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Primary Sludge Addition for Enhanced Biosludge Dewatering
by
Parthiv Amin
A thesis submitted in conformity with the requirements for the degree of Master of Applied Science
Department of Chemical Engineering & Applied Chemistry University of Toronto
© Copyright by Parthiv Amin 2014
ii
Primary Sludge Addition for Enhanced Biosludge Dewatering
Parthiv Amin
Master of Applied Science
Department of Chemical Engineering & Applied Chemistry
University of Toronto
2014
Abstract
Biosludge disposal is a costly challenge for pulp and paper mills. Primary sludge is often
combined with biosludge, and while this is known to improve downstream dewatering,
quantification of the effects of primary sludge addition is not well studied. Evaluation of sludge
properties, including mechanical dewaterability, has shown that primary sludge improves
biosludge dewaterability by a factor of 2-4 when combined with biosludge at levels as low as 20
wt%. The improvement follows a consistent pattern between different primary sludge types,
however a model derived from filtration theory is unable to fully capture the trend. Primary
sludge pretreatment is proposed as a means to improve primary sludge usage with regards to
excess water and monovalent cations. Primary sludge pretreatment, particle size and nature,
and field trials are areas recommended for further investigation in line with the objective of
better understanding dewatering enhancement by primary sludge addition.
iii
Acknowledgments
I would like to thank my supervisor Professor D. G. Allen for his guidance throughout this work,
and to Professor Honghi Tran and Professor Arun Ramchandran for serving on my committee.
I am especially grateful to the personnel at the Tembec Temiscaming and Tembec Kapuskasing
Pulp & Paper mills. In particular I would like to thank Adrew Barquin, and Eric Duchesne for
arranging to provide samples to our lab, without which this work would not have been possible.
Furthermore I would like to thank all of the additional personnel who conducted tours of these
mills for my colleagues and I. The tours provided important insights and direction to my work.
At the University of Toronto I would like to thank Susie for her never ending patience and
assistance with equipment in Biozone, as well as my fellow lab mate Sofia for her guidance. I
must also thank Doug, Igor, and Sue for their instruction and assistance on various instruments
necessary for my work. I would also like to thank the summer students in our lab who helped
conduct experiments and gather data.
Over the course of my work, I was able to meet a number of wonderful friends who contributed
greatly to my time at the U of T. Thank you to Rosanna, Doug and all my other CEGSA
colleagues for the good times, and thanks as well to the Komisar sisters for all the laughs!
This research was funded by an Industry/Academia partnership through the Natural Sciences
and Engineering Research Council of Canada Collaborative Research and Development Grant
Program.
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Table of Contents
Abstract ............................................................................................................................................ ii
Acknowledgments........................................................................................................................... iii
List of Tables .................................................................................................................................. vii
List of Figures ................................................................................................................................ viii
List of Abbreviations ....................................................................................................................... xi
1 Introduction ............................................................................................................................... 1
1.1 Objectives............................................................................................................................ 2
2 Literature Review ....................................................................................................................... 3
2.1 Biosludge ............................................................................................................................. 3
2.2 Primary Sludge .................................................................................................................... 6
2.3 Filter Aids ............................................................................................................................ 7
2.4 Assessment of Dewaterability .......................................................................................... 12
3 Materials and Methods ............................................................................................................ 15
3.1 Sludge ................................................................................................................................ 15
3.1.1 Biosludge ............................................................................................................... 15
3.1.2 Primary sludge Type A .......................................................................................... 16
3.1.3 Primary sludge Type B........................................................................................... 16
3.1.4 Primary sludge Type C ........................................................................................... 16
3.2 Chemicals .......................................................................................................................... 17
3.2.1 General Reagents .................................................................................................. 17
3.2.2 Polymer ................................................................................................................. 17
3.3 Experimental Approach .................................................................................................... 18
3.4 Test Protocols ................................................................................................................... 19
v
3.4.1 Total & Volatile Suspended Solids ........................................................................ 19
3.4.2 Total and Volatile Solids ........................................................................................ 19
3.4.3 Capillary Suction Time........................................................................................... 19
3.4.4 Particle Size ........................................................................................................... 19
3.4.5 Elemental Composition ......................................................................................... 20
3.4.6 Crown Press Dewaterability & Gravity Filtration .................................................. 21
3.4.7 pH .......................................................................................................................... 25
3.4.8 Data Analysis ......................................................................................................... 25
4 Results & Discussion................................................................................................................. 26
4.1 Sludge Storage .................................................................................................................. 27
4.2 CST and TSS ....................................................................................................................... 31
4.3 Crown Press ...................................................................................................................... 39
4.3.1 Correlation of Crown Press Cake Solids to CST and TSS ....................................... 39
4.3.2 Crown Press Cake Solids – Combined Sludge Tests .............................................. 42
4.3.3 Crown Press Cake Solids – Theoretical Basis of Understanding ........................... 48
4.3.4 Gravity Filtrate and Crown Press Pressate............................................................ 56
4.4 Particle Size ....................................................................................................................... 59
4.5 Elemental Analysis ............................................................................................................ 67
5 Conclusions .............................................................................................................................. 73
6 Recommendations ................................................................................................................... 77
7 References ................................................................................................................................ 79
8 Appendices ............................................................................................................................... 90
8.1 Appendix A – Darcy’s Law Derivation for SRF ................................................................... 90
8.2 Appendix B - Linear Regression Data for CST Dilution Tests ............................................ 92
vi
8.3 Appendix C - Regression Data for Crown Press Cake Solids – Mixed Sludge ................... 93
8.4 Appendix D – Regression Data for Crown Press Cake Solids & SRF .................................. 94
8.5 Appendix E – Linear Trends for Primary Solids vs. SRF Data & SRF vs. Cake Solids Data . 95
vii
List of Tables
Table 1. Solids Classification System ............................................................................................... 4
Table 2. Performance data for belt filter presses dewatering primary and secondary sludges .... 7
Table 3. Common Filter Aids ........................................................................................................... 9
Table 4. Dosage and Performance of Common Filter Aids ........................................................... 11
Table 5. Chemical/Reagents ......................................................................................................... 17
Table 6. ICP-OES Instrument Parameters ..................................................................................... 21
Table 7. Crown Press Calibration .................................................................................................. 24
Table 8. Linear Regression Best Fit Values – CST Dilutions........................................................... 92
Table 9. Linear Regression Best Fit Values – Crown Press Cake Solids vs. Primary Sludge Content
....................................................................................................................................................... 93
Table 10. Model Equation Best Fit Parameters ............................................................................ 93
Table 11. Linear Regression Best Fit Values – Crown Press Cake Solids vs. SRF........................... 94
Table 12. Linear Regression Best Fit Values – Primary Solids Content vs. SRF ............................. 95
Table 13. Linear Regression Best Fit Values – Cake Solids vs. SRF ............................................... 95
viii
List of Figures
Figure 1. General Overview of Conventional Wastewater Treatment Process .............................. 3
Figure 2. Filter Aid Effect ................................................................................................................. 8
Figure 3. Crown Press Belt Press Simulator (Phipps & Bird, 2013) ............................................... 13
Figure 4. Overview of Central Wastewater Treatment Plant ....................................................... 15
Figure 5. Sludge Handling System ................................................................................................. 16
Figure 6. Experimental Approach ................................................................................................. 18
Figure 7. Crown Press with Attached Gravity Filtration Apparatus .............................................. 22
Figure 8. Biosludge pH - October 2012 Batch ............................................................................... 27
Figure 9. Biosludge TSS - October 2012 Batch .............................................................................. 27
Figure 10. Biosludge CST - October 2012 Batch ........................................................................... 27
Figure 11. Biosludge CST - October 2012 Batch - Outlier Removed ............................................. 27
Figure 12. Biosludge sample 1 pH - June 2013 Batch ................................................................... 28
Figure 13. Primary Sludge Type C pH - June 2013 Batch .............................................................. 28
Figure 14. Biosludge and Primary Sludge CST - June 2013 Batches ............................................. 28
Figure 15. Biosludge TSS - June 2013 Batch .................................................................................. 28
Figure 16. CST of Sludge Samples ................................................................................................. 31
Figure 17. CST of Sludge - With and Without Polymer ................................................................. 32
Figure 18. TSS of Sludge Samples ................................................................................................. 33
ix
Figure 19. Correlation of CST with TSS ......................................................................................... 34
Figure 20. CST of Sludge - Dilution Tests ...................................................................................... 35
Figure 21. CST - Optimum Polymer Dose Determination ............................................................. 38
Figure 22. Crown Press Cake Solids vs. CST - All Sludge Samples ................................................. 40
Figure 23. Crown Press Cake Solids vs. CST - All Sludge Samples - Outlier Removed .................. 40
Figure 24. Crown Press Solids vs. Total Suspended Solids - All Sludges ....................................... 41
Figure 25. Crown Press Cake Solids vs. Primary Solids % - Primary Sludge Type A ...................... 42
Figure 26. Crown Press Cake Solids vs. Primary Solids % - Primary Sludge Type B ...................... 43
Figure 27. Crown Press Cake Solids vs. Primary Solids % - Primary Sludge Type C ...................... 43
Figure 28. Crown Press Cake Solids vs. Primary Solids % - All Sludges with Polymer .................. 45
Figure 29. Correlation of Primary Sludge Mass Fraction with Specific Resistance to Filtration .. 48
Figure 30. Correlation of Gravity Filtration Specific Resistance with Crown Press Cake Solids ... 50
Figure 31. Correlation of Gravity Filtration Specific Resistance with Crown Press Cake Solids -
Outlier Removed ........................................................................................................................... 50
Figure 32. Estimated trend versus empirical data for cake solids as a function of primary solids –
Without polymer treatment ......................................................................................................... 53
Figure 33. Estimated trend versus empirical data for cake solids as a function of primary solids -
With polymer treatment ............................................................................................................... 53
Figure 34. TSS of Gravity Filtrate + Crown Press Pressate ............................................................ 56
Figure 35. Particle Size Distribution - Biosludge with & without Polymer ................................... 60
x
Figure 36. Particle Size Distribution - Primary Sludge .................................................................. 60
Figure 37. PSD - June Biosludge - Raw and with 40% Primary Sludge .......................................... 62
Figure 38. PSD - June Biosludge - Raw and with 40% Primary Sludge and Polymer .................... 62
Figure 39. PSD - July Biosludge - Raw and with 40% Primary Sludge ........................................... 62
Figure 40. PSD - July Biosludge - Raw and with 40% Primary Sludge and Polymer ...................... 62
Figure 41. Percent of Particles in the Settleable Size Range (>100μm equivalent particle
diameter) for Various Sludge Mixtures......................................................................................... 64
Figure 42. Cation Species Concentration - Type A Primary Sludge .............................................. 67
Figure 43. Cation Species Concentration - Type B Primary Sludge............................................... 67
Figure 44. Cation Species Concentration - Type C Primary Sludge ............................................... 68
Figure 45. Cation Species Concentration – Biosludge .................................................................. 68
Figure 46. Monovalent to Divalent Cation Ratios ......................................................................... 69
Figure 47. Cation Species Concentration - Raw Sludge versus Sludge Supernatant .................... 70
Figure 48. T/V vs. V for Gravity filtration of 30%:70% Type A Primary:Biosludge mix ................. 91
xi
List of Abbreviations
ASP – Activated Sludge Process
BCTMP – Bleached Chemi-Thermo-Mechanical Pulping
COD – Chemical Oxygen Demand
CPGR – Crown Press Gauge Reading
CST – Capillary Suction Time
DO – Dissolved Oxygen
EPS – Extracellular Polymeric Substances
ICPOES – Inductively Coupled Plasma Optical Emission Spectroscopy
M:D – Monovalent to Divalent
ODT – Oven Dried Tonne
PSD – Particle Size Distribution
SN - Supernatant
SRF – Specific Resistance to Filtration
TPD – Tonnes Per Day
TS – Total Solids
TSS – Total Suspended Solids
VS – Volatile Solids
VSS – Volatile Suspended Solids
xii
WAS – Waste Activated Sludge also referred to as Biosludge
1
1 Introduction
Biosludge, also known as secondary sludge or waste activated sludge (WAS), is a byproduct of
aerobic secondary effluent treatment by the activated sludge process. Biosludges are generally
comprised of microorganisms, extracellular polymeric substances (EPS), organic and inorganic
matter, and water. The challenge posed to treatment plants by biosludge arises from the water
content, which can be greater than to 98% (Elliott & Mahmood, 2007). Disposal of the sludge
generally occurs via three methods: landfill, incineration, or landspreading. Prior to utilizing any
of the methods, the water must be removed so as to minimize the mass and volume of sludge,
and increase dryness. In the case of landfilling, transportation and disposal fees are normally
charged per unit mass, and landfill operators may also impose limits on the maximum moisture
content to prevent excessive leaching. Disposal by incineration requires increased dryness as
biomass fuels typically need a minimum of 40-60% dry solids to maintain autogeneous
combustion (depending on the type of combustor used) (ADI Limited, 2005). Dewatering of
sludge to the extent necessary for disposal can be costly, requiring dedicated equipment and an
array of treatment chemicals.
In the pulp and paper industry in Canada, incineration has commonly been used as a means of
sludge disposal with 49% of pulp and paper mills employing this method in 1998 (ADI Limited,
2005). Effective combustion, however, is still limited by high and variable moisture content,
which in turn limits the potential for energy recovery, and complicates boiler operations. Co-
combustion with higher quality fuels (e.g. natural gas, coal, bark, etc.) is sometimes necessary
to overcome excess moisture in sludge. Inefficiencies and challenges in sludge dewatering
systems are thus capable of creating cascades of challenges for mill operators. More effective
sludge dewatering is therefore an important goal for mill operations.
One method employed by mills to enhance biosludge dewatering has been to add in primary
sludge with the biosludge prior to dewatering. Primary sludge is generally much easier to
dewater as are mixes of biosludge and primary sludge with higher primary to secondary ratios
(Amberg, 1984)(Mahmood & Elliott, 2006).
2
Despite being a common practice at mills, and being known to provide significant
improvements in biosludge handling, there has been limited study into the mechanisms behind
the benefits conferred by primary sludge addition.
1.1 Objectives
The primary goal of this work is to enhance the understanding of biosludge & primary sludge
dewatering in the context of pulp and paper mills. This is in line with the overarching aims of
mill operators to improve sludge handling so as to generate opportunities for cost savings and
enhanced energy recovery in boilers.
Working towards this goal, the general objective is to identify key parameters that affect the
dewatering performance of primary sludge, biosludge, and mixtures thereof. This is
accomplished specifically through: a) the identification and quantification of a set of metrics to
characterize sludge and dewatering properties; and b) correlation of sludge properties to
dewatering performance to elucidate and quantify mechanisms by which primary sludge
enhances biosludge dewaterability.
A better understanding of the practice of primary sludge addition will allow for greater
opportunities for sludge handling optimization and improve downstream mill operations.
2 Literature Review
2.1 Biosludge
Wastewater treatment in pulp and paper
on effluent discharge quality. Prior to the 1970s, the pulp and paper industry was not subject to
regulations on wastewater effluent discharge, however, damage to fish and fish habitats
prompted the 1971 Pulp and Paper Effluents Regulation under the Fisheries Act in Canada
(Environment Canada, 2012). Amendments to the regulations in 1992 made effluent quality
standards more stringent and enforceable for all mills, with the net result that mills ad
secondary treatment to meet the new standards
treatment, in the form of aerobic biological treatment (commonly the activated sludge process
or ASP), is now a standard component of the overall effluent trea
Figure 1. General Overview of Conventional Wastewater Treatment Process
The activated sludge process uses microorganisms (bacteria and protozoa) and aeration to
biologically degrade organic matter into c
the overall content of organic matter in the effluent stream; however, the process generates
biosludge (waste activated sludge) as a byproduct. Canadian pulp and paper mills typically
generate less than 100 kg of oven dried sludge per tonne of pulp/paper product, of which the
mean secondary sludge component has been approximately 37%. These mills produce
secondary sludge in a range of 1
Literature Review
Wastewater treatment in pulp and paper mills is necessary to meet environmental regulations
on effluent discharge quality. Prior to the 1970s, the pulp and paper industry was not subject to
regulations on wastewater effluent discharge, however, damage to fish and fish habitats
Pulp and Paper Effluents Regulation under the Fisheries Act in Canada
. Amendments to the regulations in 1992 made effluent quality
standards more stringent and enforceable for all mills, with the net result that mills ad
secondary treatment to meet the new standards (Environment Canada, 2012). Secondary
treatment, in the form of aerobic biological treatment (commonly the activated sludge process
), is now a standard component of the overall effluent treatment process at mills.
General Overview of Conventional Wastewater Treatment Process
The activated sludge process uses microorganisms (bacteria and protozoa) and aeration to
biologically degrade organic matter into carbon dioxide and water (Bitton, 2005)
the overall content of organic matter in the effluent stream; however, the process generates
e (waste activated sludge) as a byproduct. Canadian pulp and paper mills typically
kg of oven dried sludge per tonne of pulp/paper product, of which the
mean secondary sludge component has been approximately 37%. These mills produce
secondary sludge in a range of 1-60 odt per day (Dorica, Harland, & Kovacs, 1999)
3
mills is necessary to meet environmental regulations
on effluent discharge quality. Prior to the 1970s, the pulp and paper industry was not subject to
regulations on wastewater effluent discharge, however, damage to fish and fish habitats
Pulp and Paper Effluents Regulation under the Fisheries Act in Canada
. Amendments to the regulations in 1992 made effluent quality
standards more stringent and enforceable for all mills, with the net result that mills adopted
. Secondary
treatment, in the form of aerobic biological treatment (commonly the activated sludge process
tment process at mills.
