i
EVALUATION OF THERMOCHEMICAL DECOMPOSITION OF VARIOUS
LIGNOCELLULOSIC BIOMASSES FOR BIOCHAR PRODUCTION
PAVITHRA SELLAPERUMAL
Department of Bioresource Engineering
Faculty of Agricultural and Environmental Sciences
McGill University
Ste Anne De Bellevue, Quebec, Canada
August 2011
A thesis submitted to the McGill University in partial fulfillment of the requirements of the
degree of
Master of Science
In
Bioresource Engineering
© 2011 Pavithra Sellaperumal
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EVALUATION OF THERMOCHEMICAL DECOMPOSITION OF VARIOUS
LIGNOCELLULOSIC BIOMASSES FOR BIOCHAR PRODUCTION
Pavithra Sellaperumal
ABSTRACT
Greenhouse gas emissions from world energy generation in 2010 were the highest in
history, according to the latest estimates from the International Energy Agency, released on
May 30th, 2011. In addition, the demand for food, feed, fiber and fuel increases to meet the
needs of a growing global population, making soil fertility management increasingly
important. In this context Biochar has proved itself to be a potential and practical solution
in combating these issues. In this present study, influence of various process parameters on
the pyrolysis of the five different types of ligno cellulosic biomasses into biochar was
investigated. The operational parameters for pyrolysis were optimized using response
surface methodology individually based on the temperature of operation and the time of
residence.
The independent process parameters for pyrolysis such as operational time and
residence time were evaluated using a central composite design to access their effects and
their interactions on the yield of biochar from lignocellulosic biomass. Optimal
temperatures for a desirability function of 0.5 for maple, balsa, bamboo, pine and ebony are
345°C, 334°C, 326.7°C, 325.8°C and 340.8°C respectively with the corresponding residence
times of 22, 43.75, 28, 24.8 and 21.75 minutes respectively. All the biomass data fitted the
proposed model very well. The least fit was observed in balsa wood biomass. Temperature
was the major influential factor compared to time. Density analysis was done to compare
the changes in density before and after pyrolysis. It was observed that the density of biochar
was 0.8 times the density of the wood from which it was originally made. Proximate
analysis was performed to compare the fuel and optimal biochar properties.
iii
Characterization of biochar revealed important details: Hyperspectral imaging
analysis which measured the mean reflectances of the biochar disclosed that porosity which
is inversely proportional to the porosity decreased as the temperature increased. Thus higher
temperature indicated greater porosity compared to average and low temperatures.
Pycnometry analysis suggested that the severity of the pyrolysis hiked the degree of porosity
as well. This result was further substantiated with the scanning electron microscope images
which showed larger sized pores at greater temperatures.
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Résumé
Émissions de gaz à effet de serre de la production d'énergie mondiale en 2010 étaient
les plus élevés dans l'histoire, selon les dernières estimations de l'Agence internationale de
l'énergie, publié le 30 mai 2011. En outre, la demande augmente aliments, la nourriture,
fibres et combustible pour répondre aux besoins d'une population mondiale croissante, ce
qui rend la fertilité des sols en plus important.Dans ce contexte biochar s'est avéré être une
solution potentielle et pratique dans la lutte contre ces problèmes. Dans la présente étude,
l'influence des paramètres du processus différents sur la pyrolyse des cinq différents types de
biomasses ligno cellulosique en biochar a été étudiée. Les paramètres opérationnels pour la
pyrolyse ont été optimisés en utilisant la méthodologie de surface de réponse individuelle
basée sur la température de fonctionnement et le temps de résidence.
Les paramètres de processus indépendant pour la pyrolyse comme le temps de
fonctionnement et le temps de séjour ont été évalués en utilisant un plan composite central
pour accéder à leurs effets et leurs interactions sur le rendement de biochar à partir de
biomasse lignocellulosique. Les températures optimales pour une fonction de désirabilité de
0,5 pour l'érable, balsa, le bambou, le pin et l'ébène sont 345 ° C, 334 ° C, 326,7 ° C, 325,8 °
C et 340,8 ° C, respectivement avec le temps de séjour correspondant de 22, 43,75, 28 , 24,8
et 21,75 minutes respectivement. Toutes les données sur la biomasse équipé le modèle
proposé très bien. Le moins bon a été observée dans la biomasse bois de balsa. La
température était le principal facteur d'influence par rapport au temps. Analyse de la densité
a été faite pour comparer les changements dans la densité avant et après pyrolyse. Il a été
observé que la densité de biochar a été 0,8 fois la densité du bois à partir de laquelle il a été
initialement faite. L'analyse immédiate a été effectuée pour comparer les propriétés du
carburant et optimale du biochar.
Caractérisation de biochar a révélé des détails importants: l'analyse d'imagerie
hyperspectrale qui a mesuré la réflectance moyenne de la biochar a révélé que la porosité
qui est inversement proportionnelle à la porosité diminué lorsque la température augmente.
Ainsi plus la température indiquée plus grande porosité par rapport aux températures
moyennes et basses. Analyse Pycnométrie suggéré que la sévérité de la pyrolyse a haussé le
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degré de porosité ainsi. Ce résultat a été étayé par les images microscope électronique à
balayage montrant des pores plus grands de taille à des températures plus élevées.
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ACKNOWLEDGEMENTS
I am highly indebted to my supervisor Dr. G.S.V.Raghavan, for his support and constant
encouragement throughout my thesis. His sound advice, great inspiration and towering
enthusiasm kept me going even during the challenging phases of this journey. It is difficult to
overstate my gratitude to him and I take a great pride in working for Dr.Raghavan, from whom I
have not only learned academics but also the values of patience and perseverance.
I extend my gratitude to Mr. Yvan Gariepy for his constant and continued technical
support and assistance in every stage of this work. I am overwhelmed to state that his great level
of patience, sense of direction helped me to pursue the work in a smooth way.
I would like thank and greatly appreciate Dr. Valérie Orsat and Dr. Micheal Nagadi for
providing access to their labs and let me perform works on Hyperspectral Imaging and
Pycnometry. I also would like to thank Dr. Suzelle Barrington for providing her waste
management lab for performing proximate analysis of biochar.
Special thanks to Ms. Line Mongeon for training me in using the Scanning Electron
Microscope for analysis. I would like to thank the faculty and staff in the Dept. of Bioresource
Engineering. Special thanks to Ms. Susan Gregus and Ms. Patricia Singleton for their help in
administrative affairs.
I also would like to thank Baishali Dutta, Ashutosh Singh and Kirupa Krishnan for
helping me to build this thesis and their valuable comments are deeply appreciated. I heart fully
thank Shrikalaa Kannan, Deepika Arumugam, and Palaniappan for making my life at MAC
campus a pleasurable one.
Last but not the least, I would proudly thank my parents, my brother and the important
people of my life: Arun Kumar Bellam, Sriharini Chellapan, Sripriya Ravindrakumar, Dhivyaa
Anandan, Malarvizhi for all their love, help and energy they have given me from a thousands of
miles away.
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Above all, I bow down to the almighty for being with me and driving me through every
joy and sorrow, making me to meet all these wonderful people and giving this contented and
fulfilled life .
viii
DEDICATION
This research is dedicated to Dr.G.S.Vijaya Raghavan, my supervisor without whom I
would not have been in a position of writing this thesis. This dedication is a small token of
gratitude that I show him for making my dream of pursuing masters come true.
Long live professor ‼
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CONTRIBUTION OF THE AUTHORS
The work reported here was performed by Pavithra Sellaperumal and supervised by
Dr.G.S.V.Raghavan from the Department of Bioresource Engineering, Macdonald Campus
of McGill University, Canada. The research was conducted in different labs including Post
Harvest Technology Laboratory, Waste Management Laboratory and Hyper spectral
Imaging Laboratory.
The authorship of the first paper (Chapter 3) includes Pavithra Sellaperumal, Dr.
G.S.V.Raghavan and Yvan Gariepy. The second paper (Chapter 4) is also authorised by
Pavithra Sellaperumal, Dr. G.S.V.Raghavan and Yvan Gariepy .
Co-authors Dr.G.S.V.Raghavan and Yvan Gariepy are from the Department of
Bioresource Engineering were involved in the development, implementation and data
analysis. Mr.Yvan Gariepy provided additional technical guidance and support in the
development of the manuscript was offered by Dr.G.S.V.Raghavan.
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TABLE OF CONTENTS
Title page………………………………..………………...………………………………………….…….i
Abstract……………………………………………...………………………….………………………….ii
Resume………………...………………...……………………………………………………….……….iv
Acknowledgements……………………...…………………………………………………….………….vi
Dedication………………………………...……………….…………………………………………….viii
Contribution of the Authors……………………………………………………………………………..ix
Table of Contents………………………………………..………………………………………………..x
List of Figures………………………………………..…..……………………………………………...xiv
List of Tables………………………………………….………………………………………………...xvii
CHAPTER I
Introduction……...………………………………………….……………………………………………..1
CHAPTER II
LITERATURE REVIEW
2.1 Benefits of Biochar: An extensive study………………………………………………………….…..6
2.1.1 Biochar as a soil amendment……………….…………………….…………………………….6
2.1.2 Biochar as a stream for waste management ……………………………………………………7
2.1.3 Energy production from Biochar…………………..……….…………………………………..8
2.1.4 Biochar as a tool for climate change mitigation………………..………….…………………..9
2.2 Biochar and charcoal-similarities and differences.…………………………...…………………....10
2.3 Physical properties of Biochar………………………………….……………………………………11
2.3.1 Molecular structure of Biochar and its influence on morphology……………………..……12
2.3.2 Structural complexity loss during thermo-chemical conversion………………..…………..14
2.3.3 Modification of physical structure of Biochar ……………………...………………………..15
2.3.4 Nano porosity of Biochar…………………………………..…………...……………………..17
2.3.5 Influence of macro porosity on Biochar…………………………………………………..….19
2.3.6 Particle size distribution of Biochar……………………………………………..……………21
2.3.7 Biochar density…………………………………………………………………..…………… 22
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2.3.8 Mechanical strength……………………………………………………………..…………… 23
2.4 Practical applications of Biochar……………………………………………………………………24
2.4.1 The role of mixtures in Biochar…………………………………………........................…24
2.4.2 Biochar as an ingredient in Bokashi…………………………………..………………………24
2.4.3 Biochar as a medium for fungal inoculants………………………………..…………………25
2.4.4 Biochar as a bulking agent in compost……………………………………..………………...25
2.4.5 Biochar and Manure………………………………………………………..………………….26
2.4.6 Land reclamation and soil remediation……………………………….……………………..26
2.4.7 Biochar as a tool for revegetation……………………………..………………………………26
2.4.8 Biochar and sorption of heavy metals………………………...………………………………27
2.4.9 Biochar and sorption of Pesticides………………………...………………………………….27
2.5 Conclusion……………………………………………………………..……………………………..29
CONNECTING TEXT
CHAPTER III
EVALUATION OF THE EFFECT OF PYROLYSIS PROCESS ON VARIOUS
LIGNOCELLULOSIC BIOMASSES THROUGH RESPONSE SURFACE METHOD
3.1 Introduction…………………………………………………………………………….…………….33
3.1.1 Pyrolysis of biomass………………………………….……………………………………….33
3.1.1.1 Slow pyrolysis…………………………………………………………………………34
3.1.1.2 Fast pyrolysis………………………………………………………………………….36
3.1.1.3 Intermediate pyrolysis………………….…………………………………………….37
3.1.1.4 Carbonisation……………………….……………………………………………… 37
3.1.2 Proximate analysis of Biochar…………………….…………………………………………38
3.1.2.1 Moisture…………………………..………………………………………………….38
3.1.2.2 Volatile Matter……………………...……………………………………………….38
3.1.2.3 Ash…………………………………………………………………...……………….38
3.1.2.4 Fixed carbon………………………………………………………..………………..39
3.2 Methods and Materials………………………………………………………………………………39
3.2.1 Preparation of Biomass………………………………………………….……………………39
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3.2.2 Pyrolysis of Biomass to produce Biochar…………………………………………………... 40
3.2.3 Ashing of Biochar for proximate analysis………………………………………………….. 42
3.3 Experimental design………………………………………………………………………………… 44
3.4 Results and Discussion………………………………………………………………………………45
3.4.1 Model Fitting…………………………………………………….……………………………45
3.4.1.1 Pine Biochar yield………………………………..………………………………….46
3.4.1.2 Balsa Biochar yield…………………………………………………………..………47
3.4.1.3 Ebony Biochar yield……………………………………….……………………….. 49
3.4.1.4 Maple Biochar yield…………………………………………………………..……..51
3.4.1.5 Bamboo Biochar yield……………………………………………………...………..53
3.4.2 Desirability ……………………………………………………………………………….…..55
3.4.3 Comparison of Biochar yield among the different types of Biomasses……………………61
3.4.4 Response surface analysis………………………………………………………….…………63
3.4.5 Estimation of Biochar Properties from proximate analysis…….…………………………68
3.4.6 Density analysis…………………………………………………………………..…………..72
3.5 Conclusion……………………………………………………………………………...……………75
3.6 Acknowledgements……………………………………………………………………..…………..75
CONNECTING TEXT
CHAPTER IV
CHARACTERIZATION OF VARIOUS BIOCHARS BY PYCNOMETRY,
HYPERSPECTRAL IMAGING AND ELECTRON MICROSCOPY IMAGING
4.1 Introduction………………………………………………………………….……………………….79
4.1.1 Hyper spectral Imaging……………………………………….………………………………79
4.1.2 Helium Pycnometer………………………………………….……………………………….81
4.1.3 Scanning Electron microscopy…………………………….………………………………...82
4.1.3.1 Fundamental principle of SEM for Biochar morphology analysis……………...83
4.2 Methods and Materials……………………………………………………………………………...85
4.2.1 Measurement of Reflectance of Biochar using Hyper spectral Imaging…………………85
4.2.2 Porosity analysis using Helium pycnometer………………………………………..……..87
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4.2.3 Imaging of Biochar using Scanning electron microscopy……………………..………..88
4.3 Results and Discussion……………………………………………………………………………89
4.3.1 Structural development analysis of Biochar from Hyper spectral imaging…………….89
4.3.2 Characterization of Biochar based on porosity using pycnometry…………………….97
4.3.3 Surface morphology studies of Biochar from SEM……………………………….……102
4.4 Conclusion……………………………………………………………………………………….110
4.5 Acknowledgements………………………………………………………………………..…….110
CHAPTER V
SUMMARY AND CONCLUSION…………………………………..…………………………111
REFERENCES
xiv
LIST OF FIGURES
Fig 1.1 Total Canadian GHG emissions [GtCO2 equivalent]…………………………….…….....1
Fig 1.2 Natural Carbon sequestration by plant………………………………………...…….………3
Fig 2.1 Social, Financial and Environmental Benefits of Biochar…………………………..……..6
Fig 2.2 The global carbon cycle of net primary productivity……………………….………….… 9
Fig 2.3 Ideal biochar structure development with highest treatment temperature (HTT)…… 13
Fig 2.4 Relationship between biochar surface area and micropore volume………………….… 18
Fig 2.5 Electron microscope image showing macroporosity of a wood-derived biochar produced
by „slow‟ pyrolysis………………………………………………………………………………………20
Fig 3.1 Illustration of a industrial slow pyrolysis system………………………..……………… 36
Fig 3.2 Illustration of a fast pyrolysis system……………………………………..……………… 37
Fig 3.3 Machine lathe and basic parts of lathe machine…………………………….………………40
Fig 3.4 Pyrolysis equipment: Thermal desorption Unit……………………………..………………41
Fig 3.5 Thermoltne furnace used for proximate analysis…………………………….……………..43
Fig 3.5a Predicted (g) vs Actual (g) PINE Yield………………………………….………………….47
Fig 3.5b Predicted (g) vs Actual (g) BALSA Yield…………………………….…………………….49
Fig 3.5c Predicted (g) vs Actual (g) EBONY Yield……………………………..……………………51
Fig 3.5d Predicted (g) vs Actual (g) MAPLE Yield…………………………….……………………53
Fig 3.5e Predicted (g) vs Actual (g) BAMBOO Yield……………………….………………………54
Fig 3.6 Overall Desirability of the Pyrolysis process for each biomass type…………..…………57
Fig 3.6a Individual Desirability of the pyrolysis process for Balsa Biochar…………….………..58
Fig 3.6b Individual Desirability of the pyrolysis process for Bamboo Biochar…………………..58
Fig 3.6c Individual Desirability of the pyrolysis process for Ebony Biochar…………….……….59
Fig 3.6d Individual Desirability of the pyrolysis process for Maple Biochar……………………..59
Fig 3.6e Individual Desirability of the pyrolysis process for Pine Biochar………………………..60
Fig 3.7 Comparison of yield of Biochar of various Biomass……………………………………….62
xv
Fig 3.8a Response Surface plots of the effect of process variables, Temperature and Time on
pyrolysis of Pine Biomass……………………………………………………………………….……..63
Fig 3.8b Response Surface plots of the effect of process variables, Temperature and Time on
pyrolysis of Bamboo Biomass………………………………………………………..………………. 64
Fig 3.8c Response Surface plots of the effect of process variables, Temperature and Time on
pyrolysis of Ebony Biomass…………………………………………………………………………….65
Fig 3.8d Response Surface plots of the effect of process variables, Temperature and Time on
pyrolysis of Maple Biomass…………………………………………………………………………….66
Fig 3.8e Response Surface plots of the effect of process variables, Temperature and Time on
pyrolysis of Balsa Biomass………………………………………………………..………………… 67
Fig 3.9a Bulk density of biochar plotted against its feedstock…………….……………………… 72
Fig 3.9b Bulk density of biochar plotted against its feedstock………………………………….… 72
Fig 3.10 Comparison of Densities (g/cc) before and after pyrolysis for various types of
biomass………………………………………………………………………….……………………….73
Fig 4.1 Illustration of capture mechanism of a linescan camera……….……………………….79
Fig 4.2 Helium Pycnometry chamber showing the different pressures…………………………...81
Fig 4.3 Components of SEM………………………………………………………………….…… 83
Fig 4.4 Scanning electron microscope (SEM) image (right)showing macroporosity of a wood-
derived biochar produced by „slow‟ pyrolysis…………………………………………….…………83
Fig 4.5 Working HyperspecTM equipment showing the camera, illumination system and sample
field (top), biochar placed in field and illuminated (Bottom left), the camera
system…………………………………………………………………………………….…………… 85
Fig 4.6 Components and sample assembling of pycnometer……………..……………………… 86
Fig 4.7 VP-SEM…………………………………………………………….………………………….87
Fig 4.8a Fisher‟s multiple comparison results of pyrolysis of maple biomass with statistical
significance (HSI)……………………………………………………………………………………. 91
Fig 4.8b Fisher‟s multiple comparison results of pyrolysis of Pine biomass with statistical
significance (HSI)………………………………………………….………………………………… 91
Fig 4.8c Fisher‟s multiple comparison results of pyrolysis of ebony biomass with statistical
significance (HIS)………………………………………………………………………………………92
Fig 4.8d Fisher‟s multiple comparison results of pyrolysis of Bamboo biomass with statistical
significance (HIS)………………………………………………………………………………………92
Fig 4.8e Fisher‟s multiple comparison results of pyrolysis of Balsa biomass with statistical
significance (HIS)………………………………………………………………………………………93
xvi
Fig 4.8f Mosaicking of Biochar samples (MOSAIC 1)…………………..…………………………94
Fig 4.8g Mosaicking of Biochar samples (MOSAIC 2)………………..………………………… 95
Fig 4.9a Fisher‟s multiple comparison results of pyrolysis of Pine biomass with statistical
significance (Pycnometry)………………………………………………..……………………………98
Fig 4.9b Fisher‟s multiple comparison results of pyrolysis of Maple biomass with statistical
significance (Pycnometry)……………………………………………………………………………..98
Fig 4.9c Fisher‟s multiple comparison results of pyrolysis of Ebony biomass with statistical
significance (Pycnometry)………………………………………………………………………….….99
Fig 4.9d Fisher‟s multiple comparison results of pyrolysis of Balsa biomass with statistical
significance (Pycnometry)………………………………………………………………….……….…99
Fig 4.9e Fisher‟s multiple comparison results of pyrolysis of Bamboo biomass with statistical
significance (Pycnometry)………………………………………………………………..……………100
Fig 4.10 Scanning electron Microscopy images of various Biochars of 1000x
magnification…………………………………………………….…………………………………….102
Fig 4.11 SEM images of Biochar at 50x magnification………………….…………………………109
xvii
LIST OF TABLES
Table 3.1 Fate of initial feed stock mass between products and pyrolysis process………………. 34
Table 3.2 The different wood used for the study for comparison of Biochar from
pyrolysis…………………………………………………………………………………….…………… 39
Table 3.3 Levels and values of the independent variables analyzed in RSM………………….…. 44
Table 3.4 Central composite uniform precision design for Response Surface Analysis of pyrolysis
of biomass………………………………………………………………………………………..………45
Table 3.5 ANOVA for the effect of Temperature and Time on Pine wood Biochar yield…..… 46
Table 3.6 ANOVA for the effect of Temperature and Time on Balsa wood Biochar
yield………………………………..……………………………………………………………………..48
Table 3.7 ANOVA for the effect of Temperature and Time on Ebony wood Biochar
yield…………………..………………………………………………………………………………….50
Table 3.8 ANOVA for the effect of Temperature and Time on Maple wood Biochar
yield……………………………………………………………………………………..………………..52
Table 3.9 ANOVA for the effect of Temperature and Time on Bamboo Biochar yield………...54
Table 3.10: Optimum values of Temperature and Time for desirability= 0.5………….………..61
Table 3.11: Proximate analysis of ebony biomass…………………………….………………….….69
Table 3.12: Proximate Analysis of Bamboo biomass……………………….………………………70
Table 3.13 Ratio of Biochar to Biomass for the lignocellulosic materials under study………71
Table 4.1: The classification of Variable-Pressure SEM…………….…………………………… 82
Table 4.2 Reference table for treatments and their actual values………………………………90,97
1
CHAPTER I
INTRODUCTION
Humankind has progressed to exponential levels over the last few decades. However,
this progress has resulted at the cost of exploitation of Mother Nature most of the times.
