Zhang and Li's algorithm ............ 91

Table A-25 - Experimental data for Protobind 1000 lignin .................................................................... 92

Table A-26 - Model data for test molecules t1, t2, t3, t4, t5 and t6............................................................ 92

Table A-27 - Model data for test molecules t7 and t8 ............................................................................. 93

Table A-28 - Experimental data for hydroconverted lignin .................................................................... 94

Table A-29 - Model data for test molecules t9, t10, t11, t12, t13 and t14 ..................................................... 94

Table A-30 - Results before the application of the algorithm for Protobind 1000 lignin ........................ 95

Table A-31 - Results before the application of the algorithm for hydroconverted lignin ....................... 95

Table A-32 - Internal results of the algorithm for Protobind 1000 lignin ................................................ 95

Table A-33 - Internal results of the algorithm for test molecules t1, t2, t3, t4, t5 and t6 ............................ 96

Table A-34 - Internal results of the algorithm for test molecules t7 and t8 ............................................. 96

Table A-35 - Internal results for hydroconverted lignin sample ............................................................. 96

Table A-36 - Internal results for test molecules t9, t10, t11, t12, t13 and t14 ............................................... 97

Table A-37 - Constraint values for Protobind 1000 lignin ...................................................................... 97

Table A-38 - Constraint values for test molecules t1, t2, t3, t4, t5 and t6 ................................................. 97

Table A-39 - Constraint values for hydroconverted lignin sample......................................................... 97

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Table A-40 - Constraint values for test molecules t9, t10, t11, t12, t13 and t14 ........................................... 98

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List of Figures

Figure 1 – World energy demand. Adapted from (WEO, 2013) .............................................................. 1

Figure 2 - Percentage of energy sources in 2010. Adapted from IEA (Key World Energy Statistics,

2012) ........................................................................................................................................................ 3

Figure 3 - Processing steps for second generation bioethanol production from lignocellulosic biomass

compared to first generation processes (Babu et al., 2013) ................................................................... 4

Figure 4 - Lignocellulosic feedstock biorefinery (Kamm et al., 2012)...................................................... 5

Figure 5 - Paths for conversion of solid biomass into fuels (Basu, 2010) ............................................... 6

Figure 6 - Combustion proceeds in stages (Bridgwater et al., 2009) ...................................................... 7

Figure 7 - Gasification process of a single particle (Bridgwater et al., 2009) .......................................... 7

Figure 8 - Products from synthesis gas after gasification of biomass (Bridgwater et al., 2009) ............. 8

Figure 9 - Product spectrum from pyrolysis (Bridgwater et al., 2009) ..................................................... 8

Figure 10 - Illustration of the three structural polymers of lignocellulosic biomass. Adapted from

Sannigrahi et al. (2010) ......................................................................................................................... 11

Figure 11 - Structure of cellulose (Zabaleta, 2012) ............................................................................... 11

Figure 12 - Structure of hemicellulose in hardwood (Zabaleta, 2012) .................................................. 11

Figure 13 - Lignin monomeric building blocks (adapted from Heitner et al., 2010)............................... 12

Figure 14 - Lignin functional groups (Heitner et al., 2010) .................................................................... 12

Figure 15 - Schematic representation of the pretreatment effect (Agbor et al., 2011) .......................... 13

Figure 16 - Classification of technical lignins (Gupta et al., 2014) ........................................................ 15

Figure 17 - First step in lignin polymerization (Heitner et al., 2010) ...................................................... 16

Figure 18 - β-O-4 bond formation via radical coupling (Heitner et al., 2010) ........................................ 16

Figure 19 - α-O-4 bond formation via radical coupling (Heitner et al., 2010)Error! Bookmark not

defined.

Figure 20 - β-5 bond formation via radical coupling (Heitner et al., 2010) ............................................ 17

Figure 21 - β-1 bond formation via radical coupling (Heitner et al., 2010) ............................................ 17

Figure 22 - Lignin linkage types and amounts (Heitner et al., 2010) .................................................... 18

Figure 23 - Example lignin structure (Heitner et al., 2010) .................................................................... 18

Figure 24 - Model molecule considered in Sato (1997) ........................................................................ 22

Figure 25 - Cp/Ca ratios of condensed aromatic compounds. Adapted from Speight (1970) .............. 23

Figure 26 - Model molecule considered in Hirsch and Altgelt (1970) ................................................... 24

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Figure 27 - Possible asphaltene molecular structures at the residue conversion level of 85 %wt

(Gauthier et al., 2008) ............................................................................................................................ 34

Figure 28 - Possible average molecular evolution of asphaltenes as a function of residue conversion

X540 °C+ (Gauthier et al., 2008) ........................................................................................................... 34

Figure 29 - Proposed structure for asphaltene molecule at 85 %wt of residue conversion (Medeiros,

2013) ...................................................................................................................................................... 35

Figure 30 - Proposed structure #1 for asphaltene sample at 85% of residue conversion .................... 35



Figure 33 - Proposed structure for component 1 .................................................................................. 37

Figure 34 - Proposed structure for component 2 .................................................................................. 38

Figure 35 – Scheme #1 to explain the deduction .................................................................................. 46

Figure 36 – Scheme #2 to explain the deduction .................................................................................. 47

Figure 37 - Scheme #3 to explain the deduction ................................................................................... 47

Figure 38 - Scheme #4 to explain the deduction ................................................................................... 48

Figure 39 – Flow diagram that illustrates the proposed algorithm ........................................................ 53

Figure 40 - Mechanism to illustrate how the methoxy groups convert into catechol groups ................ 61

Figure 41 – Flow diagram that illustrates the proposed modified algorithm .......................................... 65

Figure 42 – Conjugation types of naphthenic rings to aromatic rings ................................................... 82

Figure 43 – Structure and volume adjustments for heteroatoms. Adapted from Hirsch and Altgelt

(1970) .................................................................................................................................................... 85

Figure 44 – Structure of 1,2,3,4 tetrahydronaphthalene ....................................................................... 88

xxi

Nomenclature

Acronyms

2D HSQC NMR – 2 Dimension Heteronuclear

Single Quantum Coherence NMR

AH – Acid Hydrolysis

AMP – Average molecular parameter

ANN – Artificial neural network

AR – Aromatic ring(s)

BL – Brown-Ladner

BTL – Biomass-to-liquid diesel

DEPT 13C NMR – Distortionless enhancement

of polarization transfer carbon NMR

DHP – Dehydrogenation polymers of lignin

DFRC method – Derivatization Followed by

Reductive Cleavage method

GC-FIMS – Gas chromatography-field

ionization mass spectrometry

GRG – Generalized Reduced Gradient

HA – Hirsch and Altgelt

IFPEN – IFP Energies Nouvelles

IR – Infrared spectroscopy

IST – Instituto Superior Técnico

LCC – Lignin carbohydrate complexes

LP – Linear programming

MB – Montgomery and Boyd

MW – Molecular weight

NLP – Non-linear programming

NMR – Nuclear magnetic resonance

OS – Organosolv Pretreatment

PDF – Probability density function

PIONA – Paraffin, Isoparaffin, Olefin,

Naphthene, Aromatic

QMR approach – Quantitative molecular

representation approach

QZL – Qian, Zhang and Li

RDF – Refuse-derived fuel

SAAH – Structural analysis of aromatic

hydrocarbons

SE – Steam Explosion

SEC – Size-exclusion chromatography

SimDis – Simulated distillation

SIMREL – Software that simulates the

pyrolysis of lignin

SOL approach – Structure-oriented lumping

approach

SPYRO – Comprehensive pyrolysis model

approach

SEM-EDX – Scanning Electron Microscopy

coupled with Energy Dispersive X-ray

TEM – Transmission electronic microscopy

Symbols

1H NMR – Proton NMR

13C NMR – Carbon NMR

31P NMR – Phosphorous NMR

ARTerminal – Terminal aromatic ring

C – Carbon

C-1 – Carbon at position 1 in an AR

C-5 – Carbon at position 5 in an AR

C-α – Carbon at a α position to an AR

C-β – Carbon at a β position to an AR

Cali-C – Aliphatic carbon only connected to

another carbon atom

CA – Aromatic carbon

Cac – Peripheral quaternary aromatic carbons

Cap – Peripheral quaternary carbons

Caq – Quaternary aromatic carbons

CAliphaticInternal – Internal aliphatic carbons

CAliphaticTotal – Total aliphatic carbons

CAr-H – Aromatic carbon connected to a

hydrogen atom

Cc – Paraffinic chain carbons

CN – Naphthenic carbon

Cn – Naphthenic carbon atoms

CP – Paraffinic carbon

CPe – Peripheral carbons in fused ring units

http://en.wikipedia.org/wiki/Infrared_spectroscopy

xxii

CPeA – Peripheral aromatic carbons in fused

ring units

Ct – Total number of carbon atoms

Cti – Internal ring carbons in fused ring units

Ctp – Peripheral ring carbons in fused ring

units

CType of Carbon – Number of carbons of a type

(aromatic or aliphatic)

Cγ – Terminal methyl carbons

Ca(OH)2 – Calcium Hydroxide

CBinternal without g1 – All the construction blocks of

Table 8-1

CH4 - Methane

CO – Carbon Monoxide

CO2 – Carbon Dioxide

devj – Deviation between experimental and

proposed (calculated) value in constraint j

f – Objective function

fa – Aromaticity factor

G – Total number of terminal groups

gi – Construction block i

H – Hydrogen

HAlgorithm – Number of total hydrogens to enter

the algorithm

H2 – Hydrogen Molecule

Har – Aromatic hydrogen atoms

Hc – Paraffinic chain hydrogen atoms

HOMe – Number of methoxy hydrogens

Ht – Total number of hydrogen atoms

hi – Construction block i for the hydroconverted

lignin proposed algorithm

ki – Construction block i for the Protobind 1000

lignin proposed algorithm

KOH – Potassium Hydroxide

N – Nitrogen

NaOH – Sodium Hydroxide

NiMo – Nickel-Molybdenum

O – Oxygen

OEther – Ether oxygen atoms

OInternal – Internal oxygen atoms

OH – Number of hydroxyl group

OHAliphatic – Aliphatic OH groups

OHPhenolic – Phenolic OH groups

OHPhenolicInternal – Internal phenolic groups

OHPhenolicTerminal – Terminal phenolic groups

OMe –Number of methoxy groups

OMehydroconverted – Methoxy groups detected in

the hydroconverted lignin

pH – Indicator of the hydrogen concentration in

solution

pi – Proposed structure i

RA – Aromatic ring(s)

RN – Naphthenic ring(s)

Rt – Total number of rings

S – Sulfur

ti – Test molecule i

XAliphaticTerminal – terminal aliphatic atoms of type

X (where X can be C or H)

XCatechol – Catechol atoms of type X (where X

can be H, O or OH)

XCalc – Calculated number of X (where X can

be AR, C, OTotal, CB, Cali-C, H, OEther)

XExp – Experimental number of X (where X can

be AR, C, OTotal, CB, Cali-C, H, OEther)

XTotal – Total number of atoms of type X (where

X can be H, O or OH)

%AS – Percent substitution of an aromatic ring

%Type of Carbon – Percentage of carbons of a type

(aromatic or aliphatic)

Appendices

Experimental Data

CH3 – Aliphatic CH3 group

CH2 – Aliphatic CH2 group

CH ali – Aliphatic CH group

Cq ali – Aliphatic C group

Cq aro – Aromatic non-hydrogenated carbon

Cq cond – Condensed aromatic carbon

Cq sub – Substituted aromatic carbon

xxiii

Hα – Hydrogen in α position to an aromatic

ring

Hβ - Hydrogen in β position to an aromatic ring

Hγ - Hydrogen in γ position to an aromatic ring

Algorithm of Sato

Rna – Naphthenic neighbouring rings to an

aromatic ring

Us – Unsaturation factor

Cnα – Naphthenic carbons in a α position to an

aromatic ring

Cni – Internal naphthenic carbons

Cnp – Peripheral naphthenic carbons

L – Parameter L from the algorithm of Sato

Hn – Naphthenic hydrogens

Ccβ – Number of other paraffinic chain carbon

atoms

M – Number of fused ring systems

Cai- Number of internal aromatic carbon

Ctr – Number of ring carbons in fused ring

units

P – Number of paraffinic chain terminals on

aromatic rings

Algorithm of Speight

Cs – Saturated carbon atoms per molecule

Csa – Saturated carbon atoms α to an

aromatic ring

Cp – Peripheral carbon in a condensed

aromatic sheet

Ci - Internal carbon in a condensed aromatic

sheet

Cr - Total paraffinic carbon atoms per molecule

in locations other than α to an aromatic ring

Algorithm of Montgomery and Boyd

C1 – number of CH3, CH2, CH and C groups in

linear and branched chains

C2 – number of CH2 groups in saturated rings

(including hydrogen substitution by branched

or linear chains)

C3 – number of CH groups which are junctions

between fused saturated rings (including

hydrogen substitution by branched or linear

chains)

C4 – number of CH groups in aromatic rings

(including hydrogen substitution by branched

or linear chains)

C5 – number of C groups which are junctions

between fused aromatic rings, as well as

junctions between saturated and aromatic

rings

Algorithm of Hirsch and Altgelt

PCi – Percentage of each element i normalized

to 100% per molecule

APMi – Atoms of each element i per molecule

AFi – Atom fraction of each element i per

molecule

APMXC and APMXH – Adjusted number of

carbon and hydrogen atoms per molecule

VX - Adjusted molecular volume in cm3/mol

F1, F2, F3 – Implicit equations

Q1, Q2, Q3, Q4 – Definitions used for implicit

equation F3

CPN – Peripheral naphthenic carbons

n – Number of fused ring systems

a – Fraction of peripheral aromatic carbons

bonded to benzonaphthenic carbons

b – Average number of peripheral

benzonaphthenic carbons

CPA – Peripheral aromatic carbons

CIA – Internal aromatic carbons

CB – Total benzylic carbons

CB2 – Benzylic carbons bonded to two

hydrogens

CB3 – Benzylic carbons bonded to three

hydrogens

CPB – Peripheral benzonaphthenic carbons

CIB – Internal benzonaphthenic carbons

CIN – Internal naphthenic carbons

CLi – Aliphatic (nonbenzylic) carbons bonded

to i hydrogens

L – Number of aliphatic chains

xxiv

TRL (TAL, TNL) – Number of aliphatic chain

terminals on rings (aromatic or naphthenic

rings)

TEL – Number of CH3 terminals on aliphatic

chains

fN – Fraction of peripheral naphthenic carbons

with aliphatc chain substitutions

Sc – Conveninent constant

Brown and Ladner method

CP and CS – Number of paraffinic carbons and

saturated carbons

fN - Fraction of naphthenic carbons

fP - Fraction of paraffinic carbons

Algorithm of Williams

n - The average number of carbon atoms per

alkyl substituent

f - The average carbon-hydrogen weight ratio

of the alkyl groups

%AS - The per cent substitution of alkyl groups

on non-bridge aromatic ring carbons

#CA - The average number of aromatic carbon

atoms

#C1 and C1 - The average and the exact

number of non-bridge aromatic carbon atoms

RS - Alkyl substituents

r- The number of naphthalene rings per

substituent

CS - The exact number of saturate carbon

atoms

BI – Branchiness index

Algorithm of Knight

total #C - Total carbon atoms

A1, A2, A3 – Relative areas in 13C NMR

spectrum (lowest field band, low field band,

high field band)

#C1s and #C1

u – Substituted and unsubstituted

aromatic carbons, in weight percent

Algorithm of Cantor

Cls and Cl

u - Portion of C1 carbons which are

alkyl substituted and unsubstituted

A1 and A2 – Normalized integrals for the

aromatic and alkyl regions of the 13C NMR

spectrum

Algorithm of Dickinson

fC - The carbon-hydrogen weight ratio of total

alkyl groups

x - The hydrogen-carbon atomic ratio of alkyl

groups

CA - Percentage of aromatic carbon

C1 - Non-bridge aromatic carbon

C1S - Substituted aromatic carbon

C1U - Unsubstituted aromatic carbon

No. CA, No. C1, No. CAl and No. HAl - The

number of aromatic carbons, aromatic non-

bridge carbons, aliphatic carbons and aliphatic

hydrogens

RS – Number of alkyl substituents

AS – Percent of substitution of aromatic rings

Algorithm of QZL 1983

l - The average carbons per alkyl side chain

fc - The carbon-hydrogen weight ratio of total

alkyl groups

x - The hydrogen-carbon atomic ratio of alkyl

groups

CA% - The percentage of aromatic carbon

Cls% - Substituted aromatic aromatic carbon

Clu% and Cl% - Unsubstituted aromatic carbon

and non-bridge aromatic carbon

Cp and Cal - The aromatic non-bridge carbons

and the aliphatic carbons

Hal - The aliphatic hydrogens

n - The alkyl substituents

Algorithm of QZL 1984

Cm - Straight-chain alkyl carbons

Car,ar,ar - Internal quaternary aromatic carbons

Csl%, Cu

l% and Cl% - Percentage of

substituted, unsubstituted and non-bridge

carbon

Cp/Ca - The condensation index

xxv

fc - The carbon-hydrogen weight ratio of total

alkyl groups

Experimental Data Lignin

Xali – Aliphatic atom of type X (where X can be

C or H)

Cali-O – Aliphatic carbon connected to oxygen

Xaro – Aromatic atom of type X (where X can

be C or H)

CAR-C – Aromatic carbon connected to another

carbon

CAR-O – Aromatic carbon connected to oxygen

C=O – Carbon connected by double bond with

oxygen

HPhenolic – Hydrogen in phenolic groups

Greek Letters

ϕ – Ring “compactness factor”

ξ and Ψ – Two interdependent parameters

defining the fraction of peripheral naphthenic

carbons having aliphatic chain attachments

ε – 0,3 by definition

Ф - Degree of substitution of aromatic rings

xxvi


1

1. Introduction

Renewable Energies

As their name implies, renewable energy resources will never run out given that they are

constantly being replenished (Jansen, 2013). There are several renewable resources as solar energy,

geothermal energy, wind power, hydropower and biomass (Michaelides, 2012).

Renewable resources have become gradually more important with several factors affecting

the population, the planet, the quality of life, etc. Some of these factors are: increasing oil prices,

increasing world energy demand (Figure 1), global warming, waste recycling that is becoming more

costly and problematic, population growth that will require more energy and consumer products, etc.

(Zabaleta, 2012).

Biomass

The biomass feedstock is very embracing. There are several types of biomass, some of which

are listed below.

Table 1-1 - Different types of biomass (Basu, 2010)

Agricultural Food grain, bagasse (crushed surgarcane), corn stalks, straw, seed hulls, nutshells, and manure from cattle, poultry, and hogs

Forestal Trees, wood waste, wood or bark, sawdust, timber slash, and mill scrap

Municipal Sewage sludge, refuse-derived fuel (RDF), food waste, waste paper, and yard clippings

Energy Poplars, willows, switchgrass, alfalfa, prairie bluestem, corn, and soybean, canola, and other plant oils

Biological Animal waste, aquatic species, biological waste

Figure 1 – World energy demand. Adapted from (WEO, 2013)

2

Biomass can be transformed into liquid fuels for transportation, called biofuels. The use of

biofuels will reduce pollution and reduce a country’s dependence on non-renewable oil (Jansen,

2013).

Three types of primary fuel can be produced from biomass (Basu, 2010):

1. Liquid (ethanol, biodiesel, methanol, vegetable oil and pyrolysis oil)

2. Gaseous (biogas (CH4, CO2), producer gas (CO, H2, CH4, CO2, H2), syngas (CO, H2)

and substitute natural gas (CH4))

3. Solid (charcoal and torrefied biomass)

From these, four major categories of products can be obtained (Basu, 2010):

Chemicals such as methanol, fertilizer and synthetic fiber

Energy such as heat

Electricity

Transportation fuel such as gasoline and diesel

The different sources of harvested biomass are already very important in today's commercial

alternatives (called 1st generation biofuels) for non-renewable oil (Table 1-2), while 2nd generation

biofuels are currently being developed and tested in various research organizations worldwide.

Table 1-2 – Different sources of harvested biomass (Lorne, 2007)

1st Generation Biofuels

Farm products

Corn, sugar cane, sugar beet, wheat, etc.

Ethanol

Rape seed, soybean, palm sunflower seed,

Jatropha, etc. Biodiesel

2nd Generation Biofuels

Lignocellulosic materials

Straw or cereal plants, husk, wood, scrap,

slash, etc. Ethanol and BTL1

Three types of primary fuel can be produced from biomass (Basu, 2010):

4. Liquid (ethanol, biodiesel, methanol, vegetable oil and pyrolysis oil)

5. Gaseous (biogas (CH4, CO2), producer gas (CO, H2, CH4, CO2, H2), syngas (CO,

H2) and substitute natural gas (CH4))

6. Solid (charcoal and torrefied biomass)

From these, four major categories of products can be obtained (Basu, 2010):

Chemicals such as methanol, fertilizer and synthetic fiber

Energy such as heat

Electricity

Transportation fuel such as gasoline and diesel

The different sources of harvested biomass are already very important in today's commercial

alternatives (called 1st generation biofuels) for non-renewable oil (Table 1-2), while 2nd generation

biofuels are currently being developed and tested in various research organizations worldwide.

1 BTL : Biomass-to-Liquid Diesel (Lorne, 2007)

3

Table 1-2 lists the two types of harvested biomass in food and nonfood categories, and

indicates the potential conversion products from them. The division is important because the

production of transportation fuel (ethanol) from cereal, which is relatively easy and more established,

is already being pursued commercially on a large scale. The use of such food stocks for energy

production, however, may not be sustainable as it diverts cereal from the traditional grain market to

the energy market, with economic, social, and political consequences. Efforts are thus being made to

produce more ethanol from nonfood resources like lignocellulosic materials such that the world’s food

supply is not strained by its energy hunger (Basu, 2010).

On the vast amount of biomass, only 5% (13,5 × 109 tons) can potentially be mobilized to

produce energy. This quantity is still large enough to provide about 26% of the worldwide energy

consumption, which is equivalent to 6 × 109 tons of oil (Lorne, 2007). With biomass being such an

embracing and interesting theme from different points of view (industrial, governmental, energetic,

etc), different approaches to this theme will be presented.

2. Motivation

Biofuels Production

Biofuels are gaining increased public and scientific attention, driven by factors such as oil price

spikes, the need for increased energy security, environmental catastrophes like the 2010 oil spill in the

Gulf of Mexico, etc. For last but equally important, the feedstock of biofuels is capable of absorbing

carbon dioxide from air (Jansen, 2013).

However, biofuels still represent little compared to fossil fuels (Figure 2). Their large scale

production depends on advances in productivity in order to mitigate any negative effects associated to

them, such as decreases of indigenous forests (second generation biofuels) or the increase in price of

agricultural products due to land use (first generation biofuels). In the light of this, a worldwide

technological race is taking place to develop second and third generation biofuels (Babu et al., 2013).

2.1.1. First Generation Biofuels

First generation biofuels are those that have currently reached a stage of commercial

production. In general, they come from food crops. The first generation biofuels use agricultural

Figure 2 - Percentage of energy sources in 2010. Adapted from IEA (Key World Energy Statistics, 2012)

4

feedstocks as inputs to their production, which is the case of ethanol from sugarcane and biodiesel

from vegetable oils (Babu et al., 2013).

2.1.2. Second Generation Biofuels

Instead of only using readily extractable sugars, starches or oils as in the 1st generation

biofuels, second generation biofuels do not use edible sources as raw materials. The raw materials

can be agricultural residues such as straw and stover, residues from forestry, or biomass crops such

as grasses and wood from short rotation forestry. As second generation biofuels use different

feedstocks and bioconversion pathways, they apparently avoid the “fuel versus food” dilemma.

However, they can compete with the use of agricultural lands which could be used to grow food crops

(Babu et al., 2013). In this type of biofuels, the raw material also requires a pretreatment before the

conversion process (Figure 3).

2.1.3. Third Generation Biofuels

Biofuels of the third generation come from algae and hydrogen produced from lignocellulosic

biomass. The products resulting from their conversion are described as third generation because they

Figure 3 - Processing steps for second generation bioethanol production from lignocellulosic biomass compared

to first generation processes (Babu et al., 2013)

5

no longer require the use of land. Their production technologies use catalytic reforming routes to

convert sugar, starch and all forms of lignocellulose into targeted short-chain carbon compounds. The

technologies for third generation biofuels production are still in development phase and their large

scale production is expected in the medium to long term (Babu et al., 2013).

