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DESIGN OF EXPERIMENTS AND OPTIMIZATION OF ALGAE
BIOCRUDE HYDROTREATING FOR BIOFUEL PRODUCTION
MASTER’S THESISTEPE4- 1010 : KAROL MICHAL MICHALSKI
JUNE 2018DEPARTMENT OF ENERGY TECHNOLOGY
This document was typeset in LATEX. The cover is author's own work, reproduced from a
microscopic picture of Spiruluna microalgae [1]
Aalborg University
Title: Design of experiments and optimization of algae bio-crude hydrotreating
for biofuel production
Semester: 10th semester of Thermal Energy and Process Engineering
Semester theme: Master's Thesis
Project period: 01.02.2018 to 01.06.2018
ECTS: 30
Supervisor: Thomas Helmer Pedersen
Project group: TEPE4-1010
Karol Michal Michalski
SYNOPSIS:The development of advanced biofuels production obtained
from non-food biomass still faces challenges that impede
their commercialization. Biocrude, as the product from
thermochemical conversion of biomass has to be considered as
an intermediate requiring further upgrading to obtain drop-in
fuel properties. This study presents a set of two-level factorial
experiments, where hydrotreating of bio-crude, obtained from
HTL of micro-algae feedstock was evaluated. This was followed
by an analysis of the e�ects of operational conditions on
hydrodeoxygenation (HDO), hydrodenitrogenation (HDN) and
hydrogen consumption as response variables. It was found
that temperature is the main driver for oxygen removal,
whereas nitrogen removal also relies on hydrogen pressure
and temperature-pressure interaction, similarly as hydrogen
consumption. For an optimized conditions experiment (375
°C, 70 bar, 3h) , full deoxygenation was achieved, whereas
nitrogen level remained at around 3 %, which corresponds
to 60% reduction. However, GC-MS analysis revealed that
nitrogen is contained in higher molecular weight compounds,
which according to simulated distillation (Sim-dis) accounts
for approximately 1/3 of the total oil fraction. Therefore, light
fractions such as gasoline, jet or diesel fuel may be expected
to be nitrogen-free. As a conclusion, in�uential factors for
hydrotreating of algae bio-crude were identi�ed, but further
investigation with a greater number of experiments is required
in order to understand the process in detail.
Pages, total: 72
Appendices: A-B
By signing this document, each member of the group con�rms that all group
members have participated in the project work, and thereby all members are
collectively liable for the contents of the project. Furthermore, all group mem-
bers con�rm that the project does not include plagiarism.
iii
Executive summary
Design of experiments: hydrotreating algae bio-crude
Products from thermo-chemical conversion of bio-feeds require further upgrading in order
to obtain drop-in speci�cations and to be successfully integrated with currently existing
fuel market. The major constrain regarding the bio-crude derived from microalgae
is its high oxygen and nitrogen content. This can be addressed by hydroprocessing,
although the optimal conditions to achieve the best quality product with the least
extensive processes remain unknown for this speci�c feedstock. The present study aims
to contribute to understanding of hydrotreating mechanisms by identi�cation of the most
in�uential process parameters. This is done by designing and performing a set of two-
level factorial experiments, where the e�ect of temperature, initial hydrogen pressure
and residence time was analysed. Three essential responses were chosen to evaluate the
performance of hydrotreating: degree of deoxygenation, degree of denitrogenation and
hydrogen consumption. Additionally, a characterization of hydrotreated bio-crude samples
was carried out with regards to elemental composition, chemical structure and boiling point
distribution.
Identi�cation of in�uential parameters
The two level factorial design revealed that temperature is the main driver for
hydrodeoxygenation whereas for hydrodenitrogenation, also hydrogen pressure and the
interaction between temperature and pressure were found to be signi�cant. Similarly,
hydrogen consumption was mostly a�ected by these two parameters. In all cases, residence
time was the least substantial factor. In order to statistically validate these �ndings an
analysis of variance was done. The most severe conditions experiment yielded a complete
removal of oxygen containing compounds, whereas nitrogen content was reduced by 50
%. Based on the knowledge gained during the �rst experimental campaign and simple
linear models, a set of con�rmation experiments was proposed which were expected to
result in further nitrogen content reduction. Hydrotreating at 375 °C and 70 bar initial H2
pressure for three hours has led to the best results. However despite increasing the degree
of denitrogenation up to 60% the hydrotreated bio-crude still contained around 3% of
nitrogen. Nonetheless, the GC-MS analysis has shown that nitrogen is located in heavier
molecular weight compounds. This is considered as a positive �nding since simulated
distillation indicated that more than 60 % of the oil comprise gasoline, jet and diesel fuel
fractions. Also the calculated higher heating value of the upgraded samples was around 45
MJ/kg which corresponds to the level of conventional petroleum fuels, which indiacates
the potential of micro-algae for production of transportation biofuels.
iv
Aalborg University
Conclusions
The most in�uential parameters in hydrotreating algae bio-crude were identi�ed and a
chemical analysis of obtained products was performed. The removal of oxygenates from
algae bio-crude was successful. Although the nitrogen containing compounds are more
problematic since even hydrotreating in very severe conditions did not lead to their
complete removal. This indicates the possible limitations of this method and catalyst
related issues.
Future work should focus on a more detailed experimental design to obtain response
surfaces accounting for non linearities in the process and developing more e�cient catalysts
suited for hydrotreating of high nitrogen content oils.
v
Preface
This report was written on the 10th semester of Master of Science in Thermal Energy and
Process Engineering studies at Department of Energy Technology, Aalborg University as
a Partial Ful�lment of the Requirements for the Degree of Master of Science.
Reading guide
The layout of the report is designed for one sided print.
References are made according to the IEEE standard. In the text sources are indicated
by numbers in square brackets, sorted by their order of appearance. Citations for single
sentences are placed before the dot. If a passage of multiple sentences refers to the same
source, the citation is placed after the dot and followed by a line break. Information on
the respective source is found in the Bibliography at the end of the report.
In order to avoid excessive repetitions, synonyms are used for frequently used terms.
Software
Design Expert 11 was used for statistical analysis of the results from hydrotreating
experiments, whereas numerical calculations and plots were done in Microsoft Excel
2016. Various supplementary graphics were created in Inkscape. Additionally, for obtaining
solubulity parameters, HSPiP software was used. Programmes available in the laboratories
like LabView, LabSolutions or OMNIC Specta were helpful for acquisition and processing
of analytical chemistry results.
Acknowledgements
I would like to thank Assistant Professor Thomas Helmer Pedersen who actively supervised
this project work, as well as all my other previous activities at Aalborg University. Also
I appreciate the ability to be a part of the Biomass research programme, provided by
Professor Lasse Rosendahl.
A very special thanks goes to PostDoc Daniele Castello, who assisted with his knowledge
and skills in all experimental work in the laboratory. This also applies to all other sta�
members for being ready to support me in any inquiry related to this research project.
vi
List of Tables
2.1 Biochemical composition (%wt.) of microalgae strains on dry-ash-free basis [19] 8
2.2 Elemental analysis of di�erent microalgae [19] . . . . . . . . . . . . . . . . . . . 8
2.3 Comparison between physical properties of algal bio-oils and typical petroleum
crude oils. Data obtained from Refs.[23, 24] . . . . . . . . . . . . . . . . . . . . 9
2.4 Activation energy (EA), iso-reactive temperature (Tiso) and hydrogen consump-
tion for HDO of di�erent functional groups over a Co−MoS2/Al2O3 catalyst.
Data obtained from References [41],[42] . . . . . . . . . . . . . . . . . . . . . . 13
2.5 The average hydrogen consumption [kgH2/kgfeed] for hydrotreating studies of
di�erent authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.1 Test-factors for hydrotreating experiments . . . . . . . . . . . . . . . . . . . . . 22
3.2 Elemental composition of raw biomass and bio-crude together with HHV, ash
and water content. All on wt% dry basis. . . . . . . . . . . . . . . . . . . . . . 25
4.1 Elemental analysis results and standard deviations associated to each
measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.2 Complete experimental matrix with obtained responses and calculated e�ects. . 31
4.3 ANOVA for HDO model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4 ANOVA for HDN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.5 ANOVA for hydrogen consumption . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.6 Sim-Dis analysis of bio-crude and selected upgraded samples . . . . . . . . . . . 39
4.7 List of solvents used for solubility tests, obtained HSP for algae bio-crude. HSP
of Venezuelan crude oil was found in [69]. Estimation of HSP of wood bio-crude
and marine diesel oil was performed in an other study of the author. . . . . . . 44
4.8 Operational conditions in the second set of con�rmation experiments . . . . . . 47
4.9 Elemental analysis and degree of denitrogenation for the second set of experiments 47
A.1 Coe�cients estimates in terms of coded factors for three models . . . . . . . . . 52
A.2 R2 values for three models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
B.1 Gasoline speci�cations for a market with highly advanced requirements for
emission control and fuel e�ciency [70] . . . . . . . . . . . . . . . . . . . . . . 53
B.2 Diesel speci�cations for a market with highly advanced requirements for
emission control and fuel e�ciency [70] . . . . . . . . . . . . . . . . . . . . . . . 53
B.3 Jet A-1 speci�cations [71] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
B.4 Marine fuel speci�cations [72] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
viii
List of Figures
1.1 Catalytic upgrading step as a focus of this project . . . . . . . . . . . . . . . . 1
1.2 Biofuel demand by region 2010-2050 [8] . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Process �ow diagram of the path from algal biomass cultivation to production
of hydrocarbon fuels, adopted from Biller and Ross [13] . . . . . . . . . . . . . 3
1.4 Hydrotreating will have di�erent objectives depending on the feedstock and
desired products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 Main reactions occurring during hydroprocessing . . . . . . . . . . . . . . . . . 10
2.2 Reactivity of selected oxygen containing compounds and associated hydrogen
consumption based on data found in Reference [31] . . . . . . . . . . . . . . . . 13
2.3 Reactivity of selected nitrogen containing compounds and associated hydrogen
consumption based on data found in Reference [31] . . . . . . . . . . . . . . . . 15
2.4 Hydrodenitrogenation path of pyridine over NiMo/Al2O3 catalyst. Reproduced
from Ref. [46] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.5 Di�erent types of fuels are obtained through distillation, based on the boiling
point of di�erent fractions. The temperatures are only indicative values . . . . 19
3.1 The 23 factorial design of the hydrotreating experiment . . . . . . . . . . . . . 22
3.2 Algae biocrude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.3 NiMo/Al2O3 catalyst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4 Schematic diagram of experimental setup . . . . . . . . . . . . . . . . . . . . . 26
4.1 Half-plot for HDO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.2 Pareto for HDO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.3 Normal probability plot for HDO . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.4 Residuals vs. predicted values for HDO . . . . . . . . . . . . . . . . . . . . . . 33
4.5 Half-plot for HDN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.6 Pareto for HDN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.7 Interaction plot for temperature/initial H2 prssure interaction for HDN . . . . . 35
4.8 Contour plot for HDN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.9 Approximated hydrogen consumption calculated for all experiments . . . . . . 36
4.10 Half-plot for hydrogen consumption . . . . . . . . . . . . . . . . . . . . . . . . 37
4.11 Pareto for hydrogen consumption . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.12 HHV of the biomass, bio-crude and hydrotreated samples . . . . . . . . . . . . 38
4.13 Chromatograph of the untreated bio-crude . . . . . . . . . . . . . . . . . . . . . 39
4.14 Chromatograph of the severe conditions experiment 6 . . . . . . . . . . . . . . 39
4.15 Simulated distillation curves of all experiments and untreated bio-crude for
reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.16 FT-IR spectra of the algae bio-crude, experiment 1 and experiment 6 . . . . . . 41
4.17 High temperature EXP 6 and low temperature EXP 8 pressure data . . . . . . 42
ix
List of Figures Aalborg University
4.18 Mild conditions sample on the left with only a slight amount of water phase at
the bottom of the vial. More severe conditions sample in the middle with clear
separation of water phase. On the right the most severe conditions sample.