Pollution has reached catastrophic levels for which humans are the main reason. This has
now contributed to global warming, due to the increase in green house gas emissions
especially carbon-di-oxide.
Figure 1.1 Total Canadian GHG emissions [CO2 equivalent] (Cramer et al., 2009)
It is evident from the above graph that the increase in GHGs is alarming within a
span of 20 years. Cement industries, electronic industries, packing industries that use
aerosols and at times even farming practices and other land use processes may contribute to
the green house gas production. This has in turn caused various aftermaths like temperature
increase and melting of glaciers. According to NASA studies the extent of arctic sea ice has
declined about 10% in the last 30 years. Researchers predict that the temperatures will raise
about 2 to 10°F by the end of this century.
2
As the global economy expands, so does its waste production. An estimate showed
that the production of hazardous wastes increased from nine million tonnes to an alarming
number of 238 million tonnes within a time period of 20 years i.e. from 1970 to 1990
(Musgrave et al., 2005)
Green plants and trees which are the major consumers of carbon-di-oxide are facing
threat due to extreme deforestation required for industrialisation and urbanisation. It is
disheartening to know that the total forest cover of earth has decreased from 16 million
square kilometres to 7.5 square kilometres in a span of seventy years (Nielson, 2006). It is
estimated that one ton of carbon in wood or forest biomass embodies 3.67 tons of carbon-di-
oxide recycled in the atmosphere.
One of the potential problems that are faced by the farming and ranching
communities is soil infertility. This is crucial for the farmers because the crop does not grow,
and for the ranchers because the livestock becomes devoid of food. This problem arises
because of draining of the original soil along with its nutrients without actually replenishing
its fertility. Other reasons for infertility may be droughts or the makeup of the soil itself
which may not be able to retain water (Foth and Ellis, 1988). Farmers apply fertilizers
which put these nutrients back in to the soil and proved to help for short time. But during
surface run off, these fertilizers were washed away along with the soil causing pollution in
water bodies, thus forcing farmers to pour their money and time into soil re-fertilization.
Thus all the possible solutions have flaws since they do not provide a long term
remedy and an effective way to retain the water and nutrients. Thus, a farmer‟s and
environmentalist‟s dream solution tends towards answering these questions while making it
efficient and economical.
Thus this study focuses on “Biochar” which seems to be a golden keyword in this
context due to the fact that it serves as a soil conditioner bringing about increased crop
yields and has a potential to gain carbon credits by active carbon sequestration, ultimately
resulting in the cut down of green house gases since it removes the carbon out of the
photosynthesis cycle and locks during the pyrolysis of organic materials. Thus, the
agronomic benefits from the application of biochar include improvement of soil structure
3
and decrease in the intake of toxins that might result in good productivity. This biochar can
be made from organic and agricultural wastes. Thus it acts as a stream for waste
management.
“Biochar is thus a product of thermo chemical conversion of biomass in the absence of oxygen
through a process known as Pyrolysis”
Fig 1.2 Natural Carbon sequestration by plant; biochar sequestration clubbed to pyrolysis
process (Compiled by the author)
HYPOTHESIS
Our hypothesis was that pyrolysis of various type of lignocellulosic biomasses
through a thermal desorption unit may yield Biochars of different physical characteristics
with temperature, time and density of biomass having a major influential role.
RATIONALE FOR RESEARCH
The exponential increase of green house gas levels leading to global warming, loss of
soil fertility leading to poor productivity, increase of urban centers and industrialisation
resulting in tonnes of waste production are all creating multiple problems interlinked with
each other. The solution should be a multitasking and efficient enough to maintain all the
Pyrolysis process
4
above factors in a proper balance. Numerous green energy technologies have been
projected, but most of them answer only one of the above raised threats. Thus we propose
“Biochar” as a multitasking, efficient, cost effective, feasible means of approaching and
balancing all the above issues within a short time span. Biochar offers a stream for waste
management since it can be made from agricultural waste instead of just incinerating them.
It offers solution to carbon sequestration thus reducing the green house gases. Due to its
high porosity, it has been proved to be a good soil amendment. Also, it is a means of
employment to people since it can be made in small scale as well.
OBJECTIVES
The overall objective of this study is to assess the production and characterization of
biochar from different biomasses.
Specific objectives of each study:
1. Production of biochar from different lignocellulosic biomasses and assessment of the
pyrolysis technique based on the different process conditions (temperature, time)
used.
2. Characterization of biochar based on properties like density, porosity and surface
morphology using appropriate test methods.
5
CHAPTER II
REVIEW OF LITERATURE
ABSTRACT
Biochar is the outcome of thermal conversion of organic substances through a
process known as pyrolysis. Incorporation of this biochar into the soil has an added
advantage over these organic materials used for making it, due to its extreme resistance to
microbial decomposition. Biochar improves soil fertility by increasing the soil retention
capacity and enhancing the water infiltration as well. Thus it has been proposed as a
friendly way out to potentially sequester carbon while improving soil fertility. The social,
economic and environmental benefits of biochar have been thoroughly reviewed in this
study. The classification of biochar from the traditional charcoal has to be learned. The
physical property which includes the molecular structure and its influence on biochar
morphology is important. The pyrolysis process which leads to the loss of structural
complexity and the industrial processes for altering the physical structure have been
thoroughly examined. In addition, the soil surface area, biochar‟s nano and macro porosity
and the biochar density and the importance of mechanical strength on the quality
determination of biochar are essential components of this study. In order to understand the
practicality of the research, some of the specific applications were studied and reviewed.
Keywords: Biochar, Environmental management, Surface area, Nano & macro porosity,
Biochar Density, Mechanical strength.
6
2.1 BENEFITS OF BIOCHAR: AN EXTENSIVE STUDY
Fig 2.1 Social, Financial and Environmental Benefits of Biochar (Author)
2.1.1 Biochar as a soil amendment
Soil improvement is not a luxury but a necessity in many regions of the world. Lack
of food security is especially common in sub- Saharan Africa and South Asia, with
malnutrition in 32 and 22 per cent of the total population, respectively (FAO, 2006).While
malnutrition decreased in many countries worldwide from 1990 to 2003, many nations in
Asia, Africa and Latin America have seen increases (FAO, 2006). The „Green Revolution‟
initiated by Nobel Laureate Norman Borlaug at the International Centre for Maize and
Wheat Improvement (CIMMYT) in Mexico during the 1940s had great success in
increasing agricultural productivity in Latin America and Asia. These successes were
mainly based on better agricultural technology, such as improved crop varieties, irrigation,
and input of fertilizers and pesticides. Sustainable soil management has only recently been
demanded to create a „Doubly Green Revolution‟ that includes conservation technologies
(Tilman, 1998; Conway, 1999). Biochar provides great opportunities to turn the Green
Revolution into sustainable agro-ecosystem practice. Good returns on ever more expensive
inputs such as fertilizers rely on appropriate levels of soil organic matter, which can be
7
secured by biochar soil management for the long term (Kimetu et al., 2008; Steiner et al.,
2007). Biochar provides a unique opportunity to improve soil fertility and nutrient-use
efficiency using locally available and renewable materials in a sustainable way. Adoption of
biochar management does not require new resources, but makes more efficient and more
environmentally conscious use of existing resources. Farmers in resource-constrained agro-
ecosystems are able to convert organic residues and biomass fuels into biochar without
compromising energy yield while delivering rapid return on investment. Stockings et al.
2003 concluded that in both industrialized and developing countries, soil loss and
degradation are occurring at unprecedented rates with profound consequences for soil
ecosystem properties. In many regions, loss in soil productivity occurs despite intensive use
of agrochemicals, concurrent with adverse environmental impact on soil and water
resources Biochar is able to play a major role in expanding options for sustainable soil
management by improving upon existing best management practices, not only to improve
soil productivity, but also to decrease environmental impact on soil and water resources.
Biochar should therefore not be seen as an alternative to existing soil management, but as a
valuable addition that facilitates the development of sustainable land use: creating a truly
green „Biochar Revolution‟.
2.1.2 Biochar as a stream for waste management
Carpenter et al., 1998 and Matteson and Jenkins, 2007 found that managing animal
and crop wastes from agriculture poses a significant environmental burden that leads to
pollution of ground and surface waters. These wastes as well as other by-products are usable
resources for pyrolysis related bioenergy. Not only can energy be obtained in the process of
charring, but the volume and especially weight of the waste material is significantly reduced,
which is an important aspect. Similar opportunities exist for green urban wastes or certain
clean industrial wastes such as those from paper mills (Demirbas, 2002). At times, many of
these waste or organic by-products offer economic opportunities, with a significant reliable
source of feedstock generated at a single point location as suggested by Matteson and
Jenkins, 2007. Costs and revenues associated with accepting wastes and by-products are,
however, subject to market development and are difficult to predict. In addition, appropriate
management of organic wastes can help in the mitigation of climate change indirectly by:
8
Decreasing methane emissions from landfill;
Reducing industrial energy use and emissions due to recycling and waste reduction;
Recovering energy from waste;
Enhancing C sequestration in forests due to decreased demand for virgin paper;
Decreasing energy used in long-distance transport of waste (Ackerman, 2000).
Pathogens that may pose challenges to direct soil application of animal manures
(Bicudo and Goyal, 2003) or sewage sludge (Westrell et al., 2004) are removed by pyrolysis,
which typically operates above 350°C and is thus a valuable alternative to direct soil
application. Contents of heavy metals can be a concern in sewage sludge and some specific
industrial wastes, and should be avoided. Due to the longevity of biochar in soil,
accumulation of heavy metals by repeated and regular applications over long periods of time
that can occur for other soil additions may not occur with biochar.
2.1.3 Energy production from biochar
Capturing energy during biochar production and, conversely, using the biochar
generated during pyrolysis bioenergy production as a soil amendment is mutually beneficial
for securing the production base for generating the biomass, as well as for reducing overall
emissions. Adding biochar to soil instead of using it as a fuel does, indeed, reduce the
energy efficiency of pyrolysis bioenergy production. However, the emission reductions
associated with biochar additions to soil appear to be greater than the fossil fuel offset in its
use as fuel as suggested by Gaunt and Lehmann, 2008. This appears to be an appropriate
approach for bioenergy as a whole. In fact, bioenergy, in general, and pyrolysis, in
particular, may contribute significantly to securing a future supply of green energy.
However, it will, most likely, not be able to solve the energy crises and satisfy rising global
demand for energy on its own. In regions that rely on biomass energy, as is the case for most
of rural Africa as well as large areas in Asia and Latin America, pyrolysis bioenergy
provides opportunities for more efficient energy production than wood burning, said
Demirbas, 2004. It also widens the options for the types of biomass that can be used for
generating energy, going beyond wood to include, for example, crop residues.
9
2.1.4 Biochar as a tool for climate change mitigation
Lehmann et al., 2006 proposed the adding biochar to soils which has been described
as a means of sequestering atmospheric carbon dioxide. For this to represent true
sequestration, two requirements have to be met. First, plants have to be grown at the same
rate as they are being charred because the actual step from atmospheric CO2 to an organic C
form is delivered by photosynthesis in plants. Yet, plant biomass that is formed on an
annual basis typically decomposes rapidly.
Fig 2.2 The global carbon cycle (Sabine et al., 2004)
This decomposition releases the CO2 that was fixed by the plants back to the
atmosphere. In contrast, transforming this biomass into biochar that decomposes much
more slowly diverts C from the rapid biological cycle into a much slower biochar cycle
(Lehmann, 2007). Second, the biochar needs to be truly more stable than the biomass from
which it was formed. Several approaches have been taken to provide first estimates of the
large-scale potential of biochar sequestration to reduce atmospheric CO2, which needs to be
vetted against economic and ecological constraints and extended to include a full emission
balance Such emission balances require a comparison to a baseline scenario, showing what
10
emissions have been reduced by changing to a system that utilizes biochar sequestration.
Until more detailed studies based on concrete locations reach the information density
required to extrapolate to the global scale, a simple comparison between global C fluxes
may be to suffice to demonstrate the potential of biochar sequestration. Almost four times
more organic C is stored in the Earth‟s soils than in atmospheric CO2. And every 14 years,
the entire atmospheric CO2 has cycled once through the biosphere. Furthermore, the annual
uptake of CO2 by plants is eight times greater than today‟s anthropogenic CO2 emissions.
This means that large amounts of CO2 are cycling between atmosphere and plants on an
annual basis and most of the world‟s organic C is already stored in soil. Diverting only a
small proportion of this large amount of cycling C into a biochar cycle would make a large
difference to atmospheric CO2 concentrations, but very little difference to the global soil C
storage. Diverting merely 1 per cent of annual net plant uptake into biochar would mitigate
almost 10 per cent of current anthropogenic C emissions.
2.2 BIO“CHAR” AND “CHAR”COAL - similarities and differences
Harris, 1999 quoted that the biochar production process mirrors that of charcoal
which can be considered as one of the oldest invented industrial technologies of Homo
sapiens. However, it can be differentiated from charcoal and other similar materials by
considering the fact that biochar is synthesized completely with the intention of being
applied to the soil for improving the soil fertility, carbon sequestration and other uses. The
burning of biomass in fire will create ash mainly consisting of minerals like calcium,
magnesium or other inorganic carbonates in contrast to the organic carbon rich biochar.
Moreover, only a limited amount of vegetation will be burnt in the conditions of limited
oxygen supply, thus allowing a portion of it to remain as char. The exciting property of
biochar is that, the organic portion has very high carbon content. This comprises mainly of
six C atom rings of the aromatic compounds linked together without oxygen or hydrogen,
also well known to be present as the atoms of living organic matter. The basic difference
arises from the fact that it would have been called as graphite if these aromatic rings were
perfectly arranged and stacked into sheets. But the chances of graphite formation under the
char producing conditions are really rare. Schmidt and Noack in 2000 investigated the
biochar- type materials and concluded that the full characterization is away from feasibility
11
due to its complexity and variability. While the crystal structure of graphite was
characterised by John D. Bernal in the 1920s, biochar was successfully investigated by
Rosalind Franklin in 1950.
2.3 PHYSICAL PROPERTIES OF BIOCHAR
The physical properties of biochar determine its function as a good tool for
environmental management. They have a direct and indirect influence on the soil systems.
Brady and Weil, 2008 brought out the significant differences in the physical properties of
soil depending upon the different mineral and organic matter contained within it. Thus the
presence of biochar in such a soil system may influence the depth, texture, structure,
porosity and consistency by changing the bulk surface area, pore and particle size
distribution along with its density and packing. Thus these factors might play a very
important role in the plant growth due to the fact that the availability of air and water
around the root zone is influenced by the soil. The proportion of inorganic components also
has implications in the physical structure. The physical properties of Biochar of any given
biomass feedstock including heating rate, highest treatment temperature (HTT), pressure,
reaction residence time, reaction vessel, pre-treatment including the drying, comminution,
chemical activation, the flow rates of ancillary inputs such as nitrogen, carbon dioxide, air,
steam, etc and post treatment such as crushing, sieving and activation. Though the above
parameters contribute to the final Biochar structure, the HTT is expected to be the most
important factor studied because the fundamental physical changes are all temperature
dependent. But the temperature ranges vary with the feedstock used. Antal and Groli, 2003
reported that heating rates and pressures are expected to be the second greatest influence
since they affect the physical mass transfer of volatiles evolving at the given temperatures.
Lua et al., (2004) evaluated the relative importance of temperature, hold time, nitrogen flow
rate and heating rate during pyrolysis by assessing the standard deviations and coefficients
of variation of several physical parameters. They found that the pyrolysis temperature to
have the most significant effect, followed by the pyrolysis heating rate. The N2 flow rate and
the hold time showed the lease effects. On the other hand, BET surface areas of olive kernel
biochars measured by Zabanitou et al., (2008). Biochar is normally laced together with
macro-cracks, that may be associated with both feedstock properties and the rate at which
12
carbonization is carried out said Byrne and Nagle, 1997.Wood biochar is generally broken
and cracked as a result of shrinkage stresses developed for the reason that surface of the
material decomposes quicker than its interior. Brown et al., 2006 concluded that high-
temperature (1000°C) surface area is controlled generally by low-temperature (<450°C)
cracking and high-temperature microstructural rearrangement. By means of
experimentation, they discovered the cracks formed to be too large and too numerous to be
sealed off by microstructural rearrangement at greater carbonization. Byrne and Nagle
(1997) have designed preparation techniques for wood feedstocks based on its fundamental
characteristics, such as density and strength, under which C can be produced for advanced
applications.