Biorefineries

Biomass, similarly to petroleum, has a complex composition. Its primary separation into main

groups of substances is therefore required. Consequently, the biorefinery concept is analogous to

today’s petroleum refineries, which produce multiple fuels and products from petroleum (Xie and

Gathergood, 2013).

Among the potential large-scale industrial biorefineries, the lignocellulosic feedstock

biorefinery will most probably be the most successful. First, there is optimum availability of raw

materials (straw, grass, wood, etc), and secondly, the conversion products are well-placed on the

traditional petrochemical, and, likewise, on the future bio-based product market (Xie and Gathergood,

2013).

Biorefineries associate the essential technologies which convert biological raw materials into

the industrial intermediates and final products (Figure 4). More specifically, industrial biorefineries

have been identified as the most promising route to the creation of a new domestic bio-based industry

(Kamm et al., 2012).

Biomass Conversion

Being biomass a solid, it cannot easily be handled, stored or transported. This factor provides

motivation for the conversion of solid biomass into liquid and gaseous fuels. There are a lot of effective

and studied conversion processes for solid biomass. Nowadays, most of the ethanol for automotive

fuels is produced from corn using fermentation. Thermochemical conversion of biomass into gases

came much later (Basu, 2010). A schematic representation of the different paths for biomass

conversion is given in Figure 5.

Figure 4 - Lignocellulosic feedstock biorefinery (Kamm et al., 2012)

6

2.3.1. Biochemical Conversion Paths

Digestion

For this conversion path, enzymes are used to break the biomass molecules down into smaller

ones. This process is much slower than the thermochemical route, but it does not require much

external energy.

Both processes of anaerobic and aerobic digestion form a digestate (solid residue) and carbon

dioxide as the main products. Also, anaerobic digestion forms methane and aerobic digestion

generates heat (Basu, 2010).

Fermentation

Fermentation is the only biochemical conversion path that results in a liquid product. In this

process, part of the biomass is converted into sugars, which are converted into ethanol or other

chemicals with the help of yeasts. Lignin is not converted and has to be converted through a

thermochemical conversion path or is left directly for combustion. So, lignocellulosic biomass requires

a pretreatment if it has to be converted by a biochemical conversion path (Basu, 2010).

Acid Hydrolysis (Enzymatic)

Before lignocellulosic biomass can be subjected to fermentation, it has to be pretreated. The

typical pretreatment of lignocellulosic biomass is hydrolysis (acid, enzymatic or hydrothermal). This

step is very important to break down the cellulose and hemicellulose into simple sugars needed by the

yeast and bacteria for the fermentation process (Basu, 2010).

Sulfuric and hydrochloric acids are the most commonly used catalysts for hydrolysis of

lignocellulosic biomass. The process occurs at low temperatures, producing high hydrolysis yields of

cellulose (Verardi et al., 2012).

Figure 5 - Paths for conversion of solid biomass into fuels (Basu, 2010)

7

2.3.2. Thermochemical Conversion

Combustion

Biomass combustion is the oldest and most widely applied renewable energy technology.

Traditionally, combustion has mainly been applied in decentralized applications with relatively low

efficiencies, such as woodstoves, cooking stoves and smaller industrial furnaces using locally

available wood resources to deliver heat. Modern biomass combustion technologies are characterized

by high efficiencies, low emissions, high availability, high fuel flexibility and are automated as much as

possible.

Combustion involves high-temperature exothermic conversion of biomass in excess air into

carbon dioxide and steam (stable compounds) (Bridgwater et al., 2009).

The combustion process proceeds in three steps (Figure 6). The relative importance of each

of these steps will vary, depending on the combustion technology implemented, the fuel properties

and the combustion process conditions (Bridgwater et al., 2009).

Gasification

Gasification converts fossil or non-fossil fuels into useful gases and chemicals. It requires a

medium for reaction, which can be gas or supercritical water. It also removes most of the oxygen

content in the initial fuel (Bridgwater et al., 2009).

Figure 6 - Combustion proceeds in stages (Bridgwater et al., 2009)

Figure 7 - Gasification process of a single particle (Bridgwater et al., 2009)

8

Figure 7 shows a single particle of biomass being processed by gasification. All the shown

steps are endothermic, therefore, heat must be supplied to make the different steps to work. Given the

design of the reactor, there is a release of volatiles (Bridgwater et al., 2009).

Given the fact that there are a lot of interesting products can be made from the synthesis gas

of the gasification process of biomass, it has become one of the key technologies for future biomass

utilization since two or even three decades, as shown in Figure 8 (Bridgwater et al., 2009).

The largest problem of gasification is the cost of production compared to, for example,

combustion. Hence, combustion is still globally the most used process.

The situation is different for synthetic bio-products (e.g. synthetic bio-fuels). In this area there

is no comparable competitor (from renewable resources) and the gap to the current market price

(fossil fuel based) is even smaller than for electricity (Bridgwater et al., 2009).

Pyrolysis and Torrefaction

Pyrolysis is the thermal decomposition of biomass into gas, liquid and solid. It occurs in the

total absence of oxygen, except in cases where partial combustion is allowed to provide the thermal

energy needed. It has three variations (Figure 9): fast pyrolysis, slow pyrolysis and mild pyrolysis (or

torrefaction). Three products are always produced, but the proportions can be varied over a wide

range by adjusting the process parameters (Bridgwater et al., 2009).

Figure 8 - Products from synthesis gas after gasification of biomass (Bridgwater et al., 2009)

Figure 9 - Product spectrum from pyrolysis (Bridgwater et al., 2009)

9

The goal of fast pyrolysis is to maximize the formation of bio-oil. Hence, it uses moderate

temperatures and short vapor residence time. Fast pyrolysis for liquids production is currently of

particular interest as the liquid can be stored and transported, and used for energy, chemicals or as an

energy carrier (Bridgwater et al., 2009). Carbonization is a slow pyrolysis process, in which the

production of charcoal or char is the primary goal. The biomass is heated slowly in the absence of

oxygen during an extended period of time, at a relatively low temperature (~ 400°C) to maximize the

char formation (Bridgwater et al., 2009). The mild pyrolysis of biomass, or torrefaction, improves the

energy density, reduces its oxygen-to-carbon ratio and reduces the biomass hygroscopic nature.

During this process, the biomass dries and partially devolatilizes, decreasing its mass while largely

preserving its energy content. Also, the torrefaction removes the water and the carbon dioxide from

the biomass. This process also modifies the structure of the biomass, making it more friable or brittle.

This is caused by the depolymerization of hemicellulose (Basu, 2010).

Hydrothermal Liquefaction

The advantage of this thermochemical conversion path is that the final product is a liquid. As

known, liquids are easier to transport and handle than gaseous products. In this process, the large

feedstock molecules are decomposed into liquids having smaller molecules by an hydrothermal

process. More specifically, in this process, biomass is contacted with supercritical2 water for a period

of time. (Basu, 2010).

2.3.3. Electrochemical Conversion Paths

Typically, most review authors tend to omit the electrochemical conversion of biomass, as

shown in chapter 0. Only Schlosser and Blahušiak (2011) refer to three conversion paths in their

review, where the third one (Electrochemical Conversion) is not very used, although there are some

examples of its utilization in the literature.

Eskamani et al. (1982) shows an example describing the electrochemical conversion of

biomass. A sample of biomass substrate is converted into its constituents like cellulose, hemicellulose

and lignin and further derivatives. The process comprises placing the substrate into the anodic section

of an electrolytic cell containing an electrolyte and electrodes and applying an electromotive force

sufficient to at least partially degrade the substrate. For the purposes of this example, degradation

shall mean both the separation of lignocellulose into its components as well as the further conversion

of those parts to useful chemicals and materials.

This process can be conducted at any temperature between the freezing and boiling points of

the electrolyte solution. Although the process proceeds more rapidly at higher temperatures, ambient

or slightly higher temperatures are preferred for economic reasons. The typical duration of this

process for the operating conditions referred is about 4 to 10 days (Eskamani et al., 1982).

2 At high temperatures (300-350°C) and at high pressures (12-20MPa) (Bridgwater et al., 2009)

10

Conclusion

As seen in previous chapters, there are a lot of subjects to improve and develop concerning

the pretreatments and conversion processes for lignocellulosic biomass. These developments are

largely hampered by the lack of detailed information on the structure of lignocellulosic biomass, which

will be presented next.

3. Thesis Outline

This thesis is organized in the following way: Chapter 1 presents an introduction to the theme.

Chapter 2 contains a motivation concerning the present work. Chapter 4 concerns a background

literature review of lignocellulosic feedstock and lignin. Chapter 5 describes the objectives of the work.

Chapter 6 makes an introduction to the theme of Molecular Reconstruction. Chapter 7 first describes a

set of eleven algorithms for molecular reconstruction of heavy petroleum fractions, and their validation

with seven test molecules. Following this, the application of these algorithms to an asphaltene sample

and respective conclusions are given, together with an example of a proposed structure. This chapter

also contains a blind test applied only to the considered best algorithm and some conclusions on

asphaltenes reconstruction. Chapter 8 deals with lignin and describes the two proposed algorithms for

two samples of lignin, one for native lignin and the other for the residual lignin that remains after

passing through a hydroconversion process. Conclusions concerning the application of the proposed

algorithms are also given. Finally, Chapter 9 contains the main conclusions on this work and some

suggestions for future work.

4. Literature Review

Lignocellulosic Feedstock

Lignocellulosic material is the non-starch, fibrous part of plant materials. The most abundant

low-tech source of biomass is trees. Wood fuel can be derived from conventional forestry practice

such as thinning and trimming as part of sustainable management of woodland to ensure the

production of high-quality timber for construction and wood products (Jansen, 2013). The main

advantages of its utilization focus on the natural structures and structural elements that are being

preserved, on the raw materials that are inexpensive, on the large product varieties that are possible

and on the fact that there is no competition with food production (Zabaleta, 2012).

Lignocellulosic feedstock is composed mainly by cellulose, hemicellulose and lignin (Figure

10). It is important to stress that the proportions of these three components vary with its origin

(hardwood lignin, softwood lignin, etc), with the pretreatment/production processes and with their

operating conditions (Zabaleta, 2012).

11

4.1.1. Cellulose

Cellulose (Figure 11) is the most typical form of carbon in biomass, with a percentage of 40-

60% by weight of the biomass, depending on the biomass source. It is a complex sugar polymer

(“polysaccharide”) (Jansen, 2013). Also, cellulose is a non-branched polymer (Zabaleta, 2012). The

main function of cellulose in the plant cell is as structural component.

4.1.2. Hemicellulose

Hemicellulose (Figure 12) corresponds to a large group of polysaccharides found in the

primary and secondary cell walls constituting the second most abundant polysaccharide in nature with

a percentage of about 30%. The main function of this branched polymer is as structural component

too, where the hemicellulose, being a branched polymer, binds with cellulose, a non-branched

polymer (Zabaleta, 2012).

Figure 12 - Structure of hemicellulose in hardwood (Zabaleta, 2012)

Figure 11 - Structure of cellulose (Zabaleta, 2012)

Figure 10 - Illustration of the three structural polymers of lignocellulosic biomass. Adapted from Sannigrahi et al.

(2010)

12

4.1.3. Lignin

Lignin is a natural phenolic macromolecule present in the vegetal cell wall with a percentage of

about 10% to 24% by weight of biomass. It remains as residual material after the sugars in the

biomass have been converted to ethanol. It contains a lot of energy and can be burned to produce

steam and electricity for the biomass-ethanol process (Jansen, 2013).

The main function of lignin in the plant is as a biological barrier, protecting the plant, and as

the “glue” that retains hemicelluloses and celluloses linked, shaping the cell wall (Zabaleta, 2012).

Lignin is a polymer built up by the combination of three basic monomer types, as shown in

Figure 13. These building blocks, often referred to as phenylpropane or C9 units, differ in the

substitutions at the 3 and 5 positions, as shown in Figure 13. It is important to stress that

phenylpropane units have a different nomenclature than that of typical phenols (Heitner et al., 2010),

since in their nomenclature the side-chain attachment to the aromatic ring counts as position #1.

Hence, the aromatic carbon atoms in the phenylpropane units are named C1, C2, C3, C4, C5 and C6,

while the aliphatic carbon atoms are referred to as Cα, Cβ and Cγ (Figure 13).

The typical functional groups in lignin are illustrated in Figure 14. The three aromatics

corresponding to the three monolignols (Figure 13), when inside the lignin structure, are often referred

to as p-hydroxyphenyl units (derived from p-coumaryl alcohol, where the aromatic ring does not have

methoxy group substituents), as guaiacyl units (derived from coniferyl alcohol, where the aromatic ring

Figure 13 - Lignin monomeric building blocks (adapted from Heitner et al., 2010)

Figure 14 - Lignin functional groups (Heitner et al., 2010)

13

has one methoxy group substituent), and as syringyl units (derived from sinapyl alcohol, where the

aromatic ring has two methoxy group substituents).

Pretreatments of Lignocellulosic Feedstock

The main objective of a lignocellulosic feedstock pretreatment is to promote the separation of

the three different biopolymers in the biomass sample (cellulose, hemicellulose and lignin), as

illustrated in Figure 15.

Pretreatment processes should have a low capital and operational cost. It should be effective

on a wide range of lignocellulosic material and should result in the recovery of most of the

lignocellulosic components in a useable form in separated fractions (Agbor et al., 2011). There are

several types of pretreatments: physical, chemical, biological and physicochemical. These

pretreatments are classified through a “severity factor”, which is defined as the combined effect of

temperature, acidity, and duration of pretreatment. According to the definition of “severity factor”, the

physicochemical pretreatments have the biggest “severity factor” (Agbor et al., 2011).

4.2.1. Physical Pretreatments

In general, the aim of physical pretreatments is to break the lignocellulosic feedstock into

smaller pieces that are easier to manage. These pretreatments increase the available specific surface

area, and reduce both the degree of polymerization and cellulose crystallinity. Examples of this type of

pretreatments are: Coarse Size Reduction, Chipping, Shredding, Grinding and Milling (Agbor et al.,

2011).

4.2.2. Biological Pretreatments

Biological pretreatments have mostly been associated with the action of fungi capable of

producing enzymes that can degrade lignin, hemicellulose, and polyphenols. White-rot fungi and Soft-

rot fungi have both been reported to degrade lignocellulose material, with White-rot being the most

effective at biological pretreatment of biomass. The rate of biological pretreatment however, is too

slow for industrial purposes. The residence time of 10–14 days, the requirement of careful growth

conditions, and the large amount of space to perform biological pretreatments are disadvantages that

make this method of pretreatment less attractive on an industrial scale (Agbor et al., 2011).

Figure 15 - Schematic representation of the pretreatment effect (Agbor et al., 2011)

14

4.2.3. Chemical Pretreatments

Alkaline pretreatment disrupts the lignin structure and breaks the linkage between lignin and

the other carbohydrate fractions in lignocellulosic biomass. One typical process example of this

pretreatment is the Soda Pulping (Zakzeski et al., 2010).

As for the acid pretreatment, concentrated acids are not preferred because they are corrosive

and must be recovered to make the pretreatment economically feasible. The dilute acid pretreatment

for hydrolysis of hemicellulose into its monomeric units makes cellulose more available. In fact, acid

pretreatment may require the use of an alkali pretreatment too.

In industry, the preferred chemical pretreatments are the alkali ones. Pretreatment with alkali

(NaOH, KOH, Ca(OH)2 and others) cause swelling of biomass, which increases the internal surface

area of the biomass, and decreases both the degree of polymerization and the cellulose crystallinity

(Agbor et al., 2011). Two well known processes that use this chemical pretreatment are the Kraft

Lignin Process (also known as Kraft Pulping or Sulfate Process) and the Lignosulfonate Process (also

known as Sulfite Pulping) (Zakzeski et al., 2010). Historically, the first process that was tested was the

Soda Pulping (using only sodium hydroxide) and, years later, the process was improved by adding of

sodium sulfide and renamed Kraft Pulping (Zakzeski et al., 2010).

4.2.4. Physicochemical Pretreatments

This category includes the vast majority of pretreatment technologies such as Steam

pretreatment (or Steam Explosion), Liquid Hot Water pretreatment, Wet Oxidation pretreatment,

Ammonia Fiber/Freeze Explosion, Ammonia Recycle Percolation, Aqueous Ammonia pretreatment

and Organosolv pretreatment.

These forms of pretreatment exploit the use of conditions and compounds that affect the

physical and chemical properties of biomass. These pretreatments are often used in industry given

their high “severity factor” compared to the other pretreatments, except for the chemical

pretreatments, which are industrially used and therefore present the highest “severity factor” (Agbor et

al., 2011).

Technical Lignins

Technical lignins are obtained as a result of lignocellulosic biomass processing. These types

of lignins differ significantly from the native ones, since they suffer a combination of multiple reactions

(catalyzed biomass hydrolysis, condensation of lignin fragments, elimination of native lignin functional

groups, formation of new functional groups, etc.). As a result, they are considerably more

heterogeneous (in terms of chemical structure and molecular mass) than the native lignins. As for

native lignins, it should be stressed that the structure of technical lignins will of course also depend on

the native feedstock source (Gupta et al., 2014).

15

Technical lignins can be classified from different points of view, as can be seen in Figure 16.

The kraft (and soda) lignins3 and lignosulfonates are generated by pulp and paper industrial

processes, where they are mostly considered as waste products without controllable chemical

properties (Gupta et al., 2014). There is also a group of technical lignins from various emerging

biomass biorefining processes that use either acid hydrolysis (AH), steam explosion (SE) or

organosolv (OS) pretreatment (Gupta et al., 2014).

Another consideration about the classification of technical lignins concerns the presence or

absence of sulfur in their structure. Kraft lignin and especially lignosulfonates are sulfur-containing

lignins whereas soda, OS, AH and SE lignin are sulfur-free or low-sulfur-containing lignins (Gupta et

al., 2014).

Chemistry of Lignin

Introductory matters as the function, the main monomers that constitute lignin, its

nomenclature and a short discussion about the different monolignols within the global structure of

lignin were previously exposed in chapter 4.1.3.

Lignin is a polyphenolic random co-polymer that results from the coupling of two monomeric

radicals, but more likely grows when monomeric radicals couple with phenoxy radicals formed on the

growing polymer (Figure 17). There are at least 20 different chemical linkages that have already been

identified in lignin, but only the more predominant will be considered and discussed (Fox, 2006).

Having in mind that the most abundant linkages in lignin involve the phenoxy Cβ position

(Figure 14), it appears to be the most reactive carbon. Each monomeric radical comprises a

monolignol (which can be p-coumaryl alcohol, coniferyl alcohol or sinapyl alcohol) that goes through

an oxidation process and gives origin to a monomeric radical. Having two monomeric radicals, a

radical coupling occurs with different mechanisms (depending on the chemical linkage that is being

formed) and the resulting product is a unit formed by two monolignols with one of the 20 different

chemical linkages in lignin (Heitner et al., 2010).

3 Note that soda pulping was later improved resulting in the kraft pulping process

Figure 16 - Classification of technical lignins (Gupta et al., 2014)

16

The seven most abundant chemical linkages in lignin are known as β-O-4, α-O-4, 5-O-4, β-5,

5-5, β-1 and β-β (Figure 22).

The β-O-4 linkage is the most abundant linkage in lignin given that this linkage comes from the

oxidative coupling of coniferyl alcohol (the most abundant monolignol, as referenced in chapter 4.5).

Figure 18 and Error! Reference source not found. show the mechanism behind the formation of β-

O-4 and the similar α-O-4 linkages. In the β-O-4 mechanism, the radical Cβ of one monolignol is

attached to the radical oxygen of another monolignol via radical coupling and then a water molecule

compensates the negative deficiency of the Cα next to the linked Cβ. On the contrary, in the α-O-4

formation, another oxygen radical of another monolignol attacks the Cα with the negative deficiency

instead of a water molecule. The probability of occurring one of these linkages is directly connected

with the abundance of other radical monolignols or water molecules.

Figure 17 - First step in lignin polymerization (Heitner et al., 2010)

Figure 18 - β-O-4 bond formation via radical coupling (Heitner et al., 2010)

Figure 19 - α-O-4 bond formation via radical coupling (Heitner et al., 2010)

17

Figure 20 illustrates the mechanism behind the β-5 linkage. This linkage gives origin to a more

condensed structure. Another typical linkage that results into a condensed structure also is the 5-5

linkage (the coupling of two phenoxy radicals at their C5 positions).

Another chemical linkage very similar to the β-5 linkage is the β-1 linkage. As the name

implies, this second linkage (Figure 21) occurs by the coupling of a phenoxy radical at the C1 position

and a monolignol radical in its Cβ position.

Two other typical linkages in lignin are the β-β linkage (or α-O-γ linkage) and the 4-O-5

linkage. The first linkage, as the name suggests, results in a structure in which two radical monolignols

are coupled at their Cβ positions. This coupling results into a condensed structure also. The second

linkage is the coupling of the same radicals of the 5-5 linkage but in one of the radicals, the structure

that reacts with the other radical resonates and the linkage that is formed occurs between the C5 of

one radical and the carbonyl group of the other. The final structure results into an ether bond between

the two monolignols.

Figure 22 summarizes the seven typical linkages in lignin. Most of them group together and

give origin to characteristic structures in lignin, as shown in Figure 23.

Figure 21 - β-1 bond formation via radical coupling (Heitner et al., 2010)

Figure 20 - β-5 bond formation via radical coupling (Heitner et al., 2010)

18

Straw Lignin

Lignin can be classified into three different classes according to its origin and monolignol

content. The most frequent units in lignin are the guaiacyl units, but the amount of the other two types

of unit (syringyl and p-hydroxyphenyl) varies according to the type of lignin. Hardwood lignin has both

syringyl units and guaiacyl units in similar amounts and has no trace of p-hydroxyphenyl units. In the

case of softwood lignin, the most predominant units are the guaiacyl units and there is a small trace of

both syringyl and p-hydroxyphenyl units (that can be neglected). In the case of straw lignin, both

syringyl and guaiacyl units are present similar amounts, while a smaller (but not negligible) amount of

p-hydroxyphenyl units is present (Heitner et al., 2010).

Lignin associates with carbohydrates, mostly hemicellulose, via covalent bonds at two sites:

Cα and phenoxy C4. These associations are called lignin carbohydrate complexes (LCC). The LCC

limit the efficient separation of lignin from plant cell wall. Hence, it is important to understand the LCC

linkages. For this, model compounds have been used to demonstrate these linkages. In straw lignin,

Figure 22 - Lignin linkage types and amounts (Heitner et al., 2010)

Figure 23 - Example lignin structure (Heitner et al., 2010)

19

the LCC is largely conceived through free radical coupling of ferulates (these compounds derive from

ferulic acid, which exists in a considerable amount in grasses and compression woods) with

monolignols or the tail of the growing straw lignin polymer (Crestini and Argyropoulos, 1997). The

mechanism of this specific LCC has been demonstrated and it is generally accepted that the cross-

linking of carbohydrates and straw lignin by ferulates presents the greatest barrier to efficient

utilization of grass cell wall (Ghaffar and Fan, 2013).

Straw lignin possesses a characteristic alkali solubility, so pretreatments that involve a high pH

will not have a great impact on the original structure of straw lignin. A example of a possible

pretreatment is Soda Pulping (Ghaffar and Fan, 2013).

5. Objectives of the Work

Since lignins are complex feedstocks, a molecular structure can not be directly obtained from

the available analyses, even by means of cutting-edge analytical techniques. Hence, these samples

are generally characterized by many different analytical techniques, which all highlight different

characteristics of the sample.

The objective of this work is to develop a method or algorithm that allows to propose a

chemical structure of a lignin sample starting from the available analytical data of the sample. In this

way, the molecular-level information is “reconstructed” from the partial analytical information. This

approach is known as molecular reconstruction or composition modeling.

Because there are no molecular reconstruction techniques for lignin in the open literature, the

first part of the present work is a literature review to understand how molecular reconstruction

techniques were developed for asphaltenes. In the second part of this work, a molecular

reconstruction algorithm for lignin was developed.

6. Molecular Reconstruction

If all the information concerning composition of a feedstock was known, refinery processes

and processing facilities would be much more optimized, better process models would be proposed,

and problems with catalysts and off-spec products would be more easily and quickly recognized,

understood and solved. Most likely, catalyst development would also be much quicker and efficient.