Clear water separation and no residue on the walls indicating e�cient HDO
and an easy �owing liquid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
x
Nomenclature
General abbreviations Description
AAU Aalborg University
ANOVA Analysis of Variance
CCD Central Composite Design
CDL Coal-Derived Liquids
DOE Design of Experiments
DODO Degree of Deoxygenation
DODN Degree of Denitrogenation
FT-IR Fourier Transform - Infra Red
GHG Green House Gas
GC-MS Gas Chromatography - Mass Spectroscopy
HDN Hydrodenitrogenation
HDO Hydrodeoxygenation
HDS Hydrodesulfurization
HDT Hydrotreating
HHV Higher Heating Value
HPR Hydroprocessing
HSP Hansen Solubility Parameters
HSPiP Hansen Solubility Parameters in Practice
MDO Marine Diesel Oil
RED Relative Energy Di�erence
RSM Response Surface Methodology
Sim-Dis Simulated Distillation
xi
Contents
List of Tables viii
List of Figures ix
Contents
1 Introduction 1
1.1 Development of next generation biofuel technologies . . . . . . . . . . . . . 1
1.2 Biofuels for transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Algae as a potential feedstock for advanced biofuel production . . . . . . . . 3
1.4 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.5 Scope of the report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 State of the art 7
2.1 General characterization of algae biomass, bio-crude and di�erences with
petroleum crude oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.1 Biochemical composition of algal biomass . . . . . . . . . . . . . . . 7
2.1.2 Chemical composition and physical properties of algal bio-crude . . . 8
2.2 Hydroprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 Rationale behind hydroprocessing . . . . . . . . . . . . . . . . . . . . 11
2.2.2 Hydrodeoxygenation . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.3 Hydrodenitrogenation . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.4 Review of algal bio-crude hydrotreating studies . . . . . . . . . . . . 16
2.2.5 Issues regarding hydrotreating of bio-crudes . . . . . . . . . . . . . . 17
2.2.6 Hydrogen consumption . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3 Fuel speci�cations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Methods and materials 21
3.1 Design of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.1 Two-Level Factorial Design . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.2 Analysis of variance . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.1.3 Predictive equations modelling . . . . . . . . . . . . . . . . . . . . . 23
3.2 Feed characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2.1 Algae biomass and bio-crude . . . . . . . . . . . . . . . . . . . . . . 24
3.2.2 Catalyst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3.1 Microbatch reactors . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3.2 Sandbath and shaking device . . . . . . . . . . . . . . . . . . . . . . 27
3.3.3 Temperature and pressure measurement . . . . . . . . . . . . . . . . 27
3.3.4 Experimental procedure . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.4 Product analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Contents Aalborg University
3.4.1 Gas phase analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4.2 Elemental analysis of liquid phase . . . . . . . . . . . . . . . . . . . 28
3.4.3 GC-MS of liquid phase . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4.4 FT-IR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4.5 Simulated distilation (Sim-Dis) . . . . . . . . . . . . . . . . . . . . . 29
4 Results and discussion 30
4.1 Identi�cation of in�uencing parameters from the two- level factorial design
experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.1.1 Hydrodeoxygenation . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.1.2 Hydrodenitrogenation . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.1.3 Hydrogen consumption . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.1.4 Investigation of the e�ect of operational conditions on bio-oil properties 37
4.1.5 GC-MS analysis of compounds . . . . . . . . . . . . . . . . . . . . . 38
4.1.6 Simulated distillation . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.1.7 FT-IR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.1.8 Pressure data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1.9 Hydrtotreated samples observations . . . . . . . . . . . . . . . . . . 43
4.1.10 Additional considerations regarding bio-crude solubility . . . . . . . 43
4.1.11 Discussion and partial conclusions . . . . . . . . . . . . . . . . . . . 45
4.2 Optimization and con�rmation experiments . . . . . . . . . . . . . . . . . . 46
4.3 Results from con�rmation experiments . . . . . . . . . . . . . . . . . . . . . 47
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5 Future work 50
6 Conclusions 51
A Modelling statistics 52
B Fuel speci�cations 53
Bibliography 55
Introduction 11.1 Development of next generation biofuel technologies
In the search of clean and sustainable energy solutions, the sector of bioenergy is becoming
more and more important, being the largest renewable energy source globally, accounting
for 73 % of all renewable energy supply. From the beginning of the century, the use
of biomass fuels and feedstocks in all energy end-use sectors has substantially increased
with a growth rate of 2.3% [2]. However, most of them are referred to as conventional, �rst
generation biofuels, obtained through well-established processes from food crops feedstocks
such as cereals, sugar crops and oil seeds [3]. This brings concerns regarding the need to
explicitly use arable land for the production of fuel. Also, processing and production costs
are rather high, making them hard to compete with petroleum products [4]. Therefore, the
attention is shifting towards the advanced, second and third generation biofuels, derived
principally from non-food biomass. These include lignocellulosic, waste or algea feedstocks,
which are abundant and can be available worldwide. Nevertheless, the current stage of
their development does not allow to be fully competitive when compared to technologies of
fossil fuel alternatives [5]. The addressed challenges comprise understanding the conversion
process of di�erent feedstocks and improving its e�ciency with the ultimate goal to develop
a sustainable production of bio-fuels which could be also integrated with the existing fuel
market. Among di�erent biomass conversion technologies, the hydrothermal liquefaction
(HTL) has gained a signi�cant interest in recent years. HTL is a thermochemical process
which allows to convert wet biomass of di�erent kinds into a crude oil- like substance
called biocrude, at elevated pressure and moderate temperature. HTL has been studied
intensively at the Aalborg University (AAU) [6]. The research activities are conducted in
three main areas:
� Feedstock pretreatment to obtain continuous and e�cient biomass conversion
� Understanding the conversion path of di�erent feedstocks to ensure optimal process
conditions and high product yield
� Development of bio-crude upgrading to obtain high quality products suitable for
various end-use applications
FeedstockHydrothermal
Liquefaction
Catalytic
Upgrading
Bio-
crudeHydrocarbon
fuels
Figure 1.1. Catalytic upgrading step as a focus of this project
1
1.2. Biofuels for transportation Aalborg University
In order to achieve a commercial viability and industrial scale-up, progress in each of these
�elds is required. This project will focus on one particular bio-crude catalytic upgrading
process known as hydrotreating. It is an essential way to meet fuel speci�cations and
integrate with the existing re�nery infrastructure.
1.2 Biofuels for transportation
The transportation sector is responsible for around 14% of GHG emissions globally,
second after the power production sector. At the same time, the share of renewables
for fuel production accounts for only 2.8 % of total transport fuel [7],[2]. Therefore, a
development of new biofuel production technologies is anticipated to mitigate dependency
on oil and decarbonize especially heavy transport modes, which cannot be electri�ed.
The International Energy Agency forecasts, that the share of biofuels can raise up to
27%, leading to reductions of around 2.1 gigatonnes (Gt) of CO2 emissions per year when
produced sustainably [8].
Figure 1.2. Biofuel demand by region 2010-2050 [8]
To meet this demand the next generation biofuels from more e�cient primary liquefaction
routes with upgraded downstream re�ning processes have to become mature. However,
their successful implementation depends on achieving the same level of performance with
a signi�cantly lower carbon footprint, compared to existing biofuels at the same time. The
development of advanced biofuels has gained signi�cant political attention, with a number
of ongoing programmes and projects worldwide. As an example, the European Union
supports research activities in the �eld under Horizon 2020 framework.
2
1.3. Algae as a potential feedstock for advanced biofuel production Aalborg University
1.3 Algae as a potential feedstock for advanced biofuel
production
Microalgae are autotrophic microorganisms which are primary synthesizers of organic
matter in aquatic habitats. They are represented by a vast group of simple, single celled
organisms usually referred to as phytoplankton. Humanity has used them for a long time
as health food products, chemicals or pharmaceutical products. They have also been
cultivated for their high value oils [9].
The interest in algae as a feedstock for fuel production dates as far as back to 1960 [10]
and was later trigerred by the oil crisis of the 1970. Then, it has been comprihensively
researched for more than three decades by the US Department of Energy's funded program,
focusing on development of renewable biodiesel from high lipid- content algae grown in
ponds, utilizing waste CO2 from coal �red power plants [11]. More recently, the interest in
algal biofuels has been growing again due to concerns regarding greenhouse gas emissions,
energy security, fossil crude oil depletion and no competition for limited agricultural
resources. A currently ongoing European Union funded research as a part of the Horizon
2020 programme - HyFlexFuel [12] - examines microalgae as one of three model feedstocks.
Micro-, and macroalgae have been identi�ed as a potential source of the advanced biofuels
due to their high photosyntetic e�ciency, fast growth rate and high area-speci�c yield
compared to other biomass sources. Indeed, microalgae can have 40 times higher oil yields
than terrestrial oilseed crops such as soy and canola [9] They can also utilise a wide variety
of water sources (fresh, brackish, saline, wastewater), which is superior to terrestrial crops
that rely exclusively on fresh water. This also creates an opportunity to combine biofuel
production with wastewater treatment via nutrient removal. Nonetheless, the cost and
energy requirement for microalgae biomass production is still one of the factors hindering
the commercialization of algal biofuels.
Since microalgae need CO2 to grow, they have been found to be e�cient CO2 �xers. This
feature was proposed as a method of removing CO2 from �ue gases from power plants and
thus to reduce emission of GHG. A schematic layout of the process illustrating a path from
algae cultivation to production of hydrocarbon fuels is shown on Figure 1.3.
Figure 1.3. Process �ow diagram of the path from algal biomass cultivation to production ofhydrocarbon fuels, adopted from Biller and Ross [13]
3
1.4. Problem formulation Aalborg University
So far, most of the research in algal biofuels has been focusing in two areas: fermentative
ethanol production from algal feedstock and biodiesel synthesis from algal oils [14].
However, as it was mentioned before, HTL has become one of the most promising
technologies for thermo-chemical conversion of biofeeds into a wide range of biofuels.
As an advantage over other technologies such as pyrolysis, it does not require feedstock
dehydration prior to further processing. This is an essential feature in case of micro-algae,
due to the fact, that they are cultivated in water environment. Therefore, it is anticipated
that advances in HTL may contribute to the development of algal biofuels as well.
1.4 Problem formulation
Attempts to produce biofuels from feedstocks that are abundant and not utilized such as
forest or agricultural residues are well justi�ed and has proven to be successful. Currently,
production of biofuels based on lignocellulosic feedstocks seems to be the closest to
commercialization. Recently, a danish-canadian clean fuel company Steeper Energy has
been awarded a ¿50.6 million grant for construction of demonstration plant in Norway to
process woody residues [15]. Also Australian Licella is about to demonstrate its Cat-HTR
(Catalytic- Hydrothermal Reactor) technology on a commercial scale [16].
However, in order to meet the growing demand also other biomass sources need to be
considered. For instance, the use of fast growing and no land-base biomass such as algae
seems to be a promising alternative, with HTL as a conversion technology becoming more
mature.
Nonetheless, such obtained biocrude has to be considered as an intermediate product
requiring further upgrading to meet fuel speci�cations or to enable its co-processing at
existing re�nery utilities. On the other hand it is evident, that more upgrading leads to
least cost e�ectivity of the �nal product. Therefore the most e�cient and least intensive
procedures have to be established to obtain the desired product quality.
Hydroprocessing is a method enabling production of commercial fuels from lignocellulosic,
algae or other biofeeds. If an appropriate catalyst and operation conditions are chosen,
the �nal product may be used directly as a fuel or blended with other petroleum based
fuels. However, due to a number of process variables, �nding these optimal conditions is a
crucial and challenging step, to compromise �nal product speci�cation with economical
considerations. Hence, this thesis will focus on establishing a base for �nding the
optimization conditions for HTL bio-crude hydrotreating using a systematic approach,
based on experimental design method. Furthermore, the e�ects of changing the process
conditions will be evaluated and most signi�cant parameters identi�ed.
As a problem formulation, the following questions can be addressed:To what extent is it
viable, to obtain signi�cant information about the process, using relatively small amount
of experiments? Could it ultimately provide knowledge regarding optimal conditions for
hydrotreating to meet fuel speci�cations from algae derived bio-crude? And lastly, is micro-
algae a suitable feedstock for biofuel production?
4
1.5. Scope of the report Aalborg University
Differentbio-feeds
Conversion process
HTL
Bio-crude of
different
properties
Upgrading
Hydrotreatment
Process conditions
End-use
applications
Influence
Influence
Figure 1.4. Hydrotreating will have di�erent objectives depending on the feedstock and desiredproducts
As indicated at Figure 1.4, both the nature of the bio-crude, as well as the end
use applications have to be considered for establishing optimal process conditions of
hydrotreating. This means that the extent to which the heteroatom removal is required
will depend on di�erent product speci�cations targets. At the same time, the elemental
composition of the bio-crude will have an impact on the hydroprocessing reactions.
Therefore, the understanding of the signi�cance of each in�uencing factor, including
potential interactions between those factors is a crucial step to obtain an e�cient catalytic
upgrading process. Despite a considerable interest in upgrading HTL bio-crude of algae
origin, no comprehensive study on the e�ect of a broad range of hydrotreating process
conditions have been found. This thesis aims to contribute to �lling up this gap.
1.5 Scope of the report
The following steps will be carried out under completion of this project:
� Literature review of available algae bio-crude upgrading studies
� Preparation of a design matrix using design of experiment methodology to e�ciently
plan hydrotreating experiments
� Performing the experiments accordingly to the design matrix
� Characterization of upgraded samples using available laboratory equipment
� Analysis of acquired results via statistical methods
� Performing con�rmation experiments, based on the �ndings from previous step
In Chapter 2 all necessary knowledge regarding the di�erences between petroleum based
crude oil and HTL derived algal bio-crude and their e�ect on the upgrading process
will be presented. Also the latter will be explained in detail, with the emphasis on the
hydrotreating.
Chapter 3 introduces the methods utilized for experimental design including relevant
statistical methods and optimization techniques. The experimental setup and procedure
for hydrotreating and subsequent feedstock and bio-crude analysis will be given, along with
used laboratory equipment.
The identi�cation of in�uencing parameters will be included in Chapter 4. This will
be based on the chemical analysis results of upgraded bio-crude samples, followed by a
comprehensive and detailed discussion.
5
1.5. Scope of the report Aalborg University
Chapter 5 provides an overview of possible developments in this study and recommenda-
tions for the future work.
Lastly, in chapter 6 questions from the problem formulation are addressed and conclusion
of the main �ndings of this study is drawn.
6
State of the art 2This chapter provides all necessary background behind production of biofuel from algae
feedstock. Firstly, to understand the general di�erences between petroleum oil and algae
bio-crude, a brief characterization will be given. Subsequently, the way to upgrade such
bio-crude with emphasis on hydrotreating will be given. This will comprise the chemistry
of the process, as well as literature study on similar experiments. However, detailed
chemistry evaluations such as formulation of reaction routes, estimation of kinetics and
evaluation of catalyst performance are beyond a scope of this report and this general
hydroprocessing theory is given for overall understanding. Lastly, considerations regarding
hydrogen consumption will be mentioned and targeted fuel speci�cations
2.1 General characterization of algae biomass, bio-crude
and di�erences with petroleum crude oil
Petroleum crude oil has been formed millions years ago as a result of high temperature
and pressure conditions acting on organic materials such as algae or zooplankton for a
long period of time. Hence, it can be said that biomass conversion techniques generally
intend to imitate those conditions in a substantially smaller time-scale. Even though,
the principle of obtaining a high energy dense product is reserved, certain di�erences in
chemical composition and physical properties are present. The general characterization of
HTL derived bio-crude with comparison to standard petroleum crude oil will be given in
this section.
2.1.1 Biochemical composition of algal biomass
The chemical composition of bio-crude depends greatly on the conditions under which the
feedstock was treated. This include temperature, pressure, solvent properties, reaction
time etc. However, the most signi�cant e�ect has the composition of biomass that is fed
into the liquefaction process [17].
In general, biomass can be characterized according to its macromolecules distribution. As
an example, algae consists of mostly proteins, lipids and carbohydrates. However, algae of
di�erent origin can vary in its components structures, and hence, the diversity in product
composition may be anticipated [18]. Approximate biochemical compositions of several
microalgae are shown in Table 2.1.1, whereas an elemental analysis of di�erent microalgae
is in Table 2.1.1
7
2.1. General characterization of algae biomass, bio-crude and di�erences with petroleumcrude oil Aalborg University
Microalgae strain Protein Carbohydrate Lipid
Chlorella vulgaris 55 9 25Nannochloropsis oculata 57 8 32Porphyridium cruentum 43 40 8Spirulina 65 20 5
Table 2.1. Biochemical composition (%wt.) of microalgae strains on dry-ash-free basis [19]
An important parameter that indicates the potential of the individual macromolecules for
fuel production is the hydrogen-to-carbon ratio (H/C). In principle, the higher this ratio
is, the less hydroprocessing is required to obtain desired drop-in properties. Algae exhibit
relatively high H/C when compared to other materials, making them a promising feedstock
choice.
Spirulina Chlorella Littorale
C 46.1 47.3 35.5H 7.4 7.2 5.4H/C 1.92 1.83 1.82O 41.4 37.6 53.1N 4.8 8.2 6.0S 0.4 0.7 -Protein 57.5 80.0 37.6Fat 12.0 10.0 9.9Fatty acid 1.0 0.8 6.4Carbohydrate <0.5 <0.5 23.0Ash - 0.2 29.5
Table 2.2. Elemental analysis of di�erent microalgae [19]
However, also a signi�cant amounts of nitrogen is present due to high protein content, while
di�erent oxygenates contribute to relatively high levels of oxygen as well. In addition, low
amount of sulfur should be noted. A substantial di�erence in the ash content may occur
between marine microalgae strains which tend to have a high ash content compared to the
fresh water strains.