2.3.1 Molecular structure of biochar and its influence on morphology
The primary molecular framework of biochar creates both its surface area and
porosity. Carbonaceous solid materials similar to coals, charcoals, cokes, etc. contain
crystalline particles in the order of nanometres in diameter, composed of graphite-like layers
prepared turbostratically (Biscoe and Warren, 1942). The biochar structure, determined by
X-ray diffraction, is actually amorphous in nature, but contains some local crystalline
structure (Qadeer et al., 1994) of remarkably conjugated aromatic compounds. Crystalline
areas are often visualized as stacks of flat aromatic sheets crosslinked in a haphazard
manner as suggested by Bansal et al., 1988. Equivalent to graphite, there're good conductors
no matter their small dimensions. Thus, the microcrystallites are often referred to as the
conducting phase. The other non-conducting components that finish the biochar C matrix
are the aromatic-aliphatic organic compounds of complicated structure the mineral
compounds. This is often complemented with the voids, formed as pores like macro-, meso-
and micropores, cracks and morphologies of cellular biomass origin. Pyrolysis processing of
biomass enlarges the crystallites and brings about more order. This effect increases with
HTT. Lua et al., (2004) demonstrated, for instance, that enhancing the pyrolysis
temperature from 250°C to 500°C boosts the BET surface area due to the increasing
evolution of volatiles from pistachio-nut shells, leading to enhanced pore development in
biochars. Rosalind Franklin first demonstrated that some kinds of non-graphitic C are
converted to graphitic C during pyrolysis, presenting crystallographic order in the third
13
direction. The pyrolysis of most biomass C will finally yield graphite when heated to
3500°C; however, some feedstocks graphitize at HTTs of less than 2000°C (Setton et al.,
2002). The surface of non-graphitized C, just like wood biochars, consists of both the faces
and edges of ordered sheets (Boehm et al., 2002). The turbostratic linkage of these
crystallites leaves random interstices.
Fig 2.3 Ideal biochar structure development with highest treatment temperature
(Paul Munro et al., 2009)
(a) Increased proportion of aromatic C, highly disordered in amorphous mass (b) growing sheets
of conjugated aromatic carbon, turbostratically arranged (c) structure becomes graphitic with
order in the third dimension
A more possible cause of micropores comes from voids (holes) within hexagonal
planes was discovered by Bourke et al, 2007. Heteroatoms, in particular oxygen (O2), are
predominantly located on the edges of ordered sheets as components of various functional
groups. The interplanar distance of graphite (0.335 nm) is probably not achieved under
normal pyrolysis conditions (<1000°C) due to formation of O2 functional groups at the sheet
edges, which through steric or electronic effects avoid the close packing of the sheets (Laine
and Yunes, 1992). Pores, of whatever origin, may become filled with tars and other
amorphous decomposition products, which may partially block the microporosity created
(Bansal et al., 1988). The tars produced from thermal biomass C decomposition impede the
14
continuity of pores at low temperatures and these pores become increasingly accessible since
the temperatures increase and tar components are volatilized. Mineral matter may also
become occluded while in the pores or exposed at the surface of the biochar particles.
2.3.2 Structural complexity loss during thermo-chemical conversion
Under certain processing conditions, many research groups have reported radical
loss of structural complexity in biochar products, which is often explained by plastic
deformation, melt, fusion or sintering. High heating rates, increased pressure, high HTT,
high ash content and long retention times have all been held responsible for the loss of
surface area and porosity in biochar products. Mirasol et al., (1993) investigated the
carbonization of eucalyptus kraft lignin at different temperatures and characterized the
structure of the microporous biochar product. They found that partial fusion and swelling in
the carbonization stage was related to the ash content in the starting material.
Therefore, they developed a new pre-treatment method to eliminate the inorganic
matter by washing with diluted acidic solutions just before carbonization in order to prevent
this loss of structural complexity. High ash content is often a significant contributing factor
to loss of structure. However, even in very low ash materials, such as the hazelnut shell
(Aygun et al., 2003), some thermoplastic properties can be exhibited. A deficiency of
structure in biochars made at high heating rates has been explained by the melting of the cell
structure and by plastic transformations. Cetin et al., (2004) reported that at low heating
rates (20°C sec-1), the natural porosity of pine sawdust will allow for a volatile release with
the occurrence of no major morphological changes. However, at high heating rates like
500°C sec-1, the cell structure is ruined by devolatilization as suggested by Cetin et al., 2004.
Biagini and Tognotti (2003) documented the same phenomenon in their experimentation
and noted the re-solidification of the solid structure and formation of more compact biochar
particles. They also stated that melting and swelling are more pronounced for biomass
species that contain higher levels of volatile matter. High HTT, coinciding with the ash
melting points of the various biomass feedstocks, also causes decreases in structural
complexity. For a pistachio-nut feedstock, Lua et al., (2004) found that increasing HTT
from 500°C to 800°C progressively decreased the BET surface area. They attributed this to
the decomposition and softening of some volatile fractions to form an intermediate melt in
the biochar structure (Lua et al., 2004). Brown et al., (2006) reported similar findings with
15
biochars made from pine. At heating rates of 30°C hr-1 and 200°C hr-1, surface areas were
found to be markedly lower at a HTT of 1000°C compared with those observed at lower
final temperatures (Brown et al., 2006). Increasing the reaction retention time has also been
demonstrated to cause deformation in the physical structure; however, this may be the result
of heat transfer rates being too slow for the solid to reach a high HTT. Guo and Lua (1998)
found that at 900°C, the high surface area of oil palm stone biochar deteriorated with
increasing reaction retention time. They attributed this to both the sintering effect, followed
by a shrinkage of the biochar, and realignment of the biochar structure, which resulted in
reduced pores. Using reactor configuration, they found that maximum surface areas were
obtained when oil palm stones were pyrolysed at 800°C with a retention time of three hours
(Guo and Lua, 1998). Work by Lewis, 2000 with redwood has demonstrated, however, that
the pores do not collapse as suggested by Guo and Lua 1998. Lewis 2000 presents evidence
against such collapse by showing that the pores can be reopened by a CO2 activation process
in a manner that allows N2-accessible surface area to increase from 2 m2 g-1 to 540 m2 g-1.
This implies that the pores continue to be present and that they are only closed off at higher
temperatures (Lewis, 2000). The fusion of multiple particles, which did not occur under
atmospheric conditions, has also been reported at pressures of 10 bar to 20 bar (Cetin et al.,
2004). Cetin et al., (2004) found that at these pressures, eucalyptus sawdust particles melt
and fuse, losing their own distinctions. Similar results were obtained at atmospheric
pressures for the fast heating rate of ~500°C min-1. A number of particles fused together can
form a hollow and smooth-surfaced particle (Cetin et al., 2004).
2.3.3 Modification of physical structure of biochar
Approaches for enhancing surface areas and porosity are frequently looked into,
driven by numerous commercial programs of activated carbons that need large sorptive
capabilities. Although, as already defined, process conditions for example HTT, heating
rate, etc. influence biochar‟s physical properties, commercial possible internal surface areas
are actually produced in high C-that includes biochar precursors through physical or
chemical activation. Physical activation, that's completed most oftenly in industry, is
acquired once the initial pyrolysis responses, occurring in a inert atmosphere at moderate
temperatures , are supported into another stage where the resulting biochars are subjected to
a partial gasification in the greater temperature with oxidizing gases for example steam,
16
CO2, air or a mixture of these. This synthesises products with well toned and accessible
internal pores (Bansal et al., 1988). The activation of biochar with CO2 involves a C-CO2
reaction. This leads to eliminating C atoms or burn-off, in that way adding to the
introduction of a porous structure. Based on Reinoso et al., (1992), CO2 can open closed
pores furthermore to widen existing pores using the activation, growing the simplicity access
within the small pores for that molecules in the adsorbate. Both, area along with the
property of porosity are substantially affected with the conditions of CO2 activation, the
extent which is dependent upon the level of smoothness within the precursors (Zhang et al.,
2004). Steam is recommended to obtain a double role: it encourages both escaping volatiles
with partial devolatilization and improves crystalline C formation (Alaya et al., 2000). The
physical and adsorptive qualities of biochars rely on activation times and quantity of steam
that is helpful for activation. BET surface areas of triggered olive kernel carbons were seen
to become growing with activation times and temperature within the minimum value of
1339 m2 g-1 at 1 hour and 800°C to no more than 3049 m2 g-1 at four several hours and
900°C (Stavropoulos, 2005). Zhang et al., (2004) confirmed these trends for biochars
produced from oak, maize hulls and maize stover deposits. They found BET surface areas
of all activated carbons acquired at 700°C were under those obtained at 800°C (Zhang et al.,
2004).With physical activation for a few hours, surface areas were elevated with activation
time (Zhang et al., 2004). This expansion in area with elevated activation time may also be
known to utilise the growing burn-off (Zabaniotou et al., 2008). Chemical activation entails
adding materials for example zinc salts or phosphoric acidity for those C precursors. KOH
was adopted for planning activated carbons with abnormally high surface areas known to as
„super active‟ carbons (Rouquerol et al., 1999). Throughout activation, potassium is
intercalated and forces apart the lamellae within the crystallites define the C structure. After
cleaning the samples, K is slowly removed, departing free interlayer space that adds for that
porosity within the product (Marsh et al., 1984). Precursor material qualities for example
microcrystalline structure, reactivity and pore convenience are which might influence final
results of these treatments. Possibly the best recyclables for KOH activation are those getting
small-sized crystallites, medium reactivity and high accessibility for that internal pore
structure (Stavropoulos, 2005). Chemical activation offers many perks since it is completed
a stride, mixing carbonization and activation, is finished at lower temperatures and,
17
therefore, leads to greater advancement of porous structure. Chemical activation techniques
are not, however, as common, possibly because of the opportunity of creating secondary
atmosphere pollution throughout disposal. Reactor type has in addition proven by getting
an relation to its the physical surface and porosity of chars. Gonzalez et al., (1997)
completed their analysis of CO2 activation with both vertical and horizontal furnaces and
concluded that the horizontal furnace is beneficial for micropore development. Biochars
triggered by fast pyrolysis reactors have different physical qualities from those made under
slow pyrolysis conditions. The most effective areas of switchgrass biochars made under fast
pyrolysis conditions were seen to become low, typically between 7.7 m2 g-1 and 7.9 m2 g-1
(Boateng et al., 2007). Further examples which are typical for fast pyrolysis, due to prime
heating rates within the rather small contaminants, were created getting a fluidized bed
reactor operating at roughly 500°C, with inert N2 because the fluidizing agent (Zhang et al.,
2004). Oak, maize spend and maize stover biochars shown low surface areas of 92 m2 g-1,
48m2 g-1, and 38m2 g-1, and total pore volumes of .1458 cm3 g-1, .0581 cm3 g-1 and .0538 cm3
g-1, correspondingly (Zhang et al., 2004). Gas pressure with the pyrolysis responses provides
a relation to the dwelling within the biochar items. For instance, biochar contaminants
which have been produced at 5 bar pyrolysis pressure in the heating rate of 500°C sec-1 to
950°C were which might possess bigger tooth decay with thinner cell walls than biochars
which have been produced at atmospheric pressure. This effect was elevated at 20 bar (Cetin
et al., 2004). The pyrolysis system, particularly the activation method, comes with a relation
to the physical character of biochars.
2.3.4 Nano-porosity of biochar
The pore-size distribution of activated carbons is certainly recognized as an
important reason for industrial application. It is plausible that this physical feature of
biochars may also be of importance to their behaviour in soil processes. The connection
between total surface area and pore-size distribution is logical. As the HTT raises more
structured regular spacing between the planes results. Interplanar distances also decrease
with the increased ordering and organization of molecules, all of which result in larger
surface areas per volume. Micropores contribute most to the surface area of biochars and are
responsible for the high adsorptive capacities for molecules of small dimensions such as
gases and common solvents (Rouquerol et al., 1999). It ought to be noted that soil scientists
18
refer to all pores <200nm in diameter as micro-pores; however, the total pore volume of the
biochar will be divided into micropores, and macropores (Rouquerol et al., 1999), as this
provides a level of differentiation needed to discuss molecular and structural effects.
The pore sizes distributed in the micropore range make the greatest contribution to
total surface area. The progression of microporosity with higher temperatures and longer
retention times has been demonstrated by numerous research groups. Elevated temperatures
deliver the activation energies and longer retentions allow the time for the reactions to
achieve completion, leading to greater degrees of order in the structures. For example, the
ratios of micropore volume to total pore volume of CO2-activated carbons produced from
maize hulls generated at 700°C were lower than those of activated carbons prepared at
800°C (Zhang et al., 2004).
Fig 2.4 Relationship between biochar surface area and micropore volume,
(Adriana Downie et al., 2009)
The analysis of gas adsorption isotherms is the typical methodology used for
assessing surface areas of C materials. The array of adsorbents, degassing regimes,
temperatures, pressures and algorithms used makes comparison of literature values
demanding. Interestingly, some general developments can be observed through compiling
19
literature values. The surface area of biochars generally increases with increasing HTT until
it reaches the temperature at which deformation occurs, resulting in subsequent decreases in
surface area. A typical example is provided by Brown et al., (2006), who produced biochar
from pine in a laboratory oven purged with N2 at a range of final temperatures varying from
450°C to 1000°C, and heating rates varying from 30°C hr-1 to 1000°C hr-1. Brown et al.,
found that independent of heating rate, maximum surface area, as measured by BET (N2),
was realized at a final temperature of 750°C. At the lowest HTT (i.e. 450°C), all of the
surface areas were found to be less than 10m2 g-1, while those produced at intermediate
temperatures of 600°C to 750°C had a surface area of approximately 400m2 g-1 (Brown et al.,
2006).
Under some conditions, a high temperature causes micropores to widen because it
destroys the walls between adjacent pores, resulting in the enlargement of pores (Zhang et
al., 2004). This leads to a decrease in the fraction of volume found in the micropore range
and an increase in the total pore volume. In samples of maize hulls and maize stover, Zhang
et al., (2004) found microporosity to be appreciably greater after one hour of physical
activation than after two hours. They proposed that the rate of pore formation exceeded that
of destruction due to pore enlargement and collapse at the earlier stage and vice versa at the
later stage (Zhang et al., 2004). Heating rates also determine the extent of micropore
formation. One example was given by Cetin et al., (2004), who found that biochars
developed at atmospheric pressure under low heating rates mainly consisted of micropores,
whereas those prepared at high heating rates were largely composed of macropores as a
consequence of melting (Cetin et al., 2004). Mesopores are also present in biochar materials.
These pores are of importance to many liquid-solid adsorption processes. For example,
pistachio-nut shells have a mixture of micropores and mesopores, with micropores
dominating, indicating that these activated carbons can be used for both gas and liquid
adsorption applications (Lua et al., 2004).
2.3.5 Influence of macro porosity on biochar
In the past, when biochars and activated carbons were evaluated mainly for their role
as adsorbents, macropores were thought to be only important as feeder pores for the
transport of adsorbate molecules to the meso- and micro-pores (Wildman and Derbyshire,
1991). Nevertheless, macro-pores are extremely relevant to vital soil functions such as
20
aeration and hydrology. Macropores are also relevant to the movement of roots through soil
and as habitats for a vast variety of soil microbes. Although micropore surface areas are
significantly larger than macropore surface areas in biochars, macropore volumes can be
larger than micropore volumes. It is possible that these broader volumes could cause greater
functionality in soils than narrow surface areas.
Surface area (m²/g) Volume (cm³/g)
Micropores 750-1360 0.2-0.5
Macropores 51-138 0.6-1.0
As anticipated from the regular size and arrangement of plant cells in most biomass
from which biochars are derived, the macropore size distribution is composed of discrete
groups of pores sizes rather than a continuum (Wildman and Thompson, 1991). To put this
in perspective with typical soil particles, these discrete groups of pore diameters observed in
this sample of ~5µm to 10µm, and ~100µm compare to incredibly fine sand or silt particle
sizes, and fine sand particle sizes, respectively.
Fig 2.5 Scanning electron microscope image showing macro porosity of a wood-derived
biochar produced by „slow‟ pyrolysis (Alan Crosky et al., 2009)
Another consideration is the type of microbial communities that utilize soil pores as
a preferred habitat. Microbial cells typically range in size from 0.5µm to 5µm, and consist
predominantly of bacteria, fungi, actinomycetes and lichens (Lal, 2006). Algae are 2µm to
20µm (Lal, 2006). The macropores present in biochars therefore provide suitable dimensions
for clusters of microorganisms to inhabit.
21
Soil structure is defined in terms of peds, which are arrangements of primary soil
particles, and soil porosity is often defined as the openness between these peds. The
interaction and stacking of heterogenous agglomerated biochar particles and peds in the soil
will have a direct impact upon the bulk soil structure.
2.3.6 Particle size distribution of biochar
The particle sizes of the biochar resulting from the pyrolysis of organic material are
highly reliant upon the nature of the original material. As a result of both shrinkage and
attrition during pyrolysis, particle sizes of the organic matter feedstock are likely to be
higher than the resultant biochar. In some cases, particles may agglomerate; therefore,
increased particle sizes are found (Cetin et al., 2004). Relying after the mechanical intensity
of the pyrolysis technology employed certain amount of attrition of the biomass particles
will occur during processing. This is often particularly true in the post-handling of the
material as the biochar is really a lot more friable than the original biomass.
Biochar produced by sawdust and wood chips was prepared with unique pre-
treatments, producing contrasting particle sizes. It can also be seen that as the pyrolysis
HTT increased (450°C to 500°C to 700°C), the particle sizes tended to lessen. This can be
explained by the decreasing tensile strength of the material as it is more completely reacted,
producing less resistance to attrition during processing. Depending upon the technology
employed, biomass feedstock is prepared in alternative ways. The faster the heating rate
required, the smaller the feedstock particles need to be to aid the heat and mass transfer of
the pyrolysis reactions. Traditional batch processes enables weeks for the heat and mass
transfer of the process to come about and, hence, receive complete branches and logs. The
investigation by Cetin et al., (2004), for example, on the first-step pyrolysis of a two-stage
gasification process used biomass fuel particles with sizes between 50µm and 2000µm based
upon the reactor type and techniques used. This small size is required to achieve the high
heating rates, ranging from 500°C sec-1 to extremely high heating rates of (~~105°C sec-1)
and short residence times (Cetin et al., 2004). If larger particles are used, it is possible that
the reactions will be limited by the heat transfer into the particles and the mass transfer of
volatiles out of the biochar. In a research of the pyrolysis of oil palm stones, it was found
that the Biochar yields were affected by both the particle size of the stones and the
maximum pyrolysis temperature (Shamsuddin and Williams, 1992).