The activated sludge process uses microorganisms (bacteria and protozoa) and aeration to
(Bitton, 2005). This reduces
the overall content of organic matter in the effluent stream; however, the process generates
e (waste activated sludge) as a byproduct. Canadian pulp and paper mills typically
kg of oven dried sludge per tonne of pulp/paper product, of which the
mean secondary sludge component has been approximately 37%. These mills produce
(Dorica, Harland, & Kovacs, 1999). A survey of
4
Finnish plants found secondary sludge from activated sludge plants to be produced at a rate of
6 kg/tonne of product (5.9 tpd) and 9.5 kg/tonne of product (11.5 tpd) for paper and pulp mills
respectively (Saunamaki, 1997). Overall the mills in the above surveys typically processed on
average between 26 and 41 odt of total sludge per day. A more recent survey of Canadian mills
found that approximately 50 dry kg of sludge (70:30 primary:secondary) is produced per tonne
of production (Elliott & Mahmood, 2005).
As biosludge is a dilute slurry with a water content that usually exceeds 98% (Mahmood &
Elliott, 2007), mills often must process tens to hundreds of tonnes per day of wet biosludge
from secondary clarifiers. The requirement for high throughput as well as high water removal
efficiency in sludge handling systems necessitates expensive dewatering equipment as well as
ongoing costs for dewatering aids and conditioners. Effective removal of water from biosludge
is a challenge due to the composition and properties of biosludge. Physical properties including
particle size, compressibility, water content; biological properties including biomass content
and composition; as well as chemical composition/ properties are all contributory factors that
can influence dewaterability of biosludge.
Particle size distribution plays an important role in both the settleability of sludge as well as
filtration. In the context of wastewater sludges, the following classification system (Karr &
Keinath, 1978) has been used to describe particle sizes:
Table 1. Solids Classification System
Solids Fraction Size (μm)
Settleable ≥100
Supracolloidal 1-100
True colloidal 0.001-1
Dissolved ≤0.001
5
In general, activated sludge is comprised of particles that fall primarily in the supracolloidal and
settleable range (Liao, Droppo, Leppard, & Liss, 2006)(Dursun, Ayol, & Dentel, 2004)(Karr &
Keinath, 1978). Karr & Keinath (1978) investigated the influence of particle size fraction on
dewatering characteristics and found that particles in the supracolloidal range of 1-100 μm had
the greatest effect. Their findings indicate that higher fractions of supracolloids is correlated
with poor dewatering characteristics, due to the ability of particles of this size range to blind
filtration media and sludge cakes and increase filtrate flow resistance.
Particle size in combination with other properties can also have a negative effect on dewatering
properties. Biosludge particles are generally negatively charged due to EPS and other
biochemical components (Liao, Allen, Droppo, Leppard, & Liss, 2001; Liao, Allen, Leppard,
Droppo, & Liss, 2002). The negative charge can, by means of the electrical double layer and
subsequent electrostatic repulsion, cause the sludge to behave in a manner similar to colloidal
suspensions (Neyens & Baeyens, 2003)(Wilén, Jin, & Lant, 2003). In general, lower magnitude
surface charge is related to improved settling (Neyens & Baeyens, 2003).
In addition to fine particles, biosludge also demonstrates a high degree of compressibility.
Highly compressible sludge particles blind filter media and the sludge cake by means of reduced
porosity as sludge particles deform and close voids in the sludge cake (Qi, Thapa, & Hoadley,
2011)(Smollen & Kafaar, 1997)(Sorensen & Hansen, 1993). Compressibility is known to be
influenced by floc size, the presence of filaments, and extracellular polymeric substances (EPS)
(Jin, Wilén, & Lant, 2003). Extracellular polymeric substances can have a negative effect on
sludge compressibility. This is caused by the EPS preventing nearby cells from packing closely
together, as well as the formation of a gel matrix which retains water (Liao et al., 2001).
The EPS gel matrix is itself affected by cations, which in turn affect the biosludge bulk
properties. Cations, specifically divalent cations such as Ca2+
and Mg2+
, are known to act as
bridging agents that interact with negatively charged EPS and stabilize the gel matrix (Cousin &
Ganczarczyk, 1999)(Nguyen, Hilal, Hankins, & Novak, 2008)(Murthy, Novak, & De Haas, 1998).
Monovalent cations such as Na+ and K
+ have the opposite effect and destabilize the matrix
leading to deflocculation, increased turbidity, poorer settling, and a decrease in filterability, and
6
it is generally accepted that a monovalent to divalent (M:D) cation ratio of 2 (on a charge
equivalence basis) is the threshold at which dewatering properties deteriorate (Cousin &
Ganczarczyk, 1999)(Nguyen et al., 2008)(Murthy et al., 1998). As noted by Murthy et al. (1998),
a high M:D ratio and the associated problems are generally seen in scenarios where caustic
soda was added for pH control. For the pulp and paper industry, this is especially relevant as
wastewater streams entering the activated sludge process contain pulping chemicals which
commonly include sodium hydroxide, sodium sulphide, sodium sulphite, and/or sodium
bicarbonate. All of these pulping chemicals can contribute to a poor M:D ratio, and therefore
poor dewatering performance.
2.2 Primary Sludge
Primary sludge is generated by the primary treatment (clarification) of raw wastewater. After
screening for large debris, raw effluent is pumped into a primary clarifier and solids are allowed
to settle under gravity. The clarified effluent is pumped on to secondary treatment, and the
solids collected in the clarifier are removed and referred to as primary sludge. At a pulp and
paper mill, primary sludge is primarily composed of fibres and fines that have been lost from
the pulping and/or papermaking process (Mahmood & Elliott, 2006).
In sludge handling systems primary sludge is added to biosludge for two reasons: 1) as a
dewatering aid, and 2) to consolidate sludge streams prior to sludge processing. At the mill level
this practice has been shown to improve the dewaterability of sludge, reduce the costs for
additional chemical sludge treatments, and improve overall sludge throughput as seen in Table
2 (Amberg, 1984).
Primary sludge has been shown to improve dewatering of biosludge (H. Zhao, 2000)(Amberg,
1984) and to minimize the complexity of sludge handling systems, primary sludge and biosludge
are usually combined. In fact, sludge processing equipment (dewatering apparatus), are
commonly designed to operate with a specific mix ratio. As primary sludge and biosludge have
typically been produced at a ratio of 70:30 (Elliott & Mahmood, 2005), it would follow that
equipment would be optimized around this ratio.
7
Table 2. Performance data for belt filter presses dewatering primary and secondary sludges
Sludge Ratio Polymer cost
$/metric tonne
Actual cake solids
%
Output, metric
tonnes/metre width
Primary sludge 5-11 25-35 10-20
P:S – 2.0 22-33 20-25 8-15
P:S – 1.5 28-39 18-25 7-15
P:S – 1.0 33-44 16-20 5-10
Secondary Sludge 33-100 13-16 4-8
Designing equipment around this ratio presents the challenge whereby the mix ratio needs to
be maintained to ensure optimal performance, and yet primary sludge is a diminishing resource
at mills. In recent times, mills are engaged in efforts to optimize product yields, and minimize
fibre loss to maximize economics of mill operations (Mahmood & Elliott, 2006). The resulting
decrease in primary sludge to biosludge ratio can affect sludge handling operations, and
anecdotal evidence from select Canadian mill operators would suggest that a lower ratio results
in reduced sludge throughput, increased demand for dewatering chemicals/aids, and a lower
final cake solids. At low ratios, dewatering equipment can even be rendered completely
ineffective in solid/liquid separation.
While primary sludge is known to improve dewatering, minimal study has been conducted on
the mechanisms behind this benefit. There is a lack of knowledge in the literature that
quantitatively assesses changes in biosludge properties upon addition of primary sludge.
Studies that have been conducted in biosludge dewatering are more often focused on the
addition of other physical conditioners in the context of the filter-aid effect.
2.3 Filter Aids
A filter aid is a type of physical conditioner which serves two primary functions: 1) increase cake
porosity, and 2) decrease cake compressibility (Qi et al., 2011). Mixing in filter aids prior to
mechanical dewatering prevents the sludge cake structure from collapsing under pressure. This
ensures that pores and voids remain available for water to drain throug
pressures to be utilized than would otherwise be allowed with a compressible sludge
2011).
While an extensive range of filter aids are known to improve the dewaterability of sludges as
outlined in Table 3, it should be noted again that there
primary sludge as a filter aid.
ensures that pores and voids remain available for water to drain through, and allow for higher
pressures to be utilized than would otherwise be allowed with a compressible sludge
Figure 2. Filter Aid Effect
While an extensive range of filter aids are known to improve the dewaterability of sludges as
, it should be noted again that there is limited study on pulp and paper
8
h, and allow for higher
pressures to be utilized than would otherwise be allowed with a compressible sludge (Qi et al.,
While an extensive range of filter aids are known to improve the dewaterability of sludges as
is limited study on pulp and paper
9
Table 3. Common Filter Aids
Filter Aid Material Reference
Inorganic
Fly Ash
(Sludge or Coal boilers)
(Benitez, Rodriguez, & Suarez,
1994; Chen et al., 2010; Nelson
& Brattlof, 1979; Tenney &
Cole, 1968)
Gypsum (Y.Q Zhao & Bache, 2001; Y.Q.
Zhao, 2002)
Cement Kiln Dust (Benitez et al., 1994)
Lime (Deneux-Mustin et al., 2001;
Zall, Galil, & Rehbun, 1987)
Alum Sludge (Lai & Liu, 2004)
Carbonaceous
Coal Fines
(Albertson & Kopper, 1983;
Sander, Lauer, & Neuwirth,
1989)(Hirota, Okada, Misaka, &
Kato, 1975)
Wood Chips (Jing et al., 1999; Lin, Jing, &
Lee, 2001)
Wheat Dregs (Jing et al., 1999; Lin et al.,
2001)
Bagasse (Benitez et al., 1994)(Y.Q. Zhao,
2002)
Rice Shells & Barn (Lee, Lin, Jing, & Xu, 2001)
Sawdust, Hog fuel, Primary
sludge (H. Zhao, 2000)
Char (Smollen & Kafaar, 1997)
These literature examples are generally in consensus with Qi et al (2011), noting that the
addition of filter aids results in an increase in structural strength, permeability, and porosity of
the cake while reducing compressibility. In an effort to determine the mechanisms behind these
benefits, Tenney & Cole (1968) further investigated particle size of the filter aid (fly ash), and
10
concluded that fly ash with a higher carbon content and a 10-30μm particle size is best. This
indicates that the relationship between particle size of the sludge and the filter aid is important
in determining how effective a particular filter aid will be. Chen et al (2010) conducted an in
depth study to elicit further understanding into the mechanisms by which filter aid action is
occurring. They found, using a modified coal fly ash filter aid, that specific resistance to
filtration decreased, and proposed that the mechanisms causing this include charge
neutralization, adsorption bridging leading to improved floc formation; as well as skeleton
building. Adsorption and charge neutralization as processes generally involve electrostatic
forces and/or chemical bonding/interaction of functional groups. Thus, the chemical makeup of
a material will likely influence its ability to adsorb or neutralize charge. As Chen et al (2010)
demonstrate, chemical modification of coal fly ash was able to improve its effect as a filter aid.
In the context of primary sludge, this is an opportunity to develop more knowledge as there is a
lack of study in the literature on these properties as they relate to primary sludge. The
chemistry and physical properties of primary sludge are largely un-studied, and furthermore
there is a lack of knowledge on how any of the proposed mechanisms above may translate to
primary sludge and biosludge mixtures. The study of particle size, and sludge chemistry are
therefore a good starting point for evaluation of primary sludge as a dewatering aid.
In addition to mechanisms, important aspects in the use of filter aids are the quantity used and
final result with respect to dewatered sludge cake. For the references listed in Table 3,
information on the filter aid dosage, and resulting dewatered cake solids content has been
extracted and presented in Table 4.
11
Table 4. Dosage and Performance of Common Filter Aids
Filter Aid Dose Test Range
(Mass Fraction - %)
Optimum
Dose
(Mass
Fraction - %)
Sludge Cake
Solids
(Mass Fraction -
%)
Reference
Char 33% N/A ~29-42% (Smollen & Kafaar,
1997)
Primary Sludge,
Sawdust & Hog
fuel
67-87% primary
sludge
20-40% sawdust
and hog fuel
N/A
~33-36% with
primary sludge
~36% with 40%
sawdust
~32% with 42%
hog fuel
(H. Zhao, 2000)
Wood Chips &
Wheat Dregs
0-75% wood chips
0-47% wheat dregs >75%
~25% with 75%
wood chips
~15% with 47%
wheat dregs
(Lin et al., 2001)
Wood Chips,
Wheat Dregs, Coal
Ash
0-63% wood chips
& wheat dregs
0-38% coal ash
63% for Wood
chips &
Wheat Dregs
Coal ash
provided no
benefit
N/A (Jing et al., 1999)
Alum sludge 20-67% N/A N/A (Lai & Liu, 2004)
Gypsum 60% N/A 15-40% (Y.Q. Zhao, 2002)
Fly Ash 0-175 g/L 50 g/L ~27% (Tenney & Cole,
1968)
Fly Ash 0-70% 64% ~40% (Nelson & Brattlof,
1979)
Modified Coal Fly
Ash
Up to 91% mass
fraction 73% ~43% (Chen et al., 2010)
Fly Ash, Cement
Kiln Dust, Bagasse
53-64% for Fly ash
Not stated for kiln
dust or bagasse
60% for fly
ash
63% for kiln
dust
27% for
bagasse
36% with 60%
fly ash
44% with 63%
kiln dust
15% with 27%
bagasse
(Benitez et al.,
1994)
Notes:
• Sludge cake solids data includes conditioning with both the filter aid and another
chemical conditioner with the exception of (Tenney & Cole, 1968) and (Chen et al.,
2010) where only the filter aid was used.
• Dose test range and optimum dose have been reprocessed from sources to be
expressed as a mass fraction of total solids.
12
• For sources reporting sludge cake solids, the method of dewatering was filtration at the
lab scale (i.e. benchtop vacuum, pressure or filter press filtration apparatuses).
From Table 4 we see that dosages of filter aids vary dramatically between studies. In general,
higher dosages appear to be preferable with the range of 60-80% being reported most often.
Despite these high doses however, it appears rare in the literature to achieve a sludge cake
with a solids content above 40-45% which is generally the minimum threshold required to
achieve self-sustaining combustion for biomass (ADI Limited, 2005).
In the context of sludge dewatering at mills, while primary sludge has traditionally been used in
proportions around 70%, with production of primary sludge being reduced, using such large
quantities is generally not an option for the future (Mahmood & Elliott, 2006). Finding methods
by which primary sludge can be more effectively used at lower proportions is thus important in
a mill setting. Achieving the minimum 40% solids content in dewatered sludge cake is also of
great importance for mills as incineration in biomass boilers is a common disposal method.
2.4 Assessment of Dewaterability
A number of tests have been developed that assess various aspects of sludge dewaterability.
These tests include sludge volume index, zone settling velocity, capillary suction time (CST),
specific resistance to filtration (SRF), wedge zone simulation, Crown Press, piston press, and
gravity drainage among others. While these are all capable of providing information regarding
the dewaterability, settleability, and/or filterability of sludges, the Crown Press test is one of
the few that is specifically designed to simulate conditions inside an industrial scale dewatering
apparatus (belt filter press).
The Crown Press is a simple benchtop device that allows the user to press the sludge between
two belts over a crown. This action is designed to replicate the various stages in a commercial
belt filter press including the wedge zone, and pressure zone. Application of tension to the belts
over the crown replicates the shearing motion achieved as belts travel around rollers in a belt
filter press.
13
Work conducted at the University of Illinois – Urbana Champaign in the 1990s (Emery, 1994),
(Galla, 1996), (Galla, Freedman, Severin, & Kim, 1996), and (Graham, 1998) demonstrated that
the crown press was able to simulate the wedge and high-pressure zones in a belt filter press,
as well as evaluate polymer performance and belt fabric performance. The crown press tests
were able to accurately predict belt filter performance at multiple wastewater treatment
plants. Graham (1998) came to the additional conclusions that Crown Press tests were capable
of evaluating the effect of divalent cations on final cake solids, and provided better prediction
of dewatering performance than capillary suction time.
Figure 3. Crown Press Belt Press Simulator (Phipps & Bird, 2013)
14
Capillary suction time is a commonly used indicator of sludge filterability. Capillary suction time
is defined as the time taken for filtrate to travel from a sludge reservoir and transverse a
defined distance via capillary action in a standard filter paper. CST, however, is not a
fundamental measure of dewaterability, and as such there are limitations to the usefulness of
CST data, most significantly the dependence of the method on the sludge solids content, and
inability to compare results with other sources (Vesilind, 1988). That being said, it is
nonetheless a rapid and practical tool that can provide some useful information about sludge
conditioning. It has been used for decades for this purpose to evaluate chemical conditioners
and their effects on sludge dewaterability (Vesilind, 1988).
3 Materials and Methods
3.1 Sludge
Sludge samples were obtained from a multi
The mill is equipped with a central wastewater treatment plant with primary and secondary
(Aerobic) treatment. The sludge handling system is fed by sludge lines from the central
wastewater treatment plant as well as dedicated prim
lines. As a result, the mill generates and processes 3 different types of primary sludge and one
type of secondary sludge (biosludge). Samples were shipped in pails via courier from the mill to
the laboratory, with an average travel time of 2 days. Samples not used immediately were
stored in a 4°C cold-room.
Figure 4. Overview of
3.1.1 Biosludge
Biosludge is generated in the aerated sludge process. This
influents, as well as effluent from an upstream anaerobic d
chemical oxygen demand (COD)
with an average sludge age of approximate
of 2ppm. The effluent from the aerated sludge process is then sent to two secondary clarifiers,
from which the sludge is combined and pumped to the sludge handling system.
Materials and Methods
Sludge samples were obtained from a multi-process integrated pulp and paper mill in Canada.