Knowing every detail of heavy petroleum fractions is a very complicated matter, given the

enormous complexity of these fractions and the limitations of current analytical techniques. This is

valid for any complex mixture (Altgelt and Boduszynski, 1994). In most cases, it is therefore essential

to consider structural information from different analytical methods (NMR, IR spectroscopy, elemental

analysis, GC/MS, etc) (Altgelt and Boduszynski, 1994).

Most compositional analyses only provide average information about the sample. For simple

mixtures it is relatively simple to obtain the real structures and compositions, as the number of

combinations (isomers) remains quite limited. Naturally, when the molecular weight and the

20

heteroatom concentration of a molecule increase, the number of possible existent structures also

increases, as well as the compositional complexity.

If it is impossible to obtain all structures of the mixture, at least a set of average structures can

be proposed that are approximately representative of the sample. This approach is known as

"composition modeling" or "molecular reconstruction".

Because today there are no methods that provide representative structures of molecules for a

complex mixture, it will be useful to consider structural group analysis methods, which give average

structures. Structural group analysis occupies a position midway between ultimate analysis (also

called elemental analysis), in which atoms are the components, and molecular analysis, in which

molecules are the components. It may be seen as an analytical method, giving information

somewhere between that obtained by elemental analysis on the one hand and by analysis for

individual hydrocarbons (molecular type analysis) on the other (Speight, 2006).

Structural group analysis methods have several assumptions that make mathematical

construction of the algorithms possible, because without them the possibilities of different generated

molecules are immense. Therefore the structures will only be as reliable as the assumptions used for

the mathematical procedure (Speight, 2006).

7. Heavy Petroleum Fractions

For hydrocarbon mixtures, the various approaches can be classified into two groups. The first

group of approaches only generates a single average molecule, while the second group of methods

generates a representative set of molecules. For the heaviest fractions of petroleum, such as

asphaltenes, most approaches propose an average molecule instead of a representative set of

molecules (Speight, 2006).

Analytical methods generally do not give a final structure for the molecules in a mixture, but

instead they give different structural information on the mixture. Hence, most authors collect different

information about the mixture from different analytical methods and then propose either an average

structure, a representative set of structures or some parameters regarding the structural arrangement

of the molecules. But, as said before, only analytical data will not be enough to propose one or several

structures. Although there are cases where the authors, apart from analytical data, use only a few

relations (Groenzin and Mullins, 2000; Yen et al., 1961; Ferris et al., 1967) and with that can derive

some conclusions about the structural rearrangement; the most common approach is the construction

of structural analysis algorithms to represent an average structure or a set of average structures (Al-

Zaid et al., 1998; Ali et al., 2005; Gauthier et al., 2008; Kowalewski et al., 1996).

For the authors that propose a representative set of structures, they typically use pure

mathematical algorithms based on different optimization criteria. This type of molecular reconstruction

methods can be divided into two sub-types.

The first sub-type of the above molecular reconstruction methods allows to obtain a detailed

molecular composition through the optimization of a specific objective function that can be derived

from thermodynamic concepts like Gibbs free energy or Shannon entropy or some sort of cost function

(Pyl et al., 2010). Most of these methods start with an algorithm that generates a set of molecules

21

(group contribution methods, stochastic methods) to be considered for the molecular reconstruction.

After the set of molecules is fully defined, there is a second algorithm where an objective function is

optimized (entropy maximization method, Monte Carlo simulation, QMR approach). After the

optimization is performed, this second algorithm results in a set of chosen molecules for the complex

mixture; this set is called the representative set of structures (de Oliveira et al., 2012; Verstraete et al.,

2004; de Oliveira et al., 2013; Al Halwachi et al., 2012; Boek et al., 2008; Hudebine and Verstraete,

2004; Neurock et al., 1994; Pyl et al., 2010; Verstraete et al., 2010). Another method that allows

describing the composition, chemical reactions and properties of complex hydrocarbon mixtures is the

SOL approach (Jaffe et al., 2005). Although this method does not use two different algorithms (one to

create the experimental set of molecules and the other to choose the best set of molecules), it does

not use a training set like the second sub-type does.

The second sub-type of molecular reconstruction methods uses interpolation techniques

based on a large set of experimental data, the training set. There are several authors who tried to

obtain different algorithms based on mathematical software like SPYRO (Dente and Ranzi, 1979), in

which the kinetic parameters can be estimated while the final structures are obtained. Another

approach integrates into a computational algorithm a set of detailed structural analytical information

(PIONA, GC-FIMS and SimDis), which can derive the complete hydrocarbon-type distribution profile

(Harry et al., 2008) for low-boiling fractions. Another way of characterizing the training set is, for

example, using the Artificial Neural Network approach (Pyl et al., 2010). This ANN method has

different utilizations: characterization of the feed module (Joo et al., 2001), estimation of different

operating conditions (Lopez et al., 2001), etc. Generally, these reconstruction methods are faster than

those of the first sub-type since they are computationally less demanding. A disadvantage is that,

because of the size of the employed training set is evidently finite, the application range of these

methods is also limited (Pyl et al., 2010).

Apart from hydrocarbon mixtures, another interesting components to be studied are the

phenolic resins. These components are quite different from hydrocarbons, given that phenolic resins

also contain high contents of oxygen, besides carbon and hydrogen (Chen and Chiu, 2000).

To better understand how molecular reconstruction works, some approaches for heavy

petroleum fractions, mostly asphaltenes, will be described below.

Asphaltenes

Asphaltenes are possibly the most studied and yet least understood materials in the petroleum

industry. They are considered to be the least valuable component of the crude oil. Everything about

asphaltenes appears to be non-conclusive, elusive and complex (Yen and Chilingarian, 2000).

In order to better understand the physicochemical behavior of asphaltenes, it is of interest to

know more about their chemical structure in terms of macromolecular groups (primary bonding

structure) and intra- and intermolecular bonds (secondary bonding structure). The asphaltene

molecules are constituted of more or less condensed aromatic cores carrying alkyl or naphthenic

substituents with heteroatoms (N, S, O) interspersed within the system (Kowalewski et al., 1996).

22

These heavy molecules can precipitate very easily due to changes in the residue composition.

Moreover, asphaltenes are known to be coke precursors in acid catalysis, catalyst inhibitors and

fouling agents (Gauthier et al., 2008).

Concerning the size of these molecules, there are many different studies with different results

for the size of the molecule. Several measurements using different techniques have yielded values

that differ by a factor of 10, 100 or even more. If the very high molecular weights (typically 10,000 to

100,000 g/mol) for asphaltene molecules are correct, then each molecule must have many separate

fused ring systems. On the contrary, if low molecular weights (typically 400 to 1,000 g/mol) are correct,

then each molecule has one or perhaps two cores per molecule (Groenzin and Mullins, 2000).

7.1.1. Experimental Data

The experimental data used (SEC, elemental analysis, 1H NMR, 13C NMR, refractive index

and density) has been obtained on a sample of Buzurgan (Middle East) vacuum residue feedstock

under hydroconversion conditions (Gauthier et al., 2008). A pilot plant unit was used to produce

effluents in residue conversion conditions ranging from 55 to 85 wt% 540°C+ conversion (Gauthier et

al., 2008). It must be mentioned that the available 1H NMR data is not complete, as there is only

access to the percentages of the different types of hydrogen atoms (α, β and γ with respect to the

aromatic ring and aromatic hydrogens). The experimental data is presented in appendix A.1.

Determination of average structural parameters of

asphaltenes

As mentioned above, several methods have been proposed in the literature for the molecular

reconstruction of asphaltenes. Also, as said previously, these methods can be divided into two big

classes: those that propose an average structure and those that propose a representative set of

structures. The aim of this chapter is to test some of the different methods that propose an average

structure for an asphaltene sample.

7.2.1. Algorithm of Sato

The method developed by Sato (1997) uses the molecular weight, elemental analysis and 13C

NMR as experimental inputs and calculates different structural parameters, in different classes (Rings,

Aromatic atoms, Fused rings, Naphthenic atoms, Paraffins, Density and Parameters). This algorithm is

also known as Structural Analysis of Aromatic Hydrocarbons (SAAH). In his article, the author

validated his algorithm using the model molecule shown in Figure 24.

Figure 24 - Model molecule considered in Sato (1997)

23

The algorithm was also applied on several samples of weathered Kuwait oil spills. Gauthier et

al. (2008) also tested the SAAH algorithm of Sato (1997) for Buzurgan asphaltene samples.

7.2.2. Algorithm of Speight

The structural algorithm of Speight (1970) is based on 1H NMR, molecular weight and

elemental analysis. The algorithm provides much less calculated properties than the previous

algorithm. The conclusions of this algorithm are more oriented towards information on the core of the

molecule. This algorithm requires the hydrogen spectrum of the sample. Because the full hydrogen

spectrum was not analyzed for the Buzurgan sample, assumptions were needed to obtain the

necessary input data (appendix A.2.2). The work presented by Speight has an interesting table (Figure

25) that gives the ratios of peripheral aromatic carbons to total aromatic carbons for known fused ring

compounds. In this way, the aromatic core of the molecule can be estimated (Speight, 1970).

In his work, the author applied his structural investigation to the constituents of Athabasca

bitumen. An estimate was made of the structure of the aromatics within different fractions of the

Athabasca bitumen by determining the peripheral and internal aromatic carbon atoms by means of 1H

NMR spectroscopy. In his above conclusions, the author stresses the generally low proportion of

naphthenic carbons in all the fractions, the absence of free paraffinic molecules in the bitumen (the

paraffinic carbons preferably form long alkyl chains), the fact that the aromatic carbons appear to

consist of condensed aromatic ring systems (ranging from 1-2 aromatic rings to 40 or more in the

asphaltenes), and that asphaltene molecules appear to consist of four or more aromatic sheets,

containing 10 or more rings each, interconnected by one or more alkyl chains (Speight, 1970).

Figure 25 represents an excerpt of the complete table in Speight (1970). From this, the author

inferred the structure of the aromatic core of the asphaltenes fraction in the Athabasca bitumen.

Figure 25 - Cp/Ca ratios of condensed aromatic compounds. Adapted from Speight (1970)

24

7.2.3. Algorithm of Montgomery and Boyd

The algorithm of Montgomery and Boyd (1959) was originally developed by Van Krevelen

(Montgomery and Boyd, 1959) for the constituents of coal and was then modified for petroleum heavy

fractions. The experimental data used is molecular weight, elemental analysis, 13C NMR, refractive

index (at 20°C) and density (at 20°C), although when this method was first proposed (in 1959) the

authors initially used InfraRed spectroscopy to obtain the number of aromatic carbons in the molecule.

Because the IR spectroscopy is not available and 13C NMR is, the number of aromatic carbons was

obtained through this technique.

The authors tested the algorithm with a set of 114 hydrocarbons whose properties were

determined by API Project 42 and seven fused-ring aromatic compounds whose properties were

determined by Van Krevelen (Montgomery and Boyd, 1959).

After commenting all the results on the hydrocarbons, the authors conclude that their algorithm

can be used with confidence in extrapolating beyond the molecular weight range of the known

compounds that were used to establish the system (Montgomery and Boyd, 1959).

7.2.4. Algorithm of Hirsch and Altgelt

The algorithm presented by Hirsch and Altgelt (1970) is a much more detailed and complex

algorithm, and has even more variables than the method of Sato (1997). The necessary experimental

data comprises elemental analysis, molecular weight, 1H NMR and density (at 20°C).

In their article, the authors validated their algorithm using the model molecule shown in Figure

26.

The algorithm works pretty well for complex molecules with various fused ring systems (Hirsch

and Altgelt, 1970). In appendix A.2.4, this algorithm and the different assumptions are explained. Most

of the equations that were proposed in the algorithm were obtained for six-member ring structures, so

if a molecule with many five-member rings is tested, some errors or deviations from the real values are

to be expected. Also, the same applies to a heterogeneous mixture containing a large fraction of

aliphatic molecules (Hirsch and Altgelt, 1970). Finally the authors propose the 13C NMR technique to

Figure 26 - Model molecule considered in Hirsch and Altgelt (1970)

25

be incorporated in the algorithm because it can be traded for a floating parameter4, decreasing the

degrees of freedom of the system and thereby reducing the guess work required by the algorithm

(Hirsch and Altgelt, 1970).

7.2.5. Brown-Ladner modified Algorithm

This algorithm was initially developed for coal fractions and later modified for petroleum

fractions. The experimental data required for this structural algorithm comprises 1H NMR, molecular

weight and elemental analysis. The original algorithm of Brown-Ladner (Altgelt and Boduszynski,

1994) was purely based on 1H NMR and elemental analysis, and the authors considered three

assumptions for the algorithm to work (1 - all the oxygen is attached directly to the aromatic ring

systems and is not shared between them; 2 – aromatic rings must not be linked by C-C bonds; 3 –

values must be assumed for x and y, where x represents the average number of hydrogen atoms per

α-carbon and y represents the average number of hydrogen atoms per β-carbon) (Altgelt and

Boduszynski, 1994). Later, with the modification of the original algorithm, the 13C NMR technique was

introduced in the algorithm, so the third assumption was confirmed through the DEPT 13C NMR5

technique as x and y being both close to 2 (Altgelt and Boduszynski, 1994 and Yen and Chilingarian,

2000).

This modified algorithm was initially tested on various model molecules, of very diverse types,

ranging from alkyl naphthalenes to highly pericondensed naphtheno-aromatics consisting of several

aromatic and naphthenic ring systems found only in specific refinery streams (Altgelt and

Boduszynski, 1994). Later, the modified algorithm was applied to twelve vacuum residues of Chinese

crude oils and their fractions (Yen and Chilingarian, 2000).

7.2.6. Algorithm of Williams

The algorithm presented by Williams (1957) involves a detailed treatment of an aromatic

fraction of an oil sample (Petrakis and Allen, 1987). The experimental data required for the model are

elemental analysis, molecular weight, 1H NMR and a “branchiness index”.

The “branchiness index” is defined as the peak height ratio of the gamma to beta protons

(Williams, 1958). This variable is necessary to avoid lack of inputs. This is the oldest method, so the

results may be expected to present deviations in comparison to the more recent methods, which are

based on more recent and detailed analytical techniques.

This algorithm was applied to four asphalt fractions samples, to a virgin gas oil sample and to

a catalytic cycle stock sample (Williams, 1958). Also, other authors applied this algorithm to four

vacuum gas oil samples (Petrakis and Allen, 1987).

4 The use of this term is well explained in appendix A.2.4

5 More information concerning the assumptions is in appendix A.2.5

26

7.2.7. Algorithm of Knight

The algorithm of Knight (1967) is based on Williams (1958) but modified to use a 13C NMR

analysis. With this modification, the number of structural equations decreased compared to the original

method. Hence, the experimental data used is, as well as in Williams (1958), molecular weight,

elemental analysis, and 13C NMR.

Because this algorithm is based on a 13C NMR technique, it only needs one assumption

(appendix A.2.7). This method has the advantage of directly counting the aromatic and aliphatic

carbons, whereas all methods that are not based on 13C NMR calculate the carbon aromaticity by

indirect means (Petrakis and Allen, 1987). This advantage is especially verified when there are some

small errors in some input values (or in some intermediate variable), since, due to a propagation of

errors, the final results may deviate strongly from the actual values (Petrakis and Allen, 1987). The

algorithm of Knight (1967) has much less error propagation compared to the methods based on 1H

NMR, since it only involves spectral information (Petrakis and Allen, 1987). This is verified for all

algorithms that are based on 13C NMR (Petrakis and Allen, 1987).

This algorithm was applied to five aromatic and saturated fractions from gas oil and light

lubricating oil samples (Knight, 1967). The author concludes (using a validation criterion based on the

calculation of the number of saturated substituents per molecule and the number of naphthenic rings

per molecule) that, for the first time, this algorithm offers a reliable calculation for the naphthenic rings

in the molecule in aromatic fractions (Knight, 1967). Also, Petrakis and Allen (1987) applied this

algorithm to four vacuum gasoil samples.

7.2.8. Algorithm of Cantor

The algorithm presented by Cantor (1978) was built for coal-derived liquids. This method is

based on Williams’s and Knight’s methods. Also, the nomenclature used in the model for the structural

variables is largely taken from another method that is presented bellow (Clutter et al., 1972). The

experimental data required is 13C NMR, 1H NMR, molecular weight and elemental analysis.

Cantor applied his algorithm to six anthracene oil samples and their respective coal oil

samples. The author compared the different structural parameters for each sample, and states the

parameters of these average molecular structures can be used to develop correlations with the

feedstock reactivity and required process conditions for conversion of these fractions (Cantor, 1978).

7.2.9. Algorithm of Dickinson

This algorithm is based on several methods (Williams 1957; Hirsch and Altgelt 1970; Oka et

al. 1976; Knight 1967; Cantor 1978). The experimental data needed for this model concerns 1H NMR,

13C NMR, molecular weight and elemental analysis.

The algorithm of Dickinson (1979) was tested in three petroleum-derived materials: petroleum

pitch, decanted oil residue and ethylene tar residue. The author refers the importance of the work as

for characterizing residual petroleum fractions suitable as precursors for petroleum coke and pitches

(Dickinson, 1979). The equations used are similar to those used by Williams (1958) and Knight (1967).

27

The combination of 1H NMR and 13C NMR techniques provides average structural information

from which a representative structure can be proposed. Dickinson (1979) concludes by saying that a

more detailed structural analysis of the samples could be accomplished by fractionating them,

applying the various analytical techniques, and reconstructing a structure for each fraction (Dickinson,

1979).

7.2.10. Algorithm of Qian, Zhang and Li 1983

The algorithm of Qian, Zhang and Li (1983) is based on the methods of Knight (1967) and

Dickinson (1979). This work presents two sets of equations for structure determination: one based on

1H NMR and IR spectroscopy, and the other based on 1H NMR and 13C NMR. Because of the lack of

analytical information concerning IR spectroscopy, only the second set of equations could be used

(chapter 7.4). Only the part containing the equations derived from 13C NMR will be discussed. The

equations derived from 1H NMR could not be used because they also require structural information

acquired by IR spectroscopy. The experimental data used in this method is 1H NMR, 13C NMR,

molecular weight and elemental analysis.

Qian, Zhang and Li (1983) tested the algorithm on four aromatic fractions of decant oil, heavy

distillate from delayed coker, paraffin-based petroleum pitch and thermally cracked residues. In their

work, there is a comparison between the results of each set of equations and the Brown-Ladner

algorithm. The authors concluded that the Brown-Ladner equations may be used for molecules, which

have relatively low aromaticity and many alkyl substituents with long straight chains (Qian, Zhang and

Li, 1983). The parameter related to the number of naphthenic rings presents some discrepancies for

each sample and for each set of equations. It is therefore difficult to know which value is more precise.

The authors concluded in favor of the 13C NMR technique.

7.2.11. Algorithm of Qian, Zhang and Li 1984

This algorithm is based on Knight’s method and is also an improvement of the previous

method of the same authors (Algorithm of Qian, Zhang and Li 1983). This algorithm was specifically

made for high aromaticity samples, such as coal derivatives. The experimental data used in this

structural model concerns molecular weight, elemental analysis, 1H NMR and 13C NMR.

The algorithm of Qian, Zang and Li (1984) was applied to five pitch and oil residues. The

structural equations are based on the previous paper from Qian, Zhang and Li (1983) in chapter

7.2.10, but have some modifications concerning the determination of the naphthenic carbons (Qian,

Zhang and Li, 1984). This algorithm presents a new equation to estimate the number of naphthenic

carbons in the molecule that is demonstrated to be more accurate than Knight’s equation (appendix

A.2.7) for evaluating high aromatic samples (Qian, Zhang and Li, 1984).

7.2.12. Other Algorithms

There are two algorithms apart from the above that could not be used because of the lack of

experimental data.

The Clutter (1972) algorithm was built to characterize an aromatic fraction of a petroleum

sample through a detailed analysis of its proton magnetic resonance spectrum. This method uses the

28

Williams method to first calculate all of the average parameters, as well as the fraction of

monoaromatic and diaromatic (fused) ring systems. Given that the detailed information 1H NMR

spectrum of the Buzurgan feedstock sample is not available, this algorithm could not be applied.

The Poveda and Molina (2012) algorithm proposes a new procedure for obtaining a full set of

average molecular parameters (AMPs) of heavy crude oils and their fractions. This model is a much

more complex one; it proposes a nomenclature system for the identification of the possible chemical

groups present in an average molecular structure derived from the calculated AMPs. This algorithm

could not be applied because all of the structural equations were based on the chemical shifts of the

detailed 1H NMR spectrum, which is not available for the Buzurgan sample.

Validation

In order to compare and validate the above eleven algorithms, seven molecules whose

structures (and the different physicochemical and analytical properties) are known were tested with

these algorithms. Some conclusions were taken mostly regarding the application range of each

algorithm and are illustrated for two of these seven test molecules below. For the other 5 test

molecules, almost all methods find the correct results.

All seven molecules are identified and their analytical data is given in appendix A.1. The two

chosen test molecules are test molecule 4 (1,2,3,4-tetrahydro-1,4-dimethyl-naphthalene) and test

molecule 7 (9,10-di-1-naphthyl-anthracene). The first one contains aromatic, naphthenic and paraffinic

carbons and all the necessary analytical data is available, so all the eleven algorithms could be tested.

The second one was chosen to test the application of the algorithms to a pure aromatic compound

(even although density and refractive index are not available for this compound).

The results and comments concerning the application of the algorithms to these molecules are

given in chapter 7.4.

Comparison between Algorithms

Table 7-1 – Analytical techniques used as input in each algorithm

Experimental Data Used / Method MW Elemental Analysis

1H NMR

13C NMR

Refractive Index (20°C)

Density (20°C)

IR

Sato Speight BL (Brown-Ladner) HA (Hirsch and Altgelt) MB (Montgomery and Boyd) Williams Knight Cantor Dickinson QZL83 (Qian, Zhang and Li, 1983) QZL84 (Qian, Zhang and Li, 1984)

x x x x x x x x x x x

x x x x x x x x x x x

- x x x - x x x x x x

x - x - - - x x x x x

- - - - x - - - - - -

- - - x x - - - - - -

- - - - x - - - - - -

All the algorithms required the molecular weight and the elemental analysis. The older ones

use 1H NMR (or IR spectroscopy), while the more recent algorithms use 13C NMR or both NMR

29

techniques. Also, there are two methods that use less common experimental data such as the

refractive index and density.

After applying the above algorithms to the experimental data available, different results were

obtained for the different structural variables. Because not all structural variables are calculated in

each algorithm, there is an empty space (-) in some structural variables, indicating that the algorithm

did not return this structural variable. In case of negative values, it most likely means that the algorithm

was not designed for the type of sample used (it could be for lighter petroleum fractions, coal-derived

liquids, etc) or even that the algorithm was not well adapted to the studied sample (Buzurgan

asphaltene) and test molecules 4 and 7.

The most important structural variables are the number of aromatic, naphthenic and paraffinic

carbons, the number of aromatic and naphthenic rings, the aromaticity, the ratio of peripheral carbon

atoms per aromatic sheet to total aromatic carbon atoms per aromatic sheet, and the degree of

substitution of aromatic rings in the molecule. These variables are essential to build the core of the

molecule, which normally is the first thing to be considered (Altgelt and Boduszynski, 1994).