2.1.2 Chemical composition and physical properties of algal bio-crude
The algae biomass composition is naturally re�ected in the one of bio-crude, although
in somewhat di�erent proportions. In addition, conversion process conditions and type
of catalyst may have a great impact on the distribution of particular compounds among
di�erent product phases (aqueous phase, biocrude, residue and gaseous). In principle,
during liquefaction the relative carbon and hydrogen content is expected to increase, while
oxygen and nitrogen is desired to be reduced.
As indicated in Table 2.1.1, di�erent microalgae exhibit various elemental compositions,
which yields a very complex mix of products from the conversion process. These may
include various hydrocarbons such as n-alkadienes, trienes, triterpenoid and tetraterpenoid
[20] or a mixture of oxygenates (e.g ketones, aldehydes, phenols, alkenes, fatty acids,
8
2.1. General characterization of algae biomass, bio-crude and di�erences with petroleumcrude oil Aalborg University
esters) and aromatics [21]. Furthermore, presence of nitrogen heterocyclic compounds
is associated with the high content of chlorophyll and protein while lipids fractions may
give rise to fatty acids (e.g tetradecanoic and n-hexadecanoic acid) or cholesterol. Also,
both aromatic (toluene, ethylbenzene, and styrene) and aliphatic (1-pentadecane and
cycloalkanes) hydrocarbons may be observed [22].
Biocrude oil from microalgae is a dark colour, highly-viscous, energy-dense liquid with an
acrid smoky odour. Although again, these characteristics will also vary among di�erent
feeds and conversion process conditions. The comparison of some most important physical
properties between bio-oil and crude oil are listed in Table 2.3. From the elemental
composition it can be deducted, that the bio-crude has an energy content of 70-95 %
of that of petroleum fuel oil. Although, due to the higher viscosity, and bigger amount of
contaminants, its nature is closer to that of a heavy crude.
Bio-oil Crude oil
Water [wt.%] < 4 0.1ρ[kg/l] 0.97− 1.14 0.86µ[mm2/s] 1− 20 2− 5HHV [MJ/kg] 29− 39 44C [wt.%] 68− 78 83− 86O [wt.%] 8− 25 < 1H [wt.%] 8− 11 11− 14S [wt.%] < 1 < 4N [wt.%] 4− 8 < 1Ash 0.2− 1 0.1
Table 2.3. Comparison between physical properties of algal bio-oils and typical petroleum crudeoils. Data obtained from Refs.[23, 24]
The high viscosity is a rather undesired property, as it makes the crude more di�cult
to process in re�nery operations. Viscosity is an essential parameter in fuel standards,
as high-viscosity fuels will not be well- atomised, causing de�cient combustion, increased
engine deposits and higher energy requirements for fuel pumping [25, 26]. Even though the
conversion process conditions can have an impact on the bio-crude viscosity, for organic
compounds viscosity is rather related to the chemical structure.
It is apparent, that bio-crudes have generally lower heating value than conventional crudes.
HHV is a common criterion for evaluating a liquefaction process, since it simply describes
the energy content of the obtained product and the energy recovery from the biomass. It
is known that HHV can been correlated with chemical composition given by ultimate and
proximate analysis, as in general carbon and hydrogen positively a�ects the HHV, while
oxygen and nitrogen contents have a negative e�ect [27]. These drawbacks of bio-crudes
can be addressed by proper hydroprocessing.
9
2.2. Hydroprocessing Aalborg University
2.2 Hydroprocessing
Hydroprocessing (HPR) refers to a group of chemical engineering processes in which
pressurized hydrogen in the presence of a speci�c catalyst is used to a�ect oil properties.
Depending on the process conditions and the desired e�ect, hydrocracking, hydrogenation,
and hydrotreating can be distinguished. Typical reactions associated with these processes
are shown on Figure 2.1.
Hydrocracking:
Hydrodeoxygenation:
Hydrogenation:
R1
CH2
CH2
R2
H2+ R1 CH3+H3C R2
R OH H2+ R H+H2O
C C
R1 R2
H H
H2+R1
CH2
CH2
R2
Cracking
R1
CH2
CH2
CH2
CH2
R2
R1
CH2
CH2
+H2C
CH R2
Decarbonylation:
Decarboxylation:
R1 C
O
H
R1 C
O
OH
R1 H + CO
R1 H + CO2
Figure 2.1. Main reactions occurring during hydroprocessing
Cracking reactions aim to produce lighter products by breaking carbon-carbon bonds of
more complex molecules. Depending on whether the catalyst is present or not, thermal or
catalytic cracking can be distinguished. However, thermal cracking is rather an undesirable
reaction for bio-crudes due to their high oxygenation, which promotes coke formation at
elevated temperatures. Therefore, catalysts are used to positively a�ect selectivity and
decrease production of undesirable side products like gases and coke [28]. Hydrocracking
additionally saturates free radicals occurring after the bond cleavage.
Hydrogenation may be referred to as a less severe hydrotreating process which can be
carried out prior to more severe hydrogenation. It is typically run at temperatures below
300 °C and lower hydrogen pressures. Several studies [29, 30] propose this initial upgrading
step in order to increase thermal stability and reduce coke formation, which eventually leads
to higher yields and decreased catalyst inactivity. However, a two step hydrogenation
is more adequate for higher oxygenated feeds such as pyrolysis oil, while it is rather
unnecessary for the better quality HTL bio-crude.
Hydrotreating is an essential re�nery process for hetero-atom removal and hence preparing
the feed for further processing or �nal blending. In simple terms, hydrotreating
reactions facilitated on the surface of the metal catalyst decompose heter-atom containing
10
2.2. Hydroprocessing Aalborg University
compounds and saturate the free spot with hydrogen. In principle, the severity of the
process, as well as the choice of catalysts is determined by the initial product properties
as well as desired �nal product speci�cations. However, there are several di�erences
between the mechanism and kinetics of HPR reactions of di�erent biofeeds, which makes
�nding the optimal conditions for each process a challenging task [18]. The main
compounds to be removed from the oil are oxygen, nitrogen and sulphur and thus the
respective processes are called hydrodeoxygenation (HDO), hydrodenitrogenation (HDN),
and hydrodesulphurization (HDS). Characterization and chemical principles of HDO and
HDN will be outlined in the following sections. Due to marginal levels of sulphur in algae
bio-crude, HDS is an insigni�cant process occurring during its upgrading and therefore its
detailed description is neglected in this study.
One must be aware that these reactions may occur simultaneously and thus for any given
operating conditions a conversion equilibrium is being approached. The contribution of
each reaction is controlled by their kinetics, which are mostly dependent on the temperature
and pressure. Additionally, the reactivity might be a�ected by inhibition e�ects of some
reactants. Moreover, di�erent catalysts may favour one reaction path over another which
is why it is of great importance to tailor the process accordigly to the desired e�ect [31].
On the other hand, inappropriate process design and catalyst choice will result in low
selectivity and high hydrogen consumption in a continuous process [32].
The reaction kinetics of individual model compounds can be modelled mathematically, yet
still, estimation of mutual, interfering reaction rates of such complex substance as bio-
crude is an impossible task and it is rather more feasible to experimentally obtain optimal
process conditions, as proposed by hereby study.
2.2.1 Rationale behind hydroprocessing
There are several reasons for hydroprocessing petroleum crudes or bio-crdues. Intuitively,
the hydrogen content is increased during hydroprocessing. This naturally yields to higher
hydrogen to carbon ratio (H/C) which can be correlated with the performance of petroleum
products [24]. A signi�cant e�ort to justify usage of H/C parameter as fuel quality indicator
has been made in the literature. For instance, Mensch et.al [33] identi�ed H/C as a
combustion related property, a�ecting smoke point. Also, as suggested by Yue [34], a
higher H/C in hydrocarbon fuels has a positive e�ect on density and viscosity. Furthermore,
simultaneous increase in hydrogen content and decrease of oxygen can be directly correlated
to the heating value. It was also found that larger H/C promotes faster cracking rate and
better cooling ability [35].
Another issue which can be addressed by hydroprocessing is the bio-crude stability. A
number of studies reported that HTL bio-oils exhibited an increase in viscosity and the
amount of residue over time [36] [37]. This indicates occurrence of highly reactive organic
compunds such as ketones, aldehydes, organic acids. Reactions associated with them result
in an increase in the amount of higher molecular weight compounds due to polymerisation
and condensation [38], and an overall drop in the oil quality during storage time may be
observed, and poor performance during fuel combustion expected.
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2.2. Hydroprocessing Aalborg University
Besides contributing to products stabilisation and enriching them with hydrogen,
heteroatom removal is the main aim of hydroprocessing. This referes to elimination of
oxygen, nitrogen and sulphur.
The removal of nitrogen and sulphur is mostly environmentally driven. This is because
fuel combustion generates SOx and NOx emissions. These compounds are also undesired
since they can lead to corrosion which can be detrimental for re�nery processing units,
catalysts and quality of the end product.
As it was mentioned, the unfavourable characteristics of bio-oils are in great measure
related to oxygenated compounds. This include catalyst poisoning and corrosive e�ects,
as with other heteroatoms. Even though, a complete deoxygenation might not always be
necessary, it is evident that higher oxygen content leads to lower heating value and decrease
in stability.
2.2.2 Hydrodeoxygenation
HDO is the main reaction which occurs during hydroprocessing of the bio-feeds and its
role is much more important than for conventional feeds, which contain less then 2 wt.%
of oxygen. Liquefaction bio-crudes have signi�cant oxygen content resulting from the
depolymerisation of biomass components.
In principle HDO follows a general reaction where the exclusion of oxygen takes place by
addition of hydrogen and formation of water, as one of the two liquid phases :
R−OH + H2 −−→ R−H + H2O
Conceptually, an idealized hydrodeoxygenation reaction of bio-oil can be represented as:
C1H1,33O0,43 + 0,77H2 −−→ CH2 + 0,43H2O
As can be observed, the process utilizes about 1.5 mol of monoatomic hydrogen for every
carbon atom obtained in the upgraded oil. However, in practice, as described previously in
section 2.2, hydrotreatment is not highly selective and oxygen removal takes place together
with other reactions, which alter the formation of carbon in desired liquid phase of the
product. Alternatively, carbon can polymerize and condense to form tar and coke or gasify
to form methane or carbon oxides. Also, other reactions may occur and result in creation
of low H/C hydrocarbons such as aromatics and ole�ns. [39]
Not only the content of oxygen a�ects the hydrogen consumption but also the type of O-
compounds in the feed [40]. Some oxygenates such as alcohols, ketones, carboxylic acids or
esters are primarly reactive while the others like phenols or furans require higher hydrogen
pressure and temperatures to be successfully removed. This is due to the fact, that the
chemical bonds that have to be broken, exhibit varied strength in di�erent functional
groups. The order of HDO reactivity of di�erent oxygenates and associated hydrogen
consumption, together with examples of oxygen compounds found in bio-oil is illustrated
on Figure 2.2.
12
2.2. Hydroprocessing Aalborg University
React
ivit
y o
f O
xyg
en c
onta
inin
g c
om
poun
ds
duri
ng
HD
O
Hyd
rog
en c
on
sum
pti
on
Alcohol
Ketone
Ether
HIGH
HIGHLOW
LOW
Carboxylic
acid;
m-p.- phenol
Naphtol
Phenol
Diaryl ether
O-phenol
Furan
Benzo-furan
Dibenzo-furan
Alcohol 1H2
Ketone 2H2
Carboxylic acid 3 H2
Phenols 4 H2
Methoxyphenol
6H2
Dibenzo-furan8 H2
Figure 2.2. Reactivity of selected oxygen containing compounds and associated hydrogenconsumption based on data found in Reference [31]
Table 2.4 lists the activation energies and iso-reactive temperatures of selected oxygenates.
However, one must be aware, that the activation energy for deoxyganation of di�erent
functional groups will vary depending on the catalyst used to facilitate these reactions.
These considerations were proved by thermodynamic equilibrium calculations of phenol
reactions, showing that a complete conversion can be achieved at temperatures up to
600 °C at atmospheric pressure and stoichiometric conditions. In order to shift the
thermodynamics even further towards complete conversion, higher pressure or excess of
hydrogen is required [43].
However due to aforementioned practical di�culties to evaluate the conversion of each
individual component, a degree of deoxygenation parameter can be assessed instead.
Molecule/group EA[kJ/kmol] Tiso[C] Hydrogen consumption
Ketone 50 203 2H2 / groupCarboxylic acid 109 283 3H2 / groupMethoxy phenol 113 301 ≈ 6 H2/molecule4-Methylphenol 141 340 ≈ 6 H2/molecule2-Ethylphenol 150 367 ≈ 6 H2/moleculeDibenzofuran 143 417 ≈ 6 H2/molecule
Table 2.4. Activation energy (EA), iso-reactive temperature (Tiso) and hydrogen consumption forHDO of di�erent functional groups over a Co−MoS2/Al2O3 catalyst. Data obtainedfrom References [41],[42]
13
2.2. Hydroprocessing Aalborg University
DOD =
(1−
wt.%Oproduct
wt.%Ofeed
)· 100 (2.1)
Degree of deoxygenation indicates how e�ective oxygen removal was, by relating the weight
percent of oxygen in the oil wt.%Oproduct to the initial levels in the feed wt.%Ofeed.
However, also the yield of oil has to be taken into account when evaluating the
hydrotreating process, since high deoxygenation may lead to lower yields, as selectivity
towards water and the gas phase increases, as found by Elliot et al. [44]. Yield of oil is
therefore simply the ratio between the weight of produced oil and the weight of the feed.
Yoil =
(moil
mfeed
)· 100 (2.2)
2.2.3 Hydrodenitrogenation
Not only the nitrogen compounds are an issue of algae derived products, but the interest
in hydrodenitrogenation process has emerged with the exploration of converting petroleum
residue, coal and shale to liquid fuels, which can serve as a reasonable starting point for
HDN of algal biocrude. However the origin of nitrogen compounds in petroleum or coal is
di�erent than those of algae oil, which has to be accounted when designing a HDN process
of algal bio-crude. Just like oxygenates exhibit varying reactivity, nitrogen removal requires
hydrogenation of di�erent structures with bonds of di�erent energies. Although in general,
nitrogen compounds found in bio-oils are classi�ed as non-heterocyclic and heterocyclic
compounds. Non-heterocyclic compounds (anilines, aliphatic amines and nitriles) are more
easily de-nitrogenated, however they are rather rare in bio-oils. As will be presented later,
heterocyclic compounds (indoles, pyrrols, amines) are more dominant in algal bio-oils,
which mainly arise from depolymerisation of polypeptides and proteins in algal feedstock.