The mill is equipped with a central wastewater treatment plant with primary and secondary
(Aerobic) treatment. The sludge handling system is fed by sludge lines from the central
wastewater treatment plant as well as dedicated primary clarifiers from two additional process
lines. As a result, the mill generates and processes 3 different types of primary sludge and one
type of secondary sludge (biosludge). Samples were shipped in pails via courier from the mill to
h an average travel time of 2 days. Samples not used immediately were
Overview of Central Wastewater Treatment Plant
is generated in the aerated sludge process. This process is fed with low solids
influents, as well as effluent from an upstream anaerobic digester which serves to reduce
prior to aerobic treatment. The aeration basins are operated
with an average sludge age of approximately 12.5-13 days with a target dissolved oxygen (DO)
of 2ppm. The effluent from the aerated sludge process is then sent to two secondary clarifiers,
from which the sludge is combined and pumped to the sludge handling system.
15
nd paper mill in Canada.
The mill is equipped with a central wastewater treatment plant with primary and secondary
(Aerobic) treatment. The sludge handling system is fed by sludge lines from the central
ary clarifiers from two additional process
lines. As a result, the mill generates and processes 3 different types of primary sludge and one
type of secondary sludge (biosludge). Samples were shipped in pails via courier from the mill to
h an average travel time of 2 days. Samples not used immediately were
process is fed with low solids
igester which serves to reduce
prior to aerobic treatment. The aeration basins are operated
13 days with a target dissolved oxygen (DO)
of 2ppm. The effluent from the aerated sludge process is then sent to two secondary clarifiers,
from which the sludge is combined and pumped to the sludge handling system.
Figure
3.1.2 Primary sludge Type A
Type A primary sludge is generated in a dedicated process clarifier which is fed from a
paperboard process. The clarifier influent contains
(BCTMP) pulp residues (hydrogen
condensate, post extraction washer
produced onsite, while the Kraft pulp is sourced from another mill.
3.1.3 Primary sludge Type B
Type B primary sludge is generated in
a hardwood BCTMP pulping process (hydrogen peroxide, sodium sulphite
3.1.4 Primary sludge Type C
Type C primary sludge is generated in the prima
plant (See Figure 4 North Wemco Clarifier)
lines including but not limited to: pulping effluent
Figure 5. Sludge Handling System
Primary sludge Type A
Type A primary sludge is generated in a dedicated process clarifier which is fed from a
paperboard process. The clarifier influent contains bleached chemi-thermo-mechanical pulp
en peroxide, sodium sulphite, sodium hydroxide
asher filtrate, as well as Kraft pulp residues. The BCTMP pulp is
produced onsite, while the Kraft pulp is sourced from another mill.
Primary sludge Type B
Type B primary sludge is generated in an additional dedicated process clarifier which is fed from
pulping process (hydrogen peroxide, sodium sulphite, sodium hydroxide
Primary sludge Type C
Type C primary sludge is generated in the primary clarifier in the central wastewater treatment
North Wemco Clarifier). This clarifier is fed by the remaining mill process
ot limited to: pulping effluent, and chemical production effluent.
16
Type A primary sludge is generated in a dedicated process clarifier which is fed from a
mechanical pulp
, sodium hydroxide), acid
iltrate, as well as Kraft pulp residues. The BCTMP pulp is
s clarifier which is fed from
, sodium hydroxide).
ry clarifier in the central wastewater treatment
. This clarifier is fed by the remaining mill process
chemical production effluent.
17
3.2 Chemicals
3.2.1 General Reagents
Chemicals and reagents used in this study are summarized below.
Table 5. Chemical/Reagents
Reagent Type Reagent
Identifier
Description Supplier
Acid 7525-1 Nitric Acid – ACS Reagent Grade Caledon Laboratories
Ltd., Georgetown, ON,
Canada
Acid 6025-1 Hydrochloric Acid – ACS Reagent
Grade
Caledon Laboratories
Ltd., Georgetown, ON,
Canada
Dye 89640 Toluidine Blue Sigma-Aldrich Canada
Co., Oakville, ON,
Canada
Surface
Charge Titrant
271969 Poly(vinyl sulfate) Potassium Salt
Mw~170,000
Sigma-Aldrich Canada
Co., Oakville, ON,
Canada
Surface
Charge Titrant
409022 Poly(diallyldimethylammonium
chloride) solution 20wt% in water.
Mw~200,000-350,000.
Sigma-Aldrich Canada
Co., Oakville, ON,
Canada
Surface
Charge Titrant
H9268 Hexadimethrine Bromide Sigma-Aldrich Canada
Co., Oakville, ON,
Canada
3.2.2 Polymer
The polymer utilized in this study was BASF OrganoPol 5400 (BASF Corporation, Charlotte, NC,
USA ). It is a commercially available cationic polyacrylamide based flocculant. This polymer is
distributed in dry powdered form. Organopol 5400 is utilized as a flocculant for sludge
dewatering by the mill providing the sludge samples. It is for this reason that this polymer was
utilized, so as to be able to provide some measure of comparison from lab tests to the mill
operations.
3.2.2.1 Polymer Preparation
OrganoPol 5400 was prepared as
powder was measured in an aluminium pan, before being added to the water under high vortex
using a magnetic stir bar and stir plate
to ensure complete emulsification
minimum of 2 hours prior to use. The polymer emulsion was used within 5 days.
3.3 Experimental Approach
Figure
As outlined in Figure 6, the general experimental approach begins with biosludge and primary
sludge, the various physical and chemical properties of which are assessed. These properties
include capillary suction time, total suspended solids, pH, particle size distribution, and cation
concentrations. These properties are analyzed for trends in properties apparent between
batches of sludge, and between types of sludge. The biosludge and primary sl
combined to form mixtures at predetermined mix ratios. The mixtures are then subject to the
utilized, so as to be able to provide some measure of comparison from lab tests to the mill
Polymer Preparation
OrganoPol 5400 was prepared as a 0.5 wt% concentration in distilled water (or 5g/L). The dry
powder was measured in an aluminium pan, before being added to the water under high vortex
using a magnetic stir bar and stir plate. The mixture was vortexed for a minimum of 60 seconds
emulsification. The emulsion was then allowed to rest and age for a
minimum of 2 hours prior to use. The polymer emulsion was used within 5 days.
Approach
Figure 6. Experimental Approach
, the general experimental approach begins with biosludge and primary
sludge, the various physical and chemical properties of which are assessed. These properties
capillary suction time, total suspended solids, pH, particle size distribution, and cation
concentrations. These properties are analyzed for trends in properties apparent between
batches of sludge, and between types of sludge. The biosludge and primary sludges are then
combined to form mixtures at predetermined mix ratios. The mixtures are then subject to the
18
utilized, so as to be able to provide some measure of comparison from lab tests to the mill
concentration in distilled water (or 5g/L). The dry
powder was measured in an aluminium pan, before being added to the water under high vortex
. The mixture was vortexed for a minimum of 60 seconds
and age for a
minimum of 2 hours prior to use. The polymer emulsion was used within 5 days.
, the general experimental approach begins with biosludge and primary
sludge, the various physical and chemical properties of which are assessed. These properties
capillary suction time, total suspended solids, pH, particle size distribution, and cation
concentrations. These properties are analyzed for trends in properties apparent between
udges are then
combined to form mixtures at predetermined mix ratios. The mixtures are then subject to the
19
same tests for physical properties as the raw sludge, and are tested both with and without
polymer treatment. Again the properties are analyzed for trends. The sludge mixtures are then
evaluated for dewaterability using the Crown Press. This data is analyzed for trends, and it is
also correlated against physical properties so as to determine and quantify which physical
properties are factors in determining dewaterability.
3.4 Test Protocols
3.4.1 Total & Volatile Suspended Solids
Total suspended solids (TSS) and volatile suspended solids (VSS) were measured as per
Standard Methods 2540D (APHA, AWWA, & WEF, 1999) using Whatman Grade 934-AH Glass
Microfiber Filters (GE Healthcare Life Sciences, Piscataway, NJ, USA).
3.4.2 Total and Volatile Solids
TS and VS were measured as per Standard Methods 2540B and 2540E respectively (APHA et al.,
1999).
3.4.3 Capillary Suction Time
Capillary suction time (CST) was measured with a Type 304M Laboratory CST Meter and 7x9 cm
CST Paper (Triton Electronics Ltd., Essex, England). Sludge samples were tested in triplicate with
3mL aliquots at 23 + 2°C.
3.4.4 Particle Size
Particle size distribution was measured with a Malvern Mastersizer S equipped with a Large
Volume Dispersion Unit (Malvern Instruments Ltd., Worcestershire, UK). The instrument was
capable of measuring particles up to an equivalent diameter of 900 µm. Sludge samples at 23 +
2°C were added to the dispersion unit and stirred at low speed to evenly disperse the flocs
without disrupting them. Tap water was used to dilute the samples. Sample was added until the
laser obscuration was within the optimal range of 0.1-0.3 (a target range of 0.16-0.22 was used
to maintain consistency between samples). Samples were measured within 15 seconds of being
added to the dispersion unit to minimize time or agitation related sample degradation.
20
3.4.5 Elemental Composition
Elemental composition, specifically cation analysis, was conducted using Inductively Coupled
Plasma – Optical Emission Spectroscopy (ICP-OES). A 720 ICP-OES instrument with SPS-3
Autosampler and ICP Expert II Software was used for these analyses (Agilent Technologies
Canada Inc., Mississauga, ON, Canada).
Samples were prepared by the following protocol:
• A known volume of sample was transferred to a Pyrex tube and weighed.
• Aqua Regia was freshly prepared in a fume hood using Nitric Acid and Hydrochloric Acid
in a 1:3 volume ratio.
• 5mL of Aqua Regia was added to each tube.
• Tubes were placed in a hot water bath and brought to 95°C and allowed to digest for 2
hours.
• Additional Aqua Regia was added in 1mL increments and additional digesting time
allowed as necessary until all solid matter in the samples had been digested.
• The tubes were then removed from the hot water bath and allowed to cool to room
temperature.
• The digested samples were then diluted in a 5% Nitric Acid solution. A minimum of two
different dilution ratios were used to ensure cation concentrations fell within the
calibration limits of the ICP-OES Instrument.
• Diluted samples were transferred to 15mL conical centrifuge tubes and loaded into the
autosampler for analysis.
Samples were measured using the following instrument parameters:
21
Table 6. ICP-OES Instrument Parameters
Parameter Value
Power (kW) 1.20
Plasma Flow (L/min) 15.0
Auxiliary Flow (L/min) 1.50
Nebulizer Flow (L/min) 0.75
Replicate Read Time (s) 10.00
Instrument Stabilization
Delay (s) 15
Sample Uptake Delay (s) 30
Pump Rate (rpm) 15
Rinse Time (s) 10
Replicates 3
3.4.6 Crown Press Dewaterability & Gravity Filtration
A Crown Press belt press simulator and accompanying gravity filtration apparatus (Phipps &
Bird, Inc., Richmond, VA, USA) was utilized as the primary indication of dewaterability. The belt
filter fabric supplied with the Crown Press was a HF7-7040 white polyester belt with a 64x24
count in a 6x2 H’bone weave pattern (Clear Edge Filtration, Tulsa, OK).
22
Figure 7. Crown Press with Attached Gravity Filtration Apparatus
3.4.6.1 Crown Press Calibration
The Crown Press is designed in such a way that the gauge reading does not indicate the
filtration pressure, nor the lineal belt tension. As a result, a calibration must be performed to
correlate the Crown Press Gauge Reading (CPGR) to the pressure and lineal belt tension. The
calibration was conducted using the method and equations as described by Graham (1998):
A spring scale was utilized to apply tension to the top belt while the corresponding CPGR (lbs)
was recorded. The resulting curve is linear of the form:
���� = � ∗ � + � Equation 1
23
with
m = the regression slope, calculated as 0.7158 for this instrument
Ta = applied belt tension (lbf)
b = regression y-axis intercept, calculated as -21.88 lbs for this instrument
The pressure P (psi) can be expressed using the equation:
� = � ∗ ���� − �� ∗�� ∗ ��
Equation 2
with
Wb = belt width, 5.75 inches
Dc = crown diameter, 6.625 inches
Lineal belt tension in lb/in Tl is then calculated as follows:
�� = � ∗ �� Equation 3
Crown Press tests were conducted using CPGRs.
CPGR, pressure, and belt tension values used in this study are presented in Table 7.
24
Table 7. Crown Press
CalibrationCrown Press Gauge
Reading
(lb / N)
Pressure Applied
(psi / kPa)
Lineal Belt Tension
(lb∙in-1 / kN∙m-1)
100 / 446 8.9 / 61.4 59.2 / 10.4
150 / 669 12.6 / 86.9 83.5 / 14.6
200 / 892 16.3 / 112.4 107.8 / 18.9
3.4.6.2 Test Protocol
Gravity Filtration & Crown Press tests were conducted using the following test protocol:
• Sludge samples were retrieved from cold storage and transferred to 1L Pyrex beakers.
• Sample beakers were placed in a warm water bath and brought up to room temperature
(23+2°C).
• Beakers were then transferred to a PB-900 Programmable JarTester (Phipps & Bird Inc.,
Richmond, VA, USA) and stirred with 1 inch x 3 inch impellers at 60rpm for 1.5 hours to
allow the sludge to equilibrate.
• 250 mL samples were then withdrawn and transferred to 500mL Erlenmeyer flasks.
Samples requiring mixing of multiple sludge types were mixed to produce a total volume
of 250mL of the desired mix ratio.
• Samples were mixed using a 1.5 inch magnetic stir bar on a stir plate at high speed for
30 seconds. Polymer flocculant, if utilized, was added to the flasks at this stage.
• The mixed samples were then poured into the gravity filtration apparatus and allowed
to filter for 10 minutes. Filtrate was collected in a graduated container.
• The resulting wet cake was transferred to the Crown Press Belts and subject to 100lb
(CPGR) for 30 seconds followed by rapid release, then 150lb for 30 seconds followed by
25
rapid release, and finally 200lb for 30 seconds. Pressate was collected into the same
container as the gravity filtrate.
• The dewatered cake was then extracted from the belts with the aid of a spatula for
analysis.
Small aliquots of sample were withdrawn at various stages during the test protocol to analyze
for suspended solids, total solids and capillary suction time.
3.4.7 pH
A ThermoScientific Orion 370 Advanced PerpHecT LogR pH/ISE instrument with Orion 9206BN
PerpHecT Combination pH electrode (Thermo Fisher Scientific Inc., Waltham, MA, USA) was
used to measure pH.
3.4.8 Data Analysis
Statistical and graphical analysis of data was performed using GraphPad Prism 6 (GraphPad
Software Inc.). Statistical comparison of the mean of data sets was conducted using the built-in
t-test and ANOVA functionality with results reported at the 95% confidence level unless
otherwise stated. Correlation analysis was performed using the built-in Pearson correlation
tests with a two-tailed confidence level of 95%. Regression analysis of graphical data was
performed using the method of least squares, with confidence bands displayed that represent
the 95% confidence level unless otherwise stated.
26
4 Results & Discussion
Data obtained from laboratory testing is presented and discussed herein. First the issue of
sludge storage and use over extended periods of time is discussed with supporting data to show
that stored sludge maintains its properties over time. Evaluation of CST as an indicator of
dewaterability is presented next, with an emphasis on the challenges and limitations associated
with using CST to compare different types of sludges.
Crown Press dewaterability testing data follows and is divided into several subcategories. First,
relationships between CST, TSS and Crown Press cake solids are examined to establish whether
or not CST is capable of predicting mechanical dewaterability. Trends in cake solids are
presented next, showing data for mixtures of three types of primary sludge with biosludge,
both with and without polymer treatment. These data are analyzed to provide quantification of
the effect of primary sludge addition, and along this line of analysis, an empirical model has
been generated that is able to describe the effect of primary sludge addition on dewatered
cake solids. Suggestions for further investigation into the model are discussed, with attention
given to how this model might be validated and used in a mill setting.
A theoretical basis for the trends observed in cake solids data is developed next. Darcy’s Law is
derived to a form with which SRF can be extracted from filtration data. SRF is correlated against
cake solids, and sludge primary sludge content, in an effort to explain the trends in
dewaterability. This theoretical evaluation, however has some shortcomings, for which
strategies for future investigation are proposed so as to be able to refine and solidify the
theoretical explanation for cake solid trends.
Data for filtrate/pressate quality, particle size distribution, and elemental composition are
presented in sequential order. Attention is devoted to the ease with which low solids
filtrate/pressate can be extracted from primary sludge, and how this in combination with a
favourable disposition of monovalent cations could be used to better optimize how primary
sludge is used. Particle size distribution data is evaluated and discussed as it pertains to the
filter aid effect, demonstrating that primary sludge addition increases the proportion of larger
27
particles in mixed sludge. A shortcoming in the particle size instrument is also discussed with
regards to how the data may be skewed, and methods are proposed to compensate for or
eliminate this skew in future investigations.
4.1 Sludge Storage
As sludge was sourced from a mill over 400km from the laboratory, it was necessary to procure
large samples of sludge and store them under refrigeration for use over the course of several
weeks. Sludge properties change over time, necessitating tests to ensure that any changes from
extended storage would be insignificant. Twice, over the course of experimental work, a sample
of sludge was stored under refrigeration. These samples were monitored in daily and weekly
intervals for three bulk properties: CST, TSS, and pH. A trend in these properties with a slope
that is non-zero would indicate changes in the sludge that may influence dewaterability
properties.
Figure 8. Biosludge pH - October 2012 Batch
Figure 9. Biosludge TSS - October 2012
Batch
Figure 10. Biosludge CST - October 2012
Batch
0 30 60 90 120 150 1800
10
20
30
40
50
Days after Sampling
Figure 11. Biosludge CST - October 2012
Batch - Outlier Removed
28
Biosludge obtained October 12, 2012 was tested over the course of 180 days from the sample
date. 8 measurements for pH and TSS and 7 measurements of CST were recorded. pH
measurements for biosludge varied between 7.05 and 7.46 (Figure 8). Measurements of TSS
varied between 19.3 and 24.9 g/L (Figure 9). CST data has been presented twice in Figure 10
and Figure 11, with an outlier having been removed in the latter. CST values ranged between
16.2 and 24.6s. Regression analysis on all four data sets yielded trends that do not significantly
deviate from a slope of zero. Dashed lines indicate the 95% confidence interval on the linear
regression trends.