Table 7-2 - Most important structural variables for test molecule 4

CA CN CP RA RN fa CPe CPe/CA %AS

Sato

Speight

BL

HA

MB

Williams

Knight

Cantor

Dickinson

QZL83

QZL84

6

7

5,9

7,4

5,2

5,4

6

6

6

6

6

4

1

4

-1,1

0,2

-

-

-

-

-

2,8

2

3

1,9

4,9

4,9

-

-

-

-

-

3,2

1

2

1

0,9

-

1,2

1,5

1,5

1,5

1,5

1,5

1

-

1

0,3

-

1,1

0,5

0,5

0,5

0,5

0,5

0,5

0,58

0,5

-

-

0,5

0,5

0,5

-

-

-

8

5

-

7,7

-

-

-

-

-

-

-

1

1

-

1

-

-

-

-

-

-

-

-

20

20

-

-

21

20

20

20

20

20

Table 7-3 - Most important structural variables for test molecule 7


Sato

Speight

BL

HA

MB

Williams

Knight

Cantor

Dickinson

QZL83

QZL84

34

34

34

-

-

34

34

34

34

34

34

0

0

-4

-

-

-

-

-

-

-

0

0

0

4

-

-

-

-

-

-

-

0

7

7

8

-

-

7

7

7

7

7

7

0

-

-1

-

-

0

0

0

0

0

0

1

1

1

-

-

1

1

1

-

-

-

26

22

-

-

-

-

-

-

-

-

-

0,8

0,7

-

-

-

-

-

-

-

-

-

-

0

0

-

-

0

0

0

0

0

0

Table 7-4 - Most important structural variables for Buzurgan asphaltene sample


30

Sato

Speight

BL

HA

MB

Williams

Knight

Cantor

Dickinson

QZL83

QZL84

30,2

29,6

29,6

26,8

33,6

30,6

30,2

30,2

30,2

30,2

30,2

1,8

2,5

5,2

9,2

0

-

-

-

-

-

3,2

5,1

5

2,3

0

4,6

-

-

-

-

-

3,7

8

8,9

8,5

6,8

-

9,7

9,4

9,4

9,4

9,4

9,4

2

-

1,7

4,8

-

0,1

0,6

0,6

0,6

0,6

0,6

0,8

0,8

0,8

-

-

0,8

0,8

0,8

-

-

-

14,1

13,6

-

18,1

-

-

-

-

-

-

-

0,5

0,5

-

0,7

-

-

-

-

-

-

-

-

19

18,7

-

-

16,5

17,6

17,4

17,4

17,4

17,4

As can be observed in Table 7-2, Table 7-3 and Table 7-4, most of the values for the number

of aromatic carbon atoms (CA) are similar. This value is extremely important for petroleum fractions

and coal derived-liquids, since they are mainly composed by carbon structures. Indeed, as explained

before, Speight (1970)’s algorithm provides a table with a set of values for the variable CPe/CA, which

is used to estimate the core of the condensed aromatic compounds. In Table 7-2, there are four

algorithms whose values for variable CA (the number of aromatic carbon atoms) deviate from the

actual value of 6 aromatic carbon atoms (appendix A.1): Speight (1970), Hirsch and Altgelt (1970),

Montgomery and Boyd (1959) and Williams (1958). This is mainly due to the fact that, in the

assumptions and simplifications of these algorithms, molecules such as test molecule 4 have not been

considered. The largest error is observed for the Hirsch and Altgelt (1970) method. As can be seen in

Table 7-3 for test molecule 7, whose structure respects all the assumptions for all the algorithms, all

algorithms find exactly the correct number of aromatic carbon atoms. Based on these two test

molecules, one can conclude that the methods of Sato (1997), Brown-Ladner (1960), Knight (1967),

Cantor (1978), Dickinson (1979), Qian, Zhang and Li (1983), and Qian, Zhang and Li (1984) correctly

predict the number of aromatic carbon atoms for these test molecules. For the unknown Buzurgan

sample (Table 7-4), all methods obtain approximately 30 aromatic carbon atoms, with the exception of

the Montgomery and Boyd (1959) algorithm and the Hirsch and Altgelt (1970) algorithm, which present

quite large deviations from this average value. In conclusion, the number of aromatic carbon atoms

(CA) is correctly predicted by 7 of the 11 algorithms, while two algorithms (Hirsch and Altgelt (1970)

and Montgomery and Boyd (1959)) are wrong in all three cases.

For the number of naphthenic carbons (CN), the observed values have some discrepancy

between each other in all three tables above. For test molecules 4 and 7, the correct number of

naphthenic carbon atoms is 4 and 0, respectively. Given this, the methods of Sato (1997) and Brown-

Ladner (1960) are correct for test molecule 4, while the methods of Sato (1997), Speight (1970) and

Qian, Zhang and Li (1984) are correct for test molecule 7. Only the method of Sato (1997) is correct

for these two test molecules. For the unknown Buzurgan sample (Table 7-4), the values for CN range

from 0 to 9,24. It is therefore hard to conclude something from the range of CN values. Hirsch and

Altgelt (1970) developed their model with the objective of having a reliable and detailed estimation of

the number and structure of the naphthenic carbons, so it would be expectable to consider the value

of CN for Hirsch and Altgelt (1970) the most trustworthy. At the same time, their method did not

perform well on test molecules 4 and 7 and they do not use the 13C NMR data, which gives the most

31

information on the carbon types. Moreover, their value for CA is 26,75 (Table 7-4), the lowest value in

the list and inconsistent with the 13C NMR analysis. Indeed, 13C NMR clearly indicates that the number

of aromatic carbons has to be around 30, thus eliminating the algorithms of Hirsch and Altgelt (1970)

and of Montgomery and Boyd (1959). Hence, the values for CN of the Buzurgan sample now range

from 1,8 to 5,2. As only the method of Sato (1997) correctly predicted the number of naphthenic

carbon atoms for the test molecules, we consider that the number of naphthenic carbon atoms for the

Buzurgan sample is probably 1,8.

The values for the number of paraffinic carbon atoms (CP) also shows some variation. For test

molecules 4 and 7, the correct number of paraffinic carbon atoms is 2 and 0, respectively. For test

molecule 4, the algorithms of Speight (1970), Hirsch and Altgelt (1970), Montgomery and Boyd (1959)

and Qian, Zhang and Li (1984) show values that differ from the actual value of 2. Only the methods of

Sato (1997) and Brown-Ladner (1960) are therefore correct for test molecule 4. For test molecule 7,

the only wrong value is obtained with the Brown-Ladner (1960) algorithm, and the methods of Sato

(1997), Speight (1970) and Qian, Zhang and Li (1984) are correct for test molecule 7. Again, only the

algorithm of Sato (1997) is correct for these two test molecules. For the unknown Buzurgan sample

(Table 7-4), the values for CP range from 0 to 5,1. Table 7-4 shows the lowest value for the Hirsch and

Altgelt (1970) algorithm, and then there are two groups of similar values, the group of Sato (1997),

Speight (1970) and Montgomery and Boyd (1959), and then the group of Brown-Ladner (1960) and

Qian, Zhang and Li (1984). As only the method of Sato (1997) correctly predicted the number of

paraffinic carbon atoms for the test molecules, we consider that the number of paraffinic carbon atoms

for the Buzurgan sample probably equals 5,1.

Another important structural variable is the number of aromatic rings, RA. In the light of the

dependence between RA and CA, these two variables are almost enough to describe the core of the

molecule. Only the method of Montgomery and Boyd (1959) does not give a value for this structural

variable, but it could be obtained considering a simple relation for pericondensed or catacondensed

structures (depending on the approximation that is being considered) and based on the number of

aromatic carbons CA (Yen and Chilingarian, 2000). Another important parameter also concerns the

total number of rings in the system, which is the sum of the number of aromatic rings, RA, and number

of aromatic rings, RN. For test molecule 4, there are 2 rings, one aromatic ring and one naphthenic

ring. From Table 7-2, it can be seen that most algorithms find 2 rings, except the method of Hirsch and

Altgelt (1970), which indicates 1,28 rings, and the method of Williams (1958), which calculates 2,28

rings. Concerning the number of aromatic rings, the method of Speight (1970) presents values that

differ significantly from the actual value (2 aromatic rings), while the last five methods all obtain 1,5

aromatic rings and show the same pattern: half a unit is wrongly attributed to the aromatic rings, even

though the total number of rings is correct. Hence, for test molecule 4, only the methods of Sato

(1997) and the method of Brown-Ladner (1960) calculate the correct number of total rings, aromatic

rings and naphthenic rings. The method of Williams (1958) shows a slight deviation from the correct

result. Test molecule 7 has 7 aromatic rings and no naphthenic rings. For test molecule 7, it can be

observed that only the Brown-Ladner (1960) finds an incorrect result, indicating 8 aromatic rings and

compensating its difference by a negative value for the number of naphthenic rings, so the total

32

number of rings still equals 7. The number of aromatic rings (RA) is therefore correctly predicted by 8

of the 11 algorithms for molecule 7. Again, for these two test molecules, only the method of Sato

(1997) correctly predicts the total number of rings and the number of aromatic rings in both cases. For

the unknown Buzurgan sample, Table 7-4 shows that the total number of rings varies between 9,74

and 10,26, except for the method of Hirsch and Altgelt (1970) where the total number of rings equals

11,62. These values for the total number of rings are therefore very consistent between the different

methods. The number of aromatic rings (RA) varies between 8 and 9,67, except for the method of

Hirsch and Altgelt (1970) where the number of aromatic rings equals 6,80. It can be observed that the

value for the method of Hirsch and Altgelt (1970) is quite far below the average value for RA, but it has

to be stressed that for the Hirsch and Altgelt (1970) the low value of RA is consistent with the also low

value of CA. From Table 7-4, we can conclude that the total number of rings in the Buzurgan

asphaltenes is quite well predicted by most methods and it is consistently around 10. As only the

method of Sato (1997) correctly predicted the number of aromatic ring for both test molecules, we

consider that the number of aromatic rings will probably be equal to 8.

The number of naphthenic rings, RN, is a structural variable that is also important to

understand the complete core of the molecule. As mentioned before, the sum of RA and RN gives the

total number of rings in the molecule. With RN, RA, CN and CA it is possible to propose a structure for

the core of the molecule (or a set of fused ring systems). For the test molecules, Sato (1997)'s method

appeared to be the best. As seen in Table 7-4, Sato (1997)’s and Brown-Ladner (1960)’s RN values

are quite similar for the Buzurgan sample. The values for RN are equal for the last five algorithms, but

lower that the value of Sato (1997)'s method, exactly as for test molecule 4. In contrast, the value for

RN in Williams (1958)’s algorithm is lower than the average value. Also, in this last method, the

consistency of the lower values of CA and RA and the higher values of CN and RN in the Hirsch and

Altgelt (1970) algorithm can be observed.

The aromaticity, fa, is an important structural variable because, in a simple way, it describes

the content of aromatic carbons relative to the total number of carbon atoms. Hence, the higher is fa,

the higher is CA too. It can be observed that the values for this variable are similar for the various

methods. The aromaticity values are more reliable for the methods that are based on 13C NMR (Sato,

1997; Knight, 1967; Cantor, 1978; Dickinson, 1979; Qian, Zhang and Li, 1983; Qian, Zhang and Li,

1984) because this analytical technique directly counts the aromatic and aliphatic carbon atoms,

whereas other methods that do not use this technique calculate the carbon aromaticity via indirect

means (Speight, 1970; Brown-Ladner, 1960; Hirsch and Altgelt, 1970; Montgomery and Boyd, 1959;

Williams, 1958) (Petrakis and Allen, 1987). Regarding the test molecules, there is nothing to be

observed, except for some little deviations for the values of Speight (1970) and Williams (1958)

compared to the exact value for test molecule 4 (fa = 0,4). For test molecule 7, the results are perfect,

while for the unknown Buzurgan sample, all values are very close.

The tables above also contain some less important structural variables: CPeA, CPeA/CA and

%AS. In fact, the first variable (CPeA) is only presented so that the calculation of CPe/CA is easily

understood. CPeA is the number of peripheral aromatic carbon atoms in the fused ring systems. The

second variable (CPeA/CA) is, according to Speight (1970), an estimation of the average shape of the

33

aromatic fused ring systems (Speight, 1970). Finally, %AS represents the percent substitution of the

aromatic rings, which gives a general idea of how much substitutions aromatic rings have on average

in the molecule (Dickinson, 1979). CPeA/CA was calculated for Sato (1997), Speight (1970) and Hirsch

and Altgelt (1970), while %AS was calculated for Speight (1970), Brown-Ladner (1960) and the last six

methods. For test molecule 4, Sato (1997) obtains the correct value for CPeA. Speight (1970) shows

the value of 5 for CPeA, which deviates by 1 from the actual value of 6, and the value from Hirsch and

Altgelt (1970) shows an important deviation, but this is due to the fact that this method considered 8

aromatic carbons in test molecule 4. Looking at the shape factor CPeA/CA, whose exact value equals 1

for test molecule 4, both Sato (1997) and Hirsch and Altgelt (1970) obtain the correct value. For test

molecule 7, this exact value for CPeA is again obtained with Sato (1997)'s method, while this variable is

quite close for Speight (1970), which can be explained by the affinity of this algorithm to the pure

aromatic compounds. For the unknown Buzurgan sample, there are again only three values of CPeA/CA

to compare, which is a small sample set, so the conclusions concerning this variable may not be as

general as they should. All values obtained, both for CPeA and for CPeA/CA, are quite close. For %AS,

the eight values obtained for test molecules 4 and 7 are consistent and correct. For the unknown

Buzurgan sample, the eight values obtained can be divided into two groups: the group composed by

the five last methods, where the values are almost equal between each other; and the second group

composed by Speight (1970)’s, Brown-Ladner (1960)’s and Williams (1958)’s values, which present

bigger deviations from the average value of the first group. The methods of Speight (1970), Brown-

Ladner (1960) and Williams (1958) are based on different analytical data than the last five methods.

This fact explains the observed differences between the values of %AS.

The final conclusion regarding all the observations above establishes the algorithm of Sato

(1997) as the method that is best suited with the available analytical data and is most robust.

Structure Proposal

In the previous chapter, various methods were applied to calculate some structural

parameters. For these structural parameters, a molecule should now be proposed. These approaches

have been described in detail in Petrakis and Allen (1987) and some computer algorithms have been

developed in the literature to perform this task (Oka et al., 1977 and Chang et al., 1982). In this

chapter, an example will be given for Buzurgan asphaltenes.

As mentioned before, previous work already applied this algorithm to the high conversion

Buzurgan asphaltene sample. The results of their final structures are presented below.

Gauthier et al. (2008) tested the SAAH algorithm of Sato (1997) for the Buzurgan asphaltene

sample (Figure 27). Also, Gauthier et al. (2008) went further and applied the method to the

experimental data at different residue conversion values (Figure 28). When residue conversion

decreases, a very large increase of the apparent asphaltene molecular weight is observed. This could

be due to the association mechanism. Also, the number of possible molecules drastically increases

with molecular weight (Gauthier et al., 2008).

34

Figure 27 - Possible asphaltene molecular structures at the residue conversion level of 85 %wt (Gauthier et al., 2008)

To validate the proposed structures, the authors checked the boiling point temperature of the

reconstructed molecules at 85% conversion (Gauthier et al., 2008). The temperatures were obtained

through a group contribution method that was extrapolated towards larger polycyclic numbers

(Gauthier et al., 2008). Boiling point temperatures were found in the range of 600-800°C. This

therefore confirmed that these molecules can be found in the vacuum residue fraction (Gauthier et al.,

2008). At the end of their work, the authors propose a hydroconversion mechanism for this asphaltene

sample that is consistent with their observations.

In Medeiros (2013), the author proposes a different structure (Figure 29) than those obtained

by Gauthier et al. (2008), but with the same structural parameters given by the algorithm of Sato

(1997) (appendix A.2).

Figure 28 - Possible average molecular evolution of asphaltenes as a function of residue conversion X540 °C+ (Gauthier et al., 2008)

35

Figure 30 - Proposed structure #1 for asphaltene sample at 85% of residue conversion



In the present work, Sato's SAAH algorithm was applied to the same analytic data for the

Buzurgan asphaltene sample at 85% residue conversion, and the following three structures were

proposed:

The following table compares some data on the structures proposed by the various authors to

the experimental data for the Buzurgan asphaltene sample at 85% of residue conversion:

Table 7-5 - Comparison between previous works and present work about Buzurgan asphaltene sample at 85% of

residue conversion

Experimental [Gauthier

et al. 2008] [Medeiros, D. 2013]

Structure #1

Structure #2

Structure #3

MW (g/mol) 492,0 470 470 472 470 484

C (atoms/molecule) H (atoms/molecule) N (atoms/molecule) O (atoms/molecule)

37,1 26,1 0,4 0,3

37 26 0 0

37 26 0 0

37 28 0 0

37 26 0 0

38 28 0 0

Figure 29 - Proposed structure for asphaltene molecule at 85 %wt of residue conversion (Medeiros, 2013)

36

The first observation to be made is the difference between the experimental molecular weight

and those obtained by Gauthier et al. (2008) and Medeiros (2013). This difference is due to the

presence of heteroatoms in the experimental data. For a matter of simplification, Sato (1997) did not

consider the presence of heteroatoms. Hence, a decrease in the molecular weight is expected. The

two proposed structures from Gauthier et al. (2008), the structure from Medeiros (2013), structure #1

and structure #2 have a similar molecular weight. Structure #3 has one more carbon than the four

other structures, so the molecular weight of structure #3 is larger. Structure #3 has one additional

carbon atom and two additional hydrogen atoms than structure #2, leading to a difference in molecular

weight of 14 g/mol. For the total number of hydrogen atoms, the proposed structures #1 and #3

contain two more hydrogens than the structures proposed by Gauthier et al. (2008) and Medeiros

(2013) and than structure #2. For the aliphatic carbon types, both structures proposed by Gauthier et

al. (2008) and by Medeiros (2013), and structure #2 have values that are closer to the experimental

values than structure #1 and structure #3. For the aromatic carbon types, the structures proposed by

Gauthier et al. (2008) and by Medeiros (2013) have the same number of aromatic CH groups as the

experimental data (and hence the correct number of quaternary aromatic carbon), but they do not

have the correct amount of condensed quaternary carbon (the structure of Gauthier et al. (2008) is too

condensed, while that of Medeiros (2013) is not condensed enough). Using different rearrangements

of the various rings, the three structures proposed in the present work have the correct number of

condensed quaternary carbon atoms. Structure #1 and structure #3 have a deviation of one for the

number of aromatic CH groups compared to the experimental value. For structure #2, all values of the

different carbon types are very close.

In view of the discussion above, structure #2 seems to be the best proposal compared to the

structures proposed by Gauthier et al. (2008) and by Medeiros (2013), since it better represents the

different sub-types of aromatic carbon.

Blind Test

To test the above conclusions about the algorithm of Sato (1997), two components were

tested with this algorithm without knowing their real structures. The initial NMR spectra (appendix

A.1.3) contained a type of carbons (ester groups) that could not be included in the algorithm of Sato

S (atoms/molecule) 0,2 0 0 0 0 0

Caro (atoms/molecule) CH Cq,sub Cq,cond Cali (atoms/molecule) CH3 CH2 CH Cq

30,2 11,0 5,6

13,7 6,9

1,9 4,3 0,6

0

30 11

3 16

7 2 4 1 0

30 11

7 12

7 2 4 1 0

28 10

4 14

9 1 7 1 0

30 11

5 14

7 2 4 1 0

30 12

4 14

8 1 6 1 0

Haro (atoms/molecule) Hali (atoms/molecule)

11,0 15,1

11 15

11 15

10 18

11 15

12 16

37

(1997) given that, as referred in chapter 7.2.1, this algorithm was developed for pure hydrocarbons.

The analytical data concerning these two components is presented in appendix A.1.3.

Table 7-6 - Results of the algorithm and the real values of a proposed structure for component 1

Experimental Proposed Structure

Ct Ht Ot

MW (g/mol)

19 30 2

290,4

18 30 0

246,4

Algorithm of Sato (1997) Proposed Structure

Rt Ra Cap Caq Cac Har Cti Ctp Cn Cc Hc Cγ

1,3 1,5 6,1 1,5 0,5 5,6 0,7 6,8 0

11,6 23,6

1

1 1 6 1 1 5 0 6 0

12 25 1

Table 7-7 - Results of the algorithm and the real values of a proposed structure for component 2

Experimental Proposed Structure

Ct Ht Ot

MW (g/mol)

14 12 2

212,2

14 14 0

182

Algorithm of Sato (1997) Proposed Structure

Rt Ra Cap Caq Cac Har Cti Ctp Cn Cc Hc Cγ

2,5 2,6 10,9

4 1,8 9 2

10,7 0

1,3 3,4 0

2 2

12 2 2

10 0

12 0 2 4 0

Figure 33 - Proposed structure for component 1

38

There are some differences between the experimental values and the corresponding proposed

values for both components. These differences correspond to the neglect of the corresponding ester

carbons and, in case of component 2, also in an increase of one unit in the number of carbons.

In Table 7-6, the difference in the molecular weight values exactly corresponds to the lack of

one carbon and two oxygen atoms (the neglected ester group that was detected in the carbon NMR

spectrum, in appendix A.1.3). Also, in this table, it is possible to observe that all values have small

differences between each other in terms of number of atoms. The number of aromatic rings is slightly

bigger than the number of total rings. This is due to the fact that when there are no naphthenic rings in

the molecule, the algorithm of Sato (1997) returns a small negative value for the number of naphthenic

rings per default. Given this, after the subtraction between the total and naphthenic rings, the number

of aromatic rings will be slightly bigger than the corresponding value for total rings. Parameters related

with fused ring units can not be considered important to propose a structure, since the number of total

rings is always inferior to two (which is the minimum number of rings to obtain a fused ring unit).

Hence, Cti (internal ring carbons in fused ring units) is 0,7 in component 1 but for its proposed

structure, this value will be considered as null. With this in mind, Ctp (peripheral ring carbons in fused

ring units) will be considered as the total number of carbons in aromatic rings.

Conclusion

The question which arises now is which method gives the most accurate average description

of a petroleum sample. An evaluation to assess which of the methods above is more reliable should,

for example, analyze the propagation of errors to which the structural variables are subjected, starting

from the analytical data required for the method until the final result of each structural equation

(Petrakis and Allen, 1987). Having in mind that it is better to have direct measurements of the number

of carbons and the carbon types, methods that depend on 13C NMR are more reliable than the those

that do not use 13C NMR. Using both 1H NMR and on 13C NMR methods should be an improvement

towards the robustness of the method, because 1H NMR nicely complements and validates the 13C

NMR results.

Based on the previous chapter (chapter 7.4), the oldest algorithms clearly present more

deviations than the more recent ones. This can be concluded through the observation of the results of

each algorithm for the test molecules. Another thing to be kept in mind is the fact that most of the

algorithms were made for aromatic structures, so their results concerning variables such as the

number of aromatic carbons and rings are definitely more accurate than variables such as the number

of naphthenic carbons and rings. Furthermore, the type of input data of each algorithm has an

appreciable influence in the accuracy of the results even before making the calculations. This is due to

Figure 34 - Proposed structure for component 2

39

the information content each technique, i.e., 13C NMR technique offers more information than density

or refractive index. Moreover, the last two can not be evaluated directly from the molecular structure

and are generally estimated by means of correlations.

All things considered, the algorithm which offered the best results for the test molecules is the

SAAH algorithm of Sato (1997). Also, this conclusion had already been confirmed by previous work

(Medeiros, 2013).

Applying the SAAH algorithm of Sato (1997) to the Buzurgan asphaltenes sample showed that

different isomeric average structures (Gauthier et al., 2008; Medeiros, 2013; this work) can be

proposed starting from exactly the same analytical data. No further absolute comparisons are possible

for the different methods, since there is no technique at this time which yields the exact composition of

a complex mixture, such as petroleum heavy ends or coal-derived liquids (Petrakis and Allen, 1987).

Because none of the above methods can be applied to lignin and since no reconstruction

method for lignin exists in the open literature, an algorithm similar to those found for asphaltenes in

this chapter will be developed for lignin in the next chapter.

8. Lignocellulosic Feedstock

Lignocellulosic feedstock is a very complex mixture, mostly composed by three components:

cellulose, hemicellulose and lignin, as explained in chapter 4.1. Cellulose and hemicellulose have

already their structure well defined and studied given that they are mostly polysaccharides,

represented by linear and branched co-polymers. More information concerning lignocellulosic biomass

can be found in chapters 2.2, 4.1, 4.2 and 8.1.

Lignin

Lignin has a very important structural function, since it works as "glue" between the

components of lignocellulosic biomass. Because of this, lignin has a great capacity of branching and

ensuring strong connections, given its typical functional groups, in which some have very strong

chemical connections (ethers, phenols, etc). In contrast to heavy petroleum fractions, there is not as

much information in literature concerning molecular reconstruction of lignin as would be desired to

support the development of a reconstruction algorithm. Most of the authors that tried different

approaches to analyze the structure of lignin have only managed to discover small parts or have given

some clues.

Once structural investigation concerning lignin is done, there are some other studies that will

need to be developed. The carbohydrate connections between hemicellulose and lignin (LCC

linkages) present some complexity which makes them still a not very known subject (Lawoko et al.,

2005; Adler, 1977; Ghaffar and Fan, 2013), but once the structure of lignin is determined, further

studies can be made concerning the LCC linkages. Despite this, there are some authors that affirm

that these linkages may be a starting point to discover the real structure of lignin but this theory has

never been proven (Lawoko et al., 2005).

40

According to Ghaffar and Fan (2013), analytical studies on lignin can be divided in two

sections: qualitative and quantitative analysis. Although there are other ways of dividing these studies

(destructive and non-destructive methods), the most useful one for the present work divides analytical

studies into qualitative and quantitative methods.