22
Longer retention times would perhaps have overcome the influence of the bigger
particle sizes. A rise in linear shrinkage of the particles being pyrolysed can be seen to take
place in addition to the loss of volatile matter (Freitas et al., 1997). For example, as
pyrolysis temperatures increase from 200°C to 1000°C, the linear shrinkage of particles was
demonstrated to increase from 0 to 20 per cent for peat biochars (Freitas et al., 1997). Cetin
et al., (2004) demonstrated that increasing the pyrolysis pressure (from atmospheric to 5, 10
and 20 bars) leads to the formation of larger biochar particles. They included this as
swelling, as well as the formation of particle clusters, resulting from melting and subsequent
fusion of particles (Cetin et al., 2004).
2.3.7 Biochar density
Two types of density of biochars can be studied: the solid density and the bulk or
apparent density. Solid density is the density on a molecular level, based on the degree of
packing of the C structure. Bulk density is that of the material comprising multiple particles
and includes the macroporosity within each particle and the inter-particle voids. Often, an
increase in solid density is accompanied by a decrease in apparent densities as porosity
builds up during pyrolysis. The relationship between the two types of densities was
demonstrated by Guo and Lua (1998), who cited that apparent densities increased with the
progression of porosities from 8.3 to 24 per cent at pyrolysis temperatures up to 800°C (Guo
and Lua, 1998). However, when the temperature increased to 900°C, the apparent density
of the biochar increased and the porosity decreased owing to sintering. This inverse
relationship between solid and apparent density was also demonstrated by Pastor- Villegas
et al., (2006) for eucalyptus biochar manufactured in a continuous furnace having both the
lowest values of apparent density and the highest solid density value. The loss of volatile
and condensable compounds from the unorganized phase of the biochars and the
concomitant relative increase in the organized phase formed by graphite-like crystallites
leads to the increase in solid density of the biochars compared with their feedstocks
(Emmerich et al., 1987). The highest density of C in biochars has been reported to lie
between 2.0g cm-3 and 2.1g cm-3 based on X-ray measurements (Emmett, 1948). Such values
are only slightly below the density of solid graphite of 2.25g cm-3. Most solid densities of
biochar, however, are greatly lower than that of graphite because of residual porosity and
their turbostratic structure (Oberlin, 2002), with typical values around 1.5 g cm-3 to 1.7g cm-3
23
(Jankowska et al., 1991). Lower values such as that of a pine wood biochar collected from a
natural fire site at 1.47g cm-3 (Brown et al., 2006) are also common. Biochars activated to
produce microporosity for the adsorption of gases are denser than for those optimized to
produce meso- and macro-porosity for the purification of liquids (Pan and van Staden,
1998). The density of the biochars depends upon the nature of the starting material and the
pyrolysis process (Pandolfo et al., 1994). Solid density of biochar increases with increasing
process temperature and longer heating residence times, in accordance with the conversion
of low-density disordered C to higher-density turbostratic C (Byrne et al., 2002). Lower
amounts of volatiles, which have lower molecular weights than fixed C, and lower ash
contents result in higher solid density in biochars (Jankowska et al., 1991). However, Brown
et al., (2006) showed that density is independent of heating rate, and found a simple and
direct dependency of density upon final pyrolysis temperature. Thus, they deduced that the
He-based solid density may serve as an approximate indicator of the highest temperature
experienced by any wood biochar, no matter the exact thermal history (Brown et al., 2006).
This idea may provide a useful tool for characterizing charring conditions in order to
understand the production of biochars in archaeological soil such as Terra Preta and
possibly provide information about their creation. Bulk density is also an important physical
feature of biochars. Pastor-Villegas et al., (2006) found that the bulk densities of biochars
made from different types of woods processed in different types of traditional kilns ranged
from 0.30 g cm-3 to 0.43g cm-3. Bulk density values given in the literature for activated
carbons used for gas adsorption range from 0.40g cm-3 to 0.50g cm-3, while for activated
carbons used for decolourization, the range is 0.25g cm-3 to 0.75g cm-3 (Rodríguez-Reinoso,
1997). Byrne and Nagle (1997) established a linear relationship between the bulk densities of
wood and biochar made from the same material, which spans a range of species. They
identified that for wood pyrolysed at a heating rate of 15°C hr-1 to a HTT of 900°C, the
carbonized wood had 82 % of the bulk density of the precursor wood.
2.3.8 Mechanical Strength
The mechanical strength of biochar is associated with its solid density. Therefore, the
amplified molecular order of pyrolysed biomass provides it with a superior mechanical
strength than the biomass feedstock from which it was extracted. For example, Byrne and
Nagle (1997) reported that tulip poplar wood carbonized at a HTT of 1550°C had a 28 %
24
increase in strength. Mechanical strength is a characteristic used for defining the quality of
activated carbon as it relates to its ability to endure wear and tear during use. Agricultural
wastes, such as nut shells and fruit stones are of interest as activated carbons because of their
high mechanical strength and hardness. These properties can be explained by high lignin
and low ash contents (Aygun et al., 2003).
2.4 PRACTICAL APPLICATIONS OF BIOCHAR
2.4.1 The role of mixtures in biochar
Although biochar can be used to improve soils over the long term, it is not a long-term
source of nutrients. Depending on the feedstock and pyrolysis conditions, biochar can
contain varying amounts of ash which can provide nutrients for plant growth in the short
term. A significant advantage of biochar when added to soil is its recalcitrance and its ability
to retain nutrients which are present in soil, or added as fertilizer or decomposing organic
matter, over the long term. Biochar has also been shown to support soil microorganisms
through its highly porous structure which provides protection from predators and access to
water and nutrients (Thies and Rillig, 2009).
2.4.2 Biochar as an ingredient in Bokashi
Bokashi is a traditional Japanese soil amendment that is now used in various places
around the world. Although there are various methods for making it can be made by
combining microbes (termed “effective microorganisms”, or “EM”), molasses, biochar,
bran and animal manure with water (Reap Canada). Bokashi can be made either
anaerobically or partially aerobically, similarly to normal composting, or using a
combination of both. Some researchers found better yield of peanuts and greater numbers
and total biomass of nitrogen-fixing nodules on peanut roots when Bokashi was used
instead of synthetic fertilizer (Yan and Xu, 2002). However, Formowitz et al., (2007)
showed that the “effective microorganisms” were not likely responsible for beneficial effects
of the material on plant growth, and similar observations were made in field crops grown
over four years in central Europe (Mayer et al., 2008). However, the Bokashi made by
Formowitz et al., (2007) did not include biochar, and the reports by Yan and Xu (2002) and
Mayer et al., (2008) do not provide details on the materials used to make Bokashi and
whether or not biochar was used. Thus while Bokashi was found to provide plant growth
benefits in these studies, and these benefits could not be attributed to EM, there are no
25
reports in the literature about the role of biochar in Bokashi mixtures. Nevertheless, biochar-
containing Bokashi has been used successfully for over 15 years for growing vegetables in
Costa Rica and used by farmers in the Philippines, as outlined in Jensen et al., (2006).
2.4.3 Biochar as a medium for fungal inoculants
Peat is commonly used as a carrier for rhizobial inoculant. Rhizobia are bacteria used
to promote proper nodulation and biological nitrogen fixation in legume crops. However,
peat is not available in all regions and is arguably not a renewable resource since its
formation takes a very long time. Biochar can also be used as a carrier for microbial
inoculants. Stephens and Rask (2000) indicate that carriers for microbial inoculants should,
among other factors, support the growth of the target organisms, have high moisture
holding and retention capacity, and are environmentally safe. Properly produced biochar
has these characteristics. When testing the survival rate of rhizobial inoculum, charcoal
performed similarly to peat, oil and other carriers (Kremer and Peterson, 1983). Similar
results were found by Sparrow and Ham (1983), where rhizobial inoculant survival rates
were greater in peat, charcoal and vermiculite than in peanut hulls or corn cobs.
2.4.4 Biochar as a “bulking agent” in compost
Studies show that the composting process can be accelerated by adding biochar to
poultry manure (Steiner et al., 2010). Maximum temperatures of the compost were reached
faster when biochar was applied (Steiner et al., 2010) and the degree of humification of the
resulting compost was greater (Dias et al., 2009) with biochar application. Steiner et al.,
(2010) assumed that biochar did not decompose during the 42 day trial, and found that the
loss of poultry manure biomass was not different in cases where biochar was added as 0, 5
or 20% of the mixture on a dry weight basis. Total nitrogen losses over 42 days of
composting sewage sludge were reduced by 64% by adding 9% biochar to the sludge (Hua et
al., 2009) as opposed to a control not receiving biochar. Adding 20% biochar to poultry
litter reduced ammonia emissions by 64% over 42 days (Steiner et al., 2010) compared to a
non-amended control. Dias et al., (2009) found that N losses when using biochar as a
bulking agent were lower than when coffee husks were used, but greater than when sawdust
was used as a bulking agent. These results are promising, especially considering the
recalcitrance of biochar in soil compared to other bulking agents, and the potential for
biochar to reduce odours in compost and retain inorganic N against leaching, after soil
26
application. Indeed Steiner et al., (2007) found greater yield of maize and sorghum on an
acid soil after four years when biochar was applied with compost as opposed to being
applied with synthetic fertilizer.
2.4.5 Biochar and manure
In a column study, Laird et al., (2010) found that the addition of biochar to manure
amended soil reduced the leaching of nutrients. One method for mixing biochar with
manure is to feed biochar directly to animals. It has been known for a long time that adding
charcoal or various zeolite-like materials to the feed of livestock improves their ability to
utilize protein and assimilate protein-derived nitrogen from poor-quality fodder, most
probably via control of loss of ammonia that is subsequently used for microbial protein
synthesis in the rumen. Van et al., (2006) showed that growth rate was 20% greater, and
final animal weight was 5% greater when goats fed tannin-rich Acacia sp. fodder were also
fed less than 1 g bamboo charcoal per kg animal weight per day. This trial lasted 12 weeks.
As suggested by Blackwell et al., (2009) and McHenry (2010), biochar can thus be
“ecologically delivered” to soil as part of the animals‟ manure. A technical bulletin from the
Food and Fertilizer Technology Center in Taiwan also proposes feeding bamboo charcoal
to cattle, pigs and poultry to reduce smells in barns as well as providing other benefits to
animal health.
2.4.6 Land reclamation and soil remediation
Land reclamation generally relates to the improvement of soils degraded by human
activities, for example construction and certain forms of agriculture. Soil remediation refers
to the process of removing, neutralizing or reducing the toxicity of certain compounds, often
left by human activities such as mining and industry. Biochar can potentially facilitate the
revegetation of degraded soils through several mechanisms, and sorb a variety of
compounds in soil.
2.4.7 Biochar as a tool for revegetation
Soil may become degraded due to human activities such as mining and industrial
activities as well as the use of certain pesticides in agriculture. Some biochar materials have
a high pH and can act as liming agents, to increase soil pH (Major et al., 2010). In cases
where organic matter and clay levels in soil are low and soil is coarse textured moisture
27
retention may help the establishment of vegetation. Biochar can help with this. Nutrient
leaching can also be reduced by biochar application to soil.
2.4.8 Biochar and the sorption of heavy metals
Biochar has been found to sorb a variety of heavy metals, including lead (Pb), arsenic
(As) and cadmium (Cd). A dairy manure biochar made at 350°C sorbed several times more
Pb than AC (Cao et al., 2009). In this case, sorption by biochar was attributed mostly (85%)
to the Pb reacting with ash present in the biochar, and also to direct surface sorption (15%)
on biochar surfaces. Mohan et al., (2007) also worked on the removal of heavy metals in an
aqueous solution by biochars made from pine and oak wood and bark, at 400-450°C. Due to
its greater surface area and pore volume, oak bark biochar outperformed all others and
removed similar amounts of Pb and Cd from solution as did a commercial AC material
(~100% for Pb and ~50% for Cd). Oak bark biochar also removed ~70% of the as in
solution. Other biochars, at pH values in the range of those of most agricultural soils
removed ~5-25% Pb, ~0-10% Cd and ~0-10% as from solution. In another study, soil
amended with 0.1 and 0.5 % (w/w) pine biocharsorbed more phenanthrene than non-
amended soil, although the authors found that the amount of this contaminant sorbed by
biochar varies with the properties of the biochar, soil characteristics and contact time
between biochar and soil (Zhang et al., 2010). Uchimiya et al., (2010) found that adding
broiler litter biochar to soil enhanced the immobilization of a mixture of Pb, Cd and nickel,
and the authors attributed this effect mostly to the rise in pH brought about by the biochar.
In a different study, Uchimiya et al., tested the effect of “natural” organic matter and the
biochar‟s unstable carbon fraction, on heavy metal immobilization by biochar. They found
that these materials improve Cd immobilization by biochar, had no clear effect on
immobilization of Ni, and actually lead to greater mobility of Cu in biochar-amended soil
with very high pH (>9). Both high-ash and low-ash biochars had the ability to reduce the
mobility of Cd, Cu and Ni in this soil, and treating the biochars with phosphoric acid to
increase their negative surface charges improved the biochars‟ immobilization capacity.
2.4.9 Biochar and the sorption of pesticides
Organic contaminants include many agricultural pesticides and industrial contaminants.
Biochar and the ash contained in biochar have a high affinity for sorbing different organic
compounds. Charred organic matter generally sorbs 10 to 1000 times more organic
28
compounds than un-charred organic matter (Smernik, 2009). Indeed, the sorption of many
organic molecules in soils and sediments, including polycyclic aromatic hydrocarbons
(PAH), has been attributed to the presence of biochar or similar materials in these. While
biochar is recalcitrant in soil, many other compounds in soil can also sorb to biochar and
saturate or “block” its surfaces.
Although sorption dynamics are affected by pH and other factors in soil, many studies
have found that adding biochar to soil improved the sorption of pesticides. Cao et al., (2009)
found that biochar made from dairy manure sorbed more atrazine (herbicide) in an aqueous
solution than un-charred manure. Similar results were obtained by Zheng et al., (2010) for
atrazine and simazine, another herbicide. A study where diuron (herbicide) sorption was
compared in biochar amended vs. non-amended soils found that amended soil sorbed more
diuron (Yu et al., 2006). Similarly, Spokas et al., (2009) found that soil to which mixed
wood chip biochar was added sorbed more atrazine and acetochlor (herbicides) than
unamended soil, but organic matter applied to soil at the same rate as biochar would sorb
more of these herbicides than the fast-pyrolysis biochar they tested. In contrast, Wang et al.,
(2010) found that wood biochar sorbed more terbutylazine (herbicide) than biosolids, and
the herbicide was also more strongly sorbed by wood-based biochar than by biosolids, in
soil.
Yu et al., (2009) studied the microbial degradation of insecticides chlorpyrifos and
carbofuran in soil amended with wood-based biochar, and found that their degradation
decreased with increasing amounts of biochar applied, while the uptake of the insecticides
by onion plants also decreased with greater biochar application rates. This indicates that
while the insecticides remained in soil longer, their bioavailability to plants was reduced.
Polycyclic aromatic hydrocarbons (PAH) are potent contaminants which are produced by
fuel burning. Total PAH contents and PAH bioavailability in a contaminated field soil over
60 days was found to be reduced more by biochar than by compost (compared on a volume
basis), although not all treatment comparisons were statistically significant (Beesley et al.,
2010).
29
2.5 CONCLUSION:
The physical properties of biochar affect many of the functional roles that they may
play in environmental management applications. The large variation of physical
characteristics observed in different biochar products means that some will be more effective
than others in certain applications. It is important that the physical characterization of
biochars is undertaken before they are experimentally applied to environmental systems,
and variations in outcomes may be correlated with these features. Although the continued
examination of the influence of feedstocks and processing conditions on the physical
properties of biochars is essential, an important direction for research is to develop an
understanding of how and by what mechanisms these physical characteristics of biochars
influence processes in soils.
30
CONNECTING TEXT
The present study deals with the evaluation of the effect of pyrolysis process on various
lignocellulosic biomasses through response surface method. It also deals with the
comparison of wood and grass biochar based on proximate analysis and density analysis.
The results of the study have been statistically analysed using JMP (SAS), XLSTAT and
other basic statistical tools.
31
CHAPTER III
EVALUATION OF THE EFFECT OF PYROLYSIS PROCESS ON VARIOUS
LIGNOCELLULOSIC BIOMASSES THROUGH RESPONSE SURFACE METHOD
Pavithra Sellaperumal, Vijaya Raghavan and Yvan Gariepy
Department of Bioresource Engineering, McGill University, 21,111 Lakeshore Rd., Sainte-
Anne-de-Bellevue, QC, H9X 3V9, Canada.
Correspondence author:
Pavithra Sellaperumal, Department of Bioresource Engineering, McGill University, QC,
H9X 3V9, CANADA
Email: [email protected]
32
ABSTRACT
Thermo chemical conversion of biomass was carried out to convert the biomass to
biochar through the process known as “pyrolysis”. The independent parameters of the
pyrolysis process were time (min) and temperature (°C). These were analysed using the
central composite uniform precision (on face) design to evaluate their effects on the yield of
biochar through pyrolysis. The resulting regression model indicated that a series of linear
models best described the correlation of temperature change to pyrolysis process. It was
observed that, pine, maple, ebony and bamboo showed greater fit to the model. Balsa
showed only an average fit. Pine was the only wood which had influence from both the
factors of analysis. Both temperature and time significantly influenced the yield (p<0.0001
and p=0.0394 respectively). For maple, ebony, bamboo and balsa only temperature was an
influential factor (p=0.0002, p=0.0001, p=0.0027 and p=0.0073). The model fitted the data
for all the biomasses under study really well. This became evident from values of coefficient
of determination R² for different biomasses were significantly close to 1 except for balsa.
The values were observed to be R²=0.89 for ebony, R²=0.98 for pine, R²=0.80 for bamboo
and R²=0.88 for maple. A low value of R²=0.67 was observed for balsa which proved to be
a reasonable fit. The desirability term for the process was defined and determined. The total
desirability function for all the biomasses together was less, thus when fitted separately, gave
optimum values of temperature and time for the biomass pyrolysis. Further, response
surface plots were drawn to operate it under different experimental conditions. Density
analysis was also done to understand the relationship between the density of the wood
biomass before and after pyrolysis.
Keywords: pyrolysis, regression, maple, pine, balsa, bamboo, ebony, coefficient of
determination, response surface plots, density, desirability.
33
3.1 INTRODUCTION
Biochar and biofuel is co-produced from thermo-chemical transformation of biomass
feedstock. The thermal conversion of biomass, under the complete or partial exclusion of
oxygen, ends up with the production of biochar by-products. Biochar production processes
can easily exploit the majority of urban, agricultural or forestry biomass residues, including
wood chips, corn stover, rice or peanut hulls, tree bark, paper mill sludge, animal manure,
and recycled organics. Under controlled production conditions, the carbon inside the
biomass feedstock is captured in the biochar. Hypothetically, the biochar co-product will
preserve up to 50% of the feedstock carbon inside a porous charcoal structure; and the
remaining 50% of the feedstock carbon are going to be captured as biofuel. While it is
technically impossible to capture 100% of the biomass carbon, since energy is inevitably
used and lost in the production process, the ideal biochar production process can seize
approximately half the biomass carbon in biochar and half as biofuel. Pyrolysis systems
generate biochar largely in the absence of oxygen and most often with an external source of
heat. There are two kinds of pyrolysis systems in use today: fast pyrolysis and slow pyrolysis
systems. Gasification systems produce smaller quantities of biochar in a directly-heated
reaction vessel with air introduced. Biochar production might be optimized in the absence
of oxygen.