Figure 12. Biosludge sample 1 pH - June
2013 Batch
Figure 13. Primary Sludge Type C pH - June
2013 Batch
Figure 14. Biosludge and Primary Sludge
CST - June 2013 Batches
0 5 10 15 20 2510
11
12
13
14
15
Days after Sampling
Figure 15. Biosludge TSS - June 2013 Batch
The second sludge storage test was conducted with three sludge samples obtained June 11,
2013, consisting of two samples of biosludge and one sample of primary sludge (Type C). Both
samples of biosludge originated from the same wastewater treatment process at the mill,
however were sourced from the two separate secondary clarifiers (denoted by sample labels 1
and 2) employed by the mill. These samples were tested over a shorter time frame of 10 days.
29
pH values for biosludge sample 1 varied between 6.8 and 8.0 (Figure 12) and 6.1 and 6.4 for
type C primary sludge (Figure 13). CST varied between 9.0 and 11.1s for biosludge sample 1,
10.4 and 12.5s for biosludge sample 2, and 6.4-7.1s for type C primary sludge (Figure 14). TSS
data was collected only for biosludge sample 1 as shown in Figure 15, and over the course of 25
days after sampling, the TSS remained consistently around 12.5g/L. Regression analysis of all
pH, CST and TSS data sets yielded linear trends which did not deviate significantly from a slope
of zero. Dashed lines indicate the 95% confidence band on the linear regression trends.
As values for pH, CST, and TSS remained statistically unchanged over both the short term, and
long term for both biosludge and primary sludge, we may assume with reasonable confidence
that the overall bulk properties of sludge are stable when stored under refrigeration at 4
degrees Celsius. This assumption is valid for periods up to at least a month, if not more.
However, as the properties of the sludge samples were not tested immediately upon sampling,
it is not possible to comment on changes in sludge properties that may occur between sampling
at the mill, and arrival at the laboratory (a time period of around 2-3 days on average). Thus, to
ensure that there is not a significant change in properties during this time frame, a similar
storage experiment would need to be conducted with the initial tests of properties performed
at the sampling time, and then at regular time intervals thereafter.
While these tests do not provide any indication of changes occurring at a more microscopic
level (including changes in microbial composition), the bulk properties are preserved. While
bulk properties are not necessarily encompassing with regards to dewaterability, the consistent
properties provide a basis for comparison of data. Thus, results obtained from a batch of sludge
may be compared to results obtained a few days later from the same batch. It is important to
note at this point, that the variability of sludge properties between two different batches is
greater than the variability within a single batch stored over time. This is evident in Figure 14
where biosludge was sampled at the same time but from two different clarifiers in the same
treatment process. The two clarifiers produced two biosludges, the properties of which were
significantly different from each other. The variability between batches is also evident when
comparing between the October 2012, and June 2013 batches of sludge, i.e. Figure 9 vs. Figure
30
15 and Figure 11 vs. Figure 14, where a significant difference can be seen in average CST and
TSS values.
The greater variability in the samples presents an opportunity for improved confidence in the
data: if the results obtained from different batches of sludge demonstrate similar or identical
trends with regards to dewatering, results may then be pooled into one set, and any
conclusions drawn may be assumed as valid for the entire range of sludge bulk properties. This
is of particular utility as mill operators must contend with sludge which has properties that can
vary dramatically from day to day. Development of dewatering protocols that are valid across
the entire range of biosludge properties would therefore be useful for mill operations.
31
4.2 CST and TSS
CST data, collected as per Section 3.4.3, and TSS data, collected as per Section 3.4.1, are
presented below. CST is commonly used as an measure of the ability of water to release from
sludges and is accepted as a tool to evaluate the performance of dewatering processes (APHA
et al., 1999). Figure 16 displays the CST for sludge samples as received from the mill, and
includes biosludge and primary sludges. Data is incomplete as not all primary sludges were
sampled at the mill in each batch. Error bars represent the 95% confidence interval. For
reference, the CST for pure water was measured at 4.4-4.7 seconds, and represents the lowest
possible value for CST.
Figure 16. CST of Sludge Samples
Between October 2012, and June 2013, there is a lack of consistency in the CST of biosludge
and Type C primary sludge with values varying dramatically. Between June and July of 2013,
measurements appear more consistent and with the exception of biosludge, are statistically no
different from one month to the next. Type A primary sludge is unique in that it has a
32
significantly higher CST than the other sludge types which generally tend to fall in the range of
7-15s for the June and July 2013 batches.
The addition of polymer affects the CST of sludge samples, as particles are flocculated and
water is generally more easily released from the sludge. For the June 2013 batch of sludge, the
CST of biosludge and the three types of primary sludge was evaluated with and without
polymer. The results are presented in Figure 17, with error bars representing the 95%
confidence interval.
Figure 17. CST of Sludge - With and Without Polymer
The addition of polymer had a significant effect on Type A primary sludge, with a reduction in
CST from 38.5s to 10.1s. The CST for biosludge and Type C primary sludge are also reduced,
however the change is not statistically significant. While the CST for Type B primary sludge
increased (generally indicative of worsening dewaterability), the change was not statistically
different. In this set of experiments, a significant improvement was only present for the Type A
primary sludge. This suggests two things: first, that there may be a specific component of Type
A primary sludge that is well suited to the polymer treatment used in this study; and second,
33
that in the case of biosludge, CST may not be able to fully represent changes in dewaterability
arising from polymer treatment. This shall be further discussed in Section 4.3.1.
Another tool for assessing sludge is TSS, which is a measure of the solids content suspended
within the sludge. TSS itself is not a measure of dewaterability, however, the solids content of
sludge is known to have an influence on other tests including CST (APHA et al., 1999; Vesilind,
1988). Considering the variable nature of biosludge, it is important to assess TSS alongside CST
to determine the influence, if any, of the former on the latter. TSS for several sludge batches
has been summarized in Figure 18, with error bars representing the 95% confidence interval.
Figure 18. TSS of Sludge Samples
As was the case with CST, the TSS of the different batches of sludge varies dramatically. The TSS
for Type A primary sludge is generally above 35 g/L, with biosludge falling between 12 and 24
g/L. It is noted in the standard method for measuring CST that solids content has a strong
influence on CST (APHA et al., 1999). As such, the TSS data presented above is correlated with
CST data and is presented in Figure 19, so as to determine if this relationship holds true in this
study. This figure includes data from all types of primary sludge, biosludge, and mixtures of
34
biosludge and primary sludge, both with and without polymer. The solid line indicates the best
fit linear regression, with the dashed lines indicating the 95% confidence interval on this
regression. Error bars on data points represent one standard deviation.
Figure 19. Correlation of CST with TSS
As demonstrated above, there exists a linear relationship between solids content and the CST.
Correlation tests yielded a statistically significant Pearson coefficient of 0.7208.
Further evidence of this linear relationship was collected by performing a series of dilutions on
sludge samples and measuring the resulting CST. In order to preserve any effects that the
aqueous phase of the sludge had on CST (i.e. from dissolved salts and/or soluble organic
compounds), the diluent utilized was supernatant collected from an aliquot of the same sludge
that had been allowed to settle under gravity for 24 hours in the refrigerator. Figure 20 displays
the results of this experiment, along with regression trendlines. Error bars represent one
standard deviation.
35
CST (s)
Figure 20. CST of Sludge - Dilution Tests
As can be seen from the regression analysis for biosludge, Type A primary sludge, and Type B
primary sludge, there is a linear relationship between TSS and CST. A single data point is
presented for Type C primary as dilution was not possible due to an already low TSS of ~5g/L.
The horizontal orange line represents the CST value for pure water, and serves as a threshold
line for comparison. If extrapolated towards the y-axis, the trendlines for biosludge and Type A
primary sludge would intersect at approximately the same CST value as pure water. This is in
contrast to Type B primary sludge where the intercept would be significantly higher, indicating
that the aqueous phase of this sludge may have components that influence CST unlike
biosludge and Type A primary where this does not seem to be the case. Furthermore, while the
trendlines for the sludges are all linear, it is important to note that their slopes are statistically
different from one another (see Section 8.2).
The linear relationship between solids and CST as seen in Figure 20 is consistent with literature
(Vesilind, 1988) in which it is also noted that this relationship can be explained by Darcy’s
equation for flow through a porous medium under the assumption that solids concentration is
36
directly proportional to deposited cake depth. Thus as solids increases, so too does cake depth,
resulting in a corresponding decrease in flowrate through the cake, which manifests as an
increased CST value which is linearly proportional to the sludge solids content (Vesilind, 1988).
The exact nature of these linear trends can provide some insight into the sludge. As the
concentration of each type of sludge increases, so too does the CST, albeit at a different rate
when compared between sludge types. Generally speaking, the higher the CST, the worse the
sludge performs under dewatering, as the CST value relates to the rate at which water is
released from the sludge. Thus, a sludge with a lower CST can be expected to dewater easier.
Extending this principle over a range of solids concentrations, a sludge which exhibits a
marginal increase in CST as a function of solids would logically perform better than a sludge
which exhibits a more pronounced increase in CST. For example, consider Type B primary
sludge vs. Type A primary sludge in Figure 20. Over the range of solids concentrations from
approximately 2 g/L to 20 g/L, the CST of Type B primary sludge increases from 7.3 g/L to 9.4
g/L whereas the CST for Type A primary sludge increases from 5.5 g/L to 18.5 g/L. Over this
range one may state that the CST of Type B sludge is less dependent on solids than Type A
primary sludge, and that for a given solids concentration, Type B primary sludge is able to
release water at a greater rate than Type A primary sludge.
For a given solids concentration, it is relatively straight forward to compare the CST of multiple
types of sludge, however this is an ideal scenario. The solids concentration of different sludges
will likely be different, and even for an individual sludge, the solids concentration will vary with
time due to changes in operating parameters at the mill. Comparing the CST of multiple types
of sludge without correcting for solids concentration is not meaningful as it would be difficult to
determine the extent to which the nature of the sludge was influencing CST as oppose to the
solids concentration. Correcting for the solids concentration requires the use of a solids vs. CST
calibration chart similar to Figure 20. From such a chart it is simple to interpolate (due to the
linear trends) the estimated CST value for multiple types of sludge at a specified solids
concentration. While this allows for comparison of sludges which may not have similar solids
contents, the issue with this approach of correcting CST values for solids concentration arises
37
from the original intent of CST. CST is meant to be a rapid assessment tool which can be used
within a few minutes to assess sludge water release. The need to correct for solids
concentration when comparing CST values adds a layer of complexity to this tool, especially
when considering the time and effort required to generate a calibration chart for each type of
sludge being evaluated. Furthermore, if testing mixtures of sludge, a calibration curve must be
generated for each mixture. The amount of effort required to correct CST values for solids
concentration and solids type quickly compounds, limiting the utility and swiftness of CST as an
assessment tool.
Despite these complexities when using CST to evaluate sludges, there are scenarios in which it
is still useful and can be used with relative ease. An example of such a scenario is when CST is
used to determine optimal polymer dose rates for sludges. In this case, typically only one type
of sludge/mixture is being tested with the only manipulated variable being the polymer dose.
Since the solids concentration and type of sludge is the same for each test sample, there is no
need to perform a correction and the CST values can be measured and compared as is. For the
majority of this work, the optimal polymer dose was determined from an initial sampling of the
sludge, and then used for all subsequent tests. Samples of sludge were prepared in the same
manner as for Crown Press tests (see Section 3.4.6.2), and then divided into several aliquots of
equal volume. Polymer was added to each aliquot, in increasing quantities, and the CST was
measured and plotted. When a minimum CST value was achieved, denoting the optimum dose,
testing ceased. As polymer was added on a volumetric basis in an emulsion, the data was
subsequently reprocessed to express it in units of dry polymer per unit of dry solids in the
sludge. Figure 21 displays this data, with error bars representing one standard deviation.
Type A primary sludge began with a high TSS, and Type C with a low TSS, and once the data was
reprocessed on a mass basis, these curves were horizontally compressed, and expanded
respectively as a result. In finding the optimal polymer dose, the most important values are for
that of biosludge, as the polymer is selected and dosed based on the properties of biosludge,
and in the context of this study biosludge serves as the reference which we are trying to
improve upon. From the blue data set for biosludge, it can be seen that the CST dips slightly at a
38
dose of 4 g/kg, before rising again which is an indication of exceeding the optimum dose. Type
B and Type C primary sludge do not exhibit an optimum point, and trend upwards in a linear
fashion. Type A primary sludge responds dramatically to the polymer and reaches an optimum
at a polymer dose of only 1.3 g/kg. This confirms the previous suspicions based on Figure 17
that perhaps the polymer is better suited to the Type A primary sludge than the biosludge.
However, for the remainder of experiments, the optimum dose achieved with biosludge, 4 g/kg,
has been used, and is in general agreement with the quantities used at the mill (3-5 g/kg).
Figure 21. CST - Optimum Polymer Dose Determination
39
4.3 Crown Press
The Crown Press Belt Press Simulator was utilized as per Section 3.4.6.2 to evaluate the
dewaterability of sludge samples in a manner which simulated the action of a larger scale belt
filter. The final cake solids achieved on this instrument were used as an indication of the extent
to which mechanical dewatering could be achieved, with higher cake solids being the better
result.
4.3.1 Correlation of Crown Press Cake Solids to CST and TSS
As noted in Section 4.2, there are some complexities associated with the use of CST when
comparing between sludges of different types and different solids concentrations. While some
useful information is provided by CST, its use as an indicator/predictor of the extent of
dewaterability depends on if CST is able to reliably predict mechanical dewatering. To establish
whether or not this is the case, CST data has been compared to Crown Press cake solids data
and evaluated for any correlations. A significant correlation would indicate that CST may have
value in predicting mechanical dewaterability of sludges. An absence of correlation would
indicate that CST cannot predict the extent of mechanical dewaterability. Figure 22, below, is
comprised of CST and Crown Press data collected using the June 2013 batch of sludge including
raw biosludge, raw primary sludge (all three types), 10 & 30% mixtures thereof, all with and
without polymer. Figure 23 displays the same data with the outlier removed.
A Pearson Correlation test on the data (outlier excluded from the analysis) yielded a significant
correlation coefficient of -0.4078. While this correlation is significant, it is not useful as the large
spread in the data results in a very poor fit and large margin of error on subsequent linear
regression. Further analysis on the CST vs Crown Press data for individual sludge types (i.e.
Mixtures with Type A, Mixtures with Type A and polymer, Mixtures with Type B, etc.) did not
yield significant correlations. From this, we may conclude that CST does not predict the extent
of dewaterability of biosludge & primary sludges.
40
Figure 22. Crown Press Cake Solids vs. CST - All Sludge Samples
Figure 23. Crown Press Cake Solids vs. CST - All Sludge Samples - Outlier Removed
41
Correlation between TSS and Crown Press cake solids was evaluated next, as presented in
Figure 24, using the same data set collected from the June 2013 batch of sludge. The curved
dashed lines indicate the 95% confidence interval on the linear regression (solid black line).
Figure 24. Crown Press Solids vs. Total Suspended Solids - All Sludges
While linear regression appeared to demonstrate a trend, the slope of this regression curve is
not significantly different from zero, and the correlation coefficient, 0.3701, is not statistically
significant. If the two data points with TSS values in excess of 40g/L are treated as outliers,
linear regression again yields a trend with a slope that is not significantly different from zero,
and a correlation test yields a non-significant coefficient of 0.001. The spread in the data is
simply too large, and thus the TSS of the sludge mixture appears to have no bearing on the final
dewatered cake solids content, and so higher solids does not translate into a drier cake. While
this seems counterintuitive, the effect of a higher sludge TSS may not necessarily be borne out
in the cake solids, but rather in the quantity of water that requires removal from the sludge, as
will be further discussed in Section 4.3.4 and Section 4.4.
42
4.3.2 Crown Press Cake Solids – Combined Sludge Tests
The following results were obtained using batches of sludge from June and July of 2013 as per
Section 3.4.6.2. The aim of these tests was to characterize the exact effects on final mechanical
dewaterability when primary sludge was added to biosludge. Mixtures of primary sludge and
biosludge were prepared at 10, 20, 30, and 40% primary sludge (by mass of solids), and were
evaluated for dewaterability. Tests were conducted with and without polymer treatment. The
figures below present the results obtained with the three primary sludge types tested. Red and
blue data points represent results with and without polymer treatment respectively, and error
bars represent the 95% confidence interval.
Figure 25. Crown Press Cake Solids vs. Primary Solids % - Primary Sludge Type A
43
Figure 26. Crown Press Cake Solids vs. Primary Solids % - Primary Sludge Type B
Figure 27. Crown Press Cake Solids vs. Primary Solids % - Primary Sludge Type C
44
Considering first the data for mixtures without polymer treatment (blue data points), as seen in
Figure 25, Figure 26, and Figure 27, the final cake solids concentration follows a linear trend
with a positive slope for all three types of primary sludge. The greater the primary sludge
content, the greater the final achievable cake solids. Regression analysis yields linear trends for
all three primary sludge types, the slopes of which are statistically non zero. Best fit values are
summarized in Table 9 in Section 8.3.
The slope of the linear trend indicates the rate of improvement of dewaterability with primary
sludge addition. A greater slope indicates the primary sludge provides a greater improvement in
dewaterability. Regression of the Type A and Type B primary sludge data yielded slope and
intercept values that are not statistically different from one another, with their effect on
dewaterability being nearly identical in nature, and outperforming Type C primary sludge.
Extrapolating these linear trends to primary sludge contents greater than 40%, Type A and Type
B sludge appear able to achieve the maximum level of dewaterability (assuming the cake solids
obtained with 100% primary sludge as the maximum) at a primary sludge content in the range
of 50-60%. This is below the 70% primary solids content commonly used in industrial sludge
processing. Further data collection would be necessary to confirm this, however there is a
possibility that Type A and Type B primary sludge may exhibit a segmental-linear relationship
and may achieve a plateau cake solids value somewhere in the range of 40-60% primary sludge.
