Study of Properties of Flocs Originating from Wastewater ... · Abstract Flocculation in water systems have consequences on the performance and stability of ecosystem due to transportation

Study of Properties of Flocs Originating from Wastewater and

Biofilm Experiments

Examiner : Prof. Dr. Ing. Silke Wieprecht Supervisor : Dr. rer.nat. Sabine Ulrike Gerbersdorf

Universität Stuttgart

Lehrstuhl für Wasserbau und Wassermengenwirtschaft Institut für Wasser und Umweltsystemmodellierung

Master’s Thesis

Taranggiri Hareshgiri, Goswami (WAREM)

Matriculation Number: 3191152

February 15th, 2018

Declaration

I affirm, that the following thesis is written independently and only the sourcesand resources stated are used. No other person’s work has been used without dueacknowledgement in this thesis. This paper, in this or in a similar version has notpreviously been presented. to another examination board and has not been pub-lished.

Furthermore, I agree that the present work is made available for scientific purposesin the libraries of the Institute for Water and Environmental Systems, Universityof Stuttgart (published according § 6 Abs. 1 UrhG (Copyright Act)) and thereforecan be cited under § 51 of the UrhG (Copyright Act).

:Taranggiri Hareshgiri, GoswamiStuttgart, 15th February, 2018

i

Acknowledgement

First of all, I am grateful to the almighty God for establishing me to complete thismaster thesis.

I would like to express my gratitude to my examiner Prof. Dr.-Ing. SilkeWieprecht, University of Stuttgart, for this opportunity to perform this interestingstudy at IWS.

I take this opportunity to express my profound gratitude and deep regards tomy supervisor Dr.rer.nat. Sabine Ulrike Gerbersdorf for her exemplary guidance,monitoring and constant encouragement. The blessing, help and guidance given byher time to time shall carry me a long way in the journey of the life on which i amabout to embark.

I also thank Herr Peter Maurer from ISWA for providing me the samples ofwastewater. Also, I am extremely grateful and indebted to him for his expert, sin-cere and valuable guidance and encouragement extended to me.

I take this opportunity to record my sincere thanks to Herr Kaan Koca for hishelp and encouragement during my experiment period at any time.

I am sincerely thankful to all of my friends, Abhishek Pandey, Christene SylviaRazafimaharo, Christina Rundel, Dhruv Sehgal, Omar Nabi Dar for their construc-tive opinions, moral support and guidance. I would like to especially thank NisargSharma and Shweta Sheoran for their help in sketching instruments’ illustrationsand MATLAB support respectively.

Last but not least, I also thank my parents and brother for their unwaveringencouragement and support.

iii

Abstract

Flocculation in water systems have consequences on the performance and stabilityof ecosystem due to transportation and settling of flocs. Flocs are an aggerationof the suspended smaller particles into a larger structure forming a complex micro-ecosystem, which has given a motivation to characterize the physical and chemicalproperties of the flocs. The flocs are extracted to characterize from the wastewatertreatment plant and the biofilm experiments in which lake water is used. The waterfrom lake Altmuhlsee is circulated with two different flow conditions in 6 sets offlumes. The flumes contain cartridges with glass beads which provide substratefor biofilms to grow according to the nutrient availability. The adhesive propertiesof biofilms are measured using MagPI IP to understand the influence of flows onstability. Afterwards, flocs are eroded using a floc erosion chamber due to shearvelocity created by the shaft and impeller. On the other hand, flocs samples fromdifferent units of the wastewater treatment plant are fetched from ISWA. All thefloc samples are then introduced in the floc settling chamber, which captures im-ages for evaluating in MATLAB code, in order to obtain the physical propertiessuch as floc size, shape parameters, compactness, solidity and settling velocity. Thequantitative analysis of EPS- proteins & carbohydrate, chlorophyll is done by aspectrophotometer to acquire the chemical properties.