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2.2. Hydroprocessing Aalborg University
Reacti
vit
y o
f N
itro
gen c
onta
inin
g c
om
pou
nd
s du
ring H
DN
Hydro
gen c
on
sum
pti
on
Aniline
Alkyl
amines
Pyrrole
HIGH
HIGHLOW
LOW
Indole
Pyrridine
Piperdinne
Indole
3H2
Pyrridine
5H2
Alkyl
amines
1H2
Piperdinne
2H2
Figure 2.3. Reactivity of selected nitrogen containing compounds and associated hydrogenconsumption based on data found in Reference [31]
The general reaction for HDN is as follows:
R3 −N + 3H2 −−→ 3R−H + NH3
However in practice, the hydrodenitrogenation mechanism is a rather three step process.
First, hydrogenation of the ring containing the nitrogen atom must occur, which is
necessary in order to reduce large energy of the carbon-nitrogen double bond (147 kcal/mol)
to a lower energy carbon-nitrogen single bond (73 kcal/mol) [45]. Secondly, hydrogenolysis
takes place, during which hydrogen contributes to cleavage between nitrogen and carbon
bond and �nally allows denitrogenation. [31] A path of removal of nitrogen from pyridine
can be seen on Figure 2.4.
N
Hydrogenation
3H2
NH
HydrogenolysisH2
NH2Denitrogenation
H2NH3 +
Figure 2.4. Hydrodenitrogenation path of pyridine over NiMo/Al2O3 catalyst. Reproduced fromRef. [46]
15
2.2. Hydroprocessing Aalborg University
The degree of denitrogenation can be evaluated analogously as for deoxygenation, with
the same equation as 2.1, substituting the oxygen content with nitrogen.
2.2.4 Review of algal bio-crude hydrotreating studies
There is a substantial number of studies investigating upgrading of biocrudes of di�erent
origin. Although, only those considering hydroprocessing of bio-crude derived from algae
feedstock and hydrothermal liquefaction were found relevant for the current project. A
brief review of selected publications can be found below. This review also served as a basis
for choosing the process conditions for designed experiments.
Biller et al. [47] conducted a complete study from conversion of Chlorella microalgae
using hydrothermal liquefaction to hydroprocessing of the bio-crude with CoMo and NiMo
catalysts at two temperatures (350 °C and 405 °C ), initial hydrogen pressure between
60-66 bar and a residence time of 2h. Higher temperature resulted in better hetero-atom
removal (85 % reduction of oxygen and 65 % of reduction of nitrogen) but also in lower
yields due to coke and gas formation). Worth noting, no signi�cant di�erence in activity
of the two catalysts was observed. A further analysis of oils indicated that the majority of
remaining oxygen is contained in high molecular weight compounds, which can be removed
by solvent extraction methods, however no satisfactory reduction of nitrogen was achieved
using pentane.
Bai et al. [48] performed a two step catalytic hydrotreating of Chlorella pyrenoidosa bio-
crude and studied the e�ect of 15 di�erent catalysts on the upgraded oil composition. First,
a non-catalytic pretreatment at 350 °C was carried out, followed by upgrading experiments
at 400 °C with the presence of catalyst. In addition, deionized water was loaded to the
reactors with hydrogen at around 60 bar and kept at aforementioned temperature for 4h.
Also experiments with di�erent solvent (n-hexane) and di�erent gas (CO) was carried
out to study the e�ect of hydrogen and water. The best results were obtained using a
combination of Ru/C and Raney Ni catalysts, yielding 2 wt.% oxygen and 2 wt.% nitrogen.
Duan and Savage [49] have studied the in�uence of various reaction times (1 to 8h),
catalyst loading (5 to 80 wt.%) on hydrotreated oil yield and composition, gas products
and hydrogen consumption. Similarly to the experimental campaign by Bai et al. [48],
besides the catalyst, SCW was employed at temperature controlled around 400 °C but at
signi�cantly lower hydrogen pressure around 34 bar. It was concluded that longer reaction
time and highest catalyst loading leaded to oils with higher HHV, H/C ratio and lower
O/C and N/C ratios but at the same time promoted more side product formation such as
coke and gas.
In an another study by Duan et al. [50] catalytic hydrothermal upgrading of bio-oils
produced from di�erent thermo-chemical conversion routes of microalge, including HTL
was explored. A mixture of carbon based catalysts (Ru/C + Mo2C) was selected for
hydrotreatment. Similar to other studies, temperature was maintained at 400± °C for
4h at relatively high operating pressure of 220 bar (from initial 60 bar). An almost
complete deoxygenation was achieved for the upgraded HTL oil, wheras nitrogen containing
compounds still constitute 2.59 wt.%.
Elliott et al. [51] investigated the performance of a continuous �ow system to hydrotreat
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2.2. Hydroprocessing Aalborg University
biocrude obtained from hydrothermal liquefaction of wet algae slurry feedstock. The
biocrude was initially pre-processed in lower temperatures (120-170 °C ) with subsequent
high-temperature stage at 405 °C and operating pressure of around 136 bar. As a catalyst,
a molybdenum sul�de with cobalt promotion on a �uorinated-alumnia support was used.
An almost oxygen-free product with impressively low levels of nitrogen were reported.
However, as authors suggested it is di�cult to compare results from continuous �ow
systems to batch systems, since the latter are often equilibrium limited. Nevertheless,
complete heteroatoms removal and formation of re�nery ready blending stock is worth
noting.
Zhouhong et al. upgraded HTL biocrude from algae grown in municipal wastewater, using
metal catalysts (platinum, ruthenium, nickel and cobalt) supported on activated carbon.
The continuous reactor was held at the temperature of 350 °C for 4 h. No information
regarding the operational pressure has been given. The study concentrates rather on the
e�ect of di�erent catalysts than hydrotreating conditions. Although, the positive e�ect of
catalytic hydrotreatment on heating value, total acid number, viscosity and water content
was reported.
2.2.5 Issues regarding hydrotreating of bio-crudes
Even though the principles of hydroprocessing conventional crudes and bio-crudes are
conserved, several di�erences may arise due to peculiar properties of algae derived
oils. These di�erences have to be identi�ed and accounted for when adapting standard
hydrotreating processes for bio-crude upgrading. A brief mention of those potential issues
is given below.
Water formation
As it was described in previous sections, algae bio-crude is rich in oxygenates and removal
of those via hydrotreating reactions leads to formation of water in the liquid products.
This cannot be neglected, as proportionally to the oxygen content in the feed, considerable
amounts of water will be present in products, which will have an increasing e�ect during
process up-scaling. For once, partial pressure of water will be signi�cant, but what is more
important, water may be absorbed by active catalyst sites, reducing both activity and
selectivity or eventualy lead to catalyst detoriaration [52].
Heat release
The temperature control in hydrotreating reactors of bio-crude may be an issue, due to
a highly exothermic nature of HDO and HDN reactions. It is a substantial di�erence to
conventional process where HDS is the main mechanism. Not only the heat release for
oxygen removal is about 2-4 times higher than for sulphur, but also the total amount of
heteroatoms is much larger. This result in a heat release during hydrotreating of HTL
oil around 20-200 times larger than during HDS of petroleum feed. Rapid temperature
increase may lead to higher rate of hydrogen consumption followed by its starvation around
active catalyst sites and eventually deactivation but also a higher risk of coking. [53], [54]
17
2.2. Hydroprocessing Aalborg University
Coke formation
Again, high oxygen content leads to undesired mechanisms as for instance coke formation.
Coke may also lead to catalyst deactivation as well as lower yields. Oxygenates which
may be found in algae bio-crude such as those of phenolic origin are the main coke
precursors. Coking occurs via polymerization and polycondenstation type of reactions
which are facilitated by higher temperatures and lower partial hydrogen pressures [52].
Therefore it is desired to maintain higher partial hydrogen pressures when performing
hydrotreating at elevated temperatures as it will promote hydrogenation and conversion
of coke precursors into stable products. However it is not possible in case of the present
study, as no continuous supply of hydrogen is available in batch reactors.
Low sulphur content
While low sulphur content is in favour of the algae bio-crude quality and no need for
HDS, it may be detrimental for catalyst activity. As it will be described in section 3.2,
the catalyst has been sulphided prior to the reaction to enhance its activity by changing
the metallic oxide sites to metallic sulphides. When no sulphur in the feed is present,
the sulphur on the catalyst is gradually desorbed, imposing a stability issue. This can be
addressed by adding sulphur in form of dimethyl disulphide (DMDS), carbon disulphide
(CS2) or hydrogen sulphide (H2S) to the bio-crude before hydrotreating. Alternatively, a
co-processing with a sulphur containing feed, such as heavy oil can be performed, although
it does not seem to be a feasible route for algae bio-crude, since its oxygen and nitrogen
content poses a hydrotreating challenge itself.
2.2.6 Hydrogen consumption
The purpose of upgrading bio-crudes or any other feed using hydroprocessing methods,
should always come in pair with hydrogen consumption considerations. This is due
to both economical and environmental reasons. Hydrogen is an essential element in
re�ning industry, due to its high demand and price and its consumption is a�ected by
following factors: catalyst type, level of conversion, operating conditions and properties
of the feedstock [55]. There are several methods to quantify hydrogen consumption
during hydrotreating experiments. These include calculation of the di�erence in weight of
hydrogen before and after the reaction, measuring the volume of gas with a gasometer or
using di�ernt equation of states [56]. For continuous systems, hydrogen consumption can be
estimated by measuring H2 content at the inlet and outlet of the reactor and estimating the
mass balances. Alternatively, hydrogen content can be estimated by empirical correlations,
reaction average contributions or by kinetic modelling. [55] The average consumption for
di�erent feeds and conditions is listed in Table 2.5
18
2.3. Fuel speci�cations Aalborg University
Reference Feedstock System type H2 consumption Conditions Catalyst
Biller et al.[47]
Microalgae Batch 0.012- 0,029350/405 C138 bar2h
CoMo/NiMo
Zhu et al.[57]
MicroalgaeContinuous(Simulation)
0.05
348 C136 bar0.66/ 0.18 LHSV(two stage)
-
Elliot et al.[51]
Algae slurry Continuous 0.041105-401 °C138 bar0.20 LHSV
CoMO/Al2O3
Table 2.5. The average hydrogen consumption [kgH2/kgfeed] for hydrotreating studies of di�erent
authors
2.3 Fuel speci�cations
Various engines require di�erent type of fuel for operation. This implies, that fuels
must comply with certain speci�cations, determining their physico-chemical properties.
Furthermore, not less important are environmental factors, that impose more and more
rigorous emission standards. This causes a major constraint for the development of
biofuels, which by their nature, are much more challenging to re�ne than conventional fuels.
Figure 2.5 illustrates approximately how the di�erent transportation fuels are arranged
according to their boiling range.
Oil volume collected by distillation [Vol %]
Boili
ng p
oin
t te
mpera
ture
[oC
]
180
230
350
530
Motor
Gasoline
Jet-fuel
Diesel
Marine
fuel
Residue
Figure 2.5. Di�erent types of fuels are obtained through distillation, based on the boiling pointof di�erent fractions. The temperatures are only indicative values
Depending on the application, various parameters for di�erent fuels are evaluated. For
instance, gasoline should have a certain minimum octane number, which quanti�es fuel's
ability to prevent auto-ignition and abnormal combustion. On the other hand, the
19
2.3. Fuel speci�cations Aalborg University
characteristics of diesel engines requires high cetane number which is the opposite to the
octane number. This is due to the fact, that the compression ignition engines are based
on the auto-ignition of the fuel whereas for spark ignition engines running on gasoline, the
point of ignition is essential. Yet none of these properties are relevant for the jet fuel, for
which the proper combustion abilities at low temperatures are of the greatest importance.
This property is quanti�ed by fuel's freezing point. Marine diesel fuels, as the 'heaviest'
of all have to obey less strict emission. Peculiar for them is evaluation of the pour point
as the measure of its �owability. Besides these speci�c parameters for particular fuels,
general properties such as density, viscosity, boiling point, content of other compounds are
taken into account. Also environmentally driven factors such as smoke point or sulphur
content are considered. The detailed speci�cations of gasoline, diesel, jet, and marine fuels
are given in the Appendix B.
20
Methods and materials 3This chapter describes methods and materials utilized in the project. First, methods for
experimental design are explained, followed by statistical methods for results analysis.
Also, a detailed characterization of the analyzed bio-crude and original feedstock is given.
Furthermore, the experimental setup is shown, together with the procedure for hydrotreating.
Lastly, the methods and equipment used for product analysis are outlined.
3.1 Design of Experiments
Design of experiments is a systematic approach for e�ective planning of experiments
to determine cause and e�ect relationship in any system. It is an essential tool for
engineers and scientists to develop and improve a process or a product under investigation.
Such method enables to identify in�uencing factors as well as to limit the number of
experimental runs, which leads to resource preservation. Also, a well-planned experiment
improves the quality of information and eliminates redundant data, which is bene�cial for
further statistical analysis. Furthermore, based on the important input variables, a model
relating them to observed response can be developed. Depending on the complexity of the
process, di�erent strategies of experimentation can be used for planning and conducting
the experiment.
3.1.1 Two-Level Factorial Design
When more than one factor is known to a�ect the process, factorial design is an appropriate
strategy for experimentation. It allows to not only to consider the e�ect of varying certain
factor, but also any possible interaction between them. The e�ect of a factor is de�ned to
be the change in response produced by a change in the level of the factor [58].
Even though, hydrotreating is a�ected by a larger number of factors, three of them were
selected for designing the experiments. These are the reaction temperature, the initial
pressure of hydrogen, and the residence time. These factors are listed in table 3.1.1 along
with their low and high levels respectively. The other factors such as catalyst to oil
ratio, catalyst type, hydrogen to oil ratio were kept constant throughout the experimental
campaign.
21
3.1. Design of Experiments Aalborg University
Factor Name Unit Low level (-) High level (+)
A Temperature °C 250 350B Hydrogen pressure Bar 40 80C Residence time h 2 4
Table 3.1. Test-factors for hydrotreating experiments
The quantitative levels were set accordingly to the conditions in which theoretically
HDO and HDN can occur, which was discussed in sections 2.2.2 and 2.2.3. Values from
similar experiments found in the literature, discussed previously in section 2.2.4 were also
considered. Even though, a number of studies indicated temperature around 400 °C as a
favourable for hydrotreating of algal bio-crude, it was decided to set a lower temperature
of 350 °C as a high level. This is because of the fact, that higher temperatures could lead
to smaller yields due to side product formation, which would be not feasible in case of this
micro-batch study in which the initial amounts are already modest.
Lastly, speci�cations of the available laboratory equipment were taken into account (e.g
maximum pressure that reactors can withstand or maximum achievable temperature).