The data for Type C primary sludge, in contrast, when extrapolated, does not intersect the
maximum level of dewaterability until a primary solids content of 100% is reached.
Furthermore, the slope of this regression is significantly less than that of Type A and Type B,
indicating poorer performance in enhancing dewaterability. There is however much more
spread in the data for Type C primary sludge and the best fit value for the slope does have a
large margin for the 95% confidence interval. Further testing would be required to confirm
whether mixtures of primary sludge and biosludge reach a maximum level of dewaterability
between 40 and 100% primary solids content.
Focussing now on the data for cake solids obtained in mixtures with polymer treatment (red
data points in Figure 25, Figure 26, and Figure 27), a non-linear trend is visible with all three
45
types of primary sludge. Data from Type A and Type B sludge shows a rapid improvement in
cake solids as primary sludge content increases from 0 to 30%. While polymer treatment alone
is only able to improve the cake solids of biosludge from ~0.05 to ~0.11, the addition of primary
sludge dramatically improves the cake solids, confirming that primary sludge content is a critical
factor in improving biosludge dewaterability. Type A and B primary sludge are able to increase
the achievable cake solids to a maximum plateau value (i.e. not statistically different than the
value achieved with 100% primary sludge) at 30% and 20% primary solids content respectively.
Similar to the results without polymer, Type C primary sludge with polymer treatment confers
less of an improvement, and the cake solids do not appear to reach a plateau.
Figure 28. Crown Press Cake Solids vs. Primary Solids % - All Sludges with Polymer
Figure 28 shows the data for mixtures with all three primary sludge types, with polymer
treatment, superimposed with their respective non-linear regression trendlines for comparison.
While the curvature of the trendlines varies between the three types of primary sludge, a
simple mathematical model is able to fit all three data sets with a fair degree of precision:
46
� = ��� + � + �
Equation 4. Empirical Model Equation
Where Y is the final cake solids (grams of solids per gram of cake), a is a constant (grams of
solids per gram of cake), b is a constant (%), c is a constant (grams of solids per gram of cake),
and X is the primary sludge content (%). Best fit values (obtained via the method of least
squares) for each type of primary sludge have been summarized in Table 10 in Section 8.3.
Within this empirical model, the value of c corresponds to the y-intercept of the curve. The y-
intercept is cake solids achievable with 100% biosludge and polymer treatment, hence near-
identical values. The sum of the values of a and c corresponds to the plateau, or maximum
achievable cake solids. The value of b gives an indication of the rate at which cake solids
increase with increasing primary solids. More specifically it indicates the primary sludge content
necessary to increase the cake solids past the halfway point between the starting point (y-
intercept or c) and the plateau value (a + c).
Between the three sludge types tested, Type B primary sludge, with the lowest value of b,
provides the greatest improvement in cake solids. That being said, it must be stated that the
cake solids values achieved with Type A and Type B primary sludge are statistically no different
from each other at 10, 20, and 30% primary sludge content. The difference in dewaterability
only manifests at higher primary sludge contents. Also important to note is the fact that for
Type C primary sludge data, the calculated value of (a + c) exceeds the empirical data value by
23%. This is simply due to the fact that no plateau is achieved in the empirical data, and the
value for (a + c) would be found outside of the limits of the graph, a mathematical impossibility.
Thus, while (a + c) is a fair representation of the maximum achievable cake solids for biosludge
mixtures containing Type A and B primary sludge, the same does not hold true for Type C
primary sludge.
47
While the three types of primary sludge all confer benefits to dewaterability as evaluated by
cake solids both with and without additional polymer treatment, the magnitude of these effects
varies by primary sludge type. Type A and Type B primary sludge outperform Type C sludge in
tests both with and without polymer, especially at low primary sludge content. This is
industrially relevant as the less primary sludge necessary for biosludge dewatering, the better.
Furthermore while all three primary sludges tend towards similar values for the maximum
achievable cake solids (in the range of 0.20-0.23), non-linear regression of the data set for Type
B primary sludge suggests it is perhaps the best candidate for improving biosludge
dewaterability using minimal quantities of primary sludge. Anecdotal evidence from mill
operators support the notion that of the primary sludges available at the mill to the sludge
handling operation, Type B is, on a qualitative basis, the best.
With respect to the model equation, the regression analysis yielded a good fit for the data from
all three primary sludge types. This indicates that dewaterability performance, as evaluated in a
laboratory setting, can be reliably modeled with a fair degree of precision. The next step in
using this model would be to evaluate dewatering performance data obtained on industrial
scale machinery at the mill level. If biosludge and primary sludge mixtures respond in the same
way on mill equipment with respect to final dewatered cake solids, the model could be
validated or if necessary altered and refined. A valid model would provide operators a tool for
sludge dewatering optimization. If a desired level of biosludge dewatering is required (i.e. a
particular final cake solids), the required amount of primary sludge could simply be calculated
and added accordingly. If primary sludge production is reduced, a valid model would be a tool
to predict how downstream processes (e.g. transport for disposal, or incineration) may be
affected by changes in the achievable cake solids.
Extensive analysis of mill data would be required before this or any model could be used with
confidence, however the lab data shows there is promise in this line of investigation.
48
4.3.3 Crown Press Cake Solids – Theoretical Basis of Understanding
The model equation presented in Section 4.3.2, describes the empirical trend observed in cake
solids based on primary solids content of the sludge. A theoretical basis for the empirical trend
may be found in the specific resistance to filtration of the sludge cakes. As described in Section
8.1, a derivation of Darcy’s law as applied to cake filtration may be used to calculate the specific
cake resistance from filtration data. Using data collected for the gravity filtration phase of the
Crown Press test protocol, the specific resistance to filtration of the sludge cakes has been
calculated for the three types of primary sludge/biosludge mixtures both with and without
polymer conditioning. The specific resistance to filtration has been presented against primary
sludge fraction in Figure 29.
Figure 29. Correlation of Primary Sludge Mass Fraction with Specific Resistance to Filtration
With the exception of Type C primary sludge, the relationship between primary sludge mass
fraction and SRF appears to be consistent. SRF values for Type A and Type B Primary sludge
mixtures, are not statistically different from each other, and the same is also true for all three
primary sludge mixtures with polymer conditioning. As such, it is possible to use one linear
trend to describe the polymer treated data, and another linear trend for the data set without
49
polymer treatment. The parameters for these best fit equations may be found in Section 8.5.
The SRF values for Type A and B primary with polymer treatment are statistically lower than
Type A and B primary, with an average difference of approximately 8.6 x109 m/kg. Under a
gravity filtration regime, this indicates that polymer conditioning significantly reduces the SRF
of the sludge cake. Furthermore, the relationship between primary sludge mass fraction and
SRF exhibits a statistically significant decreasing linear trend (with the exception of Type C
primary sludge, and Type B primary with polymer). That is to say, a higher primary sludge
content serves to decrease the SRF of the overall sludge mixture.
With SRF being significantly correlated to primary sludge content, demonstrating that SRF is
also correlated to dewaterability is the next step in establishing SRF as the theoretical link
between primary sludge content and cake solids. To that end, SRF values are compared with
Crown Press cake solids. It should be noted that the SRF values were calculated using gravity
filtration data, and the cake solids data was obtained using the final dewatered sludge cake
after completing the entire Crown Press test protocol. This was done for three reasons: first,
the Crown Press apparatus design is not conducive to gathering data for pressate volume vs.
time and so only filtrate vs. time data is available; secondly, the SRF calculation requires the use
of the cake solids data in the equation, so any comparison of Crown Press cake solids with
Crown Press SRF would necessarily be correlated as the latter is calculated using the former;
and lastly, the sludge cakes are generally compressible, and controlling for and/or measuring
the Crown Press cake thickness and area is not feasible during testing. Figure 30 and Figure 31
present the Crown Press cake solids data, from July 2013, against the specific resistance to
filtration data from the corresponding gravity filtration tests. In Figure 31 the data for Type C
Primary has been removed as an outlier.
50
0 1 2 3 4 50.00
0.05
0.10
0.15
0.20
0.25
Specific Resistance to Filtration ( x1010 m/kg)
Type A Primary
Type B Primary
Type C Primary
Type A w/Poly
Type B w/Poly
Type C w/Poly
Figure 30. Correlation of Gravity Filtration Specific Resistance with Crown Press Cake Solids
Figure 31. Correlation of Gravity Filtration Specific Resistance with Crown Press Cake Solids -
Outlier Removed
51
Pearson correlation tests for the data sets in Figure 30 showed significant correlations between
SRF and Crown Press Cake Solids for Type A and Type B primary sludge mixes, with Pearson
coefficient values of -0.9999 and -0.9944 respectively. Correlations for the other four mixtures
were not significant.
While the data for each of the primary sludge mixes are not all significantly correlated on an
individual basis, when treated as a composite, Crown Press cake solids are significantly
correlated to the SRF (Pearson coefficient of -0.9590). A single linear relationship is able to
represent all the data sets (blue line in Figure 31) with a good fit (R square value of 0.92), and a
slope and intercept of -7.1 x 10-12
kg/m and 0.22 respectively.
Comparing the result from this study with literature, one finds that there are contradictory
results in other studies. Zhao (2000), demonstrated in primary deinking sludge from a pulp mill
that cake solids are independent of SRF. In contrast, Jing et al. (1999) demonstrated that when
used as physical conditioners, wheat dregs, wood chips, and diatomite were able to decrease
the SRF of digested brewery sludge in approximately linear fashion. Chen et al. (2010)
concluded that the addition of coal fly ash (modified with sulphuric acid) resulted in a decrease
in SRF of municipal sludge. With consideration given to the filter aid effect occurring by addition
of physical conditioners, the results in this study seem consistent with literature in that an
increasing proportion of physical conditioner (in this case primary sludge) translates to a
reduced filtration resistance.
With cake solids being linearly related to SRF (Figure 31), and SRF being linearly related to
primary sludge content (Figure 29), one would, on a mathematical basis, expect cake solids to
be linearly related to primary sludge content. A linear relationship nested within another linear
relationship mathematically yields another linear relationship assuming there are no variables
that are common to both equations (as is the case here):
52
��������� = ��! "�!"# + �$% ∗ ���&�# Equation 5
�$% = ��! "�!"' + ()�*��+�,�%)�� ∗ ���&�' Equation 6
��������� = ��! "�!"# + ��! "�!"' ∗ ���&�#+ ()�*��+�,�%)�� ∗ ���&�# ∗ ���&�' Equation 7
Substituting Equation 6 into Equation 5 yields Equation 7 which relates the primary sludge mass
fraction to the cake solids and as demonstrated, mathematically, cake solids should be linearly
related to the primary sludge mass fraction. Using Figure 29 and Equation 6, the values for
Constant2 and Slope2 are calculated. Note that there are two sets of values as these parameters
must be calculated for the polymer treated and untreated cases. Similarly, Figure 31 and
Equation 5, are used used to calculate the values for Constant1 and Slope1. As there is a single
trend that is able to capture both polymer treated and untreated cases (Figure 31), there is only
one set of parameters. These parameter values (summarized in Section 8.5) are then
substituted into Equation 7 to create a linear model that is able to estimate cake solids as a
function of the primary sludge fraction. Plots of this linear model superimposed on empirical
cake solids vs. primary solids data (for both the polymer treated and untreated cases) are
presented below. Dashed grey lines indicate the error region of the linear model.
It should be noted that the estimated linear trend is calculated based on data that are included
in these graphs. Cake solids data for a primary sludge fraction of 0, 0.1, 0.3 and 1 are common
between Figure 31and Figure 32, and cake solids data for a primary sludge fraction of 0, 0.1,
and 0.3 are common between Figure 31 and Figure 33. This means that the linear model
trendline and these specific data points will necessarily be correlated. That being said, it is still
necessary to evaluate whether the trendline is able to capture the trend in the data.
Additionally, cake solids values at primary sludge fractions of 0.2 and 0.4 have been added from
another data set for comparison.
53
Figure 32. Estimated trend versus empirical data for cake solids as a function of primary solids
– Without polymer treatment
Figure 33. Estimated trend versus empirical data for cake solids as a function of primary solids
- With polymer treatment
54
In Figure 32, if the data point for Type C primary at a sludge content of 0.3 is treated as an
outlier, the trendline fits the data well (as evaluated by regression with the trendline equation)
for all three types of primary sludge with R square values of 0.91, 0.88, and 0.84 for Type A, B
and C primary sludge respectively. Pearson correlation tests between the trendline and the
data indicate strong correlations (correlation coefficients of 0.97, 0.95, and 0.92 for Type A, B
and C primary sludge respectively) that are all statistically significant.
In Figure 33, while the trendline appears to fit well at lower a lower primary sludge content,
further analysis reveals that this is not the case. Regression of the data with the trendline
equation yielded negative R square values, indicating that the equation is not appropriate for
the data. This is due to the non-linear nature of the cake solids vs. primary sludge content data
for polymer treated mixtures. A linear equation is simply not able to fit the non-linear data,
especially for 100% primary sludge where the linear trend grossly overshoots the empirical
values.
There are three scenarios that may explain the inconsistency between the linear model:
• There is some other factor responsible for the non-linear nature of the cake solids trend
with polymer treated sludge mixtures. If this is the case, further mathematical analysis
would be necessary to develop a new theoretical basis for the filtration process. One
such factor could be free water that is trapped in the matrix of particles in the sludge
cake. As discussed by Zhao (2000), when dewatering a slurry of hog fuel and water
(testing the dewaterability of a physical conditioner on its own), the incompressibility of
the hog fuel allowed for significant quantities of water to remain in the void spaces of
the cake. Lin et al. (2001), studying wood chips and wheat dregs as physical
conditioners, stated that free water may be permeating into wood chips, resulting in a
reduction in the quantity of water that can be released from the sludge. Porosity and
specific resistance are intrinsically linked to one another in the context of cake filtration,
and a decrease in SRF arising from more primary sludge content, would also be linked
with a greater porosity. If the primary sludge content also generates a more rigid cake,
free water could remain trapped in the voids during Crown Press filtration. Thus, sludge
55
mixtures with more primary sludge content may gain the benefit of a reduced SRF
allowing for improved dewatering, but at the same also being susceptible to trapping
increasing amounts of free water in the porous cake matrix.
• The method used to calculate SRF fails to capture some aspect of dewaterability
resulting in a linear relationship with cake solids and/or primary sludge when the
empirical relationship is non-linear. Calculation of SRF using different methods reported
in literature may help resolve this issue by confirming the accuracy of the SRF values
calculated here.
• Cake solids measurements obtained with the Crown Press method are being
systematically reduced by the limits of the Crown Press itself. There are limits on the
pressure that can be applied by the Crown Press via a combination of its design, and the
requirement for human input to manually actuate the device. If a particular sludge
mixture can be dewatered to an extent beyond the mechanical limit of the Crown Press,
it would stand to reason that using a device capable of exerting greater pressures may
produce cake solids data that exhibits a different trend, perhaps linear (and reaching
higher cake solids values) as opposed to the non-linear data seen in this study.
Further investigation is necessary in this area to ascertain the manner in which the theoretical
model fails to fully represent the empirical data.
56
4.3.4 Gravity Filtrate and Crown Press Pressate
As a part of the Crown Press test procedure, a gravity filtration step was performed as
described in Section 3.4.6.2. An important aspect of this step is to evaluate the quantity and
quality of filtrate and pressate obtained once the cake had been pressed. The filtrate and
pressate were combined prior to testing, as both streams are usually combined in industry and
treated as one. The total suspended solids of the combined filtrate/pressate were measured to
determine the quantity of residual suspended solids. Data corresponding to the Crown Press
data from Section 4.3.2 is presented in Figure 34. Error bars represent one standard deviation.
0 10 20 30 40 50 60 70 80 90 1000
1
2
3
4
5
6
7
8
Primary Solids %
Type A Primary
Type B Primary
Type C Primary
Type A Primary w/Poly
Type B Primary w/Poly
Type C Primary w/Poly
Figure 34. TSS of Gravity Filtrate + Crown Press Pressate
Without polymer treatment, biosludge filtrate/pressate contains a significant quantity of solids,
on the order of half of the starting value in the raw sludge, indicating ineffective solid/liquid
separation. As primary sludge is added in greater proportions, the filtrate/pressate clears, and
with 40% primary solids, the residual suspended solids is in the range of 1.5g/L, which
incidentally is on the same order as that can be achieved with biosludge and polymer alone.
This reduction in residual solids is suspected to be caused by some manner of filtration of
57
particles through a matrix of primary sludge fibres, or via a particle agglomeration effect, and
shall be further discussed in Section 4.4.
Polymer treated mixtures of biosludge and primary sludge, produced a filtrate/pressate with
residual suspended solids reduced to below 0.5g/L. This is consistent with the quality of
pressate obtained directly from the mill which had an average solids content of 0.45g/L with a
standard deviation 0.07g/L. This reduction in residual suspended solids occurs with as little as
10% primary solids. A solids balance around the filtrate/pressate reveals that the gravity
filtration and crown press solids capture efficiency of biosludge is improved from approximately
0.53 to 0.86 with polymer treatment. Furthermore, 40% primary sludge mixtures with Type A
and Type B primary sludge with no polymer treatment have a solids capture efficiency of
approximately 0.94 and 0.92 respectively. When combined with polymer treatment, 10%
primary sludge mixtures with all three types of primary sludge have solids capture efficiencies
in the range of 0.97-0.99. While a higher proportion of primary solids is necessary to achieve a
maximum cake dryness with mechanical dewatering, as discussed in Section 4.3.2, if the
primary goal is effective solids capture, this can be achieved with as little as 10%, or possibly
less primary solids.
A final observation of note is the filtrate/pressate TSS of raw primary sludge. For Type B and
Type C primary sludge, the residual suspended solids are 0.84g/L and 1.16g/L respectively , low
values when compared to biosludge both untreated (6.27 g/L) and polymer treated (1.83 g/L).