The flocs from wastewater treatment plant have exhibited a larger size as com-pared to the flocs from the biofilm experiments, due to enhanced flocculation processand higher organic/inorganic activity. On the other hand, the flocs from biofilmexperiments have shown better prominence in shape parameter, compactness andsolidity due to a lack of presence of the filamentous bacteria. The settling velocityof high flow conditioned biofilms is higher compared to other floc samples due tofair size, better circularity and compactness. The EPS - protein and carbohydratecontents are observed to be higher in flocs obtained from the wastewater treatmentplant, due to higher organic activity as compared to the flocs from the biofilm ex-periments. Lastly, the chlorophyll content is rather unexpected for the flocs fromthe wastewater treatment plant as wastewater does not contain microalgae. Allthese complex physical and chemical properties of flocs affect the transport andsettlement of them by uptaking the nutrient from water bodies which increase thetreatment effieciency in any aquatic system.

Keywords: flocs, settling velocity, physical and chemical properties of flocs

v

Contents

Declaration i

Acknowledgement iii

Abstract v

List of Tables ix

List of Figures xi

1 Introduction 11.1 Roadmap of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Theoretical background 62.1 Adhesive properties of biofilm grown from lake water . . . . . . . . 62.2 Physical properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.1 Floc size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2.2 Major and minor axis . . . . . . . . . . . . . . . . . . . . . . 82.2.3 Shape parameters . . . . . . . . . . . . . . . . . . . . . . . . 92.2.4 Compactness . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2.5 Solidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.6 Settling velocity . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3 Chemical properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.3.1 Extracellular polymeric substances . . . . . . . . . . . . . . 122.3.2 Chlorophyll . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 Materials and methods 133.1 Sample site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.1.1 Lake water . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.1.2 Wastewater . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 Experimental setup to grow biofilms using lake water . . . . . . . . 163.3 Instrumentation and operation . . . . . . . . . . . . . . . . . . . . . 17

3.3.1 MagPI-IP – To measure the adhesive properties . . . . . . . 173.3.2 Floc erosion chamber - To obtain the flocs from biofilms . . 193.3.3 Floc settling chamber - To obtain the data for physical char-

acteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

vii

3.3.4 Spectrophotometer - To measure the absorbance for chemicalproperties of flocs . . . . . . . . . . . . . . . . . . . . . . . . 21

3.4 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.4.1 Evaluation of physical properties . . . . . . . . . . . . . . . 223.4.2 Chemical properties . . . . . . . . . . . . . . . . . . . . . . . 23

4 Results and discussion 294.1 Adhesive properties of the biofilm grown from lake water . . . . . . 29

4.1.1 Manual measurements . . . . . . . . . . . . . . . . . . . . . 294.1.2 Automated measurements . . . . . . . . . . . . . . . . . . . 304.1.3 Comparison of the manual measurement and automated mea-

surement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.2 Floc erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.3 Physical properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.3.1 Quantity of flocs . . . . . . . . . . . . . . . . . . . . . . . . 344.3.2 Floc size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.3.3 Major and minor axis . . . . . . . . . . . . . . . . . . . . . . 414.3.4 Shape parameters . . . . . . . . . . . . . . . . . . . . . . . . 434.3.5 Compactness . . . . . . . . . . . . . . . . . . . . . . . . . . 504.3.6 Solidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.3.7 Settling velocity . . . . . . . . . . . . . . . . . . . . . . . . . 524.3.8 Summary of the physical properties . . . . . . . . . . . . . . 54

4.4 Chemical properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 544.4.1 Protein analysis . . . . . . . . . . . . . . . . . . . . . . . . . 544.4.2 Carbohydrate analysis . . . . . . . . . . . . . . . . . . . . . 574.4.3 Chlorophyll analysis . . . . . . . . . . . . . . . . . . . . . . 59