Special attention had to be paid when establishing the high level of initial hydrogen
pressure. In accordance with fundamental thermodynamic laws, the pressure will follow the
change of the temperature and the maximum operating pressure value at given temperature
can be approximated using simple ideal gas law calculations. In addition, there was no
goal in testing operating pressures which would not be attainable for a continuous system.
Since there are three factors on two levels (noted with minus sign for low level and plus
for high level), this design is called a 23 factorial design, yielding eight possible treatment
combinations. The design space can be geometrically displayed as a cube as shown in 3.1
where each axis is associated with particular factor and possible treatment combinations
are labelled by letters A, B, C.
BC ABC
ACC
B AB
A(1) Temperature [oC]
H2 p
ress
ure
[bar]
40
80
250 350
Reside
nce
time
[h]
2
4
Figure 3.1. The 23 factorial design of the hydrotreating experiment
22
3.1. Design of Experiments Aalborg University
Response variables
The choice of the response variables was made from the standpoint of biofuel production.
Hence, those bio-crude properties that are falling short to meet fuel speci�cations and can
be in�uenced by hydrotreating were identi�ed and set to be optimized. Principally, it is
of great advantage to have the most analysis as possible, but in this study it was limited
by the volume of obtained samples from microbatch reactors. Therefore measurements
that require a small amount of oil were prioritized. These include the elemental analysis
which reveals the oxygen and nitrogen content aiming to be minimized. Moreover, the
e�ect of hydrotreating conditions on hydrogen consumption and properties like HHV was
investigated.
E�ect of factors and factor interactions
To quantify how does each parameter in�uence the response, its e�ect can be calculated.
Mathematically it can be expressed as:
Effect =
∑Y+n+
−∑Y−n−
(3.1)
The nominator represent the sum of all the responses where a particular factor was set to
high or low level and n's refer to the number of data points collected at each level.
Furthermore the e�ects, caused by interactions of factors can be estimated. The full
factorial design considers all three two-factor interactions, AB, AC and BC, plus the three
factor interaction ABC. This can be computed easily by �rst multiplying the signs of
factors and applying again the formula from 3.1.
Another approach to outline the signi�cance of the e�ects is by investigating the half-
normal probability plot. This also helps to distinguish important factors from normal
variation in the response. In principle, the unimportant factors are those that have a near-
zero e�ects and the important are those whose e�ect are considerably away from zero.
Thus, on the half normal probability plot, unimportant factors will typically lie close to
the normal curve, whereas important ones will be easily distinctive far o� this curve.
3.1.2 Analysis of variance
Analysis of variance (ANOVA) is a statistical method for evaluation of the signi�cance of
experimental results. It reveals the probability with which selected factors can contribute
to the total observed variance. ANOVA is based on the F-distribution which compare
variances by examining their ratio. The larger this ratio is, the more likely the variance
occurring in the model is signi�cantly larger than random error.
3.1.3 Predictive equations modelling
When fundamental e�ects and interaction between them have been recognized, tentative
empirical models can be used to describe the results and give an indication for future
predictions. The model provides a quantitative relationship between the response and
previously identi�ed important design factors. A basic �rst order model with an interaction
term looks as follows:
y = β0 + β1x1 + β2x2 + β12x1x2 + ε (3.2)
23
3.2. Feed characterization Aalborg University
where y is the predicted response, β 's are unknown coe�cients which are to be estimated
from the experimental data and ε is a random error term.
Adding higher order terms, will account for non linear relationship, which is often used in
optimization experiments:
y = β0 + β1x1 + β2x2 + β12x1x2 + β11x211 + β22x
22 + ε (3.3)
3.2 Feed characterization
This section gives a brief characterization of the feed used in the experiments. Figures
below illustrates the algae bio-crude, and the NiMo/Al2O3 catalyst.
Figure 3.2. Algae biocrude Figure 3.3. NiMo/Al2O3 catalyst
3.2.1 Algae biomass and bio-crude
The studied bio-crude and raw biomass was received from Aarhus University. Spirulina
microalgae was used for its production in continuous hydrothermal liquefaction plant in
Foulum, Denmark. Both the biomass and the bio-crude were primarily analyzed with
regards to the elemental composition, ash and water content.
The elemental analysis (CHNS) of biomass and biocrude was performed with a 2400 Series
II CHNS/O Element analyzer (PerkinElmer,USA). Acetanillide was used as a calibration
standard. The carbon, hydrogen and nitrogen was determined whereas oxygen was
calculated by di�erence. Sulphur was not detected by this method. These results are
shown in table 3.2.1.
24
3.2. Feed characterization Aalborg University
Biomass Elemental content HHV(MJ/kg)
Ashcontent
WatercontentC H N S O
Spirulina 53.5 7.2 12.6 - 26.6 24 5.8 6.4Biocrude 78.1 10.4 8.0 - 3.5 38 0.2 3.8
Table 3.2. Elemental composition of raw biomass and bio-crude together with HHV, ash andwater content. All on wt% dry basis.
The estimation of higher heating value (HHV) was done using the equation 3.4 proposed
by Channiwala and Parikh [59] where C, H, N, S, O and A represent the mass of carbon,
hydrogen, nitrogen, oxygen, sulphur and ash respectively, on a dry weight basis.
HHV (MJ/kg) = 0.3491C + 1.1783H + 0.1005S − 0.1034O − 0.0151N − 0.0211A (3.4)
Ash content was determined by incineration at 775 °C and measuring the mass of the
remaining products afterwards. In order to measure the water content, Karl Fischer
titration with hydranal as a reagent was performed.
3.2.2 Catalyst
There are several factors in�uencing the choice of the suitable catalyst. In principle, each
process requires a speci�cally designed catalyst to ensure the desired selectivity and high
e�ciency. Also economical and environmental impacts have to be taken into account.
However, these considerations are beyond the scope of this project, and could serve as
a basis for a separate study themselves. Therefore, a commercial NiMo/Al2O3 catalyst
provided by Shell Denmark A/S was used in the experiments. The catalyst has been
sulphided to enhance its activity. It is a standard re�nery procedure to change the metallic
oxides to metallic sulphides and thus activate the catalyst before the hydrotreating process.
According to a considerable amount of di�erent studies, this catalyst has been widely used
for hydrodeoxygenation of bio-crudes and various oils. Glic et al. [60] have studied the
e�ect of 12 di�erent catalysts for hydrotreatment of wood HTL bio-crude and NiMo/Al2O3
was found to be superior in terms of yield, decreased viscosity and high gross calori�c value.
However, the good deoxygenation performance of the aforementioned catalyst might not
be su�cient in terms of denitrogenation of algae bio-crude. Hence, a deeper analysis of
catalytic hydrotreatment of nitrogen containing materials has to be carried out.
A detailed study comparing the in�uence of 16 di�erent catalysts on algal oil has been
done by Bai et al. [48], where both commercial as well as nobel metal catalysts have been
used. Also, with regards to denitrogenation and a choice of suitable catalysts for that
particular process, worth noting is a substantial research in the �eld of the coal-derived
liquids (CDLs), which also contain a noticeable amounts of nitrogen. Several publications
may be found [61], [62], [63], [64]. Also heavier crude oils and their residua, require HDN
and research in that �eld may be helpful for developing HPR catalysts for algae bio-crodue
[65].
Nevertheless, the e�ect of di�erent catalysts has not been investigated in this work.
25
3.3. Experimental setup Aalborg University
3.3 Experimental setup
The hydrotreating experiments were conducted in the Biofuel Production Lab of Aalborg
University, using the existing facility installed in 2014. Therefore, all the necessary
equipment was available before the experimental campaign. The experimental setup
consists of 25ml Swagelok microbatch reactors, a �uidised sandbatch and a shaking device
to enhance mixing of the reactants. Also, to ensure stable conditions, the temperature and
pressure was controlled using appropriate devices. This setup is schematically shown on
Figure 3.4.
Power
Air flow
Data acquisitionsystem
(Pressure)
Fluidisedsandbath
Microbatchreactors
Pressure transducer
High pressure valve
Electric motor (shaking device)
LabView
Temperature and
air flow controller
Figure 3.4. Schematic diagram of experimental setup
Additionally, a hydrogen station was used for reactant gas supply and a nitrogen station for
performing leak test, prior to each run. The feed for the reaction is described in 3.2. The
order of experimental runs was randomized in order to satisfy the statistical requirement
of independence of observations.
3.3.1 Microbatch reactors
The experiments were decided to be performed in microbatch reactors, rather than in the
continuous hydrotreating unit, also available in the lab. This is due to the fact, that batch
reactors are believed to be more suitable for such parametric studies, where the e�ects of
various factors have to be examined to understand the investigated process. What is also
26
3.3. Experimental setup Aalborg University
essential, when using microbatch reactors, the desired reaction conditions can be achieved
immediately, which otherwise takes substantial time when running the continuous system.
Also, the small amount of required reactants made microbatch reactors a preferable choice
for this study.
The 25ml reactors build from Swagelok tubings and �ttings can facilitate conditions up
to 220 bar and 400 °C , which was enough for the purpose of hydrotreating experiments
under the conditions described in 3.1. The whole reactor is build of the main bottom part,
constituting the major reaction volume, and the top part, with a valve, pressure transducer
and a clamp needed to secure the reactor to the shaking device.
3.3.2 Sandbath and shaking device
In order to obtain high temperatures needed for the reaction, a Techne SBL-2D �uidised
sandbath was used. The �uidisation enhances the heat transfer within the sandbath,
enabling relatively quick heating. The shaking device has a signi�cant e�ect on reactants
mixing, which resembles conditions in a larger continuous �ow system. It is a small motor
with a shaft, propelling the movement of a solid steel vertical tube, to which the two
reactors are attached.
3.3.3 Temperature and pressure measurement
Only the temperature inside the sandbath was controlled, whereas the temperature inside
the reactors was assumed to be approximately the same as the ambient. This is believed to
be a valid assumption, due to a small volume of the reactor. Furthermore, the time during
which the temperature changes from initial to desired can be considered as insigni�cant
compared to a relatively long residence time. Obviously, knowing the exact temperature
inside the reactor would be a valuable information, however due to space limitations
and concerns regarding the tightness of the system a reactor design including internal
temperature measurement was not considered.
The pressure was measured with a Wika A-10 pressure transducers mounted to the top
part of the reactors. The collection and interpretations of the measurements was carried
out using a LabView programme.
3.3.4 Experimental procedure
For each experimental run, two microbatch reactors were �lled with 4 grams of algae bio-
crude, 2 grams of presulphided catalyst and 3 metallic spheres to additionally enhance
mixing. Subsequently, reactors were closed securely and checked for leaks, by pressurising
them with nitrogen and immersing in a water containing cylinder. It is an advisable
procedure to test the system with nitrogen before pressurizing with hydrogen, due to
safety reasons. Also, even small leak would lead to undesired pressure loss and eventually
not reliable results of the experiment. When no leaks were detected, the reactors were
purged with a small amount of hydrogen, to clear o� any other residual gases. Finally, the
desired pressure was obtained. Although it has to be noted that the actual initial pressure
values may di�er from the planned ones, as the gauges on the hydrogen supply are not
very accurate. Eventually, such prepared reactors were �xed to the shaking device and
27
3.4. Product analysis Aalborg University
immersed in the sandbath. After desired time of reaction, the reactors were immediately
cooled down in a water bath and both gaseous and liquid products were collected, ready
for further analysis. Following each experiments, the reactors were thoroughly cleaned to
ensure that no residue remained.
3.4 Product analysis
3.4.1 Gas phase analysis
Gas production was estimated by the di�erence in mass before and after venting the
reactors. Gaseous products were collected to gas traps, and subsequently analyzed using a
Shimadzu, model GC-2010 gas chromatograph equipped with a barrier ionization discharge
(BID) detector. A fused silica capillary column was used to separate each component in
the mixture. Helium (15 mL/min) served as the carrier gas for the analysis.
3.4.2 Elemental analysis of liquid phase
The elemental analysis of the hydrotreated oil was conducted with the same instrument
and procedure as for the feed characterization 3.2. Since the elemental composition of
the upgraded samples is the essential part of this study as it constitutes the response
variables for process optimization, a special care had to be taken during collection of the
data. Thus, every single sample was analysed in a duplicate, giving a total number of 4
measurements per sample, considering that every experiment consisted of two reactors. In
order to ensure data repeatability, standard deviation was calculated for each experiment
and when it exceeded an acceptable value of 1%, the sample had to be re-run. Finally, the
mean value of the 4 measurements was calculated, giving the oxygen and nitrogen content
as response variables.
3.4.3 GC-MS of liquid phase
Gas chromatography- mass spectroscopy (GC-MS) is a commonly used method for
determining chemical composition of bio-crudes and upgraded products. However, due
to the vast amount of components and its high complexity, e�cient chromatographic
separation is not always amenable. Therefore, analyses of higher resolution and accuracy
are performed, which includes nuclear magnetic resonance (NMR) spectroscopy and Fourier
transform ion cyclotron resonance-mass spectroscopy (FTICR-MS). [66]
GC-MS analysis was carried out using a Thermo Scienti�c Trace 1300 Gas Chromatograph
with ISQ QD Single Quadrupole Mass Spectrometer. 1 wt.% solutions of upgraded samples
with diethyl ether were prepared. The split/split less injector was set to 300 °C. The
products were separated on the column using a temperature programme from 40 °C to
300 °C with a step of around 7 °C per minute. Peaks were assigned using the NIST mass
spectral database.
3.4.4 FT-IR
While elemental analysis and GC-MS provides information regarding the amount and
type of speci�c compound, FT-IR is a spectroscopic method quantifying the amount of
28
3.4. Product analysis Aalborg University
particular functional group. The principle of FT-IR is based on the fact, that organic
compounds exhibit di�erent reactions when exposed to a beam of light in the infrared
range. Namely, the light induces vibrations of di�erent modes which corresponds to a
di�erent amount of light that is transmitted or absorbed by the sample. This results in a
unique for each sample image called spectrum, where amount of light absorbed is presented
as a function of particular wavenumber. The peaks may vary not only in wavelength but
also shape and intensity. Equation 3.5 de�nes how the absorption wavenumber (frequency
of vibration) is related to the bond strength and the mass of interacting atoms [67]:
ν =1
2πc
√k
µ(3.5)
The interpretation of a FT-IR spectra requires experience but, characteristic peaks of
di�erent functional groups can be evaluated using correlation charts.
Hereby it can be concluded, that information contained in FT-IR spectra is also indicative
for assessment of hydrotreating performance, as it reveals how hetero-atoms are bonded in
the bio-crude. However, one must be aware, that it is not an e�cient method for evaluating
compounds which are present in low concentrations, as they might be hidden in the noise
of the spectra.