This indicates that water removed from untreated primary sludge (via filtration on a similar
media as used in dewatering) is likely of sufficient quality to warrant partial dewatering prior to
any polymer treatment or mixing into biosludge. Because the addition of primary solids into
biosludge for enhanced dewaterability relies on the quantity of solids, concentrating the
primary sludge prior to mixing with biosludge, would result in a higher solids content of the
mixed sludge than would be achieved with raw primary sludge. For example: A primary sludge
with 10% solids mixed in equal parts with a biosludge with 2% solids results in a mixed sludge of
6% solids; versus a primary sludge with 4% solids mixed in equal parts with the same biosludge
resulting in a mixed sludge of only 3% solids. Use of a more concentrated primary sludge
58
automatically reduces the dewatering requirement by 3% in this example. Furthermore, if a
fixed amount of primary solids must be used, a more concentrated primary sludge has less
volume per unit of solids, thus creating a further reduction in sludge dewatering by reducing
the overall volume of sludge to be processed. Pretreatment of primary sludge thus has the
potential to create improvements in overall sludge handling by reducing dewatering
requirements. This will be further discussed in Section 4.5.
59
4.4 Particle Size
As discussed in Section 2, particle size is a factor that is known to important to the
dewaterability of sludges. Size influences the ability of particles to interact with both each
other, and filtration media, and can change how efficiently dewatering systems can perform.
Assessment of particle size is therefore important to understand the effect on dewatering.
Particle size distributions have been measured for biosludge, biosludge with polymer
treatment, primary sludge, and mixtures of primary sludge and biosludge both with and
without polymer treatment.
Error! Reference source not found. shows the particle size distributions as measured for three
different batches of biosludge both with and without polymer (dosed at 4 g/kg of sludge solids).
Features of these distributions to note include the uni-modal nature with a large volume
percent of particles in the sub 100 micron size range. As discussed in Section 2.1 these
supracolloidal particles can pose a significant challenge to dewatering. Any reductions in the
proportion of particles falling in the supracolloidal size range, generally serve to improve
dewaterability. A shift in the particle size distribution towards larger particle sizes should
therefore indicate improved dewaterability. From Error! Reference source not found., it is also
clear that the addition of polymer has a negligible influence on the particle size distribution.
From the results in Section 4.3.2, it is known that polymer treatment improves biosludge
dewaterability by almost 100% (as measured through cake solids), and yet no significant change
is apparent in the particle size distributions. This suggests that the dewaterability of biosludge
may not be reflected in particle size, and that the polymer may be affecting biosludge
dewaterability by some means other than particle agglomeration.
In contrast to biosludge, the particle size distribution of the primary sludges exhibit a different
shape, being bi-modal and exhibiting a significant proportion of large particles.
60
Volume Percent of Particles (%)
Figure 35. Particle Size Distribution - Biosludge with & without Polymer
Volume Percent of Particles (%)
Figure 36. Particle Size Distribution - Primary Sludge
61
Figure 36 displays the particle size distributions of the three different types of primary sludges
from different batches (denoted by the sampling month). Of note is the bi-modal nature, with
the first peak occurring consistently at a particle size of approximately 40-50 micrometres, and
second peak occurring in the range of 200-400 micrometres. While Type A and C primary sludge
samples appear to exhibit similar peak locations (~45μm and ~300μm) and with the exception
of the June type C batch, also tail off in the larger size ranges in a similar manner with the
largest particle sizes reaching ~750μm. Type B primary sludge, in contrast, has a distinctly
higher proportion of particles in the larger size ranges with the second peak occurring closer to
350μm. Furthermore, there are a significant proportion of particles measured at the 879μm
size, and the shape of the distribution, if extrapolated to the horizontal axis, would suggest that
larger particles also exist. The instrument utilized to measure these distributions can only
measure particles up to a size of 879 micrometres, and therefore the volume percent of
particles may be slightly over-represented in certain primary sludge samples, particularly type
B. These samples often contain particles which exceed 1mm in size, and with these larger size
fractions absent in the overall volume percent calculations, the smaller size fractions become
overestimated in the overall volume calculations. Despite this overestimation, the overall
proportion of particles in the supracolloidal size range (1-100μm) is significantly less (generally
less than half) than that of biosludge. The higher proportion of large particles present in
primary sludge, as compared to biosludge, supports the notion that primary sludge may act as a
filter-aid/skeleton builder as the particles, being larger, cannot pack as closely and maintain a
more porous cake when dewatered.
With Type B primary sludge having a higher proportion of larger particles than the other types
of primary sludge, it would be expected that this should translate into a difference in
dewatering performance. However, referring back to Section 4.3.2, specifically Figure 25, Figure
26, Figure 27, and Figure 28 we can see that Type A and B primary sludge confer improvements
to dewaterability both with and without polymer that are statistically no different from each
other. Thus, while the particle size distributions are different in the primary sludge, once mixed
with biosludge, those differences do not bear out in terms of final dewaterability.
62
Continuing along this line of investigation, the particle size distribution of mixtures of biosludge
and primary sludge were measured and have been displayed below both with and without
additional polymer treatment. Tests were conducted using two batches of sludge from June
and July of 2013.
Figure 37. PSD - June Biosludge - Raw and
with 40% Primary Sludge
Figure 38. PSD - June Biosludge - Raw and
with 40% Primary Sludge and Polymer
Figure 39. PSD - July Biosludge - Raw and
with 40% Primary Sludge
Figure 40. PSD - July Biosludge - Raw and
with 40% Primary Sludge and Polymer
From Figure 37, we may note two distinct features of the distributions: first, there is a small
decrease in the height of the peak with the addition of primary sludge, indicating a decrease in
the proportion of particles in the sub-200μm size range; and second, the addition of primary
63
sludge types A and C results in a tail region of the curve as the mixture contains particles in the
larger size ranges. In Figure 38, similar features may be observed, with the type B + polymer
mixture distribution also exhibiting a tail region. Similar to the results from the June 2013 batch
of sludge, the particle size distributions obtained with the July 2013 batch (Figure 39 and Figure
40) exhibit a similar pattern. In this case however it is mixtures with type A primary sludge that
exhibit a different tail region when comparing tests with and without polymer.
From these distributions, it is clear that mixtures of biosludge and primary sludge have a higher
proportion of particles ~200μm or larger as compared to biosludge which generally has few or
none. That being said, comparing the June data against the July data we see that there is a lack
of consistency in how the distributions are affected by a particular primary sludge type. With
the exception of the distribution for June Type B, and July Type A primary sludge, there is no
significant difference between the distributions obtained for mixtures with and without
polymer. This is in general agreement with data obtained for biosludge (see Error! Reference
source not found.) demonstrating no significant changes in particle size distribution with or
without polymer treatment.
While no differences are evident in the overall distributions, if various size ranges are bracketed
and the volume percent of particles in each bracket summed, we are able to see some
differences. Figure 41 contains this re-expressed data to show the proportion of particles in the
settleable range (>100μm) and summarizes the benefit conferred by primary sludge and
polymer, compared to polymer alone.
From Figure 41 we see that untreated biosludge contains approximately 13% of particles (by
volume) in the settleable range, and with polymer treatment the settleable proportion
increases to approximately 16%. A 60:40 mixture of biosludge and primary sludge treated with
polymer results in an increase in the proportion of settleable particles to approximately 22-
23%, a significant increase compared to untreated biosludge. The increase in settleable
particles occurs to the same extent (no statistical difference) regardless of the primary sludge
type.
64
The increase in proportion of settleable particles may be able to provide an explanation for the
observations in filtrate/pressate quality in Section 4.3.4. A greater proportion of settleable (i.e.
larger) particles may result in a greater proportion of particles being captured by the filter
media. More settleable particles may also be generating a more defined cake structure which is
able to itself act as a filtration medium, trapping yet more particles, as would be expected of
cake filtration.
Biosludge
Biosludge w/Poly
40% Type A w/Poly
40% Type B w/Poly
40% Type C w/Poly
Volume Percentage of Particles (%)
Figure 41. Percent of Particles in the Settleable Size Range (>100μm equivalent particle
diameter) for Various Sludge Mixtures
The proportion of settleable particles reported here, however, may be an underrepresentation
of the true proportion of particles in this size bracket. As discussed previously, the instrument
has a size limit of 879μm, but in the case of mixtures of primary sludge and biosludge, the
largest measurable particles present in the primary sludge appear to disappear once mixed in
with the biosludge. Consider the June batch of type B primary sludge. By volume, 1.1% of its
constituent particles were measured at a size of 754μm, and yet, once mixed with biosludge
65
and treated with polymer, no particles were measured above a size of 409μm. In the absence of
particle interactions between biosludge and primary sludge, one would expect that there would
still be measureable quantities of particles at these larger sizes, i.e. the distribution of the
mixture would simple be a weighted average of the distributions of its components. This does
not seem to be the case. Instead, it is suspected that by means of particle interaction and
agglomeration (and likely with the aid of polymer treatment), biosludge and primary sludge
particles are flocculating together to create particles larger than can be measured by the
particle sizing instrument used. If this is indeed the case, proportions of particles in the small
size ranges would be overrepresented in the volume percentage calculations. Thus, the
difference in proportion of settleable particles between biosludge and the polymer treated
biosludge/primary sludge mixtures in Figure 41 would likely be larger than reported here.
Verification of these suspicions could be achieved by two strategies assuming an instrument
with a wider measurement range is unavailable:
• Deliberately exclude large particles and focus on small particles:
This would be accomplished by sieving biosludge and primary sludge (prior to mixing
and polymer treatment and dewaterability testing) with a #20 mesh sieve (opening size
of 853μm). This would eliminate particles outside the measurement range of the
instrument, and thus one could be confident that measurements are not over or under-
representing volume proportions. While being the simplest approach, this would have
the drawback of not being able to quantify the effects of large particles and fibres which
is of importance in this line of work.
• Separate the large particles from the small and use a combination of manual and
instrument measurement:
Using the same #20 mesh sieve as before, particles can be partitioned at the 853μm
size. The mass and/or volume of particles in the two partitions can be measured. The
smaller particles may then be measured with the PSD instrument as before, and the
larger particles manually sieved into appropriate size fractions (e.g. mesh sieve sizes
#16, #14, #12 etc.) and their mass/volume measured. The measured quantities of the
large particles can then be manually added to the data obtained from the PSD
66
instrument, and the distribution can be adjusted based on the corrected calculations of
volume proportion. This method would be able to provide a more accurate particle size
distribution as it would factor in the larger particles, however, the density of the
particles would have to be estimated, and the means by which a sieve is able to
partition different particle sizes is not necessarily compatible with the method by which
the equivalent particle diameter is calculated in the PSD instrument. This would
introduce another source of error for which there is no simple method to correct for.
Regardless of the shortcomings of the instrumentation the data is able to support a few key
findings. While it has been shown that primary sludge and biosludge combined have a generally
improved particle size distribution as compared against biosludge there is a lack of consistency
in the effect. Particle size distributions for each type of primary sludge vary from one another,
but also over time as the distributions varied between June and July sludge batches. In the
context of the filter aid effect, this is important, especially if we consider the findings of Tenney
& Cole (1968) who found that a specific size range of their chosen filter aid worked best.
Attempting to determine which primary sludge has a better particle size distribution in relation
to dewaterability is a challenge since the distributions are inconsistent between different
batches, and are also bimodal in nature. This presents an opportunity for further investigation
into particle size of primary sludges. With such a wide range of particle sizes present in primary
sludge, it is worthwhile investigating if a particular subset of sizes works better than the overall
mixture. For instance, like biosludge, primary sludge also contains a significant proportion of
particles in the supracolloidal size range (see Figure 36). If these supracolloidal particles were to
be removed by some form of primary sludge pretreatment or prefiltration, and only the larger
particles added to biosludge, would the effect on dewatering be different than the raw primary
sludge? If a particular size range of primary sludge particles works best, it would allow for
primary sludge to be tailored for enhanced dewatering. Undesirable size ranges could perhaps
be returned to the pulping process (or prevented from being lost at the source), increasing pulp
output, an advantage for the mill.
67
4.5 Elemental Analysis
Cationic species concentrations were evaluated as per Section 3.4.5 with a specific emphasis on
monovalent and divalent species. Of the various species tested for, only Ca2+
, Na+, Mg
2+ and
Fe3+
were present in significant quantities. Species concentrations are presented below for
multiple batches of sludge and are arranged by sludge type. Error bars represent the 95%
confidence interval.
Ca2+
Fe3+
Mg2+
Na+
Figure 42. Cation Species Concentration - Type A Primary Sludge
Ca2+
Fe3+
Mg2+
Na+
Figure 43. Cation Species Concentration - Type B Primary Sludge
68
Ca2+
Fe3+
Mg2+
Na+
Figure 44. Cation Species Concentration - Type C Primary Sludge
Ca2+
Fe3+
Mg2+
Na+
Figure 45. Cation Species Concentration – Biosludge
As is evident with all three types of primary sludge and biosludge, cation concentrations are
generally inconsistent from one batch to the next, with no statistically significant trends with
respect to time. Sodium, however, is present at high levels in comparison to the other major
cationic constituents. This is likely due to the nature of the pulping processes utilized at this
mill, with sodium hydroxide and sodium sulphite being used as process chemicals for pH
adjustment and pulping. Excess process chemicals are carried off with fibre rejects to the
primary clarifiers and/or other wastewater streams, and subsequently sludge streams. As the
predominant monovalent cationic species, sodium quantities, and the ratio of sodium to the
divalent species, may be important in terms of sludge stability, as discussed in Section 2.1.
69
So as to evaluate the ratio of monovalent to divalent cations (M:D ratio), the above data has
been reprocessed, on a charge equivalent basis, and presented in the following graph. Error
bars represent the composite standard deviation of the calculated ratio.
Oct-12
Apr-13
Jun-13
Jul-13
Aug-13
0
2
4
6
8
10
12
Biosludge
Type A Primary
Type B Primary
Type C Primary
Figure 46. Monovalent to Divalent Cation Ratios
In the time domain, from June 2013 to August 2013 the M:D ratio for biosludge is consistently
just above 2, ranging from 2.21 to 2.66. The ratio for Type A primary and Type C primary sludge
appear to vary dramatically over time, and this is likely attributable to the multi-stream nature
of the inputs to the clarifiers that generate these sludges. Changes in any of the processes
upstream of these clarifiers would have an impact on the composition of the sludge. Type B
primary sludge displays the highest ratios, which is most likely due to the sodium sulphite based
pulping process used upstream of the clarifier.
Comparing the M:D ratios against dewatering performance in Section 4.3.2, it is difficult to
draw any conclusions. Type B primary sludge has the highest M:D ratios, and yet conferred an
almost identical benefit to biosludge dewatering as did Type A primary sludge (See Figure 28).
Factors other than cation concentration may therefore be playing a more important role in
determining dewaterability. That is not to say that cations are not important, and the generally
high M:D ratios leave opportunity for improvement.
70
An excess of monovalent cations (when the M:D ratio exceeds two) is known to cause poor
dewatering performance in biosludges, and conversely higher divalent cation concentrations
improves dewatering characteristics (Cousin & Ganczarczyk, 1999; Murthy et al., 1998; Nguyen
et al., 2008). Thus, for the biosludge and primary sludge types A and B, improvements in cation
control in the treatment and/or sludge handling processes may improve overall dewatering
properties. Furthermore, as cationic polymers operate on the principle of interacting with
negatively charged sites on sludge flocs so as to bridge them together and agglomerate them,
an excess of monovalent cations may pose the problem of competitive inhibition. While
divalent and trivalent cations such as Ca2+
and Al3+
are themselves capable of bridging (Biggs,
Ford, & Lant, 2001; Nguyen et al., 2008), sodium, as a monovalent cation, cannot do so, and
may hinder the binding ability of other cationic species, be they ions or polymer flocculants,
and may reduce the efficacy of the coagulation/flocculation processes.
In order to determine the general location of the cationic species, whether in the aqueous
phase or trapped within the sludge flocs/particles, sludge samples were centrifuged for 5
minutes at 5000g, and the centrate was then decanted and analyzed for comparison against a
raw sludge sample. Error bars represent the 95% confidence interval, and “SN” denotes
“supernatant”.
Ca2+
Fe3+
Mg2+
Na+
Figure 47. Cation Species Concentration - Raw Sludge versus Sludge Supernatant
71
While calcium is present only minimally in the supernatant, as would be expected of sodium
compounds, the majority of sodium is present in the aqueous supernatant phase. While this is
an intuitive result, it is important as it relates to the findings in Section 4.3.4 with regards to the
quality of the filtrate and pressate from both the lab tests and from mill samples. Primary
sludge is generally able to be dewatered via gravity and belt press filtration (without polymer
treatment) to produce a filtrate and pressate with low suspended solids contents. With the
majority of the sodium present in the aqueous phase, an opportunity exists to pretreat the
primary sludge in an effort to enhance overall dewatering. By partially dewatering primary
sludge first, without any added polymer or conditioners, excess water and monovalent cations
can be removed.
Removal of excess water and monovalent cations may provide two benefits:
• Reducing overall sludge handling requirements.
Gravity drainage of primary sludge yields a wet cake with approximately 6% solids
(~60g/L). Raw primary sludge contains between 0.1-5% solids (1-50g/L). Assuming
minimal loss of solids to filtrate, the partially dewatered primary sludge would have
a volume that is between 5/6th
and 1/60th
the volume of the raw primary sludge. For
dilute primary sludges, the reduction in volume could be enormous. This partially
dewatered primary sludge can then be mixed in with biosludge in the sludge
handling system, without bringing with it large quantities of excess water, which in
the case of a 0.1% solids primary sludge would actually dilute a 1% biosludge,
compounding the dewaterability problem.
• Normalization of primary sludge properties.
As seen in Figure 18, primary sludge samples vary in solids content from month to
month (and likely from day to day and hour to hour as well). Smoothing these
variations would allow for improvements in sludge processing optimization, as
partial dewatering would be able to achieve more uniform primary solids content,
and reduced monovalent cation content. The resulting mixed primary/biosludge
72
would also be of a thicker consistency going into dewatering equipment, which also
has implications to dewaterability as discussed in Section 4.3.4.