Quantitative measurements of lignin structures are an important aspect when it comes to

quantify linkages and specific structures in lignin. Examples of this type of analyses are elemental

analysis, FT-IT, FT-NMR, Thermal analysis, 1H NMR, 13C NMR, 2D HSQC NMR, DFRC and all other

methods that chemically degrade lignin in a quantitative manner (Thioacidolysis, Nitrobenzene

Oxidation, Ozonization, etc) (Ghaffar and Fan, 2013; Heitner et al., 2010; Adler, 1977; Vanholme et

al., 2010; Zeng et al., 2013; Lange, Decina and Crestini, 2013; Crestini and Argyropoulos, 1997).

Qualitative methods include SEM-EDX, TEM and Optical Microscopy (Ghaffar and Fan, 2013).

Model compounds are another strategy to study the structure of lignin. Most of the

experiments on model compounds are based on degradation studies (Heitner et al., 2010; Adler,

1977; Lu and Ralph, 1997), mostly using thioacidolysis. With this, several conclusions have become

clear, such as the conclusion that the β-O-4 linkage is the most abundant linkage in any type of lignin.

This conclusion was also obtained by Adler (1977).

Apart from analytical methods to understand the structure of lignin, some authors defend the

importance of modeling reactions with lignin, for example pyrolysis (Hou et al., 2009; Glasser, 1981;

van Parijs et al., 2010). These authors propose an algorithm with different mathematical criteria to find

a solution for the structure of lignin. Hou et al. (2009) uses the Freudenberg model (Freudenberg,

1962) as input and the mathematical criterion used by the authors was based on a PDF function with a

limited number of parameters (Hou et al., 2009). The authors claim that they could quite easily solve

and edit a complex lignin pyrolysis model with a feasible solution as a result. Glasser (1981) uses as

input data the analytical data of a “milled wood lignin”6 that has passed through a hydrolysis with

dioxane-water mixture at 180°C. Glasser (1981) does not explain so clearly his algorithm as Hou et al.

(2009). The author uses a program named SIMREL to simulate several reactions of milled wood

lignin, with the assumption of not considering LCC linkages. The author concluded that the distribution

of interunit linkages in lignin may be determined on the basis of permanganate oxidation results

(Glasser, 1981). Van Parijs et al. (2010) proposed an algorithm based on a stochastic simulation that

permits the simulation of lignin polymerization (van Parijs et al., 2010). The authors used as input

synthetic data for DHP lignins (dehydrogenation polymers of lignin (Heitner et al., 2010)), which are a

type of model compounds for lignin.

The above modeling methods can be based on two different criteria: focusing on lignin

substructure identification and on studies to estimate their relative gross frequencies, or focusing on

biopolymer sequencing (radical-radical coupling) (Davin and Lewis, 2005). The algorithm developed

by Glasser (1981) uses the first approach. On the contrary, both algorithms developed by Hou et al.

6 Native lignin that passes through a Mild Hydrolysis process or a Hydrolysis with Dioxane-Water at 180°C (Adler,

1977) and is normally considered as the most similar molecule to the native lignin.

41

(2009) and by van Parijs et al. (2010) use the more recent second approach. According to Davin and

Lewis (2005), the best approach is the second one, which focuses on radical polymerization instead of

defining each substructure (this last approach does not help because lignin is a random copolymer).

Overall, there are not enough clear reasons that permit to justify which of the two methods

(analytical or modeling) is more reliable with the smallest deviations from reality. However, Davin and

Lewis (2005) defend some inconsistencies in determining the contents of different linkages with

degradation methods like acidolysis and thioacidolysis directly on native lignin, and stipulate that these

inconsistencies do not yet have any clear explanation (Davin and Lewis, 2005) even though they could

help to unveil some structural aspects about native lignin.


The experimental data used (SEC, elemental analysis, 1H NMR, 13C NMR and 31P NMR) is for

a sample of Protobind 1000 lignin, which was obtained from soda pulping of a wheat straw (Joffres et

al., 2013). The experimental data used is the same as in Medeiros (2013).

8.1.2. Proposed Algorithm

Construction Blocks

Medeiros (2013) considered the three monolignols (Figure 13) and the six typical linkages in

lignin (Figure 22) to obtain the molecular representation through a random distribution of each linkage

between the calculated numbers for each monolignol. The author proposed a molecular

representation for a sample of Protobind 1000 lignin. The author concluded that a more systematic

way of creating a molecular representation for lignin samples was needed in the future.

For the algorithm developed in the present work, a different approach was followed. Some

assumptions were taken into account to simplify the number of possibilities when creating the

algorithm:

The percentages of ashes and water were not considered.

It is assumed that lignin only contains the three monolignols. Thus, nitrogen and sulfur

atoms were not considered.

Only ether, hydroxyl and methoxy groups were considered. Because only the

monolignols connected by the seven typical linkages should exist, ester bonds and

carboxylic groups did not enter in the analysis.

The construction blocks only connect with each other through their ends, not in the

middle.

Following Davin and Lewis (2005)’s opinion about the best approach in modeling lignin as

being the one based on the radical polymerization, the algorithm proposed in the present work is also

based on these criteria. Looking at different model molecules of lignin proposed by different authors

(mostly recent works from Medeiros (2013); Joffres et al. (2013); Vanholme et al. (2010); Heitner et al.

(2010); Zakeski et al. (2010)), a set of internal construction blocks was proposed. In Table 8-1, the six

typical linkages in lignin are represented along with a single aromatic ring each, except k4 that has

42

two. Again except k4, all the other construction blocks have two ends that can connect to other

construction blocks (internal, or terminal). These construction blocks were based on the most common

combinations of the seven linkages (Figure 23). Therefore, k1 corresponds to the β-O-4 linkage

between two monolignols, k2 corresponds to the combination of the β-5 linkage with the α-O-4 linkage,

k3 corresponds to the β-β linkage (that is also known as the α-O-γ linkage), k4 corresponds to the

combination that involves the 5-5 linkage, the β-O-4 linkage and the α-O-4 linkage, k5 corresponds to

the 4-O-5 linkage and k6 corresponds to the β-1 linkage.

Table 8-2 includes an extra internal construction block, g1, and the two terminal construction

blocks, g2 and g3. If just the internal construction blocks from Table 8-1 and the two terminal

construction blocks of Table 8-2 were considered, the final number of aromatic rings would be smaller

than the actual number of aromatic rings by one unit. So, to correct this without changing the definition

of each construction block, an extra internal construction block g1 was added to the set of internal

construction blocks to construct the algorithm. What differentiates a construction block of being

internal or terminal is the number of connections it can have: if it can only have one connection, it is

terminal, if it can have two or more connections, it is internal.

Table 8-1 - Structural representation of the six construction blocks

k1 k2 k3 k4 k5 k6

Table 8-2 - Structural representation of an extra internal CB and the two terminal CB

g1 g2 g3

The various construction blocks are composed of carbon, hydrogen and oxygen. In this

representation, oxygen can be found in 4 different functional groups: aliphatic hydroxyls, phenolic

hydroxyls, methoxy ether, or other ethers. Hydrogen can be found in 3 different functions: aromatic

hydrogen, phenolic hydrogen, and "aliphatic" hydrogen (which contains all other hydrogen atoms:

aliphatic, olefinic, alcohols, ...). For carbon, 6 different carbon types can be distinguished: aliphatic

carbon connected to a carbon, aliphatic carbon connected to methoxy oxygen, aliphatic carbon

connected to an oxygen atom from non-methoxy ether group, tertiary aromatic carbon, quaternary

43

aromatic carbon connected to a carbon, and quaternary aromatic carbon connected to an oxygen

atom. Table 8-3 shows the molecular weight of each construction block and the number of atoms of

each type, as given by the elemental analysis, 13C NMR and 1H NMR.

The structural algorithm returns a set of optimized numbers for each construction block, i.e.

their average occurrence that allows to obtain a structure that agrees (within certain ranges) with the

experimental data. The structure of the algorithm and the utilization of construction blocks (that

resembles the construction of a puzzle) were based on the works of Oka et al. (1977), Chang et al.

(1982) and Jaffe et al. (2005), in which the first two authors propose algorithms for heteroatoms with

construction blocks, while the third author extends the SOL approach to create a set of construction

blocks that are adapted for vacuum residua, and especially for asphaltene molecules.

Table 8-3 - Characteristics and composition of the various construction blocks

k1 k2 k3 k4 k5 k6 g1 g2 g3

Molecular weight (g/mol) 166 148 188 239 108 150 76 57 17

Aromatic rings 1 1 1 2 1 1 1 0 0

Elemental composition C H O N S

9 10 3 0 0

9 8 2 0 0

12 12 2 0 0

15 11 3 0 0

6 4 2 0 0

9 10 2 0 0

6 4 0 0 0

3 5 1 0 0

0 1 1 0 0

Carbon types (13C NMR) Cali(all) Cali-C Cali-O in OMe Cali-O (w/o OMe) Caro(all) CAr-H CAr-C or CAr-O C=O

3

0 0 3

6 4 2

0

3

1 0 2

6 3 3

0

6

2 0 4

6 4 2

0

3

0 0 3

12 6 6

0

0

0 0 0

6 3 3

0

3

1 0 2

6 4 2

0

0

0 0 0

6 4 2

0

3

2 0 1

0 0 0

0

0

0 0 0

0 0 0

0

Hydrogen types (1H NMR) Aromatic H (6.0 – 7.7 ppm) Phenolic H (7.8 – 9.6 ppm)

Carboxylic H (11.8 – 12.8 ppm) Other H (aliphatic, olefinic, alcohol, ...)

4 0 0 6

3 0 0 5

4 0 0 8

6 0 0 5

3 1 0 0

4 0 0 6

4 0 0 0

0 0 0 5

0 1 0 0

Oxygen types Aliphatic hydroxyl Phenolic hydroxyl

Methoxy ether Other ethers

2 0 0 1

1 0 0 1

0 0 0 2

1 0 0 2

0 1 0 1

2 0 0 0

0 0 0 0

1 0 0 0

0 1 0 0

Inputs and Calculations

Now that the construction blocks are defined, the inputs of the algorithm will be explained. The

experimental data can be consulted in appendix A.3. According to the first two assumptions, the

nitrogen and sulfur were replaced for carbon in the elemental analysis (as proposed also in the work of

Hirsch and Altgelt, (1970)) and then the elemental analysis is normalized to 100%, neglecting at the

same time the water and ashes, which are not part of the lignin structure. With the molecular weight

and the corrected elemental analysis, it is possible to calculate the total number of carbon, hydrogen

and oxygen atoms in the molecule.

44

From 13C NMR, it is possible to calculate the number of methoxy carbons in the molecule.

Given the presented construction blocks (all the structures derive from p-hydroxyphenyl units), the

methoxy groups are not considered during the assembly of the construction blocks, and will be added

afterwards, once the lignin backbone is constructed. With this is mind, the number of methoxy groups

in the molecule must be determined and replaced by hydrogen atoms. Consequently, the input data

will now represent a structure that only contains p-hydroxyphenyl units. Once a structure is proposed

for the lignin backbone, the methoxy groups will be included again in the final structure (given that the

methoxy groups only connect to the aromatic rings according to Figure 13 and Figure 14).

As the total number of carbon, hydrogen and oxygen atoms in the molecule is known at this

stage, their various sub-types will now be calculated.

To calculate the number of phenolic oxygen and hydrogen atoms, the data from 31P NMR,

given in mmol OH/g lignin, must first be converted to number of OH/g lignin. By dividing the values of

31P NMR in mmol OH/g lignin by the calculated value in Eq 1, the final results are in number of OH/g

lignin, as can be seen in appendix A.3.

1 𝑔 𝑙𝑖𝑔𝑛𝑖𝑛 = 1𝑀𝑊𝑙𝑖𝑔𝑛𝑖𝑛

⁄ × 1000 𝑚𝑚𝑜𝑙 𝑙𝑖𝑔𝑛𝑖𝑛 Eq 1

The total number of phenolic OH groups (OHPhenolicTotal) are calculated as the sum of the

phenolic groups in each monolignol type through the 31P NMR (appendix A.3). Hence, the total

number of phenolic OH groups is now known.

The next calculation involves the aliphatic OH groups in the molecule. This variable was

considered as the total oxygen connected to hydrogen in the molecule except the phenolic groups

(appendix A.3).

𝑂𝐻𝐴𝑙𝑖𝑝ℎ𝑎𝑡𝑖𝑐 = 𝑂𝐻𝑇𝑜𝑡𝑎𝑙 − 𝑂𝐻𝑃ℎ𝑒𝑛𝑜𝑙𝑖𝑐 Eq 2

The total oxygen in the molecule can be divided into oxygen connected to hydrogen (OH

groups) and ether oxygen (not connected to oxygen). The ether oxygen is obtained through the

subtraction of the total oxygen in the molecule and the oxygen in OH groups.

𝑂𝐸𝑡ℎ𝑒𝑟 = 𝑂𝑇𝑜𝑡𝑎𝑙 − 𝑂𝐻𝑇𝑜𝑡𝑎𝑙 Eq 3

Now that the oxygen in the molecule is totally discriminated, the next atom to be discriminated

is carbon. Because the methoxy carbon is already known, all the other types of carbon are obtained

through the multiplication of the total carbon and each percentage of carbon in the 13C NMR analysis

(A.3).

𝐶𝑇𝑦𝑝𝑒 𝑜𝑓 𝐶𝑎𝑟𝑏𝑜𝑛 = 𝐶𝑇 × %𝑇𝑦𝑝𝑒 𝑜𝑓 𝐶𝑎𝑟𝑏𝑜𝑛 Eq 4

Now that the aliphatic carbon is determined, it is possible to know the number of internal and

terminal aliphatic carbon. The internal aliphatic carbon consists of the total aliphatic carbon in the

internal construction blocks and the terminal aliphatic carbon consists of the total aliphatic carbon in

the terminal construction block. Hence, the internal aliphatic carbon is calculated from the number of

45

aliphatic carbon atoms in each internal construction block and the number of each internal

construction block that the algorithm returns.

𝐶𝐴𝑙𝑖𝑝ℎ𝑎𝑡𝑖𝑐𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 = 3𝑘1 + 3𝑘2 + 6𝑘3 + 3𝑘4 + 3𝑘6 Eq 5

Note that Eq 5 does not include the construction block k5 because its definition does not

include any aliphatic carbon (Table 8-1).

Provided that, the terminal aliphatic carbon is simply obtained by the subtraction between the

total aliphatic carbon and the previously determined internal aliphatic carbon.

𝐶𝐴𝑝𝑙𝑖ℎ𝑎𝑡𝑖𝑐𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 = 𝐶𝐴𝑙𝑖𝑝ℎ𝑎𝑡𝑖𝑐𝑇𝑜𝑡𝑎𝑙 − 𝐶𝐴𝑙𝑖𝑝ℎ𝑎𝑡𝑖𝑐𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 Eq 6

The same line of thought can be applied to the phenolic groups. Eq 7 is used to calculate the

internal phenolic groups, while the number of terminal phenolic groups can be calculated by

difference.

𝑂𝐻𝑃ℎ𝑒𝑛𝑜𝑙𝑖𝑐𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 = 𝑘5 Eq 7

𝑂𝐻𝑃ℎ𝑒𝑛𝑜𝑙𝑖𝑐𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 = 𝑂𝐻𝑃ℎ𝑒𝑛𝑜𝑙𝑖𝑐𝑇𝑜𝑡𝑎𝑙 − 𝑂𝐻𝑃ℎ𝑒𝑛𝑜𝑙𝑖𝑐𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 Eq 8

Note that the number of terminal phenolic groups, once determined, is also equal to the

number of terminal construction blocks of the type g3 (Table 8-2).

𝑔3 = 𝑂𝐻𝑃ℎ𝑒𝑛𝑜𝑙𝑖𝑐𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 Eq 9

The number of terminal groups of type g2 (Table 8-2) can be obtained through the number of

terminal aliphatic carbon atoms as follows:

𝑔2 = 𝐶𝐴𝑙𝑖𝑝ℎ𝑎𝑡𝑖𝑐𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙/3 Eq 10

As explained above, the number of internal groups of type g1 (Table 8-2) is always equal to 1,

hence:

𝑔1 = 1 Eq 11

For the hydrogen atoms, the "aliphatic" hydrogen (which contains all other hydrogen atoms:

aliphatic, olefinic, alcohols, ...) will be calculated first. The number of terminal aliphatic hydrogen atoms

can be calculated from the number of g2 construction blocks.

𝐻𝐴𝑙𝑖𝑝ℎ𝑎𝑡𝑖𝑐𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 = 𝑔2 × 5 Eq 12

The number of internal aliphatic hydrogen atoms is calculated from the number of aliphatic

carbon atoms in each internal construction block and the number of each internal construction block

that the algorithm returns:

𝐻𝐴𝑙𝑖𝑝ℎ𝑎𝑡𝑖𝑐𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 = 6𝑘1 + 5𝑘2 + 8𝑘3 + 5𝑘4 + 6𝑘6 Eq 13

46

The number of aromatic hydrogen atoms is obtained through the 13C NMR analysis, since the

number of aromatic hydrogens is equal to the number of aromatic carbon atoms connected to a

hydrogen atom. The results are in appendix A.3.

𝐻𝐴𝑟 = 𝐶𝐴𝑟−𝐻 Eq 14

Concerning the aromatic rings, AR, its total number has still to be determined before

determining the number of internal aromatic rings. Through Eq 4 it is possible to calculate the total

number of aromatic carbon atoms. Dividing this number by 6, the number of total aromatic rings is

obtained, since there are no fused aromatic ring structures in lignin. The terminal aromatic rings, Eq

16, are those connected to a terminal group, so by definition, half of them are also the number of

internal construction blocks of type g1 (the other half already belongs to the other internal construction

blocks).

𝐴𝑅 = 𝐶𝐴/6 Eq 15

𝐴𝑅𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 = 𝑔2 + 𝑔3 Eq 16

𝐴𝑅𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 = 𝐴𝑅 − 𝐴𝑅𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 Eq 17

The number of internal aromatic rings is obtained by difference.

Constraints

Constraint 1 – Aromatic Rings

Constraints are important to define limits for the variables. The constraints are expressed in

equations that depend on the variables and their result should vary within a range which limits are

according with the physical meaning of the problem.

The first constraint concerns the number of aromatic rings. The number of aromatic rings in

the molecule can be calculated from the number of aromatic rings in each construction block.

𝐴𝑅𝐶𝑎𝑙𝑐 = ∑ 𝑘𝑖

6

𝑖=1

+ (𝑘4 + 1) Eq 18

Eq 18 was deduced through a global view of a schematic representation of a lignin molecule.

To better understand how this equation was deduced, a small scheme is exposed below.

In Figure 35 there are two equal representations, one simple and one colored. The simple

representation schematizes a lignin test molecule without k4 groups. Because k4 groups have three

connections, they were treated differently than the others, which have just two connections. This

Figure 35 – Scheme #1 to explain the deduction

47

representation does not include the terminal groups, only the internal and terminal aromatic rings and

the different chemical linkages of each internal construction block. The colored representation serves

to distinguish each element in the global representation: the red circles represent aromatic rings and

the black bold lines represent chemical linkages in each construction block (remember that the k4

group is not included in this representation). In this representation, there are 5 aromatic rings and 4

chemical linkages, which makes 4 construction blocks plus 1 construction block of type g1.

In Figure 35, the number of aromatic rings is given by:

𝐴𝑅 = 𝐶𝐵𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑔1+ 1 = 4 + 1 = 5 Eq 19

Figure 36 has the same linear chain as Figure 35 but with a k4 group included. The k4 groups

are represented by three linear chemical linkages grouped together (green bold lines). So, when

counting the chemical linkages in this representation, the three green bold lines count as one chemical

linkage. As so, there are 6 aromatic rings and 4 chemical linkages, which makes 4 construction blocks

plus 1 construction block of type g1. The assumption for structures with k4 groups is that the number of

aromatic rings increases above the “linear” number of aromatic rings as much as there are k4 groups.

For example, it is expected for the number of aromatic rings in scheme #2 to be the number of

aromatic rings of scheme #1 plus the number of k4 groups in it, which means one aromatic ring more.


𝐴𝑅 = 𝐶𝐵𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑔1+ 𝑘4 + 1 = 4 + 1 + 1 = 6 Eq 20

Figure 36 – Scheme #2 to explain the deduction

Figure 37 - Scheme #3 to explain the deduction

48

Figure 37 is similar to the previous ones. According to what was explained above, this

representation has 9 aromatic rings and 6 chemical linkages, which makes 6 construction blocks plus

1 construction block of type g1.



The last example that permits to confirm Eq 18 is illustrated in Figure 38. This representation

has 23 aromatic rings and 17 chemical linkages. This makes 17 construction blocks plus 1

construction block of type g1.



With the above four confirmations, Eq 18 was generalized for the algorithm. According to the

number of each construction block that the algorithm attributes, the number of aromatic rings through

Eq 18 will vary. In order to limit the solution, the considered range for this variable is:

𝐴𝑅𝐸𝑥𝑝 − 4 ≤ 𝐴𝑅𝐶𝑎𝑙𝑐 ≤ 𝐴𝑅𝐸𝑥𝑝 + 4 Eq 23

Figure 38 - Scheme #4 to explain the deduction

49

Where the value of 4 is already a large range compared to the results for the validation of the

algorithm (next chapter).

Constraint 2 – Carbon Balance

The second constraint concerns the carbon balance. Here, the total carbon in the molecule is

calculated from the number of carbon atoms in each internal construction blocks and in the terminal

construction blocks.

𝐶𝐶𝑎𝑙𝑐 = 𝐶𝐴𝑙𝑖𝑝ℎ𝑎𝑡𝑖𝑐𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 + 𝐶𝐴𝑙𝑖𝑝ℎ𝑎𝑡𝑖𝑐𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 + 𝐶𝐴 Eq 24

Considering what was explained in Inputs and Calculations (chapter 8.1.2), only the number of

aromatic carbons is fixed (it depends directly on the experimental data), the other two variables

depend directly on the number of each construction blocks.

The experimental value for the number of total carbon in the molecule comes directly from the

molecular weight, the elemental analysis, and the 13C NMR analysis (to remove the methoxy carbon,

as explained above). Also, the considered range for this variable:

𝐶𝐸𝑥𝑝 − 20 ≤ 𝐶𝐶𝑎𝑙𝑐 ≤ 𝐶𝐸𝑥𝑝 + 20 Eq 25

The chosen range uses a value of 20 to be consistent with the range for the constraint on the

aromatic rings (given that each ring carries six carbons).

Constraint 3 – Oxygen Balance

The third constraint is about the total oxygen in the molecule. The expression that permits to

calculate the total oxygen in the molecule has to include all the types of oxygen (ether groups,

phenolic groups, etc).

First the oxygen inside the construction block has to be calculated (it includes OH aliphatic

groups and ether groups but not phenolic groups):

𝑂𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 = 3𝑘1 + 2𝑘2 + 2𝑘3 + 3𝑘4 + 𝑘5 + 2𝑘6 Eq 26

Having in mind that the number of OH aliphatic terminal groups is the same number of g2

groups (because each of these groups has one OH aliphatic group):

𝑂𝑇𝑜𝑡𝑎𝑙𝐶𝑎𝑙𝑐 = 𝑂𝐻𝑃ℎ𝑒𝑛𝑜𝑙𝑖𝑐𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 + 𝑂𝐻𝑃ℎ𝑒𝑛𝑜𝑙𝑖𝑐𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 + 𝑔2 + 𝑂𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 Eq 27

The experimental value for total oxygen comes from elemental analysis except the methoxy

oxygen, as explained above.

𝑂𝑇𝑜𝑡𝑎𝑙𝐸𝑥𝑝 − 20 ≤ 𝑂𝑇𝑜𝑡𝑎𝑙𝐶𝑎𝑙𝑐 ≤ 𝑂𝑇𝑜𝑡𝑎𝑙𝐸𝑥𝑝 + 20 Eq 28

The chosen range for oxygen was the same as for carbon.

50

Constraint 4 – Construction Blocks

The fourth constraint concerns the number of construction blocks in the proposed molecule.

