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was 0.8 times the density of the wood from which it was originally made.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.

iv

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

v

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.

vi

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.

vii

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.

x

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: Pavithra.sellaperumal@gmail.com

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).

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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.

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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).

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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

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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.

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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: Pavithra.sellaperumal@gmail.com

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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

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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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