5 Conclusion 625.1 Future scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

References 65

Appendix 69

viii

List of Tables

2.1 Comparison of floc shape parameters for common geometric shapes 10

3.1 Chemical composition required to prepare the protein sample . . . . 243.2 Concentration of standard solution (BSA) . . . . . . . . . . . . . . 253.3 Concentration of standard solution (D-glucose) . . . . . . . . . . . . 26

4.1 Summary of floc shape parameters for all floc samples . . . . . . . . 49

5.1 Summary of content of chemical properties . . . . . . . . . . . . . 69

ix

List of Figures

1.1 Lifecycle of biofilm . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Formation of flocs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Work plan of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1 Defining the major and minor axis . . . . . . . . . . . . . . . . . . 82.2 Solidity and convex hull . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1 Location of site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2 Schematic diagram of wastewater treatment plant,ISWA . . . . . . 143.3 Experimental setup to grow biofilms . . . . . . . . . . . . . . . . . 163.4 Magnetic Particle Induction . . . . . . . . . . . . . . . . . . . . . . 183.5 Actual images of MagPI . . . . . . . . . . . . . . . . . . . . . . . . 183.6 Assembly of floc erosion chamber . . . . . . . . . . . . . . . . . . . 193.7 De-attachment of shaft and impeller from empty erosion chamber . 203.8 Arrangement of floc settling chamber . . . . . . . . . . . . . . . . . 203.9 Working principle of spectrophotometer . . . . . . . . . . . . . . . . 213.10 Spectrophotometer . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.11 Subfolders of FloculaMazza v1.0 use . . . . . . . . . . . . . . . . 233.12 Inputparameters.xlsx file to change the parameters . . . . . . . . . 23

4.1 Surface adhesiveness by manual measurement . . . . . . . . . . . . 304.2 Surface adhesiveness by automated measurement . . . . . . . . . . 314.3 Comparison of the manual measurement and automated measurement 324.4 Erosion of flocs at certain rotating speed . . . . . . . . . . . . . . . 334.5 Quantity of flocs introduced in the floc settling chamber . . . . . . 344.6 Area of flocs samples . . . . . . . . . . . . . . . . . . . . . . . . . . 354.7 Area of flocs samples of biofilm experiment . . . . . . . . . . . . . . 364.8 Perimeter of each flocs sample . . . . . . . . . . . . . . . . . . . . . 374.9 Perimeter of flocs from the biofilm experiments . . . . . . . . . . . 384.10 Equivalent diameter of flocs from floc samples . . . . . . . . . . . . 394.11 Equivalent diameter of flocs from the biofilm experiments . . . . . . 404.12 Equivalent volume of flocs from floc samples on logarithm scale . . 414.13 Major axis length of floc samples . . . . . . . . . . . . . . . . . . . 424.14 Minor axis length of floc samples . . . . . . . . . . . . . . . . . . . 434.15 Form factor of floc samples . . . . . . . . . . . . . . . . . . . . . . . 444.16 Roundness of floc samples . . . . . . . . . . . . . . . . . . . . . . . 454.17 Aspect ratio of floc samples . . . . . . . . . . . . . . . . . . . . . . 474.18 Corey shape factor of floc samples . . . . . . . . . . . . . . . . . . . 48

x

4.19 Compactness of the floc samples . . . . . . . . . . . . . . . . . . . . 504.20 Solidity of the floc samples . . . . . . . . . . . . . . . . . . . . . . . 524.21 Settling velocity of the floc samples . . . . . . . . . . . . . . . . . . 534.22 Standard calibration curve for protein analysis . . . . . . . . . . . 554.23 Protein content on a logarithm scale . . . . . . . . . . . . . . . . . 554.24 Protein analysis of the biofilm experiments . . . . . . . . . . . . . . 564.25 Standard calibration curve for carbohydrate analysis . . . . . . . . 574.26 Carbohydrate analysis of the floc samples on a logarithm scale . . . 584.27 Carbohydrate analysis of the biofilm experiments . . . . . . . . . . 594.28 Chlorophyll a content in floc samples . . . . . . . . . . . . . . . . . 604.29 Pheopigment a content in floc samples . . . . . . . . . . . . . . . . 60