3.4.5 Simulated distilation (Sim-Dis)
Simulated distillation is a GC method that allows to quickly characterize fractions within
the oil according to their boiling point distribution. It is especially useful for micro-
scale parametric studies, for which conventional distillation may be di�cult due to a
small amount of samples. However, one must be aware about the limitations of this
method when analyzing bio-crude products. This is due to the fact, that the calibration
is performed using a pure hydrocarbon standard, while bio-crude oils may contain other
atoms as well. This a�ects the molecular weight which in turn may be not in accordance
with the correlation between the chromatograph signal and the weight of the standard.
29
Results and discussion 4In this chapter the obtained results will be presented. First, the identi�cation of the
in�uencing factors on the hydrotreating process will be given. Also, a statistical assessment
of the data will be done. This will serve as a base for further optimization and proposal of
new con�rmation experiments
4.1 Identi�cation of in�uencing parameters from the two-
level factorial design experiments
Based on the response data from the conducted experiments, the e�ects of each parameter
and e�ects of interactions between them have been calculated. This allows to gain
knowledge, whether temperature, initial hydrogen pressure or reaction time has the biggest
in�uence on the algae bio-oil properties. In order to statistically validate obtained results
an analysis of variance has been carried out. Identi�cation of in�uencing parameters allows
to derive model equations, used for further predictions. First, the results from elemental
analysis are presented in Table 4.9. Values in brackets are the standard deviations
associated with each measurement.
Numberof experiment
C H N O
176.94(0.49)
10.79(0.10)
7.19(0.05)
5.11(0.61)
282.24(0.22)
11.05(0.08)
5.44(0.04)
1.28(0.35)
377.16(0.19)
10.66(0.02)
6.66(0.50)
5.53(0.69)
482.62(0.17)
11.87(0.04)
4.35(0.12)
1.17(0.09
581.24(0.12)
11.18(0.03)
5.55(0.04)
1.95(0.11)
684.31(0.35)
12.13(0.06)
4.03(0.17)
0.00(0.23)
777.72(0.10)
10.76(0.05)
6.53(0.05)
5.00(0.20)
876.82(0.03)
10.98(0.03)
6.36(0.03)
6.13(0.03)
Table 4.1. Elemental analysis results and standard deviations associated to each measurement
30
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
Based on these results, the responses were calculated and the absolute values of estimated
e�ects are given together with the complete experimental matrix presented in Table 4.2
ExpFactor ATemperature
Factor BInitial H2
pressure
Factor CReactiontime
AB AC BC ABCR1DODO[%]
R2DODN[%]
1 250 (-) 40 (-) 2 (-) (+) (+) (+) (-) 26 62 350 (+) 40 (-) 4 (+) (-) (+) (-) (-) 82 293 250 (-) 80 (+) 2 (-) (-) (+) (-) (+) 24 104 350 (+) 80 (+) 2 (-) (+) (-) (-) (-) 83 435 350 (+) 40 (-) 2 (-) (-) (-) (+) (+) 72 276 350 (+) 80 (+) 4 (+) (+) (+) (+) (+) 100 477 250 (-) 40 (-) 4 (+) (+) (-) (-) (+) 28 158 250 (-) 80 (+) 4 (+) (-) (-) (+) (-) 12 17
E�ect R1 57.31 3.53 5.62 7.70 4.03 -5.62 -4.03
E�ect R2 23.66 8.95 4.12 6.80 -2.68 0.36 -1.80
Table 4.2. Complete experimental matrix with obtained responses and calculated e�ects.
Just by analyzing the response results, it can be seen, that the most severe conditions
(Experiment 6) have already yielded complete deoxygenation of the bio-crude. However,
at the same time, just under 50% reduction of nitrogen content was achieved. It seems
also obvious, that mild conditions (Experiment 1) just slightly a�ected the oxygen content
whereas nitrogen levels were almost intact. A more speci�c analysis of the in�uencing
factors of this outcome is given in the following sections, where the degree of deoxyganation
(DODO) and degree of denitrogenation (DODN) where analyzed separately as response
variables.
4.1.1 Hydrodeoxygenation
The absolute values of calculated e�ects already give an indication on which factors are
a�ecting the response the most. However, this should come together with the analysis of
variance, to ensure that changes in the response occur due to the e�ect of the factors, not
a normal variation. This can be checked by investigating the half-normal plot and the
pareto chart which are shown on Figures 4.1.1, 4.1.2
31
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
Figure 4.1. Half-plot for HDO Figure 4.2. Pareto for HDO
Apparently, temperature (A) is the major factor for hydrodeoxygenation, which is indicated
by the location on the half-normal plot - away from the normal line. The second factor
potentially having the a signi�cant e�ect is the temperature-pressure interaction (AB),
being located slightly o� the line. On the other hand, the initial H2 pressure (B), reaction
time (C), along with other interactions do not a�ect the response in a statistical meaningful
way, according to the half-plot. The Pareto chart con�rms those considerations, by
comparing the t-Value of di�erent factors. In principle, only those which are above the t-
Value limit are signi�cant and should be included in the process model. Hence, it is evident,
that only temperature satis�es this condition, with temperature- pressure interaction being
on the edge of the t-Value limit. The next step in the statistical assessment is the Analysis
of Variance (ANOVA). Results from ANOVA for HDO model are presented in Table 4.3
In simple terms, the high F-value indicate that the model is signi�cant, whereas p-values
below 0,05 suggest that there is a small probability that this variation could be caused by
noise. Model terms having p-value greater than 0,1 do not have an impact and do not
contribute to the model improvement.
SourceSum ofSquares
Degreeof freedom
MeanSquare
F-Value p-value Remarks
Model 8397.63 5 1679.53 90.18 0.0110 signi�cantA-Temperature 7875.12 1 7875.12 422.83 0.0024 signi�cantB-H2 Pressure 6.13 1 6.13 0.3289 0.6246 insigni�cantC- Reaction Time 55.13 1 55.13 2.96 0.2275 insigni�cant
AB 325.13 1 325.13 17.46 0.0528signi�cant(low impact)
AC 136.13 2 136.13 7.31 0.1139 insigni�cantResidual 37.25 2 18.62Cor Total 8434.88 7
Table 4.3. ANOVA for HDO model
32
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
To assess model adequacy, the residual analysis was performed (Figure 4.3). The normal
plot of residuals reveals approximately normal distribution of the residuals, which infers
that the estimated e�ects are the real. The other, residuals vs. predicted plot tests the
assumption of constant variance. A constant range of residuals across the graph satis�es
this assumption.
Figure 4.3. Normal probability plot for HDO Figure 4.4. Residuals vs. predicted values forHDO
4.1.2 Hydrodenitrogenation
The same procedure as for HDO has been performed to identify most important factors
a�ecting the performance of nitrogen removal during hydrotreating of algae bio-crude. As it
was mentioned, substantially lower degree of denitrogenation was achieved, but calculated
e�ect values suggest that not only the temperature is an in�uential variable in the process,
but also initial H2 pressure and the interaction of those two, seem to play an important
role, as seen on the half-normal plot and the pareto chart for HDN (Figure 4.5 and Figure
4.6).
33
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
Figure 4.5. Half-plot for HDN Figure 4.6. Pareto for HDN
In contrary to HDO, more than one factor exhibits true statistical signi�cance for HDN.
Again factor A- temperature contributes to the major variance in the process, however the
initial hydrogen pressure and reaction time are certainly more important for HDN than
for HDO. ANOVA for HDN con�rms this statement.
An interaction between temperature (factor A) and initial hydrogen pressure (factor B)
is shown on Figure 4.7. The appearance of two non-parallel lines suggests that the e�ect
produced by one of these factor is dependent on the value of the second factor. For instance,
when the temperature increases, the DODN is improved by 18 % at initial H2 equal to 40
bar, but when the pressure is set to 80 bar, this improvement is around 30 %. This can
be seen on the interaction plot, whereas Figure 4.8 shows a contour plot for HDN.
SourceSum ofSquares
Degreeof freedom
MeanSquare
F-Value p-value Remarks
Model 1482.50 4 370.63 83.13 0.0021 signi�cantA-Temperature 1128.12 1 1128.12 253.04 0.005 signi�cantB-H2 Pressure 231.13 1 231.13 51.84 0.0055 signi�cant
C- Reaction Time 45.13 1 45.13 10.12 0.0500signi�cant(low impact)
AB 78.13 1 78.13 17.52 0.0528signi�cant(low impact)
AC 6.13 1 6.13 1.69 0.3233 insigni�cantResidual 37.25 2 18.62Cor Total 8434.88 7
Table 4.4. ANOVA for HDN
34
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
Figure 4.7. Interaction plot for tempera-ture/initial H2 prssure interactionfor HDN
Figure 4.8. Contour plot for HDN
4.1.3 Hydrogen consumption
Approximate hydrogen consumption was calculated for each experimental run. This was
done using the pressure data recorded during hydrotreating experiments. The pressure
di�erence between the initial state when reactors were pressurized and the �nal state
when reactors were cooled down to the ambient, enabled to use the equation of state to
solve for number of hydrogen moles consumed.
nconsumed =PinitialV
RT− PendV
RT(4.1)
This is an oversimpli�cation, as there are also other gases after reaction (methane, nitrogen
removed from the feed or marginal amounts of carbon oxides), but the gas analysis revealed
that hydrogen is still the major product in the gas phase accounting for more than 70 %
of all gas products. Also an assumption has to be made regarding the volume occupied by
gas, considering that the exact volume is hard to evaluate due to the reactor design and
the extent of the volume occupied by other phases. Nevertheless, such estimation gives an
indication on how di�erent conditions a�ect the hydrogen consumption, and in the same
manner as for HDO and HDN, identi�cation on most important factors for that response
is possible. Figure 4.9 presents how hydrogen consumption varies for all experiments.
35
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
0
0,002
0,004
0,006
0,008
0,01
0,012
0,014
1 2 3 4 5 6 7 8
[kg
H2/k
g fe
ed]
Experiment
Hydrogen consumption
Figure 4.9. Approximated hydrogen consumption calculated for all experiments
As a validation of these results a stoichiometric calculation of maximum theoretical
hydrogen consumption for hydrotreating of studied bio-crude has been made. This
was based on the elemental composition of the bio-crude and assumed a complete
deoxygenation and denitrogenation as well as saturation of carbon atoms with hydrogen
to obtain the most desirable H/C ratio equal to 2. This resulted in a value of
0.0047kgH2/kgfeed. As the aforementioned assumptions has not been ful�lled during
hydrotreating, the obtained results are reasonable and are of the same magnitude as results
presented in other studies 2.5.
Similarly as for HDO and HDN, based on the calculated hydrogen consumption values,
an evaluation of the process parameters on that response has been made and presented in
Figures 4.10 and 4.11.
36
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
Figure 4.10. Half-plot for hydrogen consump-tion
Figure 4.11. Pareto for hydrogen consumption
As expected, the temperature is the main driver for hydrogen consumption. This seems
to be in accordance with the previous �nding, that temperature has the biggest impact
on the rate of HDO and HDN reactions which are the major hydrogen consumers. Thus,
increasing temperature leads to promoting those reactions and eventually results in high
hydrogen consumption. This fact itself poses a challenge for process optimization as it can
be seen that the degree of heteroatom removal and reducing H2 consumption are mutually
exclusive targets. Although, also the initial H2 pressure is a signi�cant contributor along
with the AB interaction of those two factors. From that information it may be deducted,
that since these factors were not that signi�cant for successful HDO, initial H2 pressure
may be reduced which would eventually lead to savings related to reactors design. The
statistical evaluation of signi�cance is con�rmed by the ANOVA, presented in Table 4.5.
SourceSum ofSquares
Degreeof freedom
MeanSquare
F-Value p-value Remarks
Model 0,0092 3 0,0031 46,55 0,0014 signi�cantA-Temperature 0,0058 1 0,0058 88,05 0,0007 signi�cantB-H2 Pressure 0,0023 1 0,0023 34,72 0,0041 signi�cantAB 0,0011 1 0,0011 17,19 0,0143 signi�cantResidual 0,0003 4 0,0001Cor Total 0,0094 7
Table 4.5. ANOVA for hydrogen consumption
4.1.4 Investigation of the e�ect of operational conditions on bio-oil
properties
Since it was concluded that the results from the initial experimental campaign are
statistically signi�cant, the evaluation of the e�ect of operational conditions on the
properties of the upgraded bio-crude can be carried out. This includes parameters such as
HHV, H/C ratio as they do not require additional analysis, but are related to the elemental
37
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
composition of the product. Figure 4.12 illustrates the change in the HHV from the raw
biomass through the bio-crude to the upgraded samples.
0,00
5,00
10,00
15,00
20,00
25,00
30,00
35,00
40,00
45,00
50,00
20,96
37,59 37,59 38,68 38,93 39,1841,24 41,51 42,64 43,70
0,00
10,00
20,00
30,00
40,00
50,00
HH
V [
MJ/
kg]
HHV of biomass, biocrude and upgraded samples
Figure 4.12. HHV of the biomass, bio-crude and hydrotreated samples
It is apparent, that following the changes in the elemental composition, hydrotreating
positively a�ects HHV. Although, milder conditions experiments (EXP1, EXP3, EXP7,
EXP8) are not far from the un-treated bio-crude. On the other hand, more severe
conditions experiments resulted in very satisfying HHV, similar to the ones of petroleum
products. Further reduction in nitrogen content will enable to increase HHV even more.
As for the H/C ratio, an increase from 1.6 to 1.8 was observed which also approaches the
value of commercial fuels.
4.1.5 GC-MS analysis of compounds
Chromatographs of the original bio-crude and severe conditions experiment number 6 are
presented on Figures 4.13, 4.14 respectively. The untreated bio-crude shows a complex
mixture of a vast number of di�erent compounds. It is impossible to identify all of
them, but the most abundant were labelled. These include hydrocarbons, oxygenates
and nitrogen containing compounds, as expected from the elemental analysis. Among
oxygenates, the hexadecanoic acid seems to be most abundant, whereas phenol derivatives
in the presence of p- and m-cresol can be also distinguished. Moreover, dodecanol in a
small amount was observed. Nitrogen is contained mostly in heavier molecular weight and
non-heterocyclic compounds such as various amides. However, also ring type structures
containing nitrogen such as pyrroles and indoles appear in the spectrum.
Chromatograph of the upgraded sample in severe conditions experiment reveals dominating
aliphatic hydrocarbons ranging from C14 to C21. Also a considerable amount of aromatic
hydrocarbons represented by ethylbenzene is present. At the same time, the content
of oxygen and nitrogen containing compounds has been signi�cantly reduced, with no
visible peaks related to those molecules in the analyzed spectrum. Although, since it is
known from the elemental analysis that the oil is not-nitrogen-free, it is suspected that
nitrogenates are located in heavier molecular weight compounds, not detected by the GC-
MS method.