Testing this theory would be relatively simple in a laboratory setting and would involve partially
dewatering the primary sludge prior to mixing with biosludge, and subsequent test protocols. It
has the potential for being a simple process change that could have a disproportionately large
benefit.
73
5 Conclusions
The main objective of this work was to identify key parameters that drive dewatering
performance in primary sludge, biosludge, and mixtures thereof. In order to accomplish this
objective, work was conducted to quantify the effect that primary sludge has on biosludge
dewaterability, necessitating the identification and implementation of analytical tools to that
end. Furthermore, select physical and chemical properties were chosen for quantification so as
to identify mechanisms by which the primary sludge affects biosludge. Based on the work
presented above, a number of conclusions have been made in regards to the effects of primary
sludge addition on biosludge dewatering, and how the results from this study may translate to
improvements at the mill level.
The addition of primary sludge to biosludge improves the extent of dewaterability as measured
using a Crown Press as a lab scale simulator of mechanical dewatering. The magnitude of
improvement is dependent on type and quantity of primary sludge added, as well as the
utilization of additional polymer treatment. A mixture with 20% primary sludge is generally able
to be dewatered to a final cake solids that is double what is achievable with untreated
biosludge. A mixture with 40% primary sludge, with polymer treatment, is generally able to be
dewatered to a final cake solids that is double that of polymer treated biosludge, and quadruple
that of untreated biosludge, a significant improvement in dewatering. Type A and Type B
primary sludge out-perform Type C primary sludge both with and without polymer treatment.
This indicates that the nature of the primary solids has a role to play in dewaterability.
A simple mathematical model is capable of representing Crown Press dewatering performance
data for polymer treated sludge mixtures:
� = ��� + � + �
Y - final cake solids (grams of solids per gram of cake)
a - constant (grams of solids per gram of cake)
b - constant (%)
c - constant (grams of solids per gram of cake)
74
X - primary sludge content (%)
This model fits well with data for polymer treated mixtures using all three types of primary
sludge. This indicates that at the lab scale, primary sludge performance as a dewatering aid (in
conjunction with polymer treatment) demonstrates a consistent type of non-linear trend. The
parameters of the equation depend on the type of primary sludge, and must therefore be
related to other properties of the sludge. Further investigation in the lab, and at the mill is
necessary to validate this empirical model, after which it may be useful as a tool for predicting
performance of and optimization of sludge dewatering systems.
Further analysis of Crown Press data was performed using filtration theory (the concept of
specific resistance to filtration in particular) for the development of a theoretical model to fit
the empirical data:
��������� = ��! "�!"# + ��! "�!"' ∗ ���&�# + ()�*��+�,�%)�� ∗ ���&�# ∗ ���&�'
Constant1 and Slope1 are parameters for the linear relation of Cake Solids and SRF. Constant2
and Slope2 are parameters for the linear relation of SRF and Primary Sludge Fraction.
It has been shown that this model is able to capture the linear trend observed in Crown Press
data for sludge mixtures without polymer treatment, however, as a linear model, it is unable to
capture the trend observed with data for polymer treated sludge mixtures. Further
investigation in this area is necessary to improve or modify the model such that it is able to
account for the non-linearity of polymer treated sludge data.
The use of CST as an indicator of dewaterability has been shown to require some additional
consideration in order to provide meaningful comparisons. Consistent with literature, CST has
been shown in this study to be linearly dependent on the solids concentration of a sludge and
also dependent on the type of sludge. Thus, in order to make meaningful comparisons between
CST values for multiple types of sludge with varying solids concentrations, it is necessary to
correct for these factors by means of a calibration chart. This limits the utility of CST as a rapid
assessment tool, as was its original intent. Furthermore it has been shown that there is no
75
quantitative link between CST and mechanical dewaterability as evaluated with a Crown Press.
Despite this, in scenarios where solids concentration and type can be controlled (such as
polymer dose tests), CST is still able to provide useful insights.
Analysis of particle size distributions for untreated and mixed sludges demonstrated that
primary sludge, while inconsistent in particle size, is able to significantly increase the proportion
of settleable particles when mixed with biosludge. While this is suspected to be due to particle
agglomeration effects, limitations in the particle size instrument do not allow for confirmation
of this theory. The increase in settleable particles also supports the notion that primary sludge
is acting as a filter aid through the creation of larger particles that generate a more robust and
porous sludge cake. Further investigation in particle size distribution, including particles ranging
from 1μm to 5mm, is necessary to make a quantitative determination of the influence primary
sludge has on particle size
Measurement of cation concentrations revealed that monovalent cations are present in large
quantities in biosludge and primary sludges alike. The source of monovalent cations is
suspected to be from the upstream pulping processes that rely on sodium based pulping
chemicals. When compared to divalent cations, the ratio of monovalent to divalent cations in
biosludge and primary sludges often exceed the threshold value of 2 which has been noted in
literature as the point past which dewaterability tends to deteriorate. As would be expected of
the predominant monovalent cation sodium, the majority is found in the aqueous phase.
The generally good quality (< 3 g/L of solids) of filtrate/pressate obtained from dewatering
untreated primary sludges, in combination with the predominantly aqueous monovalent
cations, presents an opportunity for improved primary sludge usage. Pretreatment of primary
sludge (partial dewatering) prior to addition into biosludge may provide the following benefits:
• Reduction in the quantity of monovalent cations in mixtures of primary sludge and
biosludge
• Reduction in the excess water being carried into the mixed sludge (especially in the
case of low solids primary sludge streams)
76
• Reduction in the overall volume of sludge that requires processing
• Reduction of the amount of dewatering required to achieve a defined level of cake
dryness.
With the addition of one unit operation, partial dewatering of the primary sludge, the sludge
handling operation could see dramatic benefits. This concept is deserving of additional study at
both the lab and mill level to confirm and quantify these potential benefits.
Lastly, in the specific context of our laboratory facilities and the mill from which samples are
obtained, it has been shown that the bulk properties of sludge remain statistically unchanged
when stored at 4 degrees Celsius. This allows for sludge samples to be stored and used up to
(and in some cases exceeding) a month after the initial sample date at the mill while
maintaining confidence that the results remain valid and comparable to earlier or later tests
using the same stored sludge.
77
6 Recommendations
While a number of conclusions have been drawn from this work, there exist opportunities to
continue specific lines of investigation to enhance knowledge of biosludge/primary sludge
dewatering. The following recommendations aim to provide direction and strategies for future
work to address shortcomings of this work, and provide strategies for translating certain
findings into practical solutions for mills.
The quantitative effect that primary sludge has on the particle size distribution in sludge
mixtures was not conclusively determined in the work presented above. Further investigation
with more robust tools is necessary to provide a more complete understanding of how particles
are interacting with each other. As discussed previously, sieving may be an appropriate strategy
to evaluate larger particles that are outside the capabilities of the particle size instrument.
Along this line of investigation, the nature of particles in each of the three primary sludge types
varies from one to the next. Factors such as particle shape, size, and functional groups may
have a role in dewaterability and/or particle interaction. An investigation into the primary
sludge specific surface area, and the various size fractions present within primary sludge could
reveal optimal sizes, shapes or types of primary fibres that are better suited to improve
biosludge dewatering.
The inconsistent nature of primary sludge production presents a key area for improvement.
Changes in day to day operations at the mill result in dramatic variation in primary sludge solids
content. This adds a layer of uncertainty in sludge handling operations at the mill. Results from
TSS measurements of filtrate/pressate quality and cation analysis indicate an opportunity to
normalize the primary sludge (with respect to solids content), and remove both excess
monovalent cations and water from the primary sludge. Excess monovalent cations may be
having a detrimental effect on downstream dewatering, while excess water from primary
sludge serves only to increase overall sludge processing requirements. In the case of dilute
primary sludges, the excess water compounds the problem by further diluting biosludge. Lab
tests using partially dewatered primary sludge would serve to establish whether or not the
hypothesized benefits actually bear true. Lab tests controlling the cation composition of sludges
78
via addition/removal of specific monovalent or divalent cations would also serve to quantify the
effects, if any, that cations have on dewatering of these particular types of sludges, and would
help establish how cation concentrations could be optimized at the mill.
While an empirical model is capable of describing the trends seen in cake solids with polymer
treated mixed sludges, and a theoretically developed model based on filtration theory is able to
capture the trend of data with mixed sludges without polymer treatment, the theoretically
developed model fails to capture the non linearity of the data from polymer treated mixtures.
Increasing confidence in the accuracy of SRF data, by calculating SRF using alternate methods
for verification; and increasing the force under which Crown Press dewatering tests are
conducted may serve to resolve the inconsistency between theory and empirical data. Further
investigation into the dewatered sludge cake itself is recommended as it is hypothesized that
free water trapped in the cake may be the reason for the non-linearity of the cake solids data.
Measurement of the free water remaining in the void spaces of the dewatered cake, correlated
against SRF, should confirm or nullify this hypothesis. Measurement of the void fraction of the
cake may also shed light on this issue.
Lastly, is the recommendation to conduct a series of trials at the mill using full scale dewatering
equipment. A series of experiments designed to validate Crown Press results, and confirm that
the empirical model equation generated at the lab scale is still applicable to large scale
operations. If validated, lab data can be used to generate operating curves for each type of
primary sludge, and would give operators a new tool to help optimize the use of primary sludge
and subsequent biosludge dewatering performance. Another series of experiments designed to
evaluate primary sludge pretreatments and their effect on dewatering performance would
build on results from the lab and confirm whether or not excess sodium and water from
primary sludge negatively influence dewatering. Cation control tests could also be conducted to
quantify the effects at the mill scale and compare/correlate to lab tests to ensure the same
trends are being observed.
79
7 References
ADI Limited. (2005). Pulp and Paper Sludge to Energy - Preliminary Assessment of Technologies.
Varennes, QC.
Albertson, O. E., & Kopper, M. (1983). Fine-coal-aided centrifugal dewatering of waste activated
sludge. Journal (Water Pollution Control Federation), 55(2), 145–156.
Amberg, H. R. (1984). Sludge dewatering and disposal in the pulp and paper industry. Journal
(Water Pollution Control Federation), 56(8), 962–969.
APHA, AWWA, & WEF. (1999). 2540 Solids. In L. Clesceri, A. Greenberg, & A. Eaton (Eds.),
Standard Methods for the Examination of Water & Wastewater (20th ed.). Washington,
DC.: American Public Health Association.
Benitez, J., Rodriguez, A., & Suarez, A. (1994). Optimization technique for sewage sludge
conditioning with polymer and skeleton builders. Water Research, 28(10), 2067–2073.
Biggs, C. a, Ford, A. M., & Lant, P. A. (2001). Activated sludge flocculation: direct determination
of the effect of calcium ions. Water Science & Technology, 43(11), 75–82. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/11443989
Bitton, G. (2005). Activated Sludge Process. In Wastewater Microbiology (Third Edit., pp. 225–
257). Hoboken, NJ, USA: John Wiley & Sons, Ltd.
Chen, C., Zhang, P., Zeng, G., Deng, J., Zhou, Y., & Lu, H. (2010). Sewage sludge conditioning
with coal fly ash modified by sulfuric acid. Chemical Engineering Journal, 158(3), 616–622.
doi:10.1016/j.cej.2010.02.021
Cousin, C. P., & Ganczarczyk, J. J. (1999). The Effect of Cationic Salt Addition on the Settling and
Dewatering Properties of an Industrial Activated Sludge. Water Environment Research,
71(2), 251–254.
80
Deneux-Mustin, S., Lartiges, B. S., Villemin, G., Thomas, F., Yvon, J., Bersillon, J. L., & Snidaro, D.
(2001). Ferric chloride and lime conditioning of activated sludges: an electron microscopic
study on resin-embedded samples. Water Research, 35(12), 3018–24. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/11471703
Dorica, J. G., Harland, R. C., & Kovacs, T. G. (1999). Sludge Dewatering Practices at Canadian
Pulp and Paper Mills [Survey]. Pulp & Paper Canada, 100(5), 19–22.
Dursun, D., Ayol, A., & Dentel, S. K. (2004). Physical characteristics of a waste activated sludge:
conditioning responses and correlations with a synthetic surrogate. Water Science &
Technology, 50(9), 129–136. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/15581004
Elliott, A., & Mahmood, T. (2005). Survey Benchmarks Generation , Management of Solid
Residues. Pulp & Paper, 79(12), 49–55.
Elliott, A., & Mahmood, T. (2007). Pretreatment technologies for advancing anaerobic digestion
of pulp and paper biotreatment residues. Water Research, 41(19), 4273–86.
doi:10.1016/j.watres.2007.06.017
Emery, B. P. (1994). Predicting belt filter press performance using laboratory techniques.
University of Illinois at Urbana-Champaign.
Environment Canada. (2012). Status Report on the Pulp and Paper Effluent Regulations. Ottawa,
ON.
Galla, C. A. (1996). Laboratory prediction of belt filter press dewatering dynamics. University of
Illinois at Urbana-Champaign.
Galla, C. A., Freedman, D. L., Severin, B. F., & Kim, B. Y. (1996). Laboratory prediction of belt
filter press dewatering dynamics. In Proceeding of the Water Environment Federation 69th
Annual Conference. Dallas, TX.
81
Graham, T. M. (1998). Predicting the performance of belt filter presses using the Crown Press for
laboratory simulation. Clemson University.
Hirota, M., Okada, H., Misaka, Y., & Kato, K. (1975). Dewatering of organic sludge by pulverized
coal. Journal (Water Pollution Control Federation), 47(12), 2774–2782.
Holdich, R. G. (2002). Chapter 4: Filtration of Liquids. In Fundamentals of Particle Technology
(pp. 29–44). Loughborough, U.K.: Midland Information Technology and Publishing.
Jin, B., Wilén, B.-M., & Lant, P. (2003). A comprehensive insight into floc characteristics and
their impact on compressibility and settleability of activated sludge. Chemical Engineering
Journal, 95(1-3), 221–234. doi:10.1016/S1385-8947(03)00108-6
Jing, S. R., Lin, Y. F., Lin, Y. M., Hsu, C. S., Huang, C. S., & Lee, D. Y. (1999). Evaluation of effective
conditioners for enhancing sludge dewatering and subsequent detachment from filter
cloth. Journal of Environmental Science and Health, 34(7), 1517–1531.
Karr, P. R., & Keinath, T. M. (1978). Influence of Particle Size on Sludge Dewaterability. Journal
(Water Pollution Control Federation), 50(8), 1911–1930.
Lai, J. Y., & Liu, J. C. (2004). Co-conditioning and dewatering of alum sludge and waste activated
sludge. Water Science & Technology, 50(9), 41–8. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/15580993
Lee, D. Y., Lin, Y. F., Jing, S. R., & Xu, Z. J. (2001). Effect of agricultural waste on the sludge
conditioning. Journal of the Chinese Institute of Environmental Engineers, 11, 209–214.
Liao, B. Q., Allen, D. G., Droppo, I. G., Leppard, G. G., & Liss, S. N. (2001). Surface properties of
sludge and their role in bioflocculation and settleability. Water Research, 35(2), 339–50.
Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11228985
82
Liao, B. Q., Allen, D. G., Leppard, G. G., Droppo, I. G., & Liss, S. N. (2002). Interparticle
interactions affecting the stability of sludge flocs. Journal of Colloid and Interface Science,
249(2), 372–80. doi:10.1006/jcis.2002.8305
Liao, B. Q., Droppo, I. G., Leppard, G. G., & Liss, S. N. (2006). Effect of solids retention time on
structure and characteristics of sludge flocs in sequencing batch reactors. Water Research,
40(13), 2583–91. doi:10.1016/j.watres.2006.04.043
Lin, Y. F., Jing, S. R., & Lee, D. Y. (2001). Recycling of wood chips and wheat dregs for sludge
processing. Bioresource Technology, 76(2), 161–163. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/11131800
Mahmood, T., & Elliott, A. (2006). A review of secondary sludge reduction technologies for the
pulp and paper industry. Water Research, 40(11), 2093–2112.
doi:10.1016/j.watres.2006.04.001
Mahmood, T., & Elliott, A. (2007). Use of Acid Preconditioning for Enhanced Dewatering of
Wastewater Treatment Sludges from the Pulp and Paper Industry. Water Environment
Research, 79(2), 168–176. doi:10.2175/106143006X111970
Murthy, S. N., Novak, J. T., & De Haas, R. D. (1998). Monitoring Cations to Predict and Improve
Activated Sludge Settling and Dewatering Properties of Industrial Wastewaters. Water
Science & Technology, 38(3), 119–126.
Nelson, R. F., & Brattlof, B. D. (1979). Sludge pressure filtration with fly ash addition. Journal
(Water Pollution Control Federation), 51(5), 1024–1031.
Neyens, E., & Baeyens, J. (2003). A review of thermal sludge pre-treatment processes to
improve dewaterability. Journal of Hazardous Materials, 98(1-3), 51–67. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/12628777
83
Nguyen, T. P., Hilal, N., Hankins, N. P., & Novak, J. T. (2008). Determination of the effect of
cations and cationic polyelectrolytes on the characteristics and final properties of synthetic
and activated sludge. Desalination, 222(1-3), 307–317. doi:10.1016/j.desal.2007.01.161
Qi, Y., Thapa, K. B., & Hoadley, A. F. A. (2011). Application of filtration aids for improving sludge
dewatering properties - A review. Chemical Engineering Journal, 171, 373–384.
Sander, B., Lauer, H., & Neuwirth, M. (1989). Process for producing combustible sewage sludge
filter cakes in filter presses. United States Patent and Trademark Office.
Saunamaki, R. (1997). Activated sludge plants in Finland. Water Science & Technology, 35(2-3),
235–243.
Smollen, M., & Kafaar, A. (1997). Investigation into alternative sludge conditioning prior to
dewatering. Water Science & Technology, 36(11), 115–119.
Sorensen, P. B., & Hansen, J. A. (1993). Extreme solid compressibility in biological sludge
dewatering. Water Science & Technology, 28(I), 133–143.