xi

Chapter 1

Introduction

Suspended particles within any aquatic system (natural or engineered system) playa significant ecological role in the way that they regulate the overall water qualitythrough their physical, chemical and biological activities, which makes the study ofproperties of flocs important (Droppo et al. [1997]). Before investigating the variousproperties of flocs, understanding the terms “biofilms and flocs” becomes essentialbecause they are interconnected.

First of all, aquatic systems sustain or provide growth environment for microbiallife. This microbial life is represented as biofilms (figure 1.1). Scientifically, (Waterresearch foundation [2015]) defines biofilms as “accumulations of microorganisms(e.g., bacteria, fungi, algae, protozoa, and viruses) and organic and inorganic mat-ter bound by extracellular polymer substances (EPS) attached to inner surfaces”.Biofilms have diverse effects on natural or artificial environments; these start withthe certain positive (e.g., self-purification, wastewater treatment) to indeed negative(e.g., biofouling, bio-corrosion, the rapeutic infection) (Gerbersdorf and Wieprecht[2015]).

Figure 1.1: Life cycle of biofilm (Schwartz [2011]) ; (1) Accumulation of molecules(2) Accumulation of bacteria (3) Formation of micro colonies (4) Biofilm maturationand EPS synthesis (5) Aging and replacement

Additionally, biofilm members are considered as “ecosystem engineers” (Ger-

1

bersdorf and Wieprecht [2015]) because of their ability to alter the ecosystem, itsfunction and its biodiversity. This is done according to the available resources nec-essary for the growth. The regulation of biofilm morphology is done by quorumsensing (communication done by biofilms). The EPS molecules are regarded as themajor factor in the microbial biofilm formation process. EPS provides the mechan-ical stability to the biofilm structure. Biofilm growth along with flow condition andconductivity determine biofilm strength (Gerbersdorf and Wieprecht [2015]).

Biofilms and flocs are analogous, in that these bio-aggregates are microbial sur-faces which produce EPS, thus improving the cohesion of the structure. So, flocproperties and behavior directly reflect EPS properties (Liss et al. [2004]).

Flocs are the aggregates of fine particles and colloids into larger size structureformed through both natural and artificial processes (Flemming et al. [2016]). Ad-ditionally, when enough external forces (such as velocity gradient) are applied to thebiofilms, the biofilms get eroded and form flocs (figure 1.2). Thus, flocs are a sortof mobile biofilms that form in the absence of substratum (Flemming et al. [2016]).Flocs can be defined accroding to Droppo (2001) as “individual micro-ecosystems(composed of matrix of water, inorganic and organic particles) with autonomousand interactive physical, chemical and biological functions or behaviors operatingwithin the floc matrix”.

Figure 1.2: Formation of flocs

These microsystems derive nutrients, energy, building materials and chemicalsfor biological growth from the surrounding aquatic medium as a result of their con-tinuous interaction. The aquatic medium is also responsible for the transport offlocs. While flocs can regulate their own environment, they will also change the wa-ter quality via their physical, chemical and biological activity (Droppo et al. [1997]).

The transportation and deposition of particulate matters in water creeks, the ac-cumulation of marine particles (plankton, organic matter etc.) into larger particlesknown as marine snow and the colloidal aggregates existing in natural surface waterare a few of the examples of flocs present in natural aqueous environments(Jarviset al. [2005a]). Flocculation in freshwater influences the performance and stabilityof ecosystems. So, descriptive knowledge about the structure of flocs and the func-tion of microbial communities of flocs is required (Liss et al. [2004]). For example,

2

1.1. Roadmap of Thesis

a study done by Krein et al. [2003] describes that a vital aspect of interstitial poreswithin gravel bed rivers are freshwater flocs.

