primary sludge addition for enhanced biosludge dewatering · primary sludge addition for enhanced...

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.

iv

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



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


), is now a standard component of the overall effluent treatment process at mills.

General Overview of Conventional Wastewater Treatment Process


biologically degrade organic matter into carbon dioxide and water (Bitton, 2005)


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


secondary sludge in a range of 1-60 odt per day (Dorica, Harland, & Kovacs, 1999)

3

mills is necessary to meet environmental regulations



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


tment process at mills.


(Bitton, 2005). This reduces


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


(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


, 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.,


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.



wastewater treatment plant as well as dedicated primary clarifiers from two additional process



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)



15

nd paper mill in Canada.



ary clarifiers from two additional process



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)



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


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


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


Polymer Preparation

OrganoPol 5400 was prepared as a 0.5 wt% concentration in distilled water (or 5g/L). The dry


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


Approach

Figure 6. Experimental Approach

, the general experimental approach begins with biosludge and primary


capillary suction time, total suspended solids, pH, particle size distribution, and cation


batches of sludge, and between types of sludge. The biosludge and primary sludges are then


18


concentration in distilled water (or 5g/L). The dry


. The mixture was vortexed for a minimum of 60 seconds

and age for a


, the general experimental approach begins with biosludge and primary


capillary suction time, total suspended solids, pH, particle size distribution, and cation


udges are then


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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

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