The algorithm varies the number of each construction block of Table 8-1 and the number of

construction blocks is therefore:

𝐶𝐵𝐶𝑎𝑙𝑐 = ∑ 𝑘𝑖

6

𝑖=1

Eq 29

The experimental value of construction blocks comes from the observation of the examples

used to explain Eq 18. As can be observed, the number of construction blocks depends always the

number of internal aromatic rings:

𝐶𝐵𝐸𝑥𝑝 = 𝐴𝑅𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 + 1 Eq 30

As so, the range for this constraint has the value of 4, it was chosen with the same reasons as

the range for the first constraint.

𝐶𝐵𝐸𝑥𝑝 − 4 ≤ 𝐶𝐵𝐶𝑎𝑙𝑐 ≤ 𝐶𝐵𝐸𝑥𝑝 + 4 Eq 31

Constraint 5 – Aliphatic Carbon not connected to Oxygen

The fifth constraint concerns the aliphatic carbon that is not connected to any oxygen. This

type of carbon only exists in groups of type g2 and in the construction blocks k2, k3 and k6 of Table 8-1.

(𝐶𝑎𝑙𝑖 − 𝐶𝑎𝑙𝑖)𝐶𝑎𝑙𝑐 = 𝑘2 + 2𝑘3 + 𝑘6 + 2𝑔2 Eq 32

The experimental value for this type of carbon comes directly from 13C NMR analysis.

(𝐶𝑎𝑙𝑖 − 𝐶𝑎𝑙𝑖)𝐸𝑥𝑝 − 20 ≤ (𝐶𝑎𝑙𝑖 − 𝐶𝑎𝑙𝑖)𝐶𝑎𝑙𝑐 ≤ (𝐶𝑎𝑙𝑖 − 𝐶𝑎𝑙𝑖)𝐸𝑥𝑝 + 20 Eq 33

This range was considered with the value of 20. It should be stressed that, due to the

simplifications, some components that were neglected, as for example carboxylic groups, may bring

deviations when determining the real number of aliphatic carbons not connected to oxygen, so a

relatively large range, equal to those for total carbon and oxygen, was used.

Constraint 6 – Hydrogen Balance

The sixth constraint involves the hydrogen balance. Here, the total hydrogen in the molecule is

calculated having in mind the quantity of hydrogen that was incremented to replace the methoxy

groups, OMe.

𝐻𝐶𝑎𝑙𝑐 = 𝐻𝐴𝑙𝑖𝑝ℎ𝑎𝑡𝑖𝑐𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 + 𝐻𝐴𝑙𝑖𝑝ℎ𝑎𝑡𝑖𝑐𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 + 𝐻𝐴𝑟 + 𝑂𝐻𝑃ℎ𝑒𝑛𝑜𝑙𝑖𝑐 + 𝑂𝑀𝑒 Eq 34

The experimental value for the number of total hydrogen in the molecule comes directly from

elemental analysis except for the methoxy hydrogen, as explained above. As so, the considered range

for this variable:

51

𝐻𝐸𝑥𝑝 − 20 ≤ 𝐻𝐶𝑎𝑙𝑐 ≤ 𝐻𝐸𝑥𝑝 + 20 Eq 35

The range for hydrogen was assumed equal to the ones for carbon and oxygen.

Constraint 7 – Ether Groups

The number of oxygen atoms in ether groups was also considered as a constraint. Its

experimental value was already explained and its proposed value depends only on the number of

each construction block.

𝑂𝐸𝑡ℎ𝑒𝑟𝐶𝑎𝑙𝑐 = 𝑘1 + 𝑘2 + 2𝑘3 + 2𝑘4 + 𝑘5 Eq 36

𝑂𝐸𝑡ℎ𝑒𝑟𝐸𝑥𝑝 − 25 ≤ 𝑂𝐸𝑡ℎ𝑒𝑟𝐶𝑎𝑙𝑐 ≤ 𝑂𝐸𝑡ℎ𝑒𝑟𝐸𝑥𝑝 + 25 Eq 37

The reasons for this high value for the range are the same as for the fifth constraint. This time,

20 was not the chosen value because the algorithm could not fit any solution within the presented

ranges, so an increment of 5 was chosen for this range.

Constraint 8 – Terminal Groups

The last constraint is a bit different from the above constraints. This one serves to control the

number of ends of the proposed molecule.

𝐺 = 𝑔2 + 𝑔3 Eq 38

According to the presented examples, it is assumed that the minimum number for the terminal

groups is 2 (Figure 35) and the maximum varies according to the number of k4 groups (Figure 36 ;

Figure 37 ; Figure 38). Each k4 group in the molecule increases the number of terminal groups in 2

units.

2 ≤ 𝐺 ≤ 𝑘4 + 2 Eq 39

Objective Function and Final Calculations

The objective function is a mathematical equation that expresses a criterion to select the best

solution. The first seven constraints have both an experimental value and a proposed value for a given

combination of the parameters k1, k2, k3, k4, k5 and k6. It should be reminded that the values for g1, g2

and g3 are not parameters, as they can be directly calculated from the analytical data. For each of

these seven constraints, a comparison between the two values (experimental and proposed) is made

and its deviation, dev, is included in the objective function, f, which is a Least Squares criterion.

𝑑𝑒𝑣𝑗 =𝑃𝑟𝑜𝑝𝑜𝑠𝑒𝑑 𝑉𝑎𝑙𝑢𝑒𝑗 − 𝐸𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙 𝑉𝑎𝑙𝑢𝑒𝑗

𝐸𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙 𝑉𝑎𝑙𝑢𝑒𝑗

Eq 40

𝑓 = ∑(𝑑𝑒𝑣𝑗2)

7

𝑗=1

Eq 41

52

When the objective function f reaches its minimum value, the corresponding solution is

considered the best one and the algorithm returns it as its final solution.

Because this algorithm is a mathematical problem that uses nonlinear programming, the

quickest way to solve it is using one of the available optimization algorithms. Because this is a typical

NLP problem, an appropriate optimization algorithm is the GRG algorithm.

Now that all the constraints have been explained, the algorithm attributes random integer

numbers between 0 and 10 (seemed reasonably according to different test molecules, in the next

chapter, and the experimental data) to each construction block of Table 8-1 and the set of solutions

that presents the minimum value of the objective function is chosen as the final one.

The last part concerns the attribution of the methoxy groups. With 31P NMR analysis (appendix

A.3), it is possible to estimate the percentage of each type of monolignol. Alternatively, having the total

number of methoxy groups (from the 13C NMR analysis) and the total number of aromatic rings in the

proposed molecule (from the algorithm), it is possible to calculate the number of each monolignol in

the molecule. Let x be the number of syringyl units (each of these have two methoxy groups), y the

number of guaiacyl units (these have only one methoxy group), and z the number of p-hydroxyphenyl

units (these do not have methoxy groups). The number of p-hydroxyphenyl units z is directly

calculated from the percentage of p-hydroxyphenyl units given by the 31P NMR analysis and the total

number of aromatic rings. The number of guaiacyl units x and the number of syringyl units y are then

calculated as follows:

2𝑥 + 𝑦 = 𝑂𝑀𝑒 Eq 42

𝑥 + 𝑦 + 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝 − ℎ𝑦𝑑𝑟𝑜𝑥𝑦𝑝ℎ𝑒𝑛𝑦𝑙 𝑢𝑛𝑖𝑡𝑠 = 𝐴𝑅 Eq 43

After calculating x and y from Eq 42 and Eq 43, the recalculated percentage of guaiacyl and

syringyl units is a little different than those directly obtained from 31P NMR analysis. This is justified

because the derivation step of 31P NMR is not well controlled and syringyl units and phenolic groups

are not precisely quantified (Joffres, 2006).

The flow diagram for the proposed algorithm is given in Figure 39.

53

Figure 39 – Flow diagram that illustrates the proposed algorithm

54

8.1.3. Validation

To validate the algorithm, some test molecules were chosen. First of all, it is worth mentioning

that every application of the algorithm was made starting with an initial value of 1 for all variables. The

analytical data is given in appendix A.3. Initially, six test molecules that use only 1 construction block

were tested (Table 8-4). In a second stage, a test molecule that uses 3 construction blocks was

tested, and finally a test molecule with a molecular weight closer to that of Protobind 1000 lignin was

used.

Table 8-4 - Test molecules based on the proposed construction blocks

t1 t2 t3 t4 t5 t6

Table 8-4 presents six test molecules based on the internal construction blocks of Table 8-1.

Each of the test molecules has only one construction block (Table 8-1). Hence, simple and unique

solutions are expected.

Table 8-5 - Obtained results after applying the algorithm to the six test molecules based on the proposed construction blocks

t1 t2 t3 t4 t5 t6

k1 k2 k3 k4 k5 k6

1 0 0 0 0 0

0 1 0 0 0 0

0 0 1 0 0 0

0 0 0 1 0 0

0 0 0 0 1 0

0 0 0 0 0 1

f 6,38E-31 3,40E-31 6,26E-31 1,62E-31 4,66E-31 0

g1 g2 g3

1 1 1

1 1 1

1 0 2

1 2 1

1 2 0

1 0 2

Table 8-5 presents the obtained results after applying the algorithm to the six test molecules.

The results are perfect, as expected for molecules that are so close to the definition of each

construction block. With these results, the mathematical equations of the algorithm can be validated.

A bigger test molecule was also tested. This molecule is a combination of 3 construction

blocks, 2 of type k2 and 1 of type k4, as can be seen below. Its analytical data is given in appendix A.3.

55

Table 8-6 - Test molecule with 2 construction blocks of type k2 and 1 construction block of type k4

t7

This molecule is also very simple, it only contains 2 construction blocks of type k2 and 1

construction block of type k4. After regression, the solution is again perfect in terms of the objective

function, just as for the simple test molecules. However, the solution presented in Table 8-7 does not

correspond to reality. The real solution should be the one presented in Table 8-8. As can be seen, the

objective function is at very similar values that are so small that they correspond to a perfect fit. This

indicates that there is a problem with the algorithm concerning construction blocks of type k2 and k4.

Table 8-7 - Obtained results for test molecule t7

t7

k1 k2 k3 k4 k5 k6

0 0 0 2 0 0

f 4,38E-31

g1 g2 g3

1 3 1

Table 8-8 - Real solution for test molecule t7

t7

k1 k2 k3 k4 k5 k6

0 2 0 1 0 0

f 2,79E-31

g1 g2 g3

1 2 1

56

If a set of 2 construction blocks of type k2 is compared with a set of 1 construction block of

type k4 together with 1 block of type g2, they are undistinguishable given that their analytical data is

exactly the same. Because the algorithm does not know what type of structure is being considered (2

k2 construction blocks or 1 k4 construction block + 1 g2 construction block), the algorithm returns one

of the multiple solutions. More analytical data is therefore needed in order to distinguish between

these two construction blocks. For example, an analysis that quantifies the number of 5-5 linkages (a

biphenyl linkage that is only present in the k4 construction block) could help to determine the exact

number of k4 blocks and in this way, the k4 variable would no longer exist. With this additional

information, the algorithm could distinguish between both and would work perfectly without problems

of multiple solutions.

The last test molecule is much bigger and its analytical data is, therefore, similar to the

experimental data of the Protobind 1000 lignin (appendix A.3).

57

Table 8-9 - Test molecule t8 with analytical data closer to the Protobind 1000 experimental data

t8

The above test molecule was tested with the algorithm having the problem with the multiple

solutions and, as a result, three different solutions were obtained (Table 8-10). Since for the third

solution the number of construction blocks of type g2 is negative, it can be eliminated, and only two

solutions remain. If the variable k4 is fixed (imagining that there is access to an experimental analysis

that gives the number of 5-5 linkages, or k4 construction blocks), there is only one best solution and

58

the algorithm returns the correct solution that corresponds to the structure t8 (Table 8-9). This unique

solution is exactly the same as the first solution in Table 8-10.

Table 8-10 - Three possible solutions for the test molecule t8

1st solution 2nd solution 3rd solution

k1 k2 k3 k4 k5 k6

9 1 1 2 2 1

9 3 1 1 2 1

9 5 1 0 2 1

f 5,48E-31 5,48E-31 7,89E-31

g1 g2 g3

1 1 3

1 0 3

1 -1 3

8.1.4. Application of the Algorithm to Protobind 1000 Lignin

The present chapter contains the results obtained through the application of the proposed

algorithm to the experimental data for Protobind 1000.

The experimental data (appendix A.3) was converted to serve properly as input for the

proposed algorithm (in Inputs and Calculations, chapter 8.1.2). Once this is done, the algorithm was

applied.


Solution #1 Solution #2 Solution #3 Solution #4 Solution #5

k1 k2 k3 k4 k5 k6

1 8 8 0 10 0

1 6 8 1 10 0

1 4 8 2 10 0

1 2 8 3 10 0

1 0 8 4 10 0

f 0,3451 0,3455 0,3460 0,3465 0,3471

g1 g2 g3

1 1,3 1,3

1 2,3 1,3

1 3,3 1,3

1 4,3 1,3

1 5,3 1,3

The optimizer only returns one solution (solution #2) regardless of the fact that multiple

solutions can be found, as shown in Table 8-11. This is probably due to the complexity of the molecule

and the fact that that the objective function values of the multiple solutions are not exactly the same.

There are some constraints that show a bigger deviation than what is desirable, but mostly this

algorithm shows an incompatibility between the definition of the construction blocks and the real

molecule or it shows that the assumptions regarding the neglect of some structures (carboxylic acids,

esters, etc) was too simplified.

A proposed structure corresponding to the solution #2 in Table 8-12 is shown below (Table

8-12). Similar structures can be drawn for the other solutions.

59

p1

Table 8-12 - Proposed structure for Protobind 1000 lignin

According to literature (Heitner et al., 2010; Joffres, 2006; Joffres et al., 2013; Ghaffar et al.,

2013; Zakzeski et al., 2010; Joffres, Laurenti et al., 2013) the most abundant linkage in lignin is the β-

O-4 linkage (corresponding to construction block k1). Medeiros (2013) also proposed a structure that

uses this as a basis. The result obtained in this work, although does not respect the above literature

view, has its analytical data closer to the experimental data than, for example, the two structures

proposed by Medeiros (2013). In fact, the algorithm was built using the analytical data as its criterion,

not which linkage is the most abundant. From a mathematical point of view, the best solution is the

one from the algorithm.

Looking at Table 8-13, some differences can be observed concerning the number of carbons

and oxygens and, therefore, the molecular weight, but the composition is pretty similar to the

experimental data, and so the proposed results are considerably good.

60

Table 8-13 - Comparison of general results between the experimental data for Protobind 1000 and the proposed structure p1

Experimental data Proposed structure p1

Total number of carbons 269,8 276

Composition (%) Carbon

Hydrogen Oxygen

65,9 6,1 28

66,5 5,6 27,9

Chemical formula C270H297O86 C276H277O87

Molecular Weight (g/mol) 4915 4986,2

Table 8-14 - Comparison of structural results between the experimental data for Protobind 1000 and the proposed structure p1

Number of Experimental data Proposed structure p1

Aliphatic carbons Aromatic carbons Methoxy groups

Phenolic OH Aliphatic hydrogens Aromatic hydrogens

108,8 161,1 29,9 11,3 225,6 60,3

108 168 30 11 203 63

Regarding structural variables (Table 8-14), some larger differences start to become more

evident. The main differences appear mostly on the aromatic carbons (the proposed structure p1

presents a deviation of +4,3%), on the aliphatic hydrogens (the proposed structure p1 presents a

deviation of -11,1%) and on the aromatic hydrogens (the proposed structure p1 presents a deviation of

+4,6%). Thus, p1 contains maybe one more aromatic ring than the experimental data indicate, and

less aliphatic hydrogens. This could be due to the approximations that neglect several components, as

previously said. Also, it must be noted that Protobind 1000 lignin is a mixture of many different

components (water, galactan, xylan, glucan, etc) and that only 93,1% is actually lignin. This could

interfere with the various analyses and misrepresent the data. Finally, one last observation is that

Joffres et al. (2013) refers that the initial Protobind 1000 lignin has 27 phenylpropane units and that

Table 8-12 shows a structure with also 27 phenylpropane units.

Hydroconverted Lignin

This section concerns an algorithm for the hydroconverted products of Protobind 1000 lignin.

Joffres, Laurenti et al. (2013) present an illustration of the different thermochemical ways to

convert lignin into liquid. There are several types of thermochemical conversion processes for lignin

(chapter 2.3.2). The thermochemical processes for conversion of lignin into liquid (bio-oil) have a

larger spectrum than just pyrolysis and liquefaction. It includes processes such as hydroconversion,

solvolysis and catalytic cracking (Joffres, Laurenti et al., 2013). In the light of the next chapters, only

hydroconversion is considered.

Hydroconversion is a general name referring to every process that chemically degrades lignin

in a hydrogen atmosphere with a catalyst (Joffres, Laurenti et al., 2013). The hydroconversion of lignin

is performed in a pressure range of 1-15 MPa and in a temperature range of 300-500 °C. Under these

conditions, high yields in liquid products can be obtained, but they depend on the origin of the lignin

(Joffres, Laurenti et al., 2013). Typical catalysts for this process are noble metal-based catalysts.

Lately, sulfide catalysts were used in the liquefaction of lignin and resulted in quite good liquid yields

61

compared to metal catalysts (Joffres, Laurenti et al., 2013). The catalyst also hydrogenates the solvent

that can then also act as a hydrogen-donor solvent in the hydroconversion process. Usually, the

liquids obtained through hydroconversion are partially deoxygenated and more stable than those

produced by pyrolysis (Joffres, Laurenti et al., 2013).


The hydroconverted lignin was obtained by subjecting 30 g of the same sample obtained in

chapter 8.1.1 (wheat straw soda lignin) to a hydroconversion process for 5 hours in a 0.3 L batch

reactor equipped with a 2 L H2 ballast, over a sulfide NiMo-based catalyst in tetralin as solvent (Joffres

et al., 2013).

8.2.2. Proposed Algorithm

To be able to propose an algorithm based on the previous one and to simultaneously adapt

the algorithm for hydroconverted lignin, some assumptions concerning the structural modifications

during hydroconversion were considered:

Every ether bond is hydrogenated (except in the k5 construction block because this

linkage is too strong to break) and water molecules are formed.

Some of the methoxy groups are hydrogenated and give origin to catechol groups

(Joffres, Laurenti et al., 2013), Figure 40.

The main structure of each chemical linkage is maintained.

The flow diagram that illustrates the algorithm and its logical decisions are maintained (Figure

39).

The catechol groups are only formed if the linkage between the ether and the methyl group is

broken. If the linkage between the aromatic ring and the methoxy group is broken, it simply turns into a

hydrogen, and therefore a phenol is formed.

The number of initial methoxy groups is known from the 13C NMR to the non-hydroconverted

lignin and from the same analysis but for the hydroconverted lignin it is possible to know the existent

methoxy groups. The catechol groups are given from the 31P NMR (appendix A.3).

𝑁𝑢𝑚𝑏𝑒𝑟𝑚𝑒𝑡ℎ𝑜𝑥𝑦 𝑔𝑟𝑜𝑢𝑝𝑠 ℎ𝑦𝑑𝑟𝑜𝑐𝑜𝑛𝑣𝑒𝑟𝑡𝑒𝑑 𝑡𝑜 𝐻 = 𝑂𝑀𝑒 − (𝑂𝐻𝑐𝑎𝑡𝑒𝑐ℎ𝑜𝑙 + 𝑂𝑀𝑒ℎ𝑦𝑑𝑟𝑜𝑐𝑜𝑛𝑣𝑒𝑟𝑡𝑒𝑑) Eq 44

Now there are two different types of groups to be substituted by hydrogen, the existing

methoxy groups and the catechol groups, but the same line of thought is maintained. With this, the

total number of hydrogens to enter the algorithm is given below.

Figure 40 - Mechanism to illustrate how the methoxy groups convert into catechol groups

62

𝐻𝐴𝑙𝑔𝑜𝑟𝑖𝑡ℎ𝑚 = 𝐻𝑇𝑜𝑡𝑎𝑙 − 𝐻𝐶𝑎𝑡𝑒𝑐ℎ𝑜𝑙 − 𝐻𝑂𝑀𝑒 + 𝑂𝑀𝑒 + 𝑂𝐻𝐶𝑎𝑡𝑒𝑐ℎ𝑜𝑙 Eq 45

The last variable, OHCatechol, represents the number of catechol groups to be added to the total

number of hydrogens to compensate the lack of both methoxy and catechol groups, already with the

removal of the total hydrogens in these two groups. It should be noted that the number of hydrogens in

the catechol groups is equal to the number of catechol groups.

The k1 group suffers a break at its ether bond, giving to two terminal groups: one phenol and

one ethyl connected to an aromatic ring (Joffres, 2006). As a matter of simplification, the ethyl terminal

group will not be accounted for in the algorithm. Groups h1 and h2 were considered as proposed in

Joffres et al. (2013), h3 and h5 are the result of hydrogenations of the previous k4 and k6 groups,

respectively. The k5 group has an ether linkage and a phenol, given that both linkages are too strong

to be hydrogenated under the present experimental conditions, h4 is exactly equal to k5. The terminal

g3 group and the internal construction block g1 are maintained as a simplification but the terminal g2

group will undergo a hydrogenation in its double bond and the aliphatic OH group will be replaced by

hydrogen, resulting in a propyl group connected to an aromatic ring (Joffres, 2006). This new terminal

group will replace the previous g2 group and will have, in this second proposed algorithm, the same

name.

Assuming all the above, the new construction blocks for this algorithm are presented below.

Table 8-15 - Structural representation of the five hydroconverted construction blocks

h1 h2 h3 h4 h5

Table 8-16 - Structural representation of the new propyl terminal construction block that replaces the previous g2 group

g2

For the algorithm for hydroconverted lignin, Table 8-17 shows the molecular weight of each

construction block and the number of atoms of each type, as given by the elemental analysis, 13C

NMR and 1H NMR.

63

Table 8-17 - Characteristics and composition of the various construction blocks

h1 h2 h3 h4 h5 g1 g2 g3

Molecular weight (g/mol) 134 160 184 108 118 76 43 17

Aromatic rings 1 1 2 1 1 1 0 0

Elemental composition C H O N S

9 10 1 0 0

12 16 0 0 0

12 8 2 0 0

6 4 2 0 0

9 10 0 0 0

6 4 0 0 0

3 7 0 0 0

0 1 1 0 0

Carbon types (13C NMR) Cali(all) Cali-C Cali-O in OMe Cali-O (w/o OMe) Caro(all) CAr-H CAr-C or CAr-O C=O

3

3 0 0

6 3 3

0

6

6 0 0

6 4 2

0

0

0 0 0

12 6 6

0

0

0 0 0

6 3 3

0

3

3 0 0

6 4 2

0

0

0 0 0

6 4 2

0

3

3 0 0

0 0 0

0

0

0 0 0

0 0 0

0

Hydrogen types (1H NMR) Aromatic H (6.0 – 7.7 ppm) Phenolic H (7.8 – 9.6 ppm)

Carboxylic H (11.8 – 12.8 ppm) Other H (aliphatic, olefinic, alcohol, ...)

3 1 0 6

4 0 0 12

6 2 0 0

3 1 0 0

4 0 0 6

4 0 0 0

0 0 0 7

0 1 0 0

Oxygen types Aliphatic hydroxyl Phenolic hydroxyl

Methoxy ether Other ethers

0 1 0 0

0 0 0 0

0 2 0 0

0 1 0 1

0 0 0 0

0 0 0 0

0 0 0 0

0 1 0 0

The modifications in some of the constraints are explained below. Note that all the ranges

considered for the previous algorithm are maintained in the present algorithm.

Constraints

Constraint 1 – Aromatic Rings

The first constraint corresponds to the number of aromatic ring knowing the number of each

construction block. In this case, where none of the construction block has more than two connections,

the molecule can only be linear.

𝐴𝑅𝐶𝑎𝑙𝑐 = ∑ ℎ𝑖

5

𝑖=1

+ 1 Eq 46

Constraint 2 – Carbon Balance

This constraint has not undergone any modification.

Constraint 3 – Oxygen Balance

The third constraint is about the total oxygen in the molecule. The only parts of the global

expression (Eq 27) that must be modified are the quantities of oxygen inside each construction block,

Ointernal, and the oxygen in the g2 group that no longer exists.

64

𝑂𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 = ℎ5 Eq 47

𝑂𝐶𝑎𝑙𝑐 = 𝑂𝐻𝑃ℎ𝑒𝑛𝑜𝑙𝑖𝑐𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 + 𝑂𝐻𝑃ℎ𝑒𝑛𝑜𝑙𝑖𝑐𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 + 𝑂𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 Eq 48

Constraint 4 – Construction Blocks

This constraint was only modified for the new construction blocks.