Tenney, M. W., & Cole, T. G. (1968). The use of fly ash in conditioning biological sludges for
vacuum filtration. Journal (Water Pollution Control Federation), 40(8), R281–R302.
Vesilind, P. A. (1988). Capillary suction time as a fundamental measure of sludge dewaterability.
Journal (Water Pollution Control Federation), 60(2), 215–220.
Wilén, B.-M., Jin, B., & Lant, P. (2003). The influence of key chemical constituents in activated
sludge on surface and flocculating properties. Water Research, 37(9), 2127–39.
doi:10.1016/S0043-1354(02)00629-2
Zall, J., Galil, N., & Rehbun, M. (1987). Skeleton builders for conditioning oily sludge. Journal
(Water Pollution Control Federation), 59(7), 699–706.
Zhao, H. (2000). Deinking and Kraft Mill Sludge Dewatering Usin a Laboratory Sludge Press. The
University of British Columbia.
84
Zhao, Y. ., & Bache, D. . (2001). Conditioning of alum sludge with polymer and gypsum. Colloids
and Surfaces A: Physicochemical and Engineering Aspects, 194(1-3), 213–220.
doi:10.1016/S0927-7757(01)00788-9
Zhao, Y. Q. (2002). Enhancement of alum sludge dewatering capacity by using gypsum as
skeleton builder. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 211(2-
3), 205–212. doi:10.1016/S0927-7757(02)00277-7
ADI Limited. (2005). Pulp and Paper Sludge to Energy - Preliminary Assessment of Technologies.
Varennes, QC.
Albertson, O. E., & Kopper, M. (1983). Fine-coal-aided centrifugal dewatering of waste activated
sludge. Journal (Water Pollution Control Federation), 55(2), 145–156.
Amberg, H. R. (1984). Sludge dewatering and disposal in the pulp and paper industry. Journal
(Water Pollution Control Federation), 56(8), 962–969.
APHA, AWWA, & WEF. (1999). 2540 Solids. In L. Clesceri, A. Greenberg, & A. Eaton (Eds.),
Standard Methods for the Examination of Water & Wastewater (20th ed.). Washington,
DC.: American Public Health Association.
Benitez, J., Rodriguez, A., & Suarez, A. (1994). Optimization technique for sewage sludge
conditioning with polymer and skeleton builders. Water Research, 28(10), 2067–2073.
Biggs, C. a, Ford, A. M., & Lant, P. A. (2001). Activated sludge flocculation: direct determination
of the effect of calcium ions. Water Science & Technology, 43(11), 75–82. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/11443989
Bitton, G. (2005). Activated Sludge Process. In Wastewater Microbiology (Third Edit., pp. 225–
257). Hoboken, NJ, USA: John Wiley & Sons, Ltd.
85
Chen, C., Zhang, P., Zeng, G., Deng, J., Zhou, Y., & Lu, H. (2010). Sewage sludge conditioning
with coal fly ash modified by sulfuric acid. Chemical Engineering Journal, 158(3), 616–622.
doi:10.1016/j.cej.2010.02.021
Cousin, C. P., & Ganczarczyk, J. J. (1999). The Effect of Cationic Salt Addition on the Settling and
Dewatering Properties of an Industrial Activated Sludge. Water Environment Research,
71(2), 251–254.
Deneux-Mustin, S., Lartiges, B. S., Villemin, G., Thomas, F., Yvon, J., Bersillon, J. L., & Snidaro, D.
(2001). Ferric chloride and lime conditioning of activated sludges: an electron microscopic
study on resin-embedded samples. Water Research, 35(12), 3018–24. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/11471703
Dorica, J. G., Harland, R. C., & Kovacs, T. G. (1999). Sludge Dewatering Practices at Canadian
Pulp and Paper Mills [Survey]. Pulp & Paper Canada, 100(5), 19–22.
Dursun, D., Ayol, A., & Dentel, S. K. (2004). Physical characteristics of a waste activated sludge:
conditioning responses and correlations with a synthetic surrogate. Water Science &
Technology, 50(9), 129–136. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/15581004
Elliott, A., & Mahmood, T. (2005). Survey Benchmarks Generation , Management of Solid
Residues. Pulp & Paper, 79(12), 49–55.
Elliott, A., & Mahmood, T. (2007). Pretreatment technologies for advancing anaerobic digestion
of pulp and paper biotreatment residues. Water Research, 41(19), 4273–86.
doi:10.1016/j.watres.2007.06.017
Emery, B. P. (1994). Predicting belt filter press performance using laboratory techniques.
University of Illinois at Urbana-Champaign.
Environment Canada. (2012). Status Report on the Pulp and Paper Effluent Regulations. Ottawa,
ON.
86
Galla, C. A. (1996). Laboratory prediction of belt filter press dewatering dynamics. University of
Illinois at Urbana-Champaign.
Galla, C. A., Freedman, D. L., Severin, B. F., & Kim, B. Y. (1996). Laboratory prediction of belt
filter press dewatering dynamics. In Proceeding of the Water Environment Federation 69th
Annual Conference. Dallas, TX.
Graham, T. M. (1998). Predicting the performance of belt filter presses using the Crown Press for
laboratory simulation. Clemson University.
Hirota, M., Okada, H., Misaka, Y., & Kato, K. (1975). Dewatering of organic sludge by pulverized
coal. Journal (Water Pollution Control Federation), 47(12), 2774–2782.
Holdich, R. G. (2002). Chapter 4: Filtration of Liquids. In Fundamentals of Particle Technology
(pp. 29–44). Loughborough, U.K.: Midland Information Technology and Publishing.
Jin, B., Wilén, B.-M., & Lant, P. (2003). A comprehensive insight into floc characteristics and
their impact on compressibility and settleability of activated sludge. Chemical Engineering
Journal, 95(1-3), 221–234. doi:10.1016/S1385-8947(03)00108-6
Jing, S. R., Lin, Y. F., Lin, Y. M., Hsu, C. S., Huang, C. S., & Lee, D. Y. (1999). Evaluation of effective
conditioners for enhancing sludge dewatering and subsequent detachment from filter
cloth. Journal of Environmental Science and Health, 34(7), 1517–1531.
Karr, P. R., & Keinath, T. M. (1978). Influence of Particle Size on Sludge Dewaterability. Journal
(Water Pollution Control Federation), 50(8), 1911–1930.
Lai, J. Y., & Liu, J. C. (2004). Co-conditioning and dewatering of alum sludge and waste activated
sludge. Water Science & Technology, 50(9), 41–8. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/15580993
Lee, D. Y., Lin, Y. F., Jing, S. R., & Xu, Z. J. (2001). Effect of agricultural waste on the sludge
conditioning. Journal of the Chinese Institute of Environmental Engineers, 11, 209–214.
87
Liao, B. Q., Allen, D. G., Droppo, I. G., Leppard, G. G., & Liss, S. N. (2001). Surface properties of
sludge and their role in bioflocculation and settleability. Water Research, 35(2), 339–50.
Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11228985
Liao, B. Q., Allen, D. G., Leppard, G. G., Droppo, I. G., & Liss, S. N. (2002). Interparticle
interactions affecting the stability of sludge flocs. Journal of Colloid and Interface Science,
249(2), 372–80. doi:10.1006/jcis.2002.8305
Liao, B. Q., Droppo, I. G., Leppard, G. G., & Liss, S. N. (2006). Effect of solids retention time on
structure and characteristics of sludge flocs in sequencing batch reactors. Water Research,
40(13), 2583–91. doi:10.1016/j.watres.2006.04.043
Lin, Y. F., Jing, S. R., & Lee, D. Y. (2001). Recycling of wood chips and wheat dregs for sludge
processing. Bioresource Technology, 76(2), 161–163. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/11131800
Mahmood, T., & Elliott, A. (2006). A review of secondary sludge reduction technologies for the
pulp and paper industry. Water Research, 40(11), 2093–2112.
doi:10.1016/j.watres.2006.04.001
Mahmood, T., & Elliott, A. (2007). Use of Acid Preconditioning for Enhanced Dewatering of
Wastewater Treatment Sludges from the Pulp and Paper Industry. Water Environment
Research, 79(2), 168–176. doi:10.2175/106143006X111970
Murthy, S. N., Novak, J. T., & De Haas, R. D. (1998). Monitoring Cations to Predict and Improve
Activated Sludge Settling and Dewatering Properties of Industrial Wastewaters. Water
Science & Technology, 38(3), 119–126.
Nelson, R. F., & Brattlof, B. D. (1979). Sludge pressure filtration with fly ash addition. Journal
(Water Pollution Control Federation), 51(5), 1024–1031.
88
Neyens, E., & Baeyens, J. (2003). A review of thermal sludge pre-treatment processes to
improve dewaterability. Journal of Hazardous Materials, 98(1-3), 51–67. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/12628777
Nguyen, T. P., Hilal, N., Hankins, N. P., & Novak, J. T. (2008). Determination of the effect of
cations and cationic polyelectrolytes on the characteristics and final properties of synthetic
and activated sludge. Desalination, 222(1-3), 307–317. doi:10.1016/j.desal.2007.01.161
Qi, Y., Thapa, K. B., & Hoadley, A. F. A. (2011). Application of filtration aids for improving sludge
dewatering properties - A review. Chemical Engineering Journal, 171, 373–384.
Sander, B., Lauer, H., & Neuwirth, M. (1989). Process for producing combustible sewage sludge
filter cakes in filter presses. United States Patent and Trademark Office.
Saunamaki, R. (1997). Activated sludge plants in Finland. Water Science & Technology, 35(2-3),
235–243.
Smollen, M., & Kafaar, A. (1997). Investigation into alternative sludge conditioning prior to
dewatering. Water Science & Technology, 36(11), 115–119.
Sorensen, P. B., & Hansen, J. A. (1993). Extreme solid compressibility in biological sludge
dewatering. Water Science & Technology, 28(I), 133–143.
Tenney, M. W., & Cole, T. G. (1968). The use of fly ash in conditioning biological sludges for
vacuum filtration. Journal (Water Pollution Control Federation), 40(8), R281–R302.
Vesilind, P. A. (1988). Capillary suction time as a fundamental measure of sludge dewaterability.
Journal (Water Pollution Control Federation), 60(2), 215–220.
Wilén, B.-M., Jin, B., & Lant, P. (2003). The influence of key chemical constituents in activated
sludge on surface and flocculating properties. Water Research, 37(9), 2127–39.
doi:10.1016/S0043-1354(02)00629-2
89
Zall, J., Galil, N., & Rehbun, M. (1987). Skeleton builders for conditioning oily sludge. Journal
(Water Pollution Control Federation), 59(7), 699–706.
Zhao, H. (2000). Deinking and Kraft Mill Sludge Dewatering Usin a Laboratory Sludge Press. The
University of British Columbia.
Zhao, Y. ., & Bache, D. . (2001). Conditioning of alum sludge with polymer and gypsum. Colloids
and Surfaces A: Physicochemical and Engineering Aspects, 194(1-3), 213–220.
doi:10.1016/S0927-7757(01)00788-9
Zhao, Y. Q. (2002). Enhancement of alum sludge dewatering capacity by using gypsum as
skeleton builder. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 211(2-
3), 205–212. doi:10.1016/S0927-7757(02)00277-7
90
8 Appendices
8.1 Appendix A – Darcy’s Law Derivation for SRF
The following equation has been developed from Darcy’s Law as described by (Holdich, 2002).
This equation relates a number of filtration parameters to filtration data (i.e. filtrate volume vs.
time).
"- = . /0
22'∆(4 5 6
1 − *8 9 - + 5/$:2∆(9
Equation 8
Thus, a knowledge of the filtrate volume as a function of time, the liquid viscosity, filtration
area, applied pressure, slurry solids mass fraction, and moisture ratio, allows for estimation of
the medium resistance and the specific cake resistance from a plot of t/V vs. V.
Figure 48 shows the processed data from the gravity filtration of a sludge mixture containing
30% Type A Primary sludge and 70% Biosludge. The slope, based on linear regression (red line),
is 7.239 x 1010
s/m6.
Gravity filtration occurs in a funnel with a filtration area of 0.007854 m2 (based on a circular
filter disc of radius 5cm). While the solids content of sludge varies from batch to batch, the
water content generally exceeds 98%. Prior measurements of bulk density of both biosludge
and primary sludge were in the range of 1005-1009 kg/m3, as such for the sludge mixtures used
in this test, the density of the sludge slurry is approximated at 1007 kg/m3. 250mL of sludge is
used for the test, which would result in an initial sludge height of 3.18cm with a resulting static
pressure at the sludge-filter interface of approximately 312 Pa. In actuality, however, the
sludge begins to immediately filter through the medium and the cake height was never
observed to exceed approximately 1cm in height. Thus as a more realistic estimate of the
91
filtration pressure, 99 Pa (based on 1cm of sludge height) has been utilized instead. The
viscosity of the filtrate is approximated as 0.001 Pa∙s, the same as water, as prior
measurements of the viscosity of sludge filtrate were within 3% of that of water. The slurry
solids mass fraction is calculated based on the TSS of the slurry. Finally, the value of mr is
obtained from taking the reciprocal of the measured cake solids of the filter cake upon
completion of gravity filtration.
Given the slope of the linear regression obtained from Figure 48 (red line), and the various
other parameters in the slope term for Equation 8, the specific resistance to filtration α has
been calculated as 1.54 x1010
m/kg with an error of approximately + 5%.
Time/Filtrate Volume ( x106 s/m3)
Figure 48. T/V vs. V for Gravity filtration of 30%:70% Type A Primary:Biosludge mix
92
8.2 Appendix B - Linear Regression Data for CST Dilution Tests
Table 8. Linear Regression Best Fit Values – CST Dilutions
Biosludge Type A Primary Type B Primary
Best-fit values YIntercept 4.227 3.981 7.299
Slope 0.5047 0.7799 0.09822 Std. Error YIntercept 0.3027 0.9138 0.2216
Slope 0.02915 0.03054 0.01852 95% Confidence
Intervals
YIntercept 3.585 to 4.869 2.044 to 5.918 6.829 to 7.769 Slope 0.4429 to 0.5665 0.7152 to 0.8447 0.05895 to 0.1375
93
8.3 Appendix C - Regression Data for Crown Press Cake Solids – Mixed Sludge
Table 9. Linear Regression Best Fit Values – Crown Press Cake Solids vs. Primary Sludge
Content
Type A Primary Type B Primary Type C Primary
Best-fit values
Y-Intercept 0.05606 0.05574 0.05594 Slope 0.002377 0.002389 0.001301
95% Confidence Intervals
Y-Intercept 0.05208 to 0.06003 0.05324 to 0.05823 0.04731 to 0.06456 Slope 0.002172 to 0.002583 0.002260 to 0.002518 0.0008557 to 0.001747
Goodness of Fit
R square 0.9632 0.9854 0.6252
Table 10. Model Equation Best Fit Parameters
Type A Primary Type B Primary Type C Primary
Best-fit values a 0.1316 0.1082 0.1694 b 13.93 7.897 43.71
c 0.1046 0.1043 0.1046 95% Confidence Intervals
a 0.1105 to 0.1527 0.09157 to 0.1247 0.1303 to 0.2084 b 6.590 to 21.27 2.752 to 13.04 21.62 to 65.81 c 0.09837 to 0.1107 0.09836 to 0.1102 0.09891 to 0.1102
Goodness of Fit R square 0.9447 0.9410 0.9405
94
8.4 Appendix D – Regression Data for Crown Press Cake Solids & SRF
Table 11. Linear Regression Best Fit Values – Crown Press Cake Solids vs. SRF
Type A Primary
Type B Primary
Type C Primary
Type A Primary w/Poly
Type B Primary w/Poly
Type C Primary w/Poly
Best-fit values
Y-Intercept 2.478E-12 2.613E-12 3.129E-12 7.080E-12 7.273E-12 5.542E-12
Slope 1.669E-11 1.978E-11 -4.667E-12 6.781E-11 9.154E-11 7.991E-11 95% Confidence
Intervals
Y-Intercept -2.567E-12
to 7.523E-12
2.848E-13 to
4.942E-12
-4.894E-12 to
1.115E-11
5.820E-13 to
1.358E-11
-3.105E-12 to
1.765E-11
-1.887E-11 to
2.995E-11
Slope -1.094E-11
to 4.432E-11
7.022E-12 to
3.253E-11
-4.861E-11 to
3.927E-11
3.222E-11 to
1.034E-10
3.469E-11 to
1.484E-10
-5.378E-11 to
2.136E-10 Goodness of Fit
R square 0.9833 0.9974 0.6455 0.9983 0.9976 0.9830
95
8.5 Appendix E – Linear Trends for Primary Solids vs. SRF Data & SRF vs. Cake Solids Data
The form of the Primary Solids vs. SRF linear trend would be as follows:
�$% = ���&� ∗ ()�*�);����� ��!"�!" + <!"�)��&"
Best fit parameters and confidence intervals are as follows:
Table 12. Linear Regression Best Fit Values – Primary Solids Content vs. SRF
With Polymer Treatment Without Polymer Treatment
Best-fit values Y-Intercept 1.451E+010 2.211E+010
Slope -2.868E+010 -2.114E+010 95% Confidence Intervals
Y-Intercept 1.347E+010 to 1.555E+010 2.078E+010 to 2.344E+010 Slope -3.437E+010 to -2.298E+010 -2.368E+010 to -1.860E+010
Goodness of Fit R square 0.9530 0.9857
The form of the SRF vs. Cake Solids linear trend would be as follows:
��������� = <!"�)��&" + �$% ∗ ���&�
Best fit parameters and confidence intervals are as follows:
Table 13. Linear Regression Best Fit Values – Cake Solids vs. SRF
Combined Polymer Treated and Untreated
Best-fit values Y-Intercept 0.2226
Slope -7.078E-012 95% Confidence Intervals
Y-Intercept 0.2062 to 0.2389 Slope -8.228E-012 to -5.927E-012
Goodness of Fit R square 0.9938