3.1.1 PYROLYSIS OF BIOMASS
The pyrolysis process greatly affects the characteristics of biochar and its potential
worth to agriculture in terms of agronomic performance and in carbon sequestration. The
process and process parameters, principally temperature and furnace residence time, are
particularly significant; nevertheless, the process and process conditions also interact with
feedstock type in establishing the nature of the product. These variables together impact
chemical, biological and physical properties, which unfortunately confine the potential
usage for biochar products.
Each and every category of pyrolysis process is characterised by a contrasting
equilibrium between biochar, bio-oil and syngas. The unique ratio in these products can
vary greatly among plants, and may be optimised at a precise installation; however, it is
34
vital that maximising the generation of biochar relative to mass of initial feedstock
(Demirbas, 2006), is always at the cost of operational energy in the liquid or gaseous form.
Although a greenhouse gas mitigation strategy may prefer maximising the biochar product
(Gaunt et al., 2008), the balance that is realised is a function of market and engineering
demands.
The net carbon gain over fossil fuel scenarios was 2-19 t CO2 Ha-1Y-1, encompassing
times higher than those for strategies based on biomass combustion. The entitled portion
of this added conserving will have to attract CO2 offset at a value sufficient to cover the
USD 47 t-1 value of residual energy in biochar.
Table 3.1 Fate of initial feedstock mass between products of pyrolysis processes
(IEA, 2007)
Process Liquid
(bio-oil)
Solid
(biochar)
Gas
(syngas)
FAST PYROLYSIS
Moderate temperature (~500 °C)
Short hot vapour residence time (<2s)
75%
12%
13%
INTERMEDIATE PYROLYSIS
Low-moderate temperature, Moderate hot
vapour residence time
50%
25%
25%
SLOW PYROLYSIS
Low-moderate temperature,
Long residence time
30%
35%
35%
GASIFICATION
high temperature (>800 °C) Long
vapour residence time
5%
10%
85%
3.1.1.1 Slow pyrolysis
Slow pyrolysis is the thermal conversion of biomass by slow heating at low to
medium temperatures (450 to 650°C) in the absence of oxygen, with the simultaneous capture
of syngas. Feedstocks in the form of dried biomass pellets or chips of various particle sizes are
fed into a heated furnace and exposed to uniform heating, generally through the use of
35
internal or external heating as retort furnace or kilns. Residence times: >5 seconds for the
production of syngas; minutes, hours or days for biochar production.
Relatively low reactor temperatures (450-650°C)
Reactor operating at atmospheric pressure
Very low heating rates, ranging from 0.01–2.0°C/s
Very short thermal quenching rate for pyrolysis products: minutes to hours.
Several commercial facilities generate syngas and biochar using a continuous flow
system in which feedstock passes slowly through a kiln in an auger feed, with combustible
syngas continuously drawn away. Biochar, bio-oil and syngas are formed in approximately
equal proportions due to the slow speed of the combustion process, which promotes
extensive secondary reactions within biochar particles and in the gas and vapour phases,
leading to condensation. The pyrolysis reaction itself is mildly endothermic, with the bulk of
energy capture being in the form of the syngas and bio-oil condensates. The biochar has a
residual energy content of about 30–35 MJ kg-1 (Ryu, 2007), and conventionally this is
extracted within the plant by burning or gasification, providing heat to drive the primary
pyrolysis (Demirbas, 2006), or to dry incoming feedstocks. The syngas product may be
combusted on site to generate heat or electricity (via gas or steam turbine), or both. Adding
steam to the pyrolysis reaction liberates additional syngas from the biochar product, mainly
in the form of hydrogen. The biochar that remains after this „secondary‟ pyrolysis displays
rather different properties from the primary product, differing in pore size and carbon to
oxygen ratio (Demirbas, 2004). Syngas can be purified through a sequence of operations to
yield pure streams of the constituent gases: hydrogen (50% of gas yield), carbon dioxide
(30%), nitrogen (15%), methane (5%), and lower molecular weight hydrocarbons, as well as
some carbon monoxide (Day et al., 2005). There is a small energy penalty associated with
these steps. Slow pyrolysis research plants currently process feedstock at a rate of 28–300 kg
hr-1 on a dry mass basis, and commercial plants operate at 48–96 t/ day. Comparison of the
efficiency of pyrolysis plants is complex since the mix and use of products vary, and the
composition and heat value of syngas differs. Feedstock quality and moisture content is also
36
variable, and there is a conversion loss in the generation of electrical power through steam
or gas turbines.
Fig 3.1 Illustration of an industrial slow pyrolysis system, (BEST Pyrolysis, Inc)
3.1.1.2 Fast pyrolysis
Very rapid feedstock heating leads to a much greater proportion of bio-oil and less
biochar. The time taken to reach peak temperature of the endothermic process is
approximately one or two seconds, rather than minutes or hours as are the case with slow
pyrolysis. Maintaining a low feedstock moisture content of around 10% and using a fine
particle size of <2mm permit rapid transference of energy. In many systems the transfer is
further increased by mechanically enhancing feedstock contact with the heat source or
maximising heat source surface area. Various technologies have been used and proposed or
tested including: fixed beds, augers, ablative methods, rotating cones, fluidized beds and
circulating fluidized beds (Demirbas, 2001). Surface charring must be continuously removed
during reaction to prevent pyrolysis of particle interiors being inhibited by its insulating
effect. Bio-oil is condensed from the syngas stream under rapid cooling, with the
combustion of syngas providing the pyrolysis process heat. The bio-oil is a low grade
product with a calorific value, on a volume basis, approximately 55% that of regular diesel
fuel. It is unsuitable as a mainstream liquid transport fuel even after refining, and is most
suitable as a fuel-oil substitute. It is considered to have an advantage over typical fuel oils in
37
zero SOx and low NOx emission on combustion (Bridgewater, 2004). In addition to
combustion for electricity generation, bio-oil may be converted to syngas for production of
clean fuels. Bio-oil also contains high value bio-chemicals of relevance to food and
pharmaceutical industries. The biochar product of fast pyrolysis is granular and displays a
lower calorific value (23–32 MJ kg-1) than that of slow pyrolysis (Demirbas, 2001).
Fig 3.2 Illustration of a fast pyrolysis system, (Laird, 2008)
3.1.1.3 Intermediate pyrolysis
This term describes a hybrid technology designed to produce bio-oil with very low tar
content, with perceived potential for use as a motor fuel (Aston‟s European Bioenergy
Research Institute). The process has been tested with woody and non-woody feedstock, and
produces biochar in greater quantity and of contrasting quality as compared to fast
pyrolysis.
3.1.1.4 Carbonisation
Carbonisation describes a number of pyrolysis processes that most closely resemble
traditional, basic methods of charcoal manufacture, and which produce biochar of the
highest carbon content. The auto-thermal carbonisation process is the small-scale method
widely used in rural communities around the world (FAO, 1987). The second requires fossil
fuel to provide an external heat source, and is associated with industrial, mass production of
charcoal (FAO, 1985). The process is optimised for the solid products of pyrolysis, but
condensed gases provide an industrial product known as „wood vinegar‟, which as well as
38
providing the basis for food flavouring ingredients, is considered to have a fertiliser value to
plants .
3.1.2 PROXIMATE ANALYSIS OF BIOCHAR
The main purpose of the proximate analysis is to assess the rank of the char and its
intrinsic characters as well. In addition to it, it can be of great use in sorting out fundamental
assumptions for future applications, for example, as in the case of trading or utilization and
of biochar in different applications. The quality of the biochar differs with the type of
biomass used and it is also quantitatively controlled by the moisture content, the residual
carbon, the volatile content etc. The terminologies related to proximate analysis and their
significances are described below.
3.1.2.1 Moisture
Since biochar production involves utilization of high temperature conditions, the
moisture is removed at various stages of the process. From the preparation of raw material,
the moisture removal is an important step prior to pyrolysis. This is done in order to
maintain uniformity in each and every sample used. The moisture present in the biochar
soon after pyrolysis is said to be called as the inherent moisture and is measured using the
proximate analysis.
3.1.2.2 Volatile matter
There are some components of the char that are liberated at very high temperatures;
these are known as the volatile matter. This does not include the moisture. And those
components which are released specifically in the absence of air i.e. pyrolysis alone are
called volatile content. This may be a mixture of short chain and long chain hydrocarbons
and some inorganic gas constituents of sulphur.
3.1.2.3 Ash
The non-combustible residue left over after the char is completely burnt represents
the ash content of the char. It symbolizes the bulk organic matter, after sulphur, water;
oxygen and carbon are completely driven off during combustion. The estimation of ash
39
content involves thorough burning of the char and ash is expressed as the percentage of the
original mass. This ash content is nothing but an approximate estimate of the mineral
content and other inorganic matter in the biomass. This estimation may also be used in
conjunction with other assays in total composition determination of the biomass samples.
3.1.2.4 Fixed carbon
The amount of carbon found in the material after the volatile matter is completely driven off
is known as the fixed carbon. This fixed carbon will be less than the ultimate carbon because
of the fact that some of the carbon is being removed in the form of hydrocarbons in volatile
matter. This fixed carbon ultimately shows how effectively the bichar behaves as carbon
negative from the environment point of view. The more the amount of fixed carbon, higher
will be its effectiveness as a climate change tool.
3.2 METHODS & MATERIALS
Feedstock biomass preparation & significance: The table below shows the feedstock
biomass used for the study.
Table 3.2 The different woods used for the study for comparison of biochar from pyrolysis
process
Biomass Density (kg/m³) Classification Climate Wood type
Balsa 170 Deciduous Tropical Hardwood
Bamboo 350 Grass Tropical Grass
Pine 455 Coniferous Temperate Softwood
Maple 755 Deciduous Temperate Hardwood
Ebony 1040 Deciduous Temperate Exotic hardwood
3.2.1 Preparation of biomasses
Biomasses (balsa, bamboo, pine, maple and ebony) were obtained from Quebec,
Canada. The wood logs were first finely shaped using a lathe and wood working facility
(Bioresource Engineering Machine shop). They were shaped into wood sticks of 50 mm
40
length and 3 mm diameter so that they might be appropriate to load into the sample holder
of the thermal desorption unit specifically adapted for the pyrolysis studies.
Fig 3.3 Machine lathe (Bioresource Engineering Machine shop, McGill University) &
Basic parts of a lathe (http://www.custompartnet.com/wu/turning)
After proper shaping of the wood samples, they are pre-treated. They are dried
overnight in hot air oven at 65 °C to remove the moisture content. They were further dried
for longer duration to make the final moisture contents approximately equal for all the five
types of samples. The samples were now ready for pyrolysis.
3.2.2 Pyrolysis of biomasses to produce biochar
Thermo-chemical conversion of the samples were carried out in the thermal
desorption unit (Supelco, Inc.). The process of pyrolysis requires control of parameters
(temperature, time). This unit consists of temperature, time and power control switches. The
volatiles produced during the pyrolysis process are continuously removed by a purge of
nitrogen gas and condensed in a water bath.
41
Fig 3.4 Pyrolysis equipment (Supelco, Inc.) (A) Thermal desorption Unit (B) Sample
holder tube (C) Ideally shaped wood sample (Maple)
The valve in the thermal desorption unit shown in figure 3.4 (A) was set to reach a
temperature of 260°C and then the samples (Figure 3.4 C ) were loaded into the sample
holder shown in Figure 3.4 (B) and they were insulated. Then the required time and
temperature are set using the tube heat set point control switch. The total time set would
include the heating time and the cooling time of the process as well.
A B C
42
Total time = Heating time (T 1 ) + Residence time (T 2 ) + Cooling time (T 3 )
The samples are subjected to various process conditions according to the
experimental design chosen for the study. Since pyrolysis process has been optimised
previously in the thermal desorption unit, fast pyrolysis is chosen for study. A heating rate
of 1000°C/min is chosen and studied. A fast pyrolysis process was chosen for this study
since earlier studies involving pyrolysis of Chinese birchwood using this thermal desorption
unit proved to produce a higher yield % of char compared to slow pyrolysis (Dutta, 2010).
Thus, a heating rate of 1000°C/min (fast pyrolysis) used throughout the study for all the
types of biomass. The aim of this study was to validate the above result with different
biomasses as well. At the end of pyrolysis, the solid char obtained are collected and weighed
to analyse yield of the process.
3.2.3 Ashing of biochars for proximate analysis
The produced biochars were then subjected to proximate analysis. The procedure
followed ASTM E1755 - 01(2007) Standard Test Method for Ash in Biomass. This test
method involved the determination of ash, expressed as a percentage of residue left behind
after dry oxidation i.e. at a temperature of 550- 600°C for all hard and soft woods. The
result reported were relative to the 105°C oven dried mass of the sample. The procedure
includes;
50 mL porcelain crucibles with a covering lid.
Barnstead Thermolyne 48000 furnace.
Analytical balance, sensitive to 0.1 mg.
43
Drying oven having a temperature control of 105±2°C
Sample pre-treatment: The biochar samples are dried at 105°C according to
Laboratory Analytical Procedure #001, Determination of Total Solids and Moisture in
Biomass, prior to proximate analysis. They are weighed before and after this drying
treatment to keep track of the moisture content.
Fig 3.5. Thermoltne furnace used for proximate analysis, 2. Biochar from pyrolysis, 3.
Crucibles placed inside the furnace operated at 550°C, 4. Ash from the furnace
The biochar samples (Figure 3.5 (2)) are pre-treated as mentioned above and placed
in the thermolyne furnace shown in Figure 3.5 (1) and ignited at about 535±20°C for a
minimum of 180 minutes, until the entire carbon is eliminated. In order to avoid the sample
from flaring up, the porcelain crucibles are covered partially. After ashing the sample the
44
furnace is allowed to cool down and then the crucibles are removed and the mass of the ash
is recorded for analysis.
3.3 Experimental design
Due to the effectiveness in revealing the effects and interactions of several factors on
a particular response (Box & Wilson 1951), a Response Surface Method is chosen to
determine the optimal conditions for pyrolysis of biomass to biochar. The RSM was
performed using JMP of Statistical Analysis Software (SAS Institute Inc.). The effect of
independent variables; time (min) and temperature (°C) each at three levels were
investigated using a central composite design. A uniform precision, on face type of design
with five central points was chosen. The fitness of the model was determined by evaluating
the Fisher test value (F- value), and the coefficient of determination (R²) as obtained from an
analysis of variance (ANOVA). The complete design consist 13 experimental levels
including five replications of the centre points for the two independent variables. The central
composite uniform precision uses linear regression to fit the experimental data to a linear
model. The linear model for the responses is as follows,
… Eqn 3.0
Where xij is the ith observation on the jth independent variable, and where the first
independent variable takes the value 1 for all i (β1 is the regression intercept)
Table 3.3 Levels and values of the independent variables analyzed in RSM
Levels Temperature
(°C)
Time (min)
1 300 15
2 350 30
3 400 45
45
The central composite design followed for the study is shown below in Table 3.4 with
appropriate combinations of temperature and time.
Table 3.4 Central composite uniform precision design for Response Surface Analysis of
pyrolysis of biomass
Run Coded values Temperature Time
1 −− 300 15
2 a0 300 30
3 00 350 30
4 0a 350 15
5 00 350 30
6 −+ 300 45
7 +− 400 15
8 A0 400 30
9 ++ 400 45
10 00 350 30
11 0A 350 45
12 00 350 30
13 00 350 30
3.4 RESULTS AND DISCUSSION
3.4.1 Model fitting
In order to study the effect of temperature and time on the pyrolysis process, statistical
analysis becomes essential. JMP software (version 8) was used for the regression analysis so
as to obtain a mathematical model for the experimental data that would achieve a
significant fit and could identify optimal operating parameters to achieve the best measured
responses.
46
3.4.1.1 Pine biochar yield
The aim was to study the effect of variables on the pyrolysis of pine wood which is
coniferous temperate softwood of moderate density. It was observed that, both temperature
(T) and time (t) significantly influenced the yield (p<0.0001 and p=0.0394), respectively) of
pine biochar. The quadratic term (T× T) had a significant effect (p=0.0001) on the pyrolysis
process whereas (t × t) did not have any effect (p>0.05). It can also be deduced from the
table 3.5 that, the quadratic term (T × t) seemed to have a diminutive influence on the
process. Thus the interaction of T and t factors made a significant contribution in
determining the yield of biochar. The predicted model for Pine Yield can be described in the
equation 3.1, with significant factors of Time and Temperature:
Pine Yield = 107.8 T2+ 0.0092 t2+0.7 Tt +2.31 T+0.01 t+0.6655
… Eqn (3.1)
Table 3.5 ANOVA for the effect of Temperature and Time on Pine wood Biochar yield
Source SS DF MS F-Value p-value
Model 8.35×10-3 5 1.67×10-3 71.19 <0.0001*
T 6.67×10-3 1 _ 283.9 <0.0001*
t 1.50×10-4 1 _ 6.38 0.0394*
T× t 1.00×10-4 1 _ 4.25 0.0779
T× T 1.34×10-3 1 _ 57.2 0.0001*
t× t 2.37×10-5 1 _ 1.01 0.3483
Lack of fit 4.43×10-5 3 1.5×10-5 0.49 0.7062
R² 0.98
*Significant factors
A plot of actual vs. predicted values of pine yield is shown in Figure 3.5a which
shows the close agreement between these values (R²=0.98), suggesting that the model and
the resulting response surface can be used to predict the pine biochar yield under different
experimental conditions. It can be perceived from the graph that almost all the actual valued
lie close to the line showing that the model obtained shows a sound fit to the data. The
47
results were in accordance with those of Gaskin et al., (2008), who studied effect of
low‐temperature pyrolysis conditions on pine biochar, and observed that temperature up to
400°C had a significant effect on the char yield. Similar results were obtained in the present
study where the pine biochar yield was influenced by the process variables.
Fig 3.5a Predicted (g) vs Actual (g) PINE Yield
3.4.1.2 Balsa Biochar Yield
The objective of this study was to examine the impact of process variables on balsa
wood when it is transformed from wood biomass to biochar. Balsa is a deciduous tropical
hardwood with an astounding low density of 170 kg/cc. From Table 3.6 which shows the
analysis of variance for balsa wood biochar yield, it was evident that, temperature was the
only factor that influenced the pyrolysis of balsa biomass ( p=0.0073). The coefficient of
determination was observed to be statistically average (R²= 0.67). Time and the quadratic
48
terms: (T × T), (t × t) nor (T × t) had no significant effect (p>0.05) on the process. The
predicted model for balsa Yield can be described in the equation, with significant factor of
Temperature:
Balsa Yield = 7.84 T2- 0.006468 t2+0.623 T + 0.000866 t + 0.14 Tt+0.0189
… Eqn (3.2)
A plot of actual vs. predicted values of balsa yield (Figure 3.5b) shows the model had
a mediocre fit the data. The actual and predicted values are observed be widely scattered
making it evident that the chosen experimental conditions favoured the process partially.