38
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
0,00E+00
2,00E+07
4,00E+07
6,00E+07
8,00E+07
1,00E+08
1,20E+08
1,40E+08
1,60E+08
1,80E+08
2,00E+08
5,0
05
,37
5,7
56
,13
6,5
16
,88
7,2
67
,64
8,0
28
,39
8,7
79
,15
9,5
39
,90
10
,28
10
,66
11
,04
11
,41
11
,79
12
,17
12
,55
12
,92
13
,30
13
,68
14
,06
14
,44
14
,81
15
,19
15
,57
15
,95
16
,32
16
,70
17
,08
17
,46
17
,83
18
,21
18
,59
18
,97
19
,34
19
,72
20
,10
20
,48
20
,85
21
,23
21
,61
21
,99
22
,36
22
,74
23
,12
23
,50
23
,87
24
,25
24
,63
25
,01
25
,38
25
,76
26
,14
26
,52
26
,89
27
,27
27
,65
28
,03
28
,40
28
,78
29
,16
29
,54
29
,91
30
,29
Inte
nsi
ty
Time (min)
Chromatograph - Bio-crude
Figure 4.13. Chromatograph of the untreated bio-crude
0,00E+00
1,00E+08
2,00E+08
3,00E+08
4,00E+08
5,00E+08
6,00E+08
7,00E+08
8,00E+08
9,00E+08
1,00E+09
5,0
05
,39
5,7
86
,16
6,5
56
,94
7,3
37
,72
8,1
08
,49
8,8
89
,27
9,6
51
0,0
41
0,4
31
0,8
21
1,2
01
1,5
91
1,9
81
2,3
71
2,7
61
3,1
41
3,5
31
3,9
21
4,3
11
4,6
91
5,0
81
5,4
71
5,8
61
6,2
51
6,6
31
7,0
21
7,4
11
7,8
01
8,1
81
8,5
71
8,9
61
9,3
51
9,7
32
0,1
22
0,5
12
0,9
02
1,2
92
1,6
72
2,0
62
2,4
52
2,8
42
3,2
22
3,6
12
4,0
02
4,3
92
4,7
82
5,1
62
5,5
52
5,9
42
6,3
32
6,7
12
7,1
02
7,4
92
7,8
82
8,2
62
8,6
52
9,0
42
9,4
32
9,8
23
0,2
0
Inte
nsi
ty
Time (min)
Chromatograph- Experiment 6
Figure 4.14. Chromatograph of the severe conditions experiment 6
4.1.6 Simulated distillation
Simulated distillation (Sim-Dis) analysis was performed to evaluate the boiling point
distribution of the hydrotreated samples and compared to the untreated oil. Typical
distillation cut points may be de�ned as: gasoline (<190°C), jet fuel (190 − 290°C),
diesel (290 − 340°C), vacuum gas oil (340 − 538°C) and vacuum residue above 538°C.
The percentage of di�erent fractions of selected experiments is given in Table 4.6, where
Experiment 1 refers to the one conducted at mild operating conditions and EXP 6
represents the most severe variant.
Fractionname
Temperaturerange
Bio-crude% Fraction
Experiment 1%Fraction
Experiment 6%Fraction
Gasoline <190 4.4 3 13.1Jet 190-290 15.9 15.5 32.2Diesel 290-340 9.5 28.3 18.4Vacuum gas oil 340-538 42.6 25.6 23.8Vacuum residue >538 27.6 27.6 12.5
Table 4.6. Sim-Dis analysis of bio-crude and selected upgraded samples
Figure 4.15 illustrates distillation curves of all samples and the original bio-crude. It
can be seen that the untreated sample contains a high fraction in the heavy molecular
weight and high boiling point fractions. The investigation of distillation curves, reveals a
39
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
desired trend of shifting towards lighter fractions. It can be also noted, that by increasing
the severity of the process, the total recovery of the fractions is improved. However,
even for the best sample, still around 15% of the compounds is located in the residue
fraction, which indicates that 350 °C is not a high enough temperature to e�ciently crack
heavier molecules. On the positive side, more than 60 % of the hydrotreated sample
from Experiment 6 incorporate gasoline, jet and diesel fuel fractions which is a signi�cant
improvement when compared to 30 % in the bio-crude. Taking into account that these
fractions are either nitrogen free or contain marginal amounts of those compounds, there
is a noticeable potential to obtain biofuels by hydrotreating of algae bio-crude.
0
10
20
30
40
50
60
70
80
90
100
0 100 200 300 400 500 600 700 800
Frac
tio
n %
Temperature [oC]
Simulated distillation curves
EXP2
EXP4
EXP5
EXP6
EXP7
EXP3
EXP8
Bio-crude
EXP1
Figure 4.15. Simulated distillation curves of all experiments and untreated bio-crude forreference
4.1.7 FT-IR
On Figure 4.16 infra-red spectra of three samples were compared - the original untreated
bio-crude, mild conditions experiment (1) and the most severe one (6). To allow comparison
and account for the e�ect of varying absorbance intensities with respect to the amount of
sample being analyzed, the spectra have been normalized. The labeled values indicates
the functional group along with a type of bond associated with particular wavenumber.
The interpretation of FT-IR spectra was done based on Reference [68].
40
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
Figure 4.16. FT-IR spectra of the algae bio-crude, experiment 1 and experiment 6
It is apparent, how hydrotreating a�ected the broad peak at around 3300cm−1 related
to the O-H bond contained in alcohols, phenols or carboxylic acids. Mild conditions
reduced concentration of those oxygenates only slightly, while more severe hydrotreating
contributed to their signi�cant decline. The remaining peak in that region is concerning,
as it was concluded from the elemental analysis that no oxygenates should be present in
that sample, but it can be also caused by alkynes or remaining nitrogen compounds such
as amides. Afterwards, all three samples exhibit similar, strong response and three peaks
below 3000cm−1, which indicates presence of an alkyl group and aldehydes. Next in the
lower frequencies region, the untreated bio-crude and experiment 1 tend to be similar, while
experiment 6 spectrum seems to di�er substantially. Sharp peak around 1700cm−1 is most
likely related to a C-O bond. Notably, for the severely hydrotreated sample, an opposite
response in that particular wavelength is visible, which indicates a good deoxyganation of
that particular group. For the severe experiment sample, only one more signi�cant peak at
1450cm−1 is seen, also related to an alkyl group. Moreover, several weaker peaks at lower
frequency correspond to aromatic hydrocarbons, which were also detected by GC-MS in a
small amount.
In the bio-crude and mildly hydrotreated sample, a peak at 1540cm−1 is expected to be a
response for aliphatic nitrogen compounds, which were detected by GC-MS. Again, as for
a C-O bond, the upgraded sample reveals a drop at that particular wavelength which is in
the accordance with the fact of not �nding those compounds by GC-MS.
Next peaks are more di�cult to interpret, as they might be an e�ect of overlapping
responses of several compounds. However, still some certain di�erences in the
41
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
concentrations of several compounds can be observed.
4.1.8 Pressure data analysis
The pressure transducers were used to record the change in pressure throughout each
experimental run. This allowed to give an estimation of the hydrogen consumption as
well as evaluate to what extent the HDO and HDN reaction might take place. A similar
pattern may be observed when analysing this data. The initial signal was very noisy,
therefore a �lter was applied to the original data in order to better indicate the trend.
The graph illustrating variation of pressure is shown in Figure 4.17. Experiments with
low and high temperature were selected to show the e�ect of temperature on hydrogen
consumption. Notice the di�erence between the pressure drop for two experiments (∆P ),
which indicates the relative di�erence in the amount of H2 gas consumed.
0
20
40
60
80
100
120
140
160
0 50 100 150 200 250
Pre
ssu
re [
bar
]
Time [min]
Pressure data of EXP6 and EXP8
EXP6-R2
EXP8-R1
ΔP
Figure 4.17. High temperature EXP 6 and low temperature EXP 8 pressure data
As for a closed thermodynamic system, a rapid increase in pressure is seen, right after the
reactors were placed in the hot sandbath. Naturally, for higher temperature the rise is more
signi�cant than for the lower. After obtaining a top value, the pressure tends to decrease,
in somewhat exponential manner as the reactions involving hydrogen proceed. This occurs
up to a certain point in which the decline stabilizes and oscillates around particular value.
This indicates that no signi�cant rates of reactions take place and further hydrotreating
does not contribute to improving the quality of the bio-crude. Such behaviour is with
accordance to the rate law which states the relationship between the reaction rate and the
concentrations or pressures of the reactants.
r = k[A]x[B]y (4.2)
Therefore, as hydrogen is consumed, the kinetics slow down dramatically. However, an
another possible explanation of this phenomena is possible. As it was mentioned, a catalyst
deactivation issue may arise due to for instance presence of water.
42
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
4.1.9 Hydrtotreated samples observations
The observation of physical properties of the hydrotreated samples may be indicative to
assess the quality of the upgraded oil. It seems evident, that low temperature treating (250
°C) did not a�ect signi�cantly the high viscosity of the algae bio-crude. Even after 4 hours
of processing, it is still a viscous and di�cult to extract from the reactors liquid. On the
other hand, hydrotreating in higher temperatures (350 °C) resulted in an easily �owing
liquid and higher yields, resembling much more a fuel-like substance. Furthermore, to
some extent, all of the samples revealed the desired phenomena of separated water phase,
indicating that the hydrodeoxygenation reaction takes place. This can be seen on Figure
4.18.
Figure 4.18. Mild conditions sample on the left with only a slight amount of water phase atthe bottom of the vial. More severe conditions sample in the middle with clearseparation of water phase. On the right the most severe conditions sample. Clearwater separation and no residue on the walls indicating e�cient HDO and an easy�owing liquid
Unfortunately, analysis of the water phase was not possible during the project period,
though it would be interesting to see its structure and whether nitrogen compounds also
migrate to that phase.
4.1.10 Additional considerations regarding bio-crude solubility
As an additional task highly relevant for co-processing, a set of solubility tests were
prepared in order to evaluate algae bio-crude potential in a re�nery context. Mixing
renewable oils with a conventional feedstock prior to any upgrading process is an another
possible application of the bio-crude. Hence, its ability to form a true solution with other
substances has to be evaluated. This is done by an experimental procedure leading to
43
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
obtain Hansen solubility parameters (HSP). HSP are three parameters accounting for
polar, dispersive and hydrogen bond forces within a molecule. Based on the fundamental
chemical principle that 'likes dissolve likes', substances with similar HSP are expected to
be soluble in each other. The experimental procedure to obtain those parameters involves
mixing bio-crude with a wide range of solvents for which HSP are known. Approximately
0.5g of bio-crude is mixed with 4ml of solvent. The mixture is left for at least 24 − 48
hours to ensure a complete dissolution. Afterwards, an initial evaluation of the degree of
solubility is done in a three grade scale where 1 means completely soluble, 2 is uncertain
or partially soluble and 0 is not soluble with visible residues. Although, due to the dark
colour of the solutions, it may be troublesome to evidently determine whether it is a true
solution or a suspension. In such cases, an auxiliary test has to be carried out. Then a
drop of solution is drizzled on a �lter paper. When a black ring is visible, a precipitate
is present, whereas a spot of constant colour indicates a true solution. Such obtained
solubility scores are entered to the HSPiP software which is able to estimate the HSP as
well as to plot the interaction sphere, showing theoretical ability to form a solution with
substances that lie within its range. Table 4.7 lists the used solvents, respective scores and
obtained HSP. Also HSP of other oils were given for reference.
Solvent Score HSPAlgaebio-crude
Woodbio-crude
Venezuelancrude oil
Marinediesel oil
Methanol 2 δD 16.73 15.72 18.6 16.05Ethanol 1 δP 7.27 7.54 3.0 4.64Acetone 2 δH 10.42 10.67 3.4 5.52DEE 2 δ 21.01 20.44 19.45 17.59Hexane 0 RED 0.13 1.29 0.82MEK 21-Octanol 1Toluene 2Cyclohexanone 1Cyclopentanone 1Ethyl Acetate 21-Pentanol 1Cyclohexane 02-Heptanone 2Heptane 02-Pentanone 2Cyclopentane 2Hexadecane 0Butanol 1
Table 4.7. List of solvents used for solubility tests, obtained HSP for algae bio-crude. HSP ofVenezuelan crude oil was found in [69]. Estimation of HSP of wood bio-crude andmarine diesel oil was performed in an other study of the author.
Once the HSP are known, one way to evaluate the solubility between two crudes, is two
compare their relative energy di�erence (RED). In principle, the smaller the RED is, the
more likely two substances will be soluble in each other. RED is the ratio of the distance
of a solvent from the centre of the sphere, divided by the radius of the sphere. HSPiP
44
4.1. Identi�cation of in�uencing parameters from the two- level factorial designexperiments Aalborg University
enables to calculate this parameters. RED below 1 indicates a good solubility, whereas
values above 1 mean that it is not likely that two substances are miscible. Following this
approach, algae bio-crude would be soluble in wood bio-crude but not soluble in Venezuelan
crude oil. However, it has potential to be blended with MDO.
Apparently, the chemical structure of the biocrude, and high content of oxygen and
nitrogen compounds a�ects its solubility performance. Bio-crude is completely insoluble
in pure hydrocarbons like alkanes, mixes quite well in alcohols and ketones while it
was de�nitely soluble in cyclic ketones such as cyclohexanone and cyclopentanone. This
indicates high polarity of the bio-crude and potential problems when mixing with fossil
crude oils. This provides an another motivation for hydroprocessing, as it was shown, the
oxygen content and hence polarity can be signi�cantly reduced by means of this method.
4.1.11 Discussion and partial conclusions
Temperature has been identi�ed as a dominating factor having the most substantial e�ect
on the degree of deoxygenation of algae bio-crude, while it was observed that other
factors did not contribute signi�cantly. At the same time, complete deoxygenation was
achievable during experiment 6, which suggests, that there might be less severe conditions
yielding similar performance. Furthermore, since the temperature also a�ects hydrogen
consumption the most, it may be feasible to reduce the severity of the process as it would
lead to less heat and hydrogen demand. Additionally, a reason to reduce retention time can
be found in the pressure data, as it is seen that after certain point the pressure stabilises,
which indicates low reaction rate.
The small contribution of hydrogen pressure for HDO was found to be a surprising �nding,
since it is known that theoretically it should have an e�ect on hydrogenation equilibrium
and improvement of saturation reactions. Although this might be due to the fact, that the
selected range for experiments was high enough and it was rather temperature/kinetics
that was a limiting parameter for successful HDO, not the pressure. Also, it is di�cult to
evaluate the real e�ect of operational pressure, as in the batch reactor it is not constant,
but decreases as reactions involving hydrogen proceed. A study in a continuous system
would be interesting to address this issue.
Hydrodenitrogenation revealed a more complicated response, showing that there are more
in�uential factors involved. Namely hydrogen initial pressure, and time were observed
to have a bigger impact for HDN than for HDO. The discrepancy between the degree of
deoxygenation and the degree of denitrogenation is problematic, as it suggests, that even
though the process might be optimized in terms of severity to reduce oxygen content,
nitrogen removal requires even more severe conditions. Furthermore, it is anticipated
that increasing temperature will result in cracking reactions and decreased yields. This
potentially leads to mutually exclusive optimization targets during simultaneous HDO and
HDN. Therefore, a two step process with initial oxygen removal in milder conditions and
subsequent nitrogen removal with elevated temperature may be worth considering.