However, the wastewater treatment plants (WWTP) and water treatment plant(WTP) need to distinguish between the solid and the liquid phase in order to treatthe water (Jarvis et al. [2005a]). This process is enhanced by adding flocculatingchemicals which increase the aggregation of suspended solids into large particles“flocs” to settle down easily. The quality of outflow of WWTP is highly influencedby solid-liquid phase separation (Liss et al. [2004]). So, this makes the understand-ing of floc properties and behavior very important because it play remarkable rolein removal of biological Carbon (C), Nitrogen (N), and Phosphorous (P) (Steinmetzand Krampe [2009]). For example, the settleability of flocs and separation of solidphase in wastewater treatment depends on physical properties of flocs such as size,shape and density and chemical properties.

There are a number of publications such as Glasgow and HSU [1984], Chu et al.[2004], Jarvis et al. [2005b], Zhang et al. [2004] available publically which focuson flocculation in wastewater but very few describe the different characteristics(physical, chemical and biological) of flocs in various aquatic bodies. This masterthesis explains the physical and chemical properties of flocs from the wastewater aswell as biofilm experiments where lake water is used.

1.1 Roadmap of Thesis

The biofilms are grown through an experimental setup under predefined conditionsin the laboratory. The growth period for biofilms has been set as 8 weeks. Then,the strength of the biofilms or adhesive properties (stickiness) is measured throughthe image processing program (MagPI code) in MATLAB for lake water.

After determining the biofilm strength, the biofilm is eroded in a floc erosionchamber to extract flocs for the biofilm experiments. Later, the extracted flocs fromthe bioflm experiments are introduced in a floc settling column in order to capturenumber of images (242 images captured in 30 second of one run). Afterward allcaptured images are analyzed via the image processing program (flocs characteris-tics code) – MATLAB to get the physical properties of the flocs.

Meanwhile, flocs from various parts of a wastewater treatment plant are pro-vided by Institut fur Siedlungswasserbau Wassergute- und Abfallwirtschaft (ISWA),Germany. The physical properties are again determined through the procedure men-tioned above.

Chemical properties (EPS - protein & carbohydrate and chlorophyll) are alsomeasured for both biofilm experiments and wastewater in order to understand theirsignificance.

3

1.2. Structure of the Thesis

Measuring sur-

face adhesiveness

using MagPI IP

Experimental

Setup to grow

biofilm using

lake water

Literature

Review

Eroding the

floc using floc

erosion chamber

Obtaining the

flocs from ISWA

Obtaining data

of the physical

properties

using floc

settling chamber

Measuring

chemical

properties

Evaluating

data to get

the physical

properties using

MATLAB

Interpretation

of results in

thesis work

Figure 1.3: Work plan of thesis

1.2 Structure of the Thesis

Starting from chapter 2, the theoretical background of the thesis is described inwhich all the physical and chemical properties that are measured is explained indetail. Additionally, the process behind the MATLAB code is described when thedata (images) obtained from a floc settling chamber is evaluated.

Chapter 3, Materials and method, illustrate the description of the sample sitesand the experimental setup for the growth of the biofilms from the lake water. Theinstruments and procedure required for obtaining the properties of the flocs are alsoexplained.

Chapter 4, Results and discussion chapter explains the detailed description ofthe obtained results for the various physical and chemical properties of flocs and

4

1.2. Structure of the Thesis

their interpreation is also explained.

Lastly, the conclusion of this thesis and future scope are discussed in chapter 5.

5

Chapter 2

Theoretical background

2.1 Adhesive properties of biofilm grown from

lake water

Physical, chemical and biological processes govern biofilm growth. The microor-ganisms adhere to the surface of the sediments and produce EPS (Garrett et al.[2008]). The EPS plays a major role in biostablization as it decides the dynamics offlow. This EPS produces the glue like structure which acts as a binding agent. Theglue structure formed because of EPS which gives the sediment stability. Sedimentstability is important for sediment transport, the release of contamination and itsoutcome on the ecosystem (Larson et al. [2009]). Additionally, Gibbs [1983] statedthat the water content, the increase in organic content, sediment density are alsoinfluenced by the adhesion of fine particles.