But this cannot conclude that all hardwoods might exhibit a similar behaviour. It is essential
that a comparison with another hardwood has to be made so as to arrive at this conclusion.
Thus a comparison of balsa and maple is made in later part of this discussion.
Table 3.6 ANOVA for the effect of Temperature and Time on Balsa wood Biochar yield
Source SS DF MS F-Value p-value
Model 4.98×10-4 5 1.00×10-4 2.88 0.1000
T 4.82×10-4 1 _ 13.9 0.0073*
t 1.13×10-6 1 _ 0.03 0.8619
T× t 4.41×10-6 1 _ 0.12 0.7316
T× T 7.82×10-6 1 _ 0.22 0.6489
t× t 7.22×10-6 1 _ 0.20 0.6615
Lack of fit 2.47×10-5 3 1.03×10-3 0.15 0.9234
R² 0.67
*Significant factors
49
Fig 3.5b Predicted (g) vs Actual (g) BALSA Yield
3.4.1.3 Ebony Biochar Yield
The intention of this study was to observe the effect of variables (temperature, T;
time, t) on the pyrolysis of ebony biomass to biochar. Table 3.7 shows the analysis of
variance for the pyrolysis of balsa. It can be seen that the model showed an excellent fit to
the experimental data (R²=0.90). The factor that had the major influence on the pyrolysis
was temperature (p=0.001). It had significantly affected the yield of balsa biochar as the
severity of pyrolysis grew. The quadratic terms including (T × T), (t × t) and (T × t) and the
linear time factor showed no significance in pyrolysis (p>0.05). Ebony is a deciduous
temperate exotic hardwood with a very high density of 1040 kg/cc.
Unlike balsa, which is also a hardwood, ebony seemed to have shown excellent fit to
the experimental conditions used. Thus the optimisation of the process can be consequently
made.
50
The predicted model for ebony Yield can be described in the equation 3.3, with significant
factor of Temperature:
Ebony Yield = 95.45 T2-0.002 t2-1.4 Tt+4.55 T+0.03 t+0.245
… Eqn (3.3)
A plot of actual vs. predicted values of ebony yield shown in Figure 3.5c proved the
close agreement between these values, suggesting that the model and the resulting response
surface can be used to predict the ebony biochar yield under different experimental
conditions. Compared to balsa, the values seems to be fairly close and regular proving that
these the chosen experimental conditions will favour the production of biochar from a
hardwood definitely.
Table 3.7 ANOVA for the effect of Temperature and Time on Ebony wood Biochar yield
Source SS DF MS F-Value p-value
Model 2.83×10-2 5 5.66×10-3 12.3 0.0023*
T 1.35×10-3 1 _ 55.0 0.0001*
t 1.35×10-3 1 _ 2.93 0.1305
T× t 4×10-4 1 _ 0.86 0.3822
T× T 1.04×10-3 1 _ 2.27 0.1749
t× t 7.4×10-7 1 _ 0.001 0.9692
Lack of fit 1.4×10-3 3 6.07×10-4 1.73 0.2978
R² 0.90
*Significant factors
51
Fig 3.5c Predicted (g) vs Actual (g) EBONY Yield
3.4.1.4 Maple Biochar Yield
Maple belongs to the group of deciduous hardwood usually found in the temperate
regions. The goal of this study is to assess the impact of process variables (Temperature, T;
time, t) on the pyrolysis of maple biomass. From ANOVA Table 3.5d, it can be seen that,
maple biochar production process was successful for the chosen experimental conditions.
This was evident from the coefficient of determination of 0.89 which suggested high
statistical significance of the process. From further observation of ANOVA, it became
apparent that temperature had a major influence on the pyrolysis of maple biomass
(p=0.0002). Time seems to a significant factor to since p=0.08, which is close to the
confidence level of 95%. The quadratic terms including (T × T), (t × t) and (T × t) showed
no significance in pyrolysis (p>0.05). The predicted model for maple yield can be described
in the equation, with significant factors of Time and Temperature:
52
Maple Yield = 14.357 T2+0.0317 t2+3.5 Tt+4.081 T+0.034 t+0.133
… Eqn (3.4)
Table 3.8 ANOVA for the effect of Temperature and Time on Maple wood Biochar yield
Source SS DF MS F-Value p-value
Model 2.29×10-2 5 4.59×10-3 10.8 0.0034*
T 2.04×10-2 1 _ 48.0 0.0002*
t 1.67×10-3 1 _ 3.92 0.0880
T× t 6.25×10-4 1 _ 1.47 0.2644
T× T 2.37×10-5 1 _ 0.05 0.8199
t× t 1.73×10-4 1 _ 0.40 0.5428
Lack of fit 1.09×10-3 3 3.64×10-4 0.77 0.5657
R² 0.89
*Significant factors
A plot of actual vs. predicted values of maple yield shown in Figure 3.5d illustrated a
close agreement between these values, suggesting that the model and the resulting response
surface can be used to predict the pine biochar yield under different experimental
conditions. The values seem to be less scattered and thus resulted in a greater fit of the data
to the model was obtained.
When a comparison was made between the hardwoods under study (maple, ebony,
balsa) it can be seen that, ebony and maple had approximately the same fit for the model
obtained despite the difference in their densities. But, balsa wood showed only an average fit
concluding that the experimental conditions favour the production of ebony and maple
more compared to balsa.
53
Fig 3.5d Predicted (g) vs Actual (g) MAPLE Yield
3.4.1.5 Bamboo biochar Yield
Bamboo is a grass that commonly grows in the tropical regions. The intention of the
study was to investigate the pyrolysis of grass and the factors that influence it. From Anova
Table 3.9, it is clearly evident that the model obtained shows a great fit and significance to
the actual values (p=0.0212 and R2=0.80). The process seemed to be controlled by
temperature to a significant extent (p=0.0027). Further, like the results obtained for other
wood biomasses under present study, bamboo was also not affected by quadratic terms
including (T × T), (t × t) and (T × t) (p>0.05). The interaction the process variables thus did
not impact the pyrolysis. The predicted model for Bamboo Yield can be described in the
equation 3.5, with significant factor of Temperature:
Bamboo Yield = 70.56 T2+0.0576 t2-1.4 Tt+2.31 T +0.012 t +0.125
… Eqn 3.5
54
Table 3.9 ANOVA for the effect of Temperature and Time on Bamboo Biochar yield
Source SS DF MS F-Value p-value
Model 9.20×10-3 5 1.84×10-3 5.63 0.0212*
T 6.66×10-3 1 _ 20.4 0.0027*
t 2.66×10-4 1 _ 0.81 0.3964
T× t 4.00×10-4 1 _ 1.22 0.3051
T× T 5.79×10-4 1 _ 1.77 0.2248
t× t 5.79×10-4 1 _ 1.77 0.2248
Lack of fit 1.36×10-3 3 4.56×10-4 1.98 0.2590
R² 0.80
*Significant factors
Fig 3.5e Predicted (gm) vs Actual (gm) BAMBOO Yield
55
A plot of actual vs. predicted values of bamboo yield shows the close agreement
between these values, suggesting that the model and the resulting response surface can be
used to predict the bamboo biochar yield under different experimental conditions. Though
the values seem to be scattered, there seems to be a good response for the experimental
conditions used.
Among the biomasses studied, pine wood exhibited the best fit for the model
developed (R2=0.98) followed by ebony (R2=0.90) maple (R2=0.89) bamboo (R2=0.80).
Balsa showed the least fit among them (R2=0.67). When hardwoods under study are
considered, ebony , an exotic hardwood of very high density favoured the production of
biochar under the appointed experimental conditions. Bamboo, a grass when compared to
wood showed to have significant fit proving that it also follows the trend in pyrolysis as any
other wood under study did. From all the above results it can be concluded that temperature
was the major influential factor that influences pyrolysis. Thus with the above results
obtained, a study the function of desirability for the pyrolysis process is attempted.
3.4.2 Desirability Function
The desirability function approach is one of the most widely used methods in
industry for dealing with the optimization of multiple response processes. It is based on the
idea that the "quality'' of a product or process that has multiple quality characteristics, with
one of them outside of some "desired" limits, is completely unacceptable. The method finds
operating conditions x that provide the "most desirable'' response values. For each response
Yi (x), a desirability function di (Yi) assigns numbers between 0 and 1 to the possible values
of Yi, with di(Yi)=0 representing a completely undesirable value of Yi and di(Yi)=1
representing a completely desirable or ideal response value.
The individual desirabilities are then combined using the geometric mean, which
gives the overall desirability D:
D= (d1(Y1) * d2 (Y2) *….* dk(Yk)) 1/k … Eqn 3.6
56
Where k denotes the number of responses. It should be noticed that if any response i is
completely undesirable di(Yi)= 1 then the overall desirability is zero (Derringer and Suich
(1980)).
Figure 3.6 showed the desirability of the predicted responses to the actual responses
at the central points. From the graph it can be predicted that at the central point, the overall
desirability is only 0.399 which meant that the conditions will only favoured 35% of the
process to be successful, giving similar yield.
The desirability of the predicted to the actual response turned out to be less when the
process is attempted to be optimised at the central point. Thus, the individual fitting for each
type of biomass was performed. It was observed that when they were fitted separately for
0.5 desirability function, a number of combinations of process variables were obtained, a
level which was predominantly advantageous.
It has been illustrated through Figures 3.6a, 3.6b, 3.6c, 3.6d, 3.6e that different
combinations of process variables arise for the same desirability function. This can be
explained as follows;
In the production of balsa biochar (Figure 3.6a), to attain a 50% desirability of the
predicted yield, the system can be operated at 334.2°C for 43.75 minutes or 344.2°C for
22.75 minutes. Thus it can be clearly seen that, an increase of just 10°C leads to the decrease
of 21 minutes of operation. Thus, desirability function aids in optimising the parameters
resulting in conservation of time and money when an industrial scale pyrolysis is attempted.
57
Fig 3.6 Overall Desirability of the Pyrolysis process for each biomass
58
Fig 3.6a Individual Desirability of the pyrolysis process for Balsa Biochar
Fig 3.6b Individual Desirability of the pyrolysis process for Bamboo Biochar
59
Fig 3.6c Individual desirability of the pyrolysis process for ebony biochar
Fig 3.6d Individual desirability of the pyrolysis process for maple biochar
60
Fig 3.6e Individual Desirability of the pyrolysis process for Pine Biochar
Thus, the individual desirability plots for each biomass showed that for a particular
desirability, it is possible to alter the independent variables of temperature and time to the
requirement. Table 3.10 shows the optimised values of time and temperature to attain a
desirability of 0.5. It can be observed from the table that, different biomass had different
optimal temperatures and time. However, pine required the lowest temperature of 314.2°C
and maple required the highest temperature of 349.2°C to attain the same yield as any other
biomass under study. While maple required the least time of 19.25 minutes, pine required
the maximum time of approximately 44 minutes to attain the same yield. Thus the results
conclude that softwood under study required the least temperature and more time compared
to hardwood which required the highest temperature and least time to attain 50% of the
desired predicted yield. It was also observed that bamboo, a grass required mediocre
temperature and medium time scale to attain the expected yield when compared to the
wood biomass under study.
61
Table 3.10 Optimum values of temperature and time for desirability factor = 0.5
Biomass type Optimum Temperature
(°C)
Optimum Time(min)
Maple 345 22
349.2 19.25
Balsa 334.2 43.75
344.2 22.75
Bamboo 326.7 28
320.0 37.5
Pine 325.8 24.8
314.2 44
Ebony 340.8 21.75
333.3 41.75
3.4.3 Comparison of biochar yield among the types of biomasses:
Biochar production from five different biomasses (Maple, Pine, Bamboo, Balsa,
Ebony) including a grass (Bamboo) was attempted. Heating rate of 1000°C min-1 was used
throughout the study for all the five types of biomasses. Thus, the aim of the study was to
optimise the fast pyrolysis of biomasses. The results have been presented and discussed in
the previous section.
In this section, an overall comparison of the fast pyrolysis of all 5 biomasses has
been attempted with the help of Figure 3.7 which represents the comparison of Biochar
Yield from the five biomasses and at different process conditions of temperature and time
(and heating rate of 1000°C min-1 throughout). As expected, the yield of biochar from
biomass decreased with increase in temperature. This was probably due to the growth in
severity of pyrolysis as there was a raise in temperature. The general observation was that, a
residence of 45 minutes had a significant effect on the pyrolysis at all temperatures. It was
after 30 minutes of residence time that the char production decelerated at all temperatures
(ebony being an exception).
62
For all the biomasses under study, it was observed that, low temperature (300°C)
aided the biochar production significantly. There was no significant difference between the
samples obtained at 350°C subjected for 45 minutes and 400°C subjected for 30 minutes for
all the five biomasses under study. As expected, temperature had a major influence on the
pyrolysis of biomass. In addition, time has also impacted the biochar production to a
significant extent. Similar to maple, bamboo has shown an analogous trend in the yield.
This concluded that bamboo also can be used for biochar production when the
process is operated at similar conditions as any wood under study. Thus, the fast pyrolysis
of five different biomasses have been attempted and optimised. Study is required in
understanding the pyrolysis behaviour of mixture of biomasses and the corresponding
parameter optimisation.
Fig 3.7 Comparison of yield of biochar from various biomasses
63
3.4.4 Response surface analysis
Regression models were used to predict the effect of the two independent variables
on the char yields. The relationships between independent and dependent variables were
illustrated in three dimensional response surfaces. The aim of this study is to compare and
understand the response surface plots for the five biomasses under study. This might assist
in executing the thermochemical decomposition under different experimental conditions. In
addition, response surface plots help in understanding the trend the pyrolysis process for
each biomass follows, in a visual manner.
From the surface plots of pine in Figure 3.8a, it can be seen that temperature has a
linear relationship with the severity of pyrolysis. This is the reason for reduction in yield
with temperature increase.
Fig 3.8a Response surface plots of the effect of process variables, temperature and time
on pyrolysis of pine biomass
64
In addition, time is another influential factor for pyrolysis. This becomes evident from the
gradual dip observed on the time axis after 30 minutes. The linear and some quadratic terms
had significant effect on the process. Similar trends were observed by K.Harris et al (2003),
who investigated the influence of temperature on soft wood pyrolysis. Pine, also being a soft
wood, showed a similar behaviour. Thus the results are in agreement with the trends of
pyrolysis studied by other researchers.
Fig 3.8b Response surface plots of the effect of process variables, temperature and time
on pyrolysis of bamboo Biomass
Application of similar process conditions to bamboo which is a grass yielded
interesting results which were in correlation with the other researches on bamboo performed
so far. Like pine, bamboo yield was also influenced by temperature to a great extent. The
Response surface plots for bamboo showed (Figure 3.8b) that there was a linear relationship
of yield with temperature. However, like pine, bamboo also followed a negative slope. Lou
et al. (2010) who performed studies on the effect of conditions on fast pyrolysis of bamboo
65
observed similar results compared to this study. It is to be noted that the bell shaped curve
indicates the influence of time on the bamboo char yield. This meant that the yield was
maximum at the extreme levels and became stagnant at the center point (350°C, 30 min).
Fig3.8c Response surface plots of the effect of process variables, temperature and time on
pyrolysis of ebony biomass
Figure 3.8c shows the response surface plot for pyrolysis of ebony. Among the woods
ebony was not affected by the variable time for producing biochar. As seen in the RS plot,
time did not have any influence on the pyrolysis process. Chew et al, (2010) who worked on
thermal degradation of wood samples observed that the pyrolysis of an exotic hardwood
was a function of temperature only. This is in agreement to the results obtained in this
study, with temperature alone playing a major role.
66
As for maple, the response surface plot (Figure 3.8d) exhibited an excellent
indication of the fact that there is a linear (exactly linear) relationship between the yield of
biochar and both the factors (temperature & time). Encinar et al (2003) who worked on the
production of oak wood biochar arrived at similar results. Oak and maple being similar kind
of hardwood have shown similar pyrolysis trend.
Fig 3.8d Response surface plots of the effect of process variables, temperature and time
on pyrolysis of maple Biomass
The response surface plot for balsa is shown in Figure 3.8e. It can be concluded that balsa
too being a hardwood, observed similar trend. It was totally unaffected by time and has a
strong influence of temperature to the biochar yield was clearly evident
67
Fig 3.8e Response surface plots of the effect of process variables, temperature and time
on pyrolysis of balsa biomass
3.4.5 Estimation of biochar properties from proximate analysis
Mobile matter % = [(Mass of sample after pyrolysis - Mass of sample @ 450°C) /
Mass of sample at 450°C] * 100
… Eqn. 3.7
Ash content % = (Mass of sample @ 550°C/Mass of sample after pyrolysis) *100 … Eqn. 3.8
Residual matter % = 100 – (Mobile matter % + Ash content %) … Eqn. 3.9
Final Moisture
content %
= 1 – [(Mass of sample after Pyrolysis – Mass of sample @ 550°C)
/ Mass of sample after Pyrolysis]
… Eqn. 3.10
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The aim of this investigation was to study the quality of biochar produced from
different biomasses under study and the comparison of fuel and good quality char properties
among the chars. The secondary objective of this study was to understand the difference
between wood and grass biochar. For this purpose, two biomasses were chosen: Ebony
(wood) and Bamboo (grass). The Table 3.11 shows the results of proximate analysis of
ebony biochar using a Barnstead furnace. Proximate analysis of a wood (ebony) and grass
(bamboo) were performed according to procedure explained in section (3.2.3). It is well
known that the char can demonstrate good fuel properties when the fixed carbon content is
significantly high. At the same time, it is essential that only a low volatile matter content is
present (Sensoz and Can, 2002, Boetang et al., 2007). In addition, the presence of higher
ash contents occurs at the expense of the carbon content of the biochar sample. And a good
quality biochar is one which retains 50 % of its carbon content after pyrolysis.
When ebony is considered, the mobile matter % appears to descend from 300°C to
400°C irrespective of the time. This indicates that, the sample obtained at 400°C for 15
minutes (Table 3.11) had the minimum mobile matter of 26.4%, thus proving to be a good
fuel. The sample obtained at 400°C for 45 minutes was also found to have less volatile
content. As expected, ebony biochars from higher temperature proved to show better fuel
properties compared to lower temperatures. In addition, volatile matter values are used as
an indication of the amount of smoke that may be emitted (Speight, 2002). This
substantiates the fact that chars from higher temperatures are cleaner fuels than those
obtained from lower temperatures. When wood and grass are compared, it was seen from
Table 3.12 that the mobile matter percentage was minimum for wood than grass. The lease
volatile content for bamboo was found to be from samples obtained at 400°C and 30
minutes (29.6%). Apparently, this is higher than in wood (ebony) considered above. Thus it
is concluded that ebony behaves as a better and cleaner fuel when compared to bamboo.
The presence of high ash content is undesirable as mentioned earlier. This is due to
the fact that, the composition of ash is the major factor for fouling and slagging problems
when used in cyclone furnaces (Yarzab, 1978).