𝐶𝐵𝐶𝑎𝑙𝑐 = ∑ ℎ𝑖

5

𝑖=1

Eq 49

Constraint 5 – Aliphatic Carbon not connected to Oxygen

This constraint has small modifications, given that this constraint is relative to the inside of the

linkages and the new g2 group.

(𝐶𝑎𝑙𝑖 − 𝐶𝑎𝑙𝑖)𝐶𝑎𝑙𝑐 = 3ℎ1 + 6ℎ2 + 3ℎ5 + 3𝑔2 Eq 50

Constraint 6 – Hydrogen Balance

This constraint has not undergone any modification.

Constraint 7 – Ether Groups

This constraint is also relative to the inside of the chemical linkages, so it has suffered some

modifications.

𝑂𝐸𝑡ℎ𝑒𝑟𝐶𝑎𝑙𝑐 = ℎ5 Eq 51

Constraint 8 – Terminal Groups

Finally the last constraint no longer exists. This constraint existed to define the number of

terminals in the molecule, which depended on the number of k4 groups. In the hydroconverted lignin,

the k4 group has given origin to the h3 group, which has only two connections. With this, it becomes

clear that the hydroconverted molecule can only have two terminals.

Objective Function and Final Calculations

This section has not undergone any modifications.

65

Figure 41 – Flow diagram that illustrates the proposed modified algorithm

66

8.2.3. Validation

As for the previous algorithm, the chosen test molecules are products of the hydroconversion

of the previous test molecules (Table 8-18). The results of the algorithm for each of these test

molecules are presented below (Table 8-19). This first test shows that the algorithm works accurately

for molecules similar to the hydroconverted construction blocks.

Table 8-18 - Structural representation of the test molecules based on the proposed construction blocks

t9 t10 t11 t12 t13

Table 8-19 - Obtained results after applying the algorithm to the six test molecules based on the hydroconverted CB

t9 t10 t11 t12 t13

h1 h2 h3 h4 h5

1 0 0 0 0

0 1 0 0 0

0 0 1 0 0

0 0 0 1 0

0 0 0 0 1

f 3,64E-31 3,64E-31 3,93 E-31 4,51E-27 0

g1 g2 g3

1 1 1

1 0 2

0 2 0

1 2 0

1 0 2

The next test is to reconstruct a fragment that resulted from the hydroconversion of the

previous test molecule t8 (Table 8-20). Note that the chosen fragment is the biggest one, because in

this fragment there were no k1 construction blocks to hydrogenate. The results are given in Table 8-21,

which shows a perfect result, meaning that the modified algorithm was also well designed for the

assumptions.

67

Table 8-20 - Structural representation of a hydroconverted fragment that resulted from test molecule t8

t14

Table 8-21 - Results of the application of the modified algorithm to test molecule t14

t14

h1 h2 h3 h4 h5

1 0 1 1 1

f 1,42E-31

g1 g2 g3

0 0 2

8.2.4. Application of the Algorithm to Hydroconverted Lignin

After validating the algorithm, the experimental data of a hydroconverted lignin sample was

introduced in the algorithm. Unfortunately, Medeiros (2013) did not extend the work to hydroconverted

lignin, so a comparison of results is not possible. The experimental data (appendix A.3) was first

converted to serve as input for this algorithm.

Table 8-22 - Obtained results from the application of the proposed algorithm to the experimental data for hydroconverted lignin

Solution

h1 h2 h3 h4 h5

0 0 0 4 8

f 0,73

g1 g2 g3

1 2 0

68

The modified algorithm returned a solution where the most abundant linkages correspond to

the last two construction blocks (Table 8-22). The algorithm returned a solution with no h1, h2 and h3

construction blocks. A construction block could indeed disappear during the hydroconversion process.

However, three construction blocks disappearing at the same time probably indicates that this solution

is most likely mathematically feasible but not the actual solution.

Because there are only three groups (two catechol groups and one methoxy group), there can

only exist three guaiacyl units or one guaiacyl unit and one syringyl unit in the structure. Given the

conformation of each catechol group (Figure 40), three guaiacyl units were proposed and no syringyl

units. This leads to the proposed structure in Table 8-23.

Table 8-23 - Structural representation of the proposed structure for hydroconverted lignin

p2

As in the previous chapter, more clarifying results are presented. Table 8-24 shows deviations

in all the variables. The total number of carbons, one of the most important variables, presents a

deviation of -31% from the experimental data. Although this deviation is much bigger than the

deviation obtained for the Protobind 1000 lignin, this algorithm has no problems concerning multiple

solutions. The elemental composition is quite similar, with a deviation for hydrogen of +11,1%, for

oxygen of -2,7% and for carbon of -0,6% compared to the experimental data. The molecular weight is

30% smaller than the experimental value, which demonstrates that the proposed structure should

have much more carbon and hydrogen. This is also illustrated by the chemical formula that shows that

the proposed structure lacks 49 carbon atoms, 32 hydrogen atoms and 5 oxygen atoms.

69

Table 8-24 - Comparison of general results between the experimental data and the proposed structure

Experimental data Proposed structure p1

Total number of carbons 157,7 109

Composition (%) Carbon

Hydrogen Oxygen

82,2 6,5 11,3

81,7 7,3 11

Chemical formula C158H148O16 C109H116O11

Molecular Weight (g/mol) 2303 1602,1

Table 8-25 - Comparison of structural results between the experimental data and the proposed structure

Number of Experimental data Proposed structure p1

Aliphatic carbons Aromatic carbons Methoxy groups

Catechol OH Phenolic OH

Aliphatic hydrogens Aromatic hydrogens

30,3 126,6 0,7 2,3 4,2 98,8 43

31 78 1 2 6 65 45

Table 8-25 shows that the proposed structure should have more aromatic carbons.

Apparently, the algorithm did not choose correctly the number of construction blocks given that the

number of aliphatic carbons is very similar between the experimental data and proposed data, but that

the aliphatic hydrogen is much smaller in the proposed data. A last thing to be pointed out is that

Joffres et al. (2013) calculated that after 5 h of hydroconversion, the hydroconverted lignin has only 14

phenylpropane units. Table 8-23 shows approximately 13 phenylpropane units. This difference implies

that, apart from the lack of aromatic carbons and aliphatic hydrogens, this choice of construction

blocks was relatively successful. Joffres et al (2013) also stated that after 5 h of hydroconversion, no

syringyl units were detected and that the signals for aliphatic carbons were more intense in

hydroconverted lignin. This proofs the continuous decrease of methoxy groups and that the cyclic

ether β-β and β-5 bond types were converted into aliphatic chains between two aromatic rings.

Conclusion

The proposed algorithm for Protobind 1000 lignin returned a solution with structural

characteristics that were very close to the experimental data. Although it must not be forgotten that

this algorithm still has the multiple solutions problem, this problem can be solved if an analytical

technique is available that can quantify the 5-5 linkage (this specific linkage only belongs to the k4

group and so, this could help distinguish between the k2 and k4 groups). This could help justify the

deviation between the obtained solution and what literature work states regarding the abundance of

the β-O-4 linkage. Also, other fact that can explain this deviation is that the analytical data is an

average measurement of the real sample, which is a mixture of different structures. Moreover, when

chemical processes cleave chemical linkages, the number of different structures increases strongly

and so the average experimental data is increasingly less representative of the real structures

(chapter 6).

With this, the experimental data for hydroconverted lignin is even less accurate than for

Protobind 1000 lignin. Even so, the algorithm returns a feasible solution but this solution has structural

70

characteristics that differ more of the experimental data (comparatively to the proposed algorithm for

native lignin). Given the fact that experimental data is more like a set of average data instead of

representative data (as with asphaltenes), a hypothesis is that aromatic carbons and aliphatic

hydrogens come from different structures that can be very easily detected but do not take part in the

final structure. Finally, Joffres et al. (2013) confirms the absence of cyclic ether β-β and β-5 bond

types and of syringyl units. This fact confirms the calculations above and the assumption that there

were no syringyl units in the proposed structure.

The final conclusion concerning the algorithm is that it can be upgraded after the analytical

data becomes more accurate or when more analytical data is available. In terms of mathematical

performance, the algorithm works pretty well. Medeiros (2013) proposes two final structures to

represent Protobind 1000 lignin. The recalculated analytical data for these structures are close to the

experimental data in some of the criteria, but they deviate very strongly for other criteria. The obtained

solution in this work for Protobind 1000 lignin is much closer to the experimental data for all criteria,

but it does not reflect what literature states about the abundance of the β-O-4 linkage. This

discrepancy is most likely due to the assumptions, and the fact that several neglected functional

groups (carboxylic acids, esters) can have a major influence on the final solution. More accurate

analytical data can improve this algorithm to give not just a feasible solution but a solution very close

to reality. The same can be concluded for hydroconverted lignin. Although the second proposed

algorithm no longer has the multiple solution problems, it returns an average structure to represent a

mixture of different structures. This mixture is, obviously, more complex than a sample of Protobind

1000 lignin, so bigger deviations are observed.

9. Conclusions and Future Perspectives

The main objectives of the present work were to understand how molecular reconstruction is

done and to propose a molecular reconstruction algorithm for lignin structures.

In chapter 2, it was explained that a lot of topics can be improved once the actual structure of

the lignin feedstock is known. Biorefineries will be more rapidly improved and integrated if the

structure of the feedstock is known. This will also be extended to research in biofuels and in the

different pretreatment methods of lignocellulosic feedstock. Even the existing processes for biomass

conversion could be improved. With all these subjects that could be impacted, the knowledge of the

structure of lignin has become a major investigation theme.

The first part of the work consisted of a literature review on different algorithms for molecular

reconstruction of asphaltenes. A total of eleven different algorithms for asphaltenes were found and

validated on several test molecules. One important observation was that the accuracy of each

analytical technique played an important role, for example to calculate the number of the aromatic

carbons, 13C NMR resulted in less error than 1H NMR. Of the eleven algorithms, the algorithm of Sato

(1997) is the most accurate one and gave results with smaller deviations from experimental data. This

was validated on several test molecules were used and even a blind test was carried out. The results

were consistently good. A major disadvantage of these algorithms is that all of them were specifically

made for pure hydrocarbons. This is an important issue because lignin, even if sulfur and nitrogen can

71

be neglected, has large amounts of oxygen. Finally, the algorithms were also tested on a sample of

Buzurgan asphaltenes (Gauthier et al., 2008).

The second part of the present work consisted of a bibliographic study about various

composition modeling techniques for lignin. It was verified that there is not much of improvement in

this area, so a completely new algorithm has to be proposed. Based on heteroatom modeling

techniques found in Oka et al. (1977) and Chang et al. (1982) and a set of important analytical data, it

was possible to create two specific algorithms, one for Protobind 1000 lignin and one for

hydroconverted lignin. The two final algorithms were validated on several test molecules. When

applied to the Protobind 1000 lignin, the algorithm proposes a molecule that satisfies all criteria, even

though it does not propose many β-O-4 linkages, as suggested by literature. This algorithm can also

find multiple solutions which cannot be distinguished due to the lack of informative analytical data. The

algorithm for hydroconverted lignin has quite some deviations from the experimental data, especially

for the number of aromatic carbons and aliphatic hydrogens. These discrepancies are most likely due

to the assumptions, but maybe also due to neglecting several functional groups (carboxylic acids,

esters) that may have a major influence on the final solution. In terms of mathematical performance,

both algorithms work pretty well.

In any event, even though both proposed algorithms still have room for improvements, this

work has shown the great potential of this type of molecular reconstruction techniques.

Future work concerning the first proposed algorithm is to solve the multiple solution problem

by using additional and more accurate analytical data. One possibility can be the quantification of the

5-5 linkage. In both algorithms, a decrease in the assumptions would also be desirable, for example

by directly including ferulic acids, carboxylic acids and ester groups in the algorithm. Although they

can be neglected, nitrogen and sulfur should also be part of the set of atoms in the proposed

algorithms in order to decrease the observed deviations in molecular weight (this is specially important

for nitrogen, which represents about 1,1% (w/w)). Also, in the second algorithm, a major improvement

is required, but the lack of time did not allow it: to add the ethyl terminal groups in the equations. For

this, some kind of analysis could be useful to differentiate between the propyl terminal groups and the

ethyl terminal groups. Eventually, with these modifications, the final solutions can be closer to the

experimental data. A statistical analysis to study the most abundant linkages in wheat straw lignin

would be most suited to improve the proposed algorithms. This type of data commonly exists only for

softwood and hardwood lignins. Another interesting task would be to perform a sensitivity analysis to

investigate whether small variations on the input data (ex. elemental analysis, ...) have a significant

impact on the proposed solutions.

72

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77

A. Appendix

A.1. Experimental Data for Asphaltenes

A.1.1. Buzurgan asphaltenes at 85 wt% conversion

The experimental data for the Buzurgan asphaltene sample obtained at 85 wt% conversion

are given in Table A-1 (Gauthier et al., 2008).

Table A-1 - Experimental data for the Buzurgan asphaltene sample

Elemental Analysis %wt

C H N O S

90,5 5,3 1,2 1

1,4

Molecular Weight (SEC) g/mol

Molecule 492

13C NMR wt %

Total aliphatic carbon CH3 CH2

CH ali Cq ali

18,6 5,2 11,7 1,7 0

Total aromatic carbon CAr-H

Cq aro Cq cond Cq sub

81,4 29,6 51,8 36,8 15

1H NMR wt %

Har Hα Hβ Hγ

42,5 19,5 37,5 0,5

Density (20° C) 1,3

Refractive Index (20° C) 2

A.1.2. Test molecules

To test the various algorithms, 7 test molecules were selected (Table A-2). Their experimental

data are given in Table A-3.

78

Table A-2 - Name and respective structure of each test molecule (http://webbook.nist.gov/chemistry/)

Test Molecule n° 1 Test Molecule n° 2

Name Benzene Name Naphtalene

Structure

Structure


Name Acenaphtene Name 1,2,3,4-tetrahydro-1,4-dimethyl-naphthalene

Structure

Structure


Name 1-butyl-naphthalene Name 4-decyl-1,2,3,6,7,8-hexahydro-pyrene

Structure

Structure

Test Molecule n° 7

Name 9,10-di-1-naphthyl-

anthracene

Structure

79

Table A-3 - Analytical data for the seven test molecules (http://webbook.nist.gov/chemistry/)

Test Molecule number 1 2 3 4 5 6 7

MW 78,1 128,2 154,2 160,3 184,3 348,6 430,5

Elemental Analysis %wt

C H N O S

92,2 7,7 0 0 0

93,6 6,2 0 0 0

93,4 6,5 0 0 0

89,9 10 0 0 0

91,2 8,7 0 0 0

89,5 10,3

0 0 0

94,8 5,1 0 0 0

1H NMR %wt

Har Hα Hβ Hγ

100 0 0 0

100 0 0 0

60 40 0 0

25 12,5 25

37,5

43,8 12,5 25

18,8

8,3 27,8 55,6 8,3

100 0 0 0

13C NMR %wt

Total aliphatic carbon CH3 CH2 CH

Cq ali

0 0 0 0 0

0 0 0 0 0

16,7 0

16,7 0 0

50 16,7 16,7 16,7

0

28,6 7,1 21,4

0 0

61,5 3,8 57,7

0 0

0 0 0 0 0

Total aromatic carbon CAr-H


100 100

0 0 0

100 100 80 20 20

83,3 50

33,3 16,7 16,7

50 33,3 16,7 16,7

0

71,4 50

21,4 14,3 7,1

38,5 11,5 26,9 7,7 19,2

100 64,7 35,3 23,5 11,8

Density 20°C 0,9 1,1 - 0,9 1 - -

Refractive Index 20°C 1,5 1,6 - 1,5 1,6 - -

A.1.3. Blind Test

The analytical data concerning the two “unknown” components used in the blind test can be

found in the tables below (Table A-4; Table A-5; Table A-6; Table A-7).

Table A-4 – 1H NMR spectrum for component 1

What means Ppm % Integration

Ha Ha Ha Hα Hα Hα Hα Hβ Hβ Hγ Hγ Hγ Hγ Hγ

7,361 7,339 5,110 2,436 2,352 2,271 2,266 1,339 1,253 0,935 0,921 0,910 0,904 0,879

44 758 334 30 80 59 65 53

1000 42 35 32 32 3

80

Table A-5 – 13C NMR spectrum for component 1


C in O-C=O Cq sub

CAr-H CAr-H

CH2 connected to O-C=O CH CH2 CH2 CH2 CH2 CH2 CH2 CH2 CH2 CH3

173,63 136,28 128,54 128,16 66,06 34,37 31,96 29,65 29,50 29,36 29,30 29,18 25,02 22,73 14,11

237 227 747 1000 284 299 351 675 433 443 459 381 278 314 294

Table A-6 – 1H NMR spectrum for component 2


Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha Ha

Hα (benzylic H connected to ester group)

8,128 8,111 8,095 8,084 8,067 8,048 8,043 8,020 8,007 7,629 7,553 7,541 7,510 7,498 7,447 7,452 7,428 7,418 7,398 7,382 7,375 7,341 7,325 7,313 7,294 7,274 5,354

204 222 88 81 74 168 181 274 56 31 82 112 75 189 521 284 158 220 603 609 547 243 198 75 159 33

1000

81

Table A-7 - 13C NMR spectrum for component 2


C in O-C=O Cq sub (connected to ester group)

CAr-H

CAr-H CAr-H CAr-H CAr-H

Cq sub (connected to benzylic carbon) CH2 (benzylic carbon connected to ester group)

166,34 136,20 133,01 130,28 129,76 128,65 128,43 128,23 66,69

103 185 321 201 620 750 755 1000 270

Table A-8 - Analytical data as input for the algorithm of Sato (1997)

Component 1 Component 2

Elemental Analysis wt % wt %

Carbon Hydrogen Oxygen Nitrogen

Sulfur

78,575 10,411 11,017

0 0

79,154 5,654 15,077

0 0

Molecular Weight (SEC) g/mol g/mol

Molecule 290,44 212,244

13C NMR wt % wt %

Total aliphatic carbon CH3 CH2 CH

Cq ali

66,548 4,982 56,499 5,067

0

6,582 0

6,582 0 0

Total aromatic carbon CH


37,468 29,605 3,847

0 3,847

93,418 64,529 28,888

0 28,888

1H NMR wt % wt %

Haro Hα Hβ Hγ

33,940 9,903 44,562 11,595

84,585 15,415

0 0

Density (20° C) 0,943 1,112

Refractive Index (20° C) 1,481 1,568

A.2. Algorithms for the Reconstruction of Asphaltenes

A.2.1. Algorithm of Sato

The method developed by Sato (1997) is based on the calculation of different structural

parameters, in different classes (Ring, Aromatic atoms, Fused rings, Naphthenic atoms, Paraffins,

Density and Parameters) and uses molecular weight, elemental analysis and 13C NMR as

experimental inputs.

82

In fact not all of the variables of these seven classes in Sato (1997) can be obtained because

of the lack of structural information, as can be seen by comparing Table A-10 and the proposed set of

structural equations from Sato (1997). As for all the structural algorithms, there is a set of assumptions

made to construct the equations, or else there would be too much alternatives to have a general

equation. This is one of the facts why more than one algorithm should be studied. For this algorithm

the list of assumptions is presented:

1. No aliphatic quaternary carbon should exist nor heteroatoms;

2. No double bond should exist except in aromatic ring;

3. Fused ring systems in an average molecule should be connected by only one aliphatic chain.

The number of chains should be the number of fused ring systems-1;

4. Every fused ring system should include at least one naphthenic ring if naphthenic rings exist;

5. Naphthenic rings in a single fused ring system should not be apart from each other (type I, not

type II in Figure 42);

6. The numbers of naphthenic and aromatic carbons substituted by aliphatic chains should be

proportional to the numbers of naphthenic and aromatic peripheral carbons;

7. No aliphatic chain substitution should exist on carbons in α-position;

8. The number of branches per aliphatic chain should be null or one.

Among those assumptions, 6. and 7. sometimes do not agree with the estimated structure in

small molecules. Therefore, the assumptions should be guidelines for deciding the structure (Sato et

al. 1997).

Figure 42 – Conjugation types of naphthenic rings to aromatic rings

The values for the parameters were firstly randomly assumed and then the Least Square

Method was applied to minimize the difference between the experimental data and the calculated

values for Sato (1997)’s algorithm. From the minimization, the results are in Table A-9 and Table A-10.

Table A-9 - Optimized values for the parameters according to Least Square Method for Buzurgan asphaltene

sample with Sato (1997)’s algorithm

Parameters Min Max Value

M Cai Ctr P

1 12 32 0

5 19 42 3

1 14 32 1

I II

83

Table A-10 - Structural variables calculated for Buzurgan asphaltene sample with Sato (1997)'s algorithm

N° equation Ring N° equation Naphthenic atoms

1 2 3 4 5

Us Rt Ra Rn Rna

25 10 8 2 3

11 12 13 14 15 16 17 18

Cnα Cni Cn Cnp

L (max) L (min)

L Hn

4 0 2 2 2 1 1 4

N° equation Aromatic atoms

6 7 8

Cap Caq Cac

16 19 5 N° equation Paraffins

N° equation Fused rings 19 20 21 22 23

N Cc Hc Cγ

Ccβ

0 5

11 2 2

9 10 - -

Cti Ctp

- -

18 14 - -

A.2.2. Algorithm of Speight

The structural algorithm of Speight (1970) is based on 1H NMR, SEC and elemental analysis.

It results into much less variables than the previous algorithm. The conclusions of this algorithm are

more specific for the core of the molecule.

To construct this algorithm it was necessary to propose assumptions. It was assumed that Hn,

Hr and Hm, in 1H NMR, are associated with naphthenic methylene, paraffinic methylene and paraffinic

methyl, respectively. Also, it was necessary to assume that each carbon atom α to an aromatic ring

carries two protons. The principal deviation here may be in the lower-boiling fractions, where

substituents on aromatic nuclei may be largely methyl groups accompanied by one, long, relatively

unbranched, alkyl group. This algorithm also neglects the existence of heteroatoms.

Because the analytical data required concerning 1H NMR is different from the available (Table

A-1), the only 1H NMR parameters that can be easily obtained are, according to Speight (1970), Ha

(fraction of aromatic hydrogen), Hα (fraction of benzylic hydrogen) and Hm (fraction of paraffinic methyl

hydrogen). The main problem is to differentiate between Hn (fraction of naphthenic hydrogen) and Hr

(fraction of paraffinic methylene hydrogen) because the only data available in Table A-1 is the sum of

Hn and Hr. As so, it was assumed a criteria to help calculate each parameter: the ratio of Hn and Hr

was studied from different authors: Yen and Chilingarian (2000) considered the value of 2,2 for Hn/Hr,

Qian, Zhang and Li (1984) considered 0,84 for the same ratio and Speight (1970) considered 0,4 for

the Hn and Hr ratio in the studied sample. The average value of these three is, approximately, 1. So

the assumption made to obtain Hn and Hr and considering the sum of these two being 0,375 (Table

A-1) is Hn/Hr = 1. With this the values obtained for Hn and Hr were 0,1875 each.

84

Table A-11 - Structural variables calculated for Buzurgan asphaltene sample with Speight (1970)'s algorithm

Calculated Value % w/w

Cs Csa Ca Cp Ci Cr Cn Ra

Csa/Cp Cs/Csa Cp/Ca

11,18 2,54

25,92 4,68

21,24 6,55 2,09

11,62 0,54 4,39 0,18

3,02 0,69 7,01 1,27 5,74 1,77 0,56

- - - -

A.2.3. Algorithm of Montgomery and Boyd

To obtain this algorithm, some assumptions were made: polycyclic nonfused structures, spiro

compounds, three-dimensional ring systems and compounds containing olefinic or acetylenic bonds

are excluded. Both molar volume and molar refraction were expressed in terms of the refractive index

and density, through the approximation of a linear combination of the same groups of chemical types

(the five-type carbon classification). Furthermore it was also assumed that there was no heteroatoms

in the molecule.

Given the iterative resolution, the Least Square Method was applied to minimize the

differences between the experimental values and the values calculated initially with the algorithm. The

molar volume and the molar refraction were firstly estimated with both the density and the refractive

index at 20°C (Table A-1).