The MagPI-IP technique to measure the stability is discussed in detailed (chap-ter 3.3.1) at later stages. This technique works on the principle of electromagnetism.The magnetic force required to pull the ferrous particles from the surface of thebiofilm can be represented as surface adhesion.

2.2 Physical properties

The physical properties of flocs control their formation and stability in natural andengineered aquatic systems, which makes their study important. Floc formationcan also be influenced by surrounding environments such as temperature, pH andthe concentration of organic contents.

Microscopy and photography are two of the most used methods to determinethe physical properties of the flocs. Due to the special attention required to theflocs sampling and time consuming method to analyse the flocs under microscope,microscopy to evaluate the properties of flocs is not preferred (Jarvis et al. [2005a]).In this work, the determination of physical properties is achieved by a sequenceof processes : (1) Flocs are extracted from the wastewater and biofilm experiment(lake water), (2) The extracted flocs are introduced in a floc settling chamber where

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2.2. Physical properties

sequences of images is gathered, (3) The sequence of images is then evaluated inMATLAB image processing code.

All the physical properties evaluated in MATLAB with a description of theprocess running behind the MATLAB code during evaluation to acquire the physicalproperties are explained below.

2.2.1 Floc size

Floc size is one of the most used parameters to represent floc properties in anyaquatic bodies. The transportation and settling of flocs (suspended particles),biomass separation and sludge dewatering process are controlled by the floc sizedistribution (Li and Ganczarczyk [1993]). This floc size distribution also has animpact on the settling velocity of floc particles, as larger compacted flocs have agreater settling velocity. Flocs have various fractions and nature of organic andinorganic particles. These floc particles are also bound together due to EPS in flocswhich allow microorganisms to trap the organic matter and adhere, resulting in thevariation of floc size (Mikkelsen et al. [2007]) (Pen et al. [2015]).

Equivalent diameter is by far the most used parameter to represent the flocsize(Liss et al. [2004]). Due to highly irregular three dimensional shapes of flocsrelying on one parameter ’equivalent diameter’ is not sufficient. So, various otherparameters are also chosen to represent the floc size such as area, perimeter andequivalent volume.

Area

The measurement of floc size in terms of area is preferred when the photography(in stu) method is used because it is easier to get measurements of two dimensionalproperties from 2D images compared to one or three-dimensional properties. So,the area is first calculated from the pixels of images and then the measured area offlocs can be used to further calculate perimeter, equivalent diameter and volume.The sum of pixels (ap) within an image of a particular floc is described as the areaof the floc (Olson [2011]). Generally, floc size areas cover a range of a few micronssquared to a few millimeters squared.

A =n∑p=0

ap (2.1)

Perimeter

Mathematically, perimeter is total length of sides or edges of a two dimensional floc.Perimeter is used to represent the fractal dimensions of flocs. The fractal dimensionindicate the space filling capacity and is used in the modeling of morphologicalproperties of flocs (porosity, density and settling velocity) (Maggi [2007]). Theperimeter is also a linear characteristic which is calculated by counting the pixel

7


sets of the boundary region of the flocs which can be expressed by the followingequation (Olson [2011]).

P =π

N

π∑α

Iα · dL (2.2)

Where,I = number of intercept formed by parallel linesdL = spacing between linesN = directions

Equivalent diameter

Equivalent diameter represent the diameter of the circle with a same area as mea-sured previously (Jarvis et al. [2005b]). In other words, the equivalent diameter(Xa) is calculated using the measured area of a particle with the following equation.