69
Proximate analysis of wood (EBONY)
Table 3.11 Proximate analysis of ebony biomass
Temperature Time Mobile
matter %
Ash
content %
Residual
matter %
Final
moisture
content%
300 15 82.4 3.33 11.2 3.33
300 30 74.3 3.64 19.0 3.64
300 45 84.4 3.02 9.53 3.02
350 15 53.5 2.51 41.0 2.51
350 30 41.2 4.00 51.7 4.00
350 30 56.4 2.97 37.5 2.97
350 30 52.3 3.00 41.6 3.00
350 30 56.5 4.70 35.7 4.70
350 30 48.0 4.28 44.7 4.28
350 45 55.6 3.47 38.0 3.47
400 15 26.4 3.81 66.7 3.81
400 30 35.1 4.73 57.2 4.73
400 45 29.1 3.92 64.0 3.92
From the results of proximate analysis it is seen generally that bamboo (grass) was
found to contain more ash content than ebony (wood). Within the biochar samples of ebony
it could be seen that, samples obtained at 350°C at 30 minutes and the sample at 400°C at
30 minutes was found to have the highest ash content of 4.7%. This high ash% was at the
expense of carbon content and decreases the quality of the biochar produced. The sample
obtained at 350°C at 15 minutes was found to have the least ash content of 2.51%. This
concludes that this could be used for carbon sequestration since the expense of carbon is
least.
70
Proximate analysis of grass (BAMBOO)
Table 3.12 Proximate analysis of bamboo biomass
Temperature Time Mobile
matter %
Ash
content %
Residual
matter %
Final
moisture
content%
300 15 76.2 4.25 16.5 4.25
300 30 74.0 4.47 18.4 4.47
300 45 70.3 4.44 22.1 4.44
350 15 50.8 5.92 40.1 5.92
350 30 42.2 6.98 47.7 6.98
350 30 51.7 7.18 38.0 7.18
350 30 47.9 6.67 42.3 6.67
350 30 52.9 7.21 36.8 7.21
350 30 47.8 19.1 30.1 19.0
350 45 48.7 6.41 41.8 6.40
400 15 33.4 9.01 54.5 9.00
400 30 29.0 5.86 62.0 5.86
400 45 31.6 7.44 57.8 7.44
An interesting result obtained was the drastic change of residual matter % of ebony
biochar from 9.53% (300°C, 45 minutes) to 40.2% (350°C, 15 minutes). This might be due
to the sudden increase in severity of the process that leads to increase in residual matter. The
presence of high fixed carbon indicates good quality of the biochar. In this aspect, samples
at 400°C for 15 minutes (Ebony) and 400°C for 30 minutes (Bamboo) found to have the
highest fixed carbon content of 66.8% and 62.07% respectively. These are capable of
behaving as a greenhouse gas absorbent (especially CO2). It should also be noticed that
wood (ebony) showed better fuel and good quality char properties than grass (Bamboo).
71
3.4.6 Density analysis
The density of the biochars depends upon the nature of the starting material and the
pyrolysis process (Pandolfo et al, 1994). Solid density of biochar increases with increasing
process temperature and longer heating residence times, in accordance with the conversion
of low-density disordered C to higher-density turbostratic C (Byrne, 1996; Kercher and
Nagle, 2002). Lower amounts of volatiles, which have lower molecular weights than fixed
C, and lower ash contents result in higher solid density in biochars (Jankowska et al, 1991).
The aim of this investigation is to evaluate the density changes of the biomasses
under study during their transformation into biochars. Prior researches have been focussed
in comparing the density of the biomass before and after pyrolysis process. Here, a
relationship between the biomass density and biochar density has been established from the
study. For this purpose, the bulk densities of biomass and biochar were obtained. It was
observed that, when the bulk densities of wood and biochar were plotted against each other
(Figure 3.9a) , a (average) ratio of Biochar bulk density to Biomass bulk density was found
to be 0.80. Table 3.13 shows the individual ratios obtained for various biomass under study.
Table 3.13 Ratio of biochar to biomass for the lignocellulosic materials under study
Biomass type (Biochar/Biomass) Ratio
Balsa 0.77
Bamboo 0.70
Pine 0.78
Maple 0.82
Ebony
Mean
0.87
0.80
72
Fig 3.9a Bulk density of biochar plotted against its feedstock (Present Study)
Fig 3.9b Bulk density of biochar plotted against its feedstock (Byrne and Nagle (1997))
73
Thus, the relationship between the bulk densities of biochar and biomass was established to
be;
Biochar bulk density = 0.80 (Biomass bulk density)
… Eqn 3.11
Byrne and Nagle (1997) who worked on pyrolysis of different woods (Figure 3.9b)
including oak, pine balsa and maple have established a similar relationship. They proposed
that, the ratio of biochar to biomass bulk density was 0.8176. Thus the results obtained in
this study were in agreement with those of Byrne and Nagle.
From Figure 3.10, the reduction in density before and after pyrolysis is clearly
illustrated. Among the biomasses pine, maple and ebony showed similar density change
after pyrolysis. When bamboo and maple which are of similar densities (but different origin)
were compared, it was seen that bamboo (a grass), showed more reduction in density
compared to maple. Among the hard woods, maple was found to show more density
reduction than ebony and balsa. The softwood, pine showed similar results as of ebony (an
exotic hardwood).
Fig 3.10 Comparison of densities (g/cc) before and after pyrolysis for various types of
biomasses
74
3.5 CONCLUSION:
Utilization of wood and other biomass for biochar production through pyrolysis not
only provides soil amendment, but also forms a stream for waste management. In our study,
RSM proved to be effective in estimating the impact of the two independent variables on the
biochar yield. It was interpreted that both temperature and time had significant influence on
pine biochar production, while temperature alone had a very great influence on balsa,
ebony, maple and bamboo. Also it was proved that bamboo which is a grass showed similar
pyrolysis trend like woods (maple) of similar density.
3.6 ACKNOWLEDGEMENTS:
The authors are grateful to NSERC (Natural sciences and engineering research
council of Canada) for the financial support of this study.
75
CONNECTING TEXT
The biochars obtained through pyrolysis in the thermal desorption unit were subjected to
Pycnometry and Hyperspectral imaging to assimilate their porosity characteristics. Biochar
characterization reveals details which make the classification of chars from various
biomasses significant. Further, they were analysed using the Variable- Pressure Scanning
Electron Microscopy to reveal the fine details hiding within the chars.
76
CHAPTER IV
CHARACTERIZATION OF VARIOUS BIOCHARS BY PYCNOMETRY,
HYPERSPECTRAL IMAGING AND ELECRON MICROSCOPY IMAGING
Pavithra Sellaperumal, G.S.V.Raghavan and Yvan Gariepy
Department of Bioresource Engineering, McGill University, 21,111 Lakeshore Rd., Sainte-
Anne-de-Bellevue, QC, H9X 3V9, Canada.
Correspondence author:
Pavithra Sellaperumal, Department of Bioresource Engineering, McGill University, QC,
H9X 3V9, Canada
Email: [email protected]
77
ABSTRACT
Characterization of biochar reveals details which make the classification of chars
from various biomass materials easier. The biochars which were produced by pyrolysis in
the thermal desorption unit were subjected to Pycnometry and Hyperspectral imaging to
collect details on their porosity. Further, they were analysed using the Variable- Pressure
Scanning Electron Microscopy to reveal the fine details hiding inside the chars. The SEM
imaging was an additional evidence for the pore size (referring “porosity”) of each kind of
char due to different temperature and time. And the results from the SEM were in
agreement to the results obtained from hyperspectral imaging and pycnometry. The
classification of the spectral data from hyper spectral imaging was performed by mosaicking
the images from HSI instrument. Duncan‟s multiple comparison analysis was done and
fisher‟s test was used for representation and comparison. The procedure involves clarity
examination of the mosaic images and recording of mean reflectances. The mean of
reflectances observed were to be related to the porosity of the char produced under variable
conditions. Pyconometry results were obtained and they were analysed using Fisher‟s test.
The results indicated that as the temperature increased the porosity also increased. This was
due to the fact that the surface texture of biochar became more irregular because of
devolatilization of the volatile matter. The images from the electron microscopy also
substantiated the above results.
Keywords: characterization, pycnometry, hyperspectral imaging, scanning electron
microscope, fisher‟s test
78
4.1 INTRODUCTION
Characterisation aims to document the basic features of a biochar and to
ensure that it is safe to apply as a soil amendment. It is also appropriate to quantify the key
properties that may give rise to the beneficial qualities of biochar.
4.1.1 Hyper spectral imaging
Hyper spectral imaging of biochar is done in order to collect and compare the
spectral data of reflectance in the IR region. The data is classified on the clarity of the
images which meant low reflectance. The data obtained was used to investigate the
structural development of the biochar and the influence of the pyrolysis temperature and
residence time.
Hyperspectral imaging is the ability to collect an image of a scene or object, with
complete spectral fidelity, where each pixel point in one dimension representing a spectrum
band on the image, compared to conventional color photography where each pixel is
represented by three colors. Hyperspectral analysis may be used to remotely study the
chemical composition of objects and scenes with speed and accuracy using a variety of
spectral techniques, namely optical emission, reflectance, fluorescence or Raman
spectroscopy. One of the largest differences between hyper spectral spectroscopy and single
point spectroscopy is that the use of imaging spectrographs produces information of much
greater detail. For use as a remote chemical sensor it is critical that the hyperspectral imager
be able to capture each pixel-spectrum in a manner similar to that which would be collected
by a conventional single point –single pixel spectrometer, except at a much higher
acquisition speed. The need for large quantities of spectral details, fast acquisition speeds,
and for analysing large three-dimensional hyperspectral images is necessary and it is driving
advanced image analysis techniques to be developed and honed in order to maximize the
information which may be extracted from hyperspectral images. From this perspective it is
realized that much of hyperspectral technology revolves around the application of high
speed mathematical techniques which can be applied to the large three dimensional data
sets created from the hyperspectral spectrometer. Of equal, if not greater importance, is the
79
constant innovation which is taking place in the development of new and innovative
hyperspectral scanning instruments which is required for high-speed data acquisition.
Fig 4.1 Illustration of the capture mechanism of a linescan camera,
(Aikio, 2001) It uses a two dimensional detector perpendicular to the surface of the sample. The
sample is imaged by a narrow line of radiation falling on the sample or by a narrow slit in
the optical path leading to the detector. Hyperspectral images can easily be created by
collecting sets of these matrices while moving the sample scan line. Since no filter change is
necessary, the speed of image acquisition is limited only by camera read out speeds.
Commercial instruments are available with frame rates of 90 Hz or higher with 256×320
pixel resolution InGaAs detectors. This speed allows images to be acquired in a matter of
seconds. This configuration is also amenable to continuous operation for online monitoring
of rocess streams (Aikio, 2001; Wold et al., 2006).
80
4.1.2 Helium pycnometer
Pynometry technique is employed to biochar to estimate its porosity. The
pycnometer equipment is used to measure the volume of the samples. The Multivolume
Pycnometer is designed to measure rapidly the skeletal volume of powders, granules, or any
other solid objects having low vapor pressures and to permit computation of absolute
density when weight information is supplied. A skeletal volume is the volume that includes
the open pores of the sample but does not include the closed pores .The specimen chamber
of this equipment has a volume of 8.16 cc. This apparatus operates by detecting the pressure
change of pure helium gas resulting from displacement of the gas by a solid object. After
sealing, a vacuum (with a pressure of 1atm) was created in the specimen chamber for 20
minutes. After this period, helium gas was made to flow through the specimen chamber for
five minutes. These steps are recommended by the pycnometer‟s manufacturer to eliminate
any residual vapors (water) that could be present in the specimen chamber or on the surface
of the specimen and would interfere with the pressure ratios measured. The specimen
chamber was pressurized (purged) with helium to a value P1 (P1= ± 19.5psig) according to
pycnometer manufacturer‟s instructions. The pycnometer has an internal expansion
chamber of known volume, which is isolated from the specimen chamber by a valve. When
this valve is opened, the pressure of the system is allowed to reach equilibrium and this
resultant value is measured (P2).
The volume V sample was calculated from P1 and P2.
V sample = {Vcell + V exp} / [(P1/P2) – 1]
… Eqn 4.1
Where,
Vcell =Empty cell volume (m³)
Vexp = Expansion Volume (m³)
V sample = Sample Volume (m³)
This was used to compute the apparent density, ρApparent (excluding the open, interconnected
and inter particle pore spaces). Using standard methods, the bulk density, ρBulk (including all
the pores, inter particle spaces, moisture and air) of biochar can be found.
ε = 1- ( ρ Bulk /ρ Apparent) … Eqn 4.2
81
Thus the porosity, ε (the air or void volume per total volume of material) can be found.
Fig 4.2 Helium Pycnometry chamber showing the different pressures
(http://www.azonano.com/images/Article_Images/ImageForArticle_2637 (4).jpg)
4.1.3 Scanning electron microscope
The VP-SEM has made the investigation of nearly all kind of specimens, namely,
non-conducting or hydrated, in their original state under very stable conditions. This can be
accomplished by holding the sample inside the specimen chamber under controlled
ambience of gas at pressures as high as 1000 Pa. The added advantage of using VP-SEM is
that, it can image most of the specimens without prior sample preparation. This can be
attained at trivial increase of gas pressure of 100 Pa. A typical Scanning electron microscope
utilizes a focused beam of high energy electrons that helps to generate a variety of signals on
the surface of solid specimens. The electron sample interactions give rise to signals that
divulge information including the external surface morphology, the chemical composition,
the crystalline structure and sometimes the orientation of the materials making up the
sample as well.
82
Table 4.1 The Classification of Variable-Pressure SEM, (Füting Fraunhofer, 1988)
VARIABLE PRESSURE SCANNING ELECTRON MICROSCOPES
Work without high vacuum inside the specimen chamber
LOW VACUUM SEM
Water vapour pressure inside the specimen chamber is
always below 612 Pa.
(typical the maximum pressure is 300 Pa)
AMBIENT SEM
Water vapour pressure inside the specimen chamber
can be higher than 612 Pa.
(until now only achieved by ESEM)
ESEM: Environmental scanning electron microscope
SEM is also capable of analyzing selected point locations on a particular sample as in
the case of semi- quantitative or qualitative determination of chemical compositions and
orientation or structure of some crystalline forms.
4.1.3.1 Fundamental principle of SEM for biochar morphology analysis
The kinetic energy of the accelerated electrons in the SEM is dissipated as signals,
when they hit and become decelerated by the solid Biochar sample through electron-sample
interactions. This signal consists of secondary electrons, backscattered electrons and the
diffracted backscattered electrons which govern the crystal structure and orientation of
minerals. These may also include photons that are utilized for elemental analysis, visible
light and sometimes heat. But the commonly used electrons for imaging are the secondary
electrons and the backscattered electrons. The role of both these are however different and
significant. While the secondary electrons provide valuable information on the morphology
and topography on samples, the contrasts in composition of multiphase samples are given
by the backscattered electrons. The inelastic collisions between the incident electrons and
the electrons in the discrete orbitals produce X-rays. When the excited electrons return back
to the lower energy state, it leads to the release of X- rays of fixed wavelength. This
wavelength is nothing but the difference in energy levels of electrons belonging to different
shells. Thus, the X-rays produced are characteristic to the mineral which “excites” because
of the electron beam. The SEM analysis is a non-destructive method since it does not result
83
in loss of mass or volume of sample which makes repetition of analysis of the same
materials feasible.
Fig 4.3. Components of SEM (Lui et al., 2003)
The macroporous structure of a wood biochar imaged using a scanning electron
microscope (SEM) can be seen in Figure 4.3..
Fig 4.4 Scanning electron microscope (SEM) image (right) showing macroporosity of a
wood-derived biochar produced by „slow‟ pyrolysis
(Paul Munro et al., 2009)
84
Microporosity has been examined by many researchers of which Paul Munro et al.,
is significant. The image of wood derived biochar produced through slow pyrolysis is shown
in Figure 4.4.
To put this in perspective with typical soil particles, these discrete groups of pore
diameters observed in this sample of ~5μm to 10μm, and ~100μm compare to very fine
sand or silt particle sizes, and fine sand particle sizes, respectively and the porosity of the
char will indicate the degree of accommodation of nutrients , better absorption and
supportive microbes
4.2 METHODS AND MATERIALS
4.2.1 Measurement of reflectance of biochar using hyper spectral imaging
The biochar produced by thermo chemical conversion in the thermal desorption unit
through the pyrolysis process was characterised using the hyper spectral imaging equipment
(Figure 4.5) by studying the structural development based on the reflectances. The
equipment used is HyperspecTM (Headwall Photonics Inc. USA). The spectral range of the
equipment is about 900 to 1700 nm.
The HyperspecTM consists of a InGaAs camera, mounted above a conveyor that is
driven by a motor of the desired speed. The illumination system consisted of a tungsten
halogen lamp which illuminated the samples as they drove across the field. After recording
the reflectance data, it was classified using ENVI software 4.8 (ITT Visual Information
Solutions, CO, USA). Classification of the spectral data was obtained by performing
moisaicking the images of the five types of biomasses into groups, and multiple comparison
analysis of the same regions of interest was done. For the classification purpose, Duncan‟s
analysis was done. Using Duncan‟s multiple comparison test it is possible to classify
samples into groups that have similar mean reflectances, those with the least and highest
mean reflectances. The procedure for interpretation of porosity from mean reflectances is
elaborately discussed in section 4.3.1.
85
Fig 4.5 Working HyperspecTM equipment showing the camera, illumination system and sample
field (top), biochar placed in field and illuminated(Bottom left), the camera system(bottom right )
86
4.2.2 Porosity analysis using helium pycnometer
The samples were subjected to pynometry after they were imaged with hyper spectral
imaging since both are non – destructive techniques of characterization. Pycnometry
technique was employed to biochar to calculate its porosity. The Helium Pycnometer
(Model 1305 Multivolume, Micromeritics Instrument Corporation, Norcross, GA) shown
in Figure 4.6 was used to measure the volume (Vsamp) of the samples.(principle elaborated in
the introduction section).
This is used to find out the apparent density (excluding the open, interconnected and
inter particle pore spaces). Using standard methods, the bulk density (including all the
pores, inter particle spaces, moisture and air) of biochar can be found.
Fig 4.6 Components and sample assembling of pycnometer (Top) the pycnometer
(Bottom) sample placed in the holder and sample holder placed in the equipment
87
Thus the porosity (the air or void volume per total volume of material) can be found. This
may be statistically compared for further assessment.
4.2.3 Imaging of biochar using scanning electron microscopy
HITACHI S-3000N Scanning Electron Microscope shown in Figure 4.7 was used for
imaging the surface morphologies of biochar samples. The samples were cut into thin
sections for better view of the cross section. Images were taken at 50X and 1000X
magnification. A high voltage of 25kV was applied and a vacuum of 50 Pa was used for our
study.
Fig 4.7 Variable Pressure Scanning Electron Microscope (HITACHI S-3000N)
Figure 4.7 shows the complete set up of SEM and was taken during imaging of maple
biochar which can be seen on the screen.