Table A-12 - Simultaneous resolution of the three carbon balances and the two correlations of the Montgomery

and Boyd’s algorithm

Equation n° 1° term 2° term 1° term-2° term (1° term-2° term)2

1

2

3

4

5

38,20

26,73

33,56

383,23

186,71

37,10

26,08

33,37

383,33

186,86

1,10

0,66

0,19

-0,10

-0,15

Sum

1,22

0,43

0,04

0,01

0,02

1,72

Table A-13 - Calculated values for the five parameters of the Montgomery and Boyd’s algorithm

Structural Parameters

C1 C2 C3 C4 C5

4,64 0 0

17,46 16,11

85

A.2.4. Algorithm of Hirsch and Altgelt

The algorithm presented by Hirsch and Altgelt (1970) is a much more detailed algorithm, has

as much or even more variables than Sato (1997). The experimental data necessary comprises

elemental analysis, SEC, 1H NMR and density (20°C).

The way of solving such a complex algorithm has to be well understood:

First it is the Preliminary Calculations and Normalizations which concerns a series of

calculations to obtain the molecular volume, to normalize the percentages of each

atomic specie to 100% and to convert each atomic specie to atoms/average molecule

and to atom fractions as can be seen in Table A-14.

Secondly there’s the Disposition of Heteroatoms where the “average” molecule is

reduced to a pure hydrocarbon; this is accomplished by converting groups containing

heteroatoms to corresponding hydrocarbon groups. A consequence of this conversion

is the need for adjustments in the average molecular volume, the total number of

carbon and hydrogen atoms per molecule. The distribution of heteroatoms into

functional groups is approximated on the basis of results obtained from infrared

spectrometry (Figure 43).

Figure 43 – Structure and volume adjustments for heteroatoms. Adapted from Hirsch and Altgelt (1970)

Next step is to solve simultaneously three nonlinear equations, F1, F2 and F3 (Table

A-15 and Table A-16) to estimate the unknown variables CI, CPN and n. After having

the values of these three variables, it is possible to estimate the values of the five

floating parameters that optimize the solution required according to the authors. This

86

optimized solution is achieved when all the nonlinear equations are equal to zero and

then the rest of the variables can be obtained.

To obtain the present algorithm it was necessary to create a set of assumptions that helped

reduce the number of possibilities to create a molecule. First, the distribution of heteroatoms into

functional groups is approximated on the basis of results obtained from infrared spectrometry. About

the development of the mathematical treatment, it is important to refer that the authors derived

quantities for an “average” molecule and that within the molecule they considered “average” fused ring

systems. It was assumed that no aliphatic chain branching occurred at benzylic carbons. Also,

aliphatic chains attached to internal naphthenic carbons were treated as though they were bonded to

peripheral naphthenic carbons. About fused ring systems, it was assumed that they were linked by

single aliphatic chains and that no cyclization occurred through the aliphatic links. The last assumption

was made to obtain the peripheral distribution relation; a fused ring system of a given compactness

factor and containing a given number of aromatic and of naphthenic rings will have a fixed number of

peripheral carbons. That is why there are a number of isomers that’ll satisfy the structural

requirements. To simplify these fact, it was assumed that every isomer is statistically equally possible.

As well as for Sato (1997)’s algorithm, in this algorithm the analytical data is also incomplete.

There are several 1H NMR inputs: HA (number of aromatic hydrogen), HB (number of benzylic –CH

and –CH2 hydrogens), HB3 (number of benzylic –CH3 hydrogens), HL3 (number of aliphatic –CH3

hydrogens) and HR (number of other hydrogens). According to Table A-1, there is data available for

HA, HL3 and HR. The problem to solve is to distinguish between HB and HB3. The available data

concerning these two parameters is their sum, which is 0,195 (Table A-1). Based on the assumptions

made by Williams (1957), is was assumed that the ratio HB/HB3 is equal to Hβ/Hγ (Table A-1). With this

assumption and knowing that the sum of these two parameters is Hα (Table A-1), it is possible to come

with two values for HB and HB3; they are 5,331 and 0,0711 respectively.

Table A-14 - Reduction of the "average" molecule to a pure hydrocarbon

Element PCi APMi AFi APMXC and APMXH VX

Carbon Hydrogen Nitrogen Oxygen Sulfur

91,11 5,34 1,19 0,98 1,39

37,35 26,26 0,42 0,30 0,21

0,58 0,41

0,0065 0,0047 0,0033

37,99 27,70

0 0 0

383,34 - - - -

Table A-15 - Simultaneous resolution of three non-linear equations F1, F2 and F3 to estimate the three unknown variables CI, CPN and n

1° term 2° term Minimizing the differences Auxiliary Calculations Variables

F1 F2 F3

9,28E-06 -1,09E+01 8,16E+00

0 0 0

9,28E-06 -1,09E+01 8,16E+00

Sum

8,61E-11 1,19E+02 6,65E+01 1,85E+02

Q1 Q2 Q3 Q4

0,020 6,03 1,61 5,87

CI CPN n

16,78 2,68 0,50

87

Table A-16 - Obtained values for the five parameters of the algorithm, this step is only possible after the non-linear resolution

Parameters Value Min Max

φ a b ξ ψ ε

0,34 0,3 1 1

0,1 0,3

0 0,25

1 0,7 0

1 0,4 2 1

0,3

Table A-17 - Calculated values for the structural variables

CPA CIA CB CB2 CB3 CPB CIB CIN CPe CL1 CL2 CL3 C

18,53 13,95 4,53 -2,03

1 0,50 5,05 -7,78 16,15 3,01 1,02 2,05

37,99

CPe(max) CPe(min)

Rt RA RN L

TRL TAL TNL TEL fA fN SC

19,81 9,01

36,60 7,48 1,41 -0,46 -0,96 -1,03 0,076 3,05

-0,080 0,028 50,27

A.2.5. Brown-Ladner modified algorithm

The Brown-Ladner algorithm (Yen and Chilingarian, 2000) was initially made for coal and later

modified for petroleum fractions (the one presented). The experimental data required for this structural

algorithm comprises 1H NMR, SEC and elemental analysis.

For the assumptions, the molecules of the resins and the asphaltenes with large average

molecular weights were assumed to be composed of more than one substituted pericondensed

nucleus (more than one pericondensed fused ring system). The hydrogen to carbon atom ratio of the

saturated part of the average molecule of the samples equals 2 (it was confirmed by the DEPT 13C

NMR technique in Yen and Chilingarian (2000)). It was also assumed that aliphatic quaternary

carbons are absent in the chemical structure (it was confirmed also by the same technique that the

content of aliphatic quaternary carbons in the sample is negligible). As the last assumption, the

heteroatoms have not been considered in calculation.

Table A-18 - Calculated values for the structural parameters of Brown-Ladner modified method

fa σ CA RA RT RN CN CS CP fN fP

0,98 0,19

36,47 8,62 9,96 1,34 5,37 0,62 -4,74 0,14 -0,13

88

A.2.6. Algorithm of Williams

The algorithm presented by Williams (1957) involves a detailed treatment of an aromatic

fraction of an oil sample. The experimental data required for the model are elemental analysis, SEC

and 1H NMR.

The assumptions made by Williams consider that: first, the carbon to hydrogen ratio of the

alkyl groups needs to be accurately estimated and this involves the determination of a “branchiness

index” (BI, in Table A-19, which is defined as the peak height ratio of the gamma to beta protons).

Second, the carbon to hydrogen ratio of the α-alkyl groups was assumed equal to that of the other

alkyl groups. Finally, it is assumed that a system such as tetralin (1,2,3,4 tetrahydronaphthalene) in

Figure 44 has two alkyl groups of two carbons each (Petrakis and Allen, 1987).

Figure 44 – Structure of 1,2,3,4 tetrahydronaphthalene

Table A-19 - Calculated values for the structural variables of Williams's algorithm

n f r

CA CS fa C1

#C1 %AS #CA RA RN RS BI

2,95 5,18 0,03

74,70 15,78 0,83

32,38 13,28 16,53 30,63 9,67 0,07 2,19 0,01

A.2.7. Algorithm of Knight

The algorithm of Knight (1967) is based on Williams (1957) but modified with 13C NMR. With

the modification, the number of structural equations decreased compared to the original one. As so,

the experimental data used is, as well as in Williams (1957), SEC and elemental analysis, and instead

of 1H NMR, 13C NMR is used.

The only assumption involved in this technique is that the carbon to hydrogen ratio of the α-

alkyl and other groups is the same (second assumption of Williams’s algorithm). It is worth mentioning

that because of 13C NMR results used in this algorithm, the average structural parameters are based

upon the direct observation of the carbon skeleton. In addition to being able to directly measure the

aromaticity, this method represents a reliable means of estimating the number of naphthenic rings per

average molecule (Petrakis and Allen, 1987).

89

Table A-20 - Calculated values for the structural variables of Knight's algorithm

n #C1 #CA RA RS fa

#C1s

#C1u

total #C %AS

f r

RN A1 A2 A3

2,95 21,56 30,89 5,67 2,34 0,83 2,34

19,22 37,10 10,86 5,52 0,24 0,57 0,67 0,26 0,19

A.2.8. Algorithm of Cantor

The algorithm presented by Cantor (1978) was built for coal-derived liquids. This method is

based on Williams’s and Knight’s methods. Also, the nomenclature used in the model for the structural

variables is largely taken from the method of Clutter et al. (1972). The experimental data used

concerns 13C NMR, 1H NMR, SEC and elemental analysis.

Two assumptions were made in order to obtain the mathematical equations. First, it was

assumed that all alkyl groups were present as substituents on aromatic ring. This is not strictly true for

coal-derived liquids, but the concentrations of saturate compounds were low enough that any errors

introduced were minimal (Cantor 1978). Second, it was assumed that the C/H ratio (carbon-to-

hydrogen ratio) at the α position was equal to the C/H ratio in the remainder of the alkyl groups. This

last assumption can be a problem when branching the α-alkyl carbon (a common chemical bond), as

so it is to expect better results for a sample with short-chain substituents (it precludes high

concentrations of such structures) (Cantor 1978).

Table A-21 - Calculated values for structural variables of Cantor's algorithm

fa n

CA C1

s C1

u C1

%AS f r

#CA #C1 #CS RA RS RN f1 A1 A2

0,81 2,95 0,74 0,06 0,27 0,33

17,43 5,52 0,24

30,20 13,42 6,90 9,39 2,34 0,57

32,70 0,81 0,19

90

A.2.9. Algorithm of Dickinson

This algorithm is based on several methods (Williams 1957; Hirsch and Altgelt 1970; Oka et

al. 1976; Knight 1967; Cantor 1978). The experimental data needed for this model concerns 1H NMR,

13C NMR, SEC and elemental analysis.

Concerning the assumptions for this model, initially there were four: first it was assumed that

the H/C atomic ratio of α-alkyl groups attached directly to aromatic ring was the same as that of the

remainder of the side chain; secondly that the alkyl groups could be estimated form the 1H NMR

spectrum, third, that aromatic ring systems were directly linked with no intervening alkyl groups; finally

the heteroatoms were neglected in order to simplify the average structures (Dickinson, 1979). More

recently, after the 13C NMR technique was available, it was possible to eliminate the second and third

assumptions because then 13C NMR enables to calculate what was assumed above. As so, with 13C

NMR in the experimental data, only the first assumption still remains.

Table A-22 - Calculated values for the structural variables of Dickinson's algorithm

n fC x

CA CS

1 Cu

1 C1

N° CA N° C1

RA AS RS RN

N° CAl N° HAl

2,95 5,52 2,17

73,65 5,71

27,03 32,74 30,20 13,42 9,39

17,43 2,34 0,57 6,90

14,99

A.2.10. Algorithm of Qian, Zhang and Li (1983)

The algorithm of Qian, Zhang and Li (1983) is based on the methods of Knight (1967) and

Dickinson (1979), but only the part containing the equations derived from 13C NMR. The equations

derived from 1H NMR could not be used because of the lack of structural information concerning IR

spectroscopy. The experimental data used in this method is 1H NMR, 13C NMR, SEC and elemental

analysis.

Concerning the assumptions and given that this model is based on Knight’s and Dickinson’s

methods, the assumptions for this method are the same that for those.

91

Table A-23 - Calculated values for the structural variables of Qian, Zhang and Li's algorithm

l fC x

CA% Cl

s% Cl

u% Cl% CA CP RA

AS% n

RN Cal Hal

2,95 5,52 2,17

73,65 5,71

27,03 32,74 30,20 13,42 9,39 0,17 2,34 0,57 6,90

14,99

A.2.11. Algorithm of Qian, Zhang and Li (1984)

The algorithm of Qian, Zhang and li (1984) is based on Knight’s method, but more accurate

and is also an improvement of the previous method of the same authors. This algorithm was

specifically made for high aromaticity samples like coal. The experimental data used in this structural

model concerns SEC, elemental analysis, 1H NMR and 13C NMR. Of course, because this algorithm is

more recent than the previous ones (except for Sato’s algorithm), it has the advantage of having

access to a set of more recent analytical methods.

Because this algorithm is based on Knight’s method, the assumptions are the same and

therefore, can be read above.

Table A-24 - Calculated values for the structural variables of Qian, Zhang and Li's algorithm

C l

Ca Cl

s% Cl

u% Cl% Cp

Cp/Ca RA

AS% n

Car,ar,ar Cal Hal Cn Cm fC RT RN

37,10 2,95

30,20 0,06 0,27 0,33

13,42 0,44 9,39 0,17 2,34 9,35 6,90

14,99 3,16 3,74 0,53 9,96 0,57

A.3. Experimental Data for Lignins

The experimental data for the Protobind 1000 lignin is given in Table A-25 (Joffres et al., 2013).

92

Table A-25 - Experimental data for Protobind 1000 lignin

Direct data

Normalized to 100%

Elemental Analysis

wt % wt %

C H O N S

Ashes Water

59,4 5,6 25,7 1,1 0,1 4,9 3

65,9 6,1 28 0 0 0 0

Molecular Weight (SEC)

g/mol g/mol

Molecule

4915 4915

13C NMR

mmol/g lignin

%wt

Cali(all) Cali-Cali

OMe Cali-O (without OMe)

Caro(all) CAr-H CAr-C CAr-O C=O

20,4 8,4 5,6 6,4 30,2 11,3 9,8 9,1 1,8

40,3 16,6 11,1 12,6 59,7 22,3 19,4 18 0

1H NMR

%wt -

Aliphatic H Aromatic H

Phenolic OH Carboxylic COOH

CH-CO, CH-O, Cal-OH

16,4 12,9

4 1,8 20

- - - - -

31P NMR

mmol/g lignin

OH/g lignin

Aliphatic OH group Syringyl phenolic units + condensed phenolic units

Guaiacyl phenolic units p-Hydroxyphenolic units

Carboxylic COOH

1,6 1,1 0,8 0,4 0,9

7,9 5,4 3,9 2

4,4

The model data for test molecules t1, t2, t3, t4, t5 and t6 are given inTable A-26, and those for

test molecules t7 and t8 are given in Table A-27.

.

Table A-26 - Model data for test molecules t1, t2, t3, t4, t5 and t6

t1 t2 t3 t4 t5 t6

Elemental Analysis

wt %

C H O N S

Ashes Water

68,3 6,4 25,3

0 0 0 0

72,5 6,1 21,5

0 0 0 0

72,5 6,1 21,5

0 0 0 0

72,6 5,9 21,5

0 0 0 0

72,5 6,1 21,5

0 0 0 0

69,2 6,2 24,6

0 0 0 0


g/mol

93

Molecule

316,4 298,3 298,3 446,5 298,3 260,3

13C NMR

%wt

Cali(all) Cali-Cali



33,3 11,1

0 22,2 66,7 44,4 11,1 11,1

0

33,3 16,7

0 16,7 66,7 38,9 16,7 11,1

0

33,3 11,1

0 22,2 66,7 44,4 11,1 11,1

0

33,3 14,8

0 18,5 66,7 37

18,5 11,1

0

33,3 22,2

0 11,1 66,7 38,9 11,1 16,7

0

20 6,7 0

13,3 80

53,3 13,3 13,3

0

1H NMR

%wt



CH-CO, CH-O, Cal-OH

55,0 40,0 5,0 0 0

55,6 38,9 5,6 0 0

44,4 44,4 11,1

0 0

57,7 38,5 3,8 0 0

55,6 38,9 5,6 0 0

37,5 50,0 12,5

0 0

Table A-27 - Model data for test molecules t7 and t8

t7 t8

Elemental Analysis

wt %

C H O N S

Ashes Water

72,8 5,7 21,5

0 0 0 0

62,6 5,9 31,5

0 0 0 0


g/mol

Molecule

742,8 3551,7

13C NMR

%wt

Cali(all) Cali-Cali



33,3 13,3

0 20

66,7 35,6 20

11,1 0

38,4 3,2 12,4 22,7 61,6 24,9 24,3 12,4

0

1H NMR

%wt



CH-CO, CH-O, Cal-OH

59,5 38,1 2,4 0 0

75,5 22,1 2,4 0 0

The experimental data for the hydroconverted lignin is given in Table A-28.

94

Table A-28 - Experimental data for hydroconverted lignin

Direct data

Normalized to 100%

Elemental Analysis

wt % wt %

C H O N S

Ashes Water

80 6,5 11,3 2,4 0 0 0

82,2 0,1 0,2 0 0 0 0


g/mol g/mol

Molecule

2303,00 2303,00

13C NMR

mmol/g lignin

%wt

Cali(all) Cali-Cali



13 11,6 0,3 1,1 53 18

25,6 9,4 0,7

19,7 17,6 0,5 1,7 80,3 27,3 38,8 14,2

0

1H NMR

%wt -



CH-CO, CH-O, Cal-OH

31,0 16,8 12,2 1,7 38,3

- - - - -

31P NMR

mmol/g lignin

OH/g lignin

Aliphatic OH group Syringyl phenolic units + condensed phenolic units

Guaiacyl phenolic units p-Hydroxyphenolic units

Catechol OH Carboxylic COOH

0 0,6 0,3 0,9 1

0,2

0 1,4 0,7 2,1 2,3 0,5

The model data for test molecules t9, t10, t11, t12, t13 and t14 is given in Table A-29.

Table A-29 - Model data for test molecules t9, t10, t11, t12, t13 and t14

t9 t10 t11 t12 t13 t14

Elemental Analysis

%wt

C H O N S

Ashes Water

80,0 8,2 11,8

0 0 0 0

80,0 8,2 11,8

0 0 0 0

80,0 8,2 11,8

0 0 0 0

80,0 8,2 11,8

0 0 0 0

78,9 7,1 14,0

0 0 0 0

70,0 6,0 23,9

0 0 0 0


g/mol

95

Molecule

270,4 270,4 270,4 270,4 228,3 668,7

13C NMR

%wt

Cali(all) Cali-Cali



33,3 33,3

0 0

66,7 38,9 16,7 11,1

0

33,3 33,3

0 0

66,7 44,4 11,1 11,1

0

33,3 33,3

0 0

66,7 33,3 22,2 11,1

0

33,3 33,3

0 0

66,7 38,9 11,1 16,7

0

20 20 0 0 80

53,3 13,3 13,3

0

23,1 15,4 7,7 0

76,9 33,3 15,4 28,2

0

1H NMR

%wt



CH-CO, CH-O, Cal-OH

59,1 31,8 9,1 0 0

54,5 36,4 9,1 0 0

63,6 27,3 9,1 0 0

63,6 31,8 4,5 0 0

37,5 50,0 12,5

0 0

52,5 32,5 15,0

0 0

A.4. Results for the Reconstruction of Lignins

The results of the preliminary calculations for the Protobind 1000 lignin are given in

Table A-30.

Table A-30 - Results before the application of the algorithm for Protobind 1000 lignin

Results before the algorithm

Total oxygen in the molecule (mmol/g lignin) Total oxygen in the molecule (O/g lignin)

Total ether groups in the molecule (O/g lignin) Total phenolic groups in the molecule (O/g lignin)

Total aliphatic OH groups in the molecule (O/g lignin)

16,8 82,6 59

11,3 12,3

The preliminary calculations’s results for the hydroconverted lignin are given in Table A-31.

Table A-31 - Results before the application of the algorithm for hydroconverted lignin

Results before the algorithm

Total oxygen in the molecule (mmol/g lignin) Total oxygen in the molecule (O/g lignin)

Total ether groups in the molecule (O/g lignin) Total phenolic groups in the molecule (O/g lignin)

Total aliphatic OH groups in the molecule (O/g lignin)

3,4 7,8 3,2 4,1 0,5

Table A-32 - Internal results of the algorithm for Protobind 1000 lignin

Protobind 1000 lignin

Cali-O (without OMe) Internal Cali

g3 CB Number of hydrogens in g3 CB

Internal Hali Haro

Total HPhenolic Internal HPhenolic

78,9 73,8 1,7 8,5

107,5 60,3 11,3 10

96

Terminal HPhenolic Total AR

Internal AR Terminal AR

1,3 26,8 23,8

3

Table A-33 - Internal results of the algorithm for test molecules t1, t2, t3, t4, t5 and t6

t1 t2 t3 t4 t5 t6



Internal Hali Haro

Total HPhenolic Internal HPhenolic Terminal HPhenolic

Total AR Internal AR Terminal AR

6 3 1 5 6 8 1 0 1 2 0 2

6 3 1 5 5 7 1 0 1 2 0 2

6 6 0 0 8 8 2 0 2 2 0 2

9 3 2 10 5 10 1 0 1 3 0 3

6 0 2 10 0 7 1 1 0 2 0 2

3 3 0 0 6 8 2 0 2 2 0 2

Table A-34 - Internal results of the algorithm for test molecules t7 and t8

t7 t8



Internal Hali Haro



15 9 2

10 15 16 1 0 1 5 2 3

48 45 1 5

83 46 5 2 3

19 15 4

Table A-35 - Internal results for hydroconverted lignin sample

Hydroconverted lignin



Internal Hali Haro



30,3 24,3

2 14

48,7 45,9 4,1 4,1 0

21,1 19,1

2

97

Table A-36 - Internal results for test molecules t9, t10, t11, t12, t13 and t14

t9 t10 t11 t12 t13 t14



Internal Hali Haro



6 3 1 7 6 7 2 1 1 2 0 2

6 6 0 0

12 8 2 0 2 2 0 2

6 0 2 14 0 6 2 2 0 2 0 2

6 0 2 14 0 7 1 1 0 2 0 2

3 3 0 0 6 8 2 0 2 2 0 2

6 6 0 0 12 13 6 4 2 5 3 2

Table A-37 - Constraint values for Protobind 1000 lignin

Constraint number 1 2 3 4 5 6 7 8

Protobind 1000 lignin

Calculated Value Experimental Value

28,3 26,8

248,9 240

57,7 56

26,3 24,8

26,0 44,8

217,4 237,4

35,6 59,0

3 -

Table A-38 - Constraint values for test molecules t1, t2, t3, t4, t5 and t6


t1

t2

t3

t4

t5

t6

t7

t8

Calculated Value Experimental Value Calculated Value

Experimental Value Calculated Value






Experimental Value

2 2 2 2 2 2 3 3 2 2 2 2 5 5

19 19

18 18 18 18 18 18 27 27 18 18 15 15 45 45 162 162

5 5 4 4 4 4 6 6 4 4 4 4

10 10 47 47

1 1 1 1 1 1 1 1 1 1 1 1 3 3

16 16

2 2 3 3 2 2 4 4 4 4 1 1 6 6 6 6

20 20 18 18 18 18 26 26 18 18 16 16 42 42 162 162

1 1 1 1 2 2 2 2 1 1 0 0 4 4

18 18

2 - 2 - 2 - 3 - 2 - 2 - 1 - 3 -

Table A-39 - Constraint values for hydroconverted lignin sample


Hydroconverted lignin

Calculated Value Experimental Value

13,3 21,1

109,9 157,7

8,3 16,2

12,3 20,1

30,3 27,7

115,7 148,2

4,1 3,2

2 -

98

Table A-40 - Constraint values for test molecules t9, t10, t11, t12, t13 and t14


t9

t10

t11

t12

t13

t14

Calculated Value Experimental Value Calculated Value





Experimental Value

2 2 2 2 2 2 2 2 2 2 5 5

18 18 18 18 18 18 18 18 15 15 36 36

2 2 2 2 2 2 2 2 2 2 7 7

1 1 1 1 1 1 1 1 1 1 4 4

6 6 6 6 6 6 6 6 3 3 6 6

22 22 22 22 22 22 22 22 16 16 34 34

0 0 0 0 0 0 1 1 0 0 1 1

2 - 2 - 2 - 2 - 2 - 2 -

Documents

Chemical Representation of Various Biomass Compounds