Xa =

√4A

π(2.3)

Equivalent volume

Floc volume has an influence on the turbidity in aquatic bodies because larger vol-ume flocs cover more space in the water system. Equivalent volume is the functionof the equivalent diameter that is dependent on the measured area of flocs. Themeasured volume should be given more attention as some larger floc diameters havea prevailing impact on the whole volume distribution (Liss et al. [2004]).

V =1

6(Xa)

3 (2.4)

2.2.2 Major and minor axis

Defining the major and minor axis in flocs is necessary as it is very difficult tofind flocs that are symmetrical. Also, several physical properties such as the Coreyshape factor (CSF), the aspect ratio, compactness and roundness are calculatedbased on the major and minor axis lengths.

Figure 2.1: Defining the major and minor axis

8


The major and minor axes are defined for a particular floc by the axes of ellipsefitted onto the pixels of the image (Santolamazza [2013]). According to MathOpen-Reference [2011], the major axis is the largest diameter of flocs passing through thecenter of mass, while the minor axis is the smallest diameter of flocs perpendicularto the major axis. Their lengths are expressed in micrometers or millimeters.

2.2.3 Shape parameters

The shape of flocs also contributes to settling and to the transportation of flocs infreshwater and wastewater. The shape of flocs is influenced by the microorganismliving on it e.g. filamentous bacteria which reduces the roundness of the flocs.Differently shaped flocs behave differently. According to Liss et al. [2004], circularshaped flocs have higher settling ability followed by cylindrical, disc shaped flocs.The shape parameters which are selected to understand the shape of the flocs arethe form factor, roundness, aspect ratio. They are dimensionless parameters.

(A) Form factor (FF)

Form factor is one of the most used shape parameter of the flocs. Form factor canbe defined Banadda et al. [2005] as the variation in the geometry of the flocs froma circle. It is sensitive to roughness of particles because roughness cause a decreasein the form factor. The form factor is expressed as;

Formfactor =4π × AreaPerimeter2

(2.5)

(B) Roundness (RO)

Roundness is similar to the form factor with consideration for the elongation of flocparticle (Banadda et al. [2005]). The elongation of the floc particle is expressed fromthe major axis value. Roundness varies between 0 and 1 with 1 being a circular flocand other values show the difference from a circle. Roundness is calculated fromthe following equation.

Roundness =4× Area

π × (Major axis)2(2.6)

(C) Aspect ratio (AR)

Aspect ratio is a function of the major and minor axes of flocs, used to assess theregion of the floc particle. The mathematical representation of aspect is shownbelow. If aspect ratio is higher when the major axis value is very high, then flocsare more elongated.

Aspect ratio =Major axis

Minor axis(2.7)

9


(D) Corey shape factor (CSF)

The corey shape factor is developed by Corey, John S. McNown and Jamil Malaikain 1950, but investigated in detail by Komar and Reimers [1978]. They observedthat all three axes (minor, major and median axes) of floc particles influence thesettling velocity of flocs, but minor axis has extra significance because it causes thelargest deviation from circle. The equation of the corey shape factor is;

CSF =Minor axis√

Minor axis×Median axis(2.8)

Median axis is the intermediate length (diameter) of the floc particle. Coreyshape factor have a range between 0 and 1 where 1 is the best fit for a circle.

Shape parameter comparison

Shape Length Peri Area Form Round Aspect Corey-meter factor -ness ratio shape factor

1 3.14 0.79 1 1 1 1

1 4 1 0.78 0.64 1 1

1 3 0.4 0.60 0.55 1.15 0.55

Table 2.1: Comparison of floc shape parameters for common geometric shapes

2.2.4 Compactness

As already mentioned before that compact large flocs tend to settle faster in aquaticbodies. Additionally, the compacted flocs indicate better quality of bacteria becausebacteria hold the flocs firmly which make flocs denser, and thus more surface areafor adsorption resulting in efficient treatment.

Compactness is an important factor in a wastewater treatment plant as com