88
4.3 RESULTS AND DISCUSSION
4.3.1 Structural development analysis of biochar from hyper spectral imaging
The goal of this study is to assimilate data from spectral studies of the biochars and
correlate them to their porosity. The procedure involves clarity examination of the mosaic
images of biochars as shown in Figure 4.8f and Figure 4.8g. It can be observed that only at
certain wavelengths the images appear with maximum clarity. Thus the most appropriate
images are the ones with maximum clarity. There were a total of 845 bands from which a
single band had to be chosen. The additional issue was that there are five different types of
biomass. Thus selecting the appropriate band which gives maximum clarity for all was done
by trial and error method. Band 99 (1368.4353) was chosen. This band gave maximum
clarity for all the five biomasses. According to the International Commission on
Illumination (CIE) (Henderson & Roy, 2007), the optical radiation is classified as:
IR-A: 700 nm–1400 nm - Near IR
IR-B: 1400 nm–3000 nm - Short range IR
IR-C: 3000 nm–1 mm - Mid and Long range IR
Thus the wavelengths found to be optimum for all five biomasses were 1368 nm
which belongs to short range infrared radiation and 4353 nm which was a mid range
infrared radiation.
The next step of the study was to classify the biochars based on the spectra. The
classification of the spectral data was performed by mosaicking the images from HSI
instrument (Figure 4.8f and 4.8g). Duncan‟s multiple comparison was done and fisher‟s test
is used for representation and comparison. It has been suggested by Tang et al. in 2005 that
with the increase in coal reflectance, the porosity of the formed char decreases. Thus, the
interpretation of the spectral data was done on the fact that, the mean of reflectances
observed were to be related to the porosity of the char produced under variable conditions.
Figure 4.8a, 4.8b, 4.8c, 4.8d and 4.8e shows the fisher‟s multiple comparison tests for
maple, pine, ebony, bamboo and balsa and the treatments T1 to T13 can be referred from
Table 4.2.
Maple: It was observed from Figure 4.8a, that T1 (300o C at 15 min) had the highest
mean reflectance indicating low porosity while T12 (400o C at 30 min) showed the least
89
reflectance mean showing that it had the highest porosity among the group. Also, T2 ( 300o
C at 30 min) and T3 is (300o C at 45 min) showed no significant difference in their mean
reflectances. When there are significant differences between their mean reflectances it
denotes that they have approximately the same porosity.
Pine: From Figure 4.8b it can be deduced that except four samples, almost all the
samples showed similar porosity. T1 (300o C at 15 min) and T3 (300o C at 45 min) had the
highest mean of reflectances showing that they had the least porosity. In addition, T12 (
400o C at 30 min) had the least mean reflectance signifying high porous structure of the
biochar sample. T6 is 350o C at 30 min, T7 (350o C at 30 min), T8 (350o C at 30 min), T9
(350o C at 30 min) had no significant difference in their means. Thus these samples had the
same porosity.
Ebony: Ebony being an exotic hardwood was found to show low porosity in
comparison with other woods. It can be seen in Figure 4.8c that the overall mean
reflectances are quite high for all the samples proving the above fact. Within the group, T1
(300o C at 15 min) had the highest mean reflectance indicating least porosity and as
expected T13 (400o C at 45 min) had the highest porosity since it showed the least mean
reflectance.
Bamboo: The porosity trend of the samples can be observed in Figure 4.8d. Bamboo
showed a range of porosities with change in temperature and time. It can very well be seen
that T1 (300o C at 15 min) had the least porosity followed by T2 (300o C at 30 min) and (T3
300o C at 45 min). However, as the severity of pyrolysis grew, the porosity seemed to
increase gradually. This became evident from T10 (350o C at 45 min), T11 (400o C at 15
min) and T12 (400o C at 30 min) which showed decreasing mean of reflectances implying an
increasing porosity as temperature increased.
Balsa: There was a wide range of porosities in balsa biochar samples similar
to bamboo. T1 (300o C at 15 min) had the highest mean of reflectance indicating that the
sample had the lowest porosity. An interesting observation was that T5 (350o C at 30 min)
and T11 (400o C at 15 min) had no significant difference in their porosities and the mean
reflectance values were quite low. The highest porosity was found to be in T3 (300o C at 45
90
min) indicating that time was an influential factor for determining the porosity of the
sample.
The treatments (T) are;
Table 4.2 Reference table for treatments and their actual values
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
300o C at 15 min
300o C at 30 min
300o C at 45 min
350o C at 15 min
350o C at 30 min
350o C at 30 min
350o C at 30 min
350o C at 30 min
350o C at 30 min
350o C at 45 min
400o C at 15 min
400o C at 30 min
400o C at 45 min
91
Fig 4.8a : Fisher‟s multiple comparison results of pyrolysis of maple biomass with
statistical significance. Note : The means followed by the same letter are not significant
at P<0.05 level(HSI).
Fig 4.8b : Fisher‟s multiple comparison results of pyrolysis of pine biomass with
statistical significance. Note : The means followed by the same letter are not significant
at P<0.05 level( HSI)
92
Fig 4.8c : Fisher‟s multiple comparison results of pyrolysis of ebony biomass with
statistical significance. Note : The means followed by the same letter are not significant
at P<0.05 level( HSI)
Fig 4.8d : Fisher‟s multiple comparison results of pyrolysis of bamboo biomass with
statistical significance. Note : The means followed by the same letter are not significant
at P<0.05 level(HSI)
93
Fig 4.8e : Fisher‟s multiple comparison results of pyrolysis of balsa biomass with
statistical significance. Note : The means followed by the same letter are not significant
at P<0.05 level (HSI)
94
Pine 3000 C-30 min Pine 4000 C-30 min Pine 3500 C-30 min Pine 3500 C-30 min Pine 3500 C-30 min pine 3500 C-30 min pine 3500 C-30 min
Balsa 3000 C-30 min Balsa 3500 C-30 min Balsa 4000 C-30 min Balsa 3500 C-30 min Balsa 3500 C-30 min Balsa 3500 C-30 min Balsa 3500 C-30 min
Bamboo 3000 C-15 min Bamboo 3500 C-15 min Bamboo 4000 C-15 min Bamboo 3000 C-45 min Bamboo 3500 C-45 min Bamboo 4000 C-45 min
Pine 3000 C-15 min Pine 4000 C-15 min Pine 3500 C-15 min Pine 3500 C-45 min Pine 3000 C-45 min Pine 4000 C-45 min
Maple 3000 C-30 min Maple 4000 C-30 min Maple 3500 C-30 min Maple 3500 C-30 min Maple 3500 C-30 min Maple 3500 C-30 min Maple 3500 C-30 min
Fig 4.8f Mosaicking of biochar samples (Mosaic 1)
95
Balsa3000 C-15 min Balsa 4000 C-15min Balsa 3500 C-15 min Balsa 3500 C45 min Balsa 4000 C-45 min Balsa 3000 C-45 min
Maple 3000 C-15 min Maple 3500 C-15 min Maple 4000 C-15 min Maple 3000 C-45 min Maple 3500 C-45 min Maple 4000 C-45 min
Ebony 3500 C-30 min Ebony 3000 C-30 min Ebony 4000 C-30 min Ebony 3000 C-30 min Ebony 3500 C-30 min Ebony 3500 C-30 min Ebony 3500 C-30 min
Ebony 4000 C-15 min
Ebony 3500 C-15 min Ebony 3000 C-15 min Ebony 3500 C-45 min Ebony 4000 C-45 min Ebony 3000 C-45 min
Bamboo 3000 C-30 min
Bamboo 4000 C-30 min Bamboo 3500 C-30 min Bamboo 3500 C-30 min Bamboo 3500 C-30 min Bamboo 3500 C-30 min Bamboo 3500 C-30 min
Fig 4.8g Mosaicking of biochar samples (Mosaic 2)
96
4.3.2 Characterisation of biochar based on porosity using pycnometry
The objective of this study is to characterise the biochar based on the data obtained
from pycnometry. Pyconometry results were obtained and they were analysed using Fisher‟s
test. Using the procedure described in section 4.2.2 and the equations 4.1 and 4.2 the
porosity of the samples are calculated. Figures 4.9a, 4.9b, 4.9c, 4.9d and 4.9e show the
fisher‟s multiple comparison tests for pine, maple, ebony, balsa and bamboo and the
treatments T1to T13 can be referred form Table 4.2
Pine: Among pine biochar (in Figure 4.9a), T2 (300o C at 30 min) had minimum
porosity. Most of the members of this pine group were found to have similar porosity.
Among them, T6 (350o C at 30 min), T7 (350o C at 30 min), T8 (350o C at 30 min) and T9
(350o C at 30 min) had exactly the same high porosity. But the highest porosity was found in
T4 (350o C at 15 min) which meant that as the process approached the central point the
increase in porosity was also high.
Maple: Maple, the deciduous hard wood had the highest porosity for T8 (350o C at
30 min) and the lowest porosity was found to for the sample T1 (300o C at 15 min). It was
also observed from Figure 4.9a that T6 (350o C at 30 min), T7 (350o C at 30 min), T9 (350o
C at 30 min), T13 (400o C at 45 min) showed remarkably the same porosity. As expected,
T2 (300o C at 30 min) had a low porosity. This may be due to the inefficiency of the lower
temperatures to pyrolyse the biomasses completely to produce biochars of high porosity.
Ebony: From Figure 4.9c it can be seen that, T10 (350o C at 45) min has the highest
porosity. Similar high porosity was observed in T7 (350o C at 30 min), T6 350o C at 30 min,
T8 (350o C at 30 min), T9 (350o C at 30 min) did not have significant differences in their
porosity %. Among the group of ebony biochars, moderate porosities were found in T3
(300o C at 45 min) and T13 (400o C at 45 min).
Balsa: Figure 4.9d shows the pycnometry results for balsa. It can be seen that T2
(300o C at 30 min) had the lowest porosity among the group of balsa biochar samples. T8
(350o C at 30 min) showed an astonishingly high value of porosity among all the members
97
of the group. This might be due to the growth in severity of pyrolysis as a result of
increasing temperature.
Bamboo: Bamboo, a tropical grass showed very high porosity in the overall sense.
From Figure 4.9c , it became clearly evident that, T6 (350o C at 30 min), T7 (350o C at 30
min), T8 (350o C at 30 min), T9 (350oC at 30 min) and T10 (350o C at 45 min) had the
highest and similar porosities among the group. As expected, T2 (300o C at 30 min) had a
very low porosity and was found to be the least.
The results indicated that as the temperature increased the porosity also increased.
This was due to the fact that the surface texture of biochar became more irregular because of
devolatilization of volatile matter. Karosmanog et al., (2003) had also reported increase in
porosity with increasing temperature of pyrolysis.
Table 4.2 Reference table for treatments and their actual values
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
300o C at 15 min
300o C at 30 min
300o C at 45 min
350o C at 15 min
350o C at 30 min
350o C at 30 min
350o C at 30 min
350o C at 30 min
350o C at 30 min
350o C at 45 min
400o C at 15 min
400o C at 30 min
400o C at 45 min
98
Fig 4.9a : Fisher‟s multiple comparison results of pyrolysis of pine biomass with
statistical significance. Note : The means followed by the same letter are not significant
at P<0.05 level (pycnometry)
Fig 4.9b : Fisher‟s multiple comparison results of pyrolysis of maple biomass with
statistical significance. Note : The means followed by the same letter are not significant
at P<0.05 level( pynometry)
99
Fig 4.9c : Fisher‟s multiple comparison results of pyrolysis of ebony biomass with
statistical significance. Note : The means followed by the same letter are not significant
at P<0.05 level( pynometry)
Fig 4.9d : Fisher‟s multiple comparison results of pyrolysis of balsa biomass with
statistical significance. Note : The means followed by the same letter are not significant
at P<0.05 level ( pynometry)
100
Fig 4.9e : Fisher‟s multiple comparison results of pyrolysis of bamboo biomass with
statistical significance. Note : The means followed by the same letter are not significant
at P<0.05 level( pynometry)
101
4.3.3 Surface morphology studies of biochar from scanning electron microscopy imaging
HITACHI S-3000N Scanning Electron Microscope was used for imaging the surface
morphologies of biochar samples. The SEM images were captured at 1000X and 50X
magnification. At 1000X, the internal pores became clearly visible that even the size could
be measured. The purpose of 50X magnification was to view of the cross section of the
biochar samples. The results from the SEM were in agreement with the results of
pycnometry and hyper spectral imaging. The pores at higher temperature were observed to
be bigger compared to those at lower temperatures. As the temperature and severity of
pyrolysis process elevated, enlargement of pores could be observed. Pore size at 300°C,
15min was smaller than those at 400°C 15 min. Most of the biochars exhibit macro porosity
similar to the cellular structures of the botanical origin from which they were formed. This
was confirmed through the microscopy analysis showing the alignment of honey comb
pores in the order of 10µm in diameter, which represents the carbonaceous skeleton (Laine
et al., 1991). Fukuyama et al., in 2001 who investigated the meso and micro pores of carbon
materials said that the large sized pores serve as the feeder for pores of smaller dimension.
In our studies also, similar observation was noticed. A mixture of micro and macro pores
was present. In Figure 4.10, 1000X magnification images of maple, pine, bamboo, ebony
and balsa are given in order. Each biomass was said to have its own pore shape similar to
the cells from which it originated. Every sample was found to have a distinct pattern which
can be witnessed from Figure 4.11 which shows images taken from samples treated at the
central point of 350°C for 30 minutes. Brennan et al., 2001 showed evidences of existence of
a range of different functional groups that existed in the grapheme sheets. Koutcheiko et al.,
(2007) who worked on preparation of chicken manure biochar by heating to 360°C in a fast
pyrolysis unit observed the existence of pyrrolic or pyridinic amine groups. Thus there are
evidences of presence of poly aromatic hydrocarbons in the biochars. It is suspected from
Figure 4.10 (Those images boxed in red) that there might be existence of pyridinic and
quaternary groups (Patch of bright white spots). EDS or Energy Dispersive X-Ray, FTIR
Spectroscopy studies and other techniques are required for elucidate these details.
102
Fig 4.10 Scanning Electron Microscopy images of various biochars at 1000X magnification
Maple 300o C 15 min Maple 350 o C 15 min
Maple 300 o C 30 min Maple 350 o C 30 min
Maple 300 o C 45 min Maple 350 o C 45 min
103
Maple 400 o C 15 min Pine 300 o C 15 min
Maple 400 o C 30 min Pine 300 o C 30 min
Maple 400 o C 45 min Pine 300 o C 45 min
104
Pine 350 o C 15 min Pine 400 o C 15 min
Pine 350 o C 30 min Pine 400 o C 30 min
Pine 350 o C 45 min Pine 400 o C 45 min
105
Bamboo 300 o C 15 min Bamboo 350 o C 15 min
Bamboo 300 o C 30 min Bamboo 350 o C 30 min
Bamboo 300 o C 45 min Bamboo 350 o C 45 min
106
Bamboo 400 o C 15 min Ebony 300 o C 15 min
Bamboo 400 o C 30 min Ebony 300 o C 30 min
Bamboo 400 o C 45 min Ebony 300 o C 45 min
107
Ebony 350 o C 15 min Ebony 400 o C 15 min
Ebony 350 o C 30 min Ebony 400 o C 30 min
Ebony 350 o C 45 min Ebony 400 o C 45 min
108
Balsa 300 o C 15 min Balsa 350 o C 15 min
Balsa 300 o C 30 min Balsa 350 o C 30 min
Balsa 300 o C 45 min Balsa 350 o C 45 min
109
PINE MAPLE
EBONY BALSA
BAMBOO
Fig 4.11 SEM images of Biochars at 50X magnification taken at the central point of
350°C for 30 minutes
110
4.4 CONCLUSION
Characterization of biochar revealed information that could help us to study the
physical properties. The biochar from pyrolysis was subjected to analysis, all of which are
non-destructive. Initially, using a hyper spectral imaging, the mean reflectance was
recorded. This was used to predict the porosity of the char. It was observed that, as the
temperature increased the porosity also increased. From the pycnometry data, using fisher
test, statistical analysis was conducted. Similar to the hyper spectral imaging analysis, it was
shown that the increase in temperature ultimately led to the increase in porosity. This fact
was again substantiated through the images of scanning electron microscope. Another
important result was that there were no cracks unlike other researchers who observed
cracking of char material. Thus, all the results indicate that, temperature is a key factor for
pyrolysis process. Characterization thus provides a wide knowledge about the char and the
modifications required to attain the desired result.
4.5 ACKNOWLEDGEMENTS
The authors are grateful to NSERC (Natural sciences and engineering research council of
Canada) for the financial support of this study.
111
CHAPTER V
SUMMARY AND CONCLUSIONS
Biochar is a fine-grained, porous charcoal substance that, when used as a soil
amendment in combination with sustainable production of the biomass feedstock,
effectively removes net carbon dioxide from the atmosphere. In the soil, biochar provides a
habitat for soil organisms, but is not itself consumed by them to a great extent, and most of
the applied biochar can remain in the soil for several hundreds to thousands of years. When
used as a soil amendment along with organic and inorganic fertilizers, biochar significantly
improves soil tilth, productivity, and nutrient retention and availability to plants. Thus,
instead of letting the wood biomass to decompose or incinerate, biochar production would
be the best fate for the waste stream.
The first objective of the research was to produce biochar from different
lignocellulosic biomasses and assessment of the pyrolysis technique based on the different
process conditions like temperature and time used. These were analysed using the central
composite uniform precision design to evaluate their effects on the yield of biochar through
pyrolysis. The resulting regression model indicated that a series of linear models best
described the correlation of temperature change to pyrolysis process. SAS software was used
to statistically fit the obtained data. It was observed that, pine, maple, ebony and bamboo
showed great fit to the model. Balsa showed only an average fit. Pine was the only wood
which had influence from both the factors of analysis. Both temperature and time
significantly influenced the yield (p<0.0001 and p=0.0394 respectively). For maple, ebony,
bamboo and balsa only temperature was an influential factor (p=0.0002, p=0.0001,
p=0.0027 and p=0.0073). The model fitted the data for all types of biomass well. This
became evident from the values of coefficient of determination R² for different biomasses
which were significantly close to unity except for balsa. The values were observed to be
R²=0.90 for ebony, R²=0.98 for pine, R²=0.80 for bamboo and R²=0.89 for maple. A lower
value of R²=0.67 was observed for balsa which proved to be a reasonable fit. The
desirability term for the process was defined and determined. The total desirability of the
predicted yield to the actual yield for all the biomasses together was less, thus when fitted
112
separately, gave optimum values of temperature and time for the biomass pyrolysis. Further,
response surface plots were drawn to operate it under different experimental conditions.
From density analysis it was concluded that, the density of biochar was 0.80 times the
density of wood. Proximate analysis of biochar indicated that at higher temperatures there
was low mobile matter percentage and high residual matter percentage proving its good fuel
properties.
The second objective was to characterize using pycnometry, hyper spectral imaging
and electron microscopy. ENVI 4.8 was used to analyse the spectral data. It was observed
from the hyperspectral imaging data analysis that the mean reflectances were lower for
treatments at higher temperatures. This was due to the fact the porosity of chars increased
when the temperature increased. Similar trends were shown by bamboo though it is a grass;
it showed the same characteristic porosity as any wood under study. From the pycnometry
analysis it was observed that the porosity of the char increased and the pores started
becoming roughly surfaced due to devolatilation. The analysis was done using XLSTAT
2011. The results from scanning electron microscopy were a visual proof that the pore size
increased with the gradual increase in temperature. Further studies are required to
understand the micro properties of biochar.
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