In order to interpret those �ndings, the GC-MS and FT-IR analysis of the compounds
found in the bio-crude and upgraded samples comes in hand. As it was mentioned in
sections 2.2.2, 2.2.3, various compounds exhibit di�erent reactivity, depending on the
45
4.2. Optimization and con�rmation experiments Aalborg University
process conditions. It might be said, that the oxygenates found in the bio-crude, were
relatively easy to reduce, which resulted in total deoxygenation. On the other hand,
nitrogen compounds are more persistent to hydrotreating. This suggest that either, HDO
reactions have an inhibiting e�ect on HDN, or the nitrogen containing compounds require
principally more severe conditions to be successfully hydrogenated and removed from the
feed. Otherwise, this means that the studied catalyst is simply more selective for HDN
than for HDN.
Among identi�ed compounds in the untreated bio-crude, various amides were present
in abundance. As a matter of fact, as these are non-heterocyclic compounds, they are
expected to be removed at higher temperatures with relative ease. Also, no trace of
molecules containing multiple nitrogen atoms suggests that increasing temperature may
lead to better HDN e�ciency.
Furthermore, even though the upgrading under severe conditions bio-crude seems to
achieve the primal goal of hydrotreating to remove O and N atoms to obtain pure
hydrocarbons, it is not able to crack higher molecular compounds into more valuable
lighter, saturated hydrocarbons. This can be also observed from the Sim-Dis analysis, as
still around 30 % mass fraction is con�ned in non gasoline, jet or diesel range. Hence again,
elevating operational temperature may be bene�cial, as it will promote hydrocracking
reactions.
Lastly, it was observed that simultaneous analysis of results from elemental composition,
GC-MS, Sim-Dis can lead to an interesting hypothesis. This is due to the fact, that
hardly any nitrogen containing compounds were identi�ed in GC-MS of the upgraded
sample from the most severe conditions. Yet still, elemental analysis revealed around 4
wt.% of nitrogen. This would suggest, that these N-compounds are located in the heavier
fractions, not detected by Gas-Chromatography, as it can only detect molecules volatilizing
at temperatures below 300 °C. Hence, taking into account the results from Sim-Dis, it may
be deducted, what percentage of obtained oil hypothetically contains oxygen and nitrogen
free fractions. Nonetheless, in order to prove that statement, a real distillation shall be
performed followed by an elemental analysis of the distillates.
4.2 Optimization and con�rmation experiments
As it was concluded in the previous section, an improvement in the process e�ciency is
anticipated with an optimization of operational conditions. Ideally, in order to detect the
curvature in the process, higher order terms shall be added to the model. This means that
following an approach such as central composite design at least 8 center points experiments
would have to be augmented to the experimental design. Ideally, to obtain a full quadratic
polynomial, total number of 27 (33) experimental runs would have to be performed. This
was not possible during the working period of this project, therefore an optimization based
on a linear model was carried out instead. Although it is di�cult to expect, that a response
in hydrotreating process will follow a linear model, it can give a rough estimation on the
maximum possible improvement.
46
4.3. Results from con�rmation experiments Aalborg University
Also knowledge about the process, gained from the �rst experimental part was helpful to
propose a second set of con�rmation experiments with new operation conditions, given in
Table 4.8
Number ofexperiment
Temperature[oC]
Initialhydrogenpressure[bar]
Reactiontime[h]
2.1 375 70 32.2 400 65 2.52.3 400 70 2
Table 4.8. Operational conditions in the second set of con�rmation experiments
4.3 Results from con�rmation experiments
As the oxygen containing compounds has been already removed in previous experiments,
the goal was to achieve further reductions in nitrogen content. Although one remark
regarding the results of this campaign has to be made. Due to the failure of elemental
analyzer used to estimate CHNO content in previous experiments, samples from the last
three experiments were analyzed with di�erent instrument at the Department of Chemistry
and Bioscience in Esbjerg. Therefore, there is some uncertainty expected, and a repeated
analysis using the same instrument as before is anticipated to ensure the accuracy of the
results. Nevertheless, Table 4.9 shows the results from the elemental analysis and respective
degree of denitrogenation.
Number ofexperiment
C H N ODegree ofdenitrogenation[%]
2.1 84,55 12,36 3,09 0 602.2 84.17 11.99 3,84 0 502.3 84,37 12,44 3,19 0 58
Table 4.9. Elemental analysis and degree of denitrogenation for the second set of experiments
It can be seen, that increasing the severity of the process resulted in further nitrogen
reductions, whereas as previously in Experiment 6, oxygen was not present in analyzed
samples. This corresponds to an improvement from 48 % to 60 % in degree of
denitrogenation. However, the nitrogen content of more than 3 % is still far beyond a
satisfactory levels for fuels standards, which would de�nitely lead to production of nitrogen
oxides (NOx) during combustion as well as storage stability. As the operating conditions
were set towards rather high boundaries of practical hydrotreating, the constrains have to
be searched in other aspects. Also, since the highest temperature did not result in the best
HDN performance, it may be suspected that there might be some inhibiting e�ect, not
observed in the previous factorial experiments. This could be for instance an increased,
rapid hydrogen consumption by HDO reactions in higher temperatures, hindering the
catalyst ability to facilitate HDN reactions.
47
4.4. Discussion Aalborg University
4.4 Discussion
The reason for the presence of the remaining nitrogen compounds could be found in
thermodynamics of the process. In the studied experiments, as hydrogen is consumed, its
partial pressure decreases and the position of the equilibrium of hydrogenation is a�ected,
which in turn results in lower hydrodenitrogenation rates. It is expected that maintaining
constant, higher hydrogen partial pressure would lead to more e�ective hydrogenation of
heterocyclic structures and removal of nitrogen atoms.
As a discussion, it is also worth to compare obtained results with other studies. For
instance, hydrotreating of algae bio-crude carried out by Biller et al. [47] resulted in an
upgraded bio-oil with a nitrogen content between 2.4 − 4.7wt.% for di�erent operating
conditions and catalysts. Considering the initial level of nitrogen in the Chlorella bio-
crude, signi�cantly lower than the one of Spirulina, 60% reduction was achieved. This is
in accordance to the HDN performance observed in the present study.
Also other studies [49], [50] where hydrotreating of algae bio-crude was performed, report
denitrogenation of approximately 50 % which indicates a general di�culty in e�cient
upgrading of these kind of feedstocks.
Even though a complete denitrogenation of algae bio-crude is possible, as stated by Elliott
et al. [51] it has to be noted that the result of hydrotreating may depend greatly on the
composition of the feedstock subjected to the thermo-chemical conversion and subsequent
upgrading. In that case, the initial nitrogen content was around half of the one in the
present study. Also as mentioned before, continuous processing, where pressure is kept
constant throughout the whole reaction and hydrogen is supplied in great excess of the
process requirements seems to enhance the rate of heteroatom removal.
Furthermore, since it has been observed that high molecular weight compounds containing
nitrogen are not easily hydrogenated even in severe conditions, it may be viable to perform
a solvent extraction on the bio-crude and investigate the structure of obtained fractions.
This could possibly save the e�ort on intensive hydrotreating of the whole bio-crude and
prevent catalyst poisoning from high nitrogen containing feed. Such experiment may pose
an additional task for the future work of upgrading microalgae bio-crudes. This alternative
pathway for dealing with algae bio-crud would result in overall lower yield of the fuel
products but on the other hand, it would allow to signi�cantly cut down the severity of
the process and avoid the necessity to hydrogenate problematic, high molecular weight
compounds containing nitrogen. As a consequence, hydrogen consumption can be also
reduced.
From the re�nery point of view, at least partial upgrading is required before enabling
co-processing with a petroleum crude. This is dictated by the potential incompatibility
of the algae bio-crude with petroleum feeds, as indicated in section 4.1.10. Although, co
processing of such partially upgraded bio-crude is not of re�ners' greatest interest, since
it induces a risks associated with potential corrosion, fouling etc. On the positive side, an
ability to mix bio-crude with heavier feeds such as marine diesel oil was indicated, and
practical test could be performed to assess this statement.
48
4.4. Discussion Aalborg University
Additionally it would be of great interest to take into consideration also the yields from
both HTL and hydroprocessing conversion. Together with the boiling point distribution
analysis, this would allow to evaluate the e�ciency od the overall process from the feedstock
to production of biofuel and compare with other biofuel technologies.
It seems apparent that with a hydroprocessing feed that contains high levels of more than
one heteroatom to be removed, it is di�cult to obtain a high quality product by the means
of a single stage process. Therefore, as previously mentioned, a two stage treatment seems
to be more adequate for processing microalgae feedstock, where appropriate conditions and
catalysts for each reaction mechanism should be employed. Also continuous processing is
expected to show higher rates of conversion towards pure hydrocarbon products.
49
Future work 5Experimental studies are an essential part in technical research. However since they are
dependent on instriments performance and resources availability they might be very time
consuming. They might be supported by simultaneous modelling studies, although in the
end, a full factorial experimental design will only lead to con�rmation of the assomptions
made. Therefore, based on the gained experience, following further tasks are recommended
for a more profound analysis of the hydrotreating of algal bio-crude:
� A full factorial design including center points to enable creating models for Response
Surface Methodology. This would result in a total number of 27 experiments (33)
� A study investigating e�ects of other parameters such as catalyst to oil ratio,
hydrogen to oil ratio
� Examination of di�erent catalysts, including heterogeneous, novel metals catalysts
� Scaling up the experiments to account for the yield and enable more analysis requiring
samples of greater size
� Addressing a given in the discussion proposal, of a two step process, for primary
HDO and subsequent HDN
� Once a desired upgrading performance is achieved, testing the continuous system
� Further optimization and techno-economic assessment of the process, with regards
to hydrogen consumption, heat demand, catalyst cost etc.
50
Conclusions 6Investigation of the most in�uential parameters a�ecting hydrotreating of algae bio-crude
was the major objective of this study. For this purpose, a set of two-level factorial
experiments was designed and performed. Analytical characterization of obtained samples
served as a basis for evaluation of the e�ects of temperature, hydrogen pressure and
residence time on selected response variables. These include the degree of deoxygenation,
degree of denitrogenation and hydrogen consumption. Moreover, experimental results were
aiming to assess the feasibility of microalgae as a feedstock for biofuel production using
available analytical chemistry analysis.
Therefore, the questions raised in the problem formulation may be now addressed.
Indeed, relevant information about the process was acquired throughout the experimental
campaign. The most in�uential parameters were identi�ed and their statistical signi�cance
was validated by the analysis of variance (ANOVA). It was observed that rather severe
conditions in terms of temperature and pressure are required to obtain the highest degree
of heteroatom removal.
Since one conditions yielded a complete HDO, it can be said that oxygenates contained in
the analyzed bio-crude are relatively easy to be removed and less severe conditions could be
found for this purpose. Unfortunately removal of nitrogen containing compounds appeared
to be more problematic and more severe conditions were proposed to increase the e�ciency
of HDN. Hydrotreating in the temperature of 375 °C, 70 bar of initial hydrogen pressure
and 3h residence time resulted in maximum 60 % degree of denitrogenation. This value
was found to be in accordance with other similar studies. This in fact poses a general
question regarding the limitations of hydrotreating nitrogen rich feedstocks.
Although, even more severe HDT did not contribute to further reduction of nitrogen,
which indicates that the response of the process is more complicated and not possible to
be detected by simple linear models given in this study. This implies a need for performing a
greater number of experiments and eventually obtaining more data to create more complex
response surfaces. Hence it may be concluded that design of experiments method such as a
two-level factorial may provide a certain amount of information but it is rather insu�cient
for the purpose of practical optimization.
From the fuel production point of view, micro-algae can be concluded to be a di�cult to
re�ne feedstock requiring more than average intensive hydroprocessing to obtain drop-in
properties. However, as it was found that a major amount of upgraded sample consist of
nitrogen and oxygen free fractions it may be feasible for production of light fuels such as
gasoline or jet.
51
Modelling statistics ACoe�cient Estimate
Factor HDO HDN Hydrogen consumptionIntercept 52.88 24.63 0.0606A 31.37 11.88 0.0269B 0.8750 5.38 0.0169C 2.62 2.37 -AB 6.38 3.13 0.0119AC 4.13 -0.8750 -
Table A.1. Coe�cients estimates in terms of coded factors for three models
HDO HDN Hydrogen consumption
R2 0.9956 0.9952 0.9722Adjusted R2 0.9845 0.9830 0.9514Predicted R2 0.9293 0.9225 0.8889
Table A.2. R2 values for three models
52
Fuel specifications BGasoline
RON, Research octane number min 95.0MON, Motor octane number min 85.0Density at 15 C kg/m3 720-775Vapor pressure kPa max 65-80T10 boiling point C max 55T90 boiling point C max 130-175Ole�n content V ol% max 10.0Aromatic content V ol% max 35.0Benzene content V ol% max 1.0Oxygenate content wt% max 2.7Sulphur content ppm max
Table B.1. Gasoline speci�cations for a market with highly advanced requirements for emissioncontrol and fuel e�ciency [70]
Diesel
Cetane number min 95.0Density at 15 C kg/m3 820-840Viscosity at 40 C cSt max 2.0-4.0T90 boiling point C max 320Final boiling point C max 350Flash point C min 55Total aromatic content %m/m max 15.0Polycyclic aromatic content %m/m max 2.0Sulphur content ppm max 10Water content ppm max 200
TAN mgKOHg max 0.08
FAME %v/v max 5
Table B.2. Diesel speci�cations for a market with highly advanced requirements for emissioncontrol and fuel e�ciency [70]
53
Aalborg University
Jet A-1 fuel
Freezing point max -47Density at 15 C kg/m3 775-840Viscosity at -20 C cSt max 8T10 boiling point C max 205.0Final boiling point C max 300Flash point C min 38Smoke point mm min 25.0Aromatic content V ol% max 25Sulphur content ppm max 3000Thiol content ppm max 30Stability 260C torr max 25.0Speci�c energy content MJ/kg min 42.80
Table B.3. Jet A-1 speci�cations [71]
Marine Distillate Fuels
Density at 15C kg/m3 890Viscosity at 40C cSt max 2.0-60Micro carbon residue wt% max 0.3Water content vol.% max 0.30Flash point C min 60Pour point summer C max 0Pour point winter C max -6Sulphur content wt.% max 0.1-1.50H2S content ppm max 2.0Ash content wt.% max 0.040
TAN mgKOHg max 0.5
Calc.cetane index min 40.0
Table B.4. Marine fuel speci�cations [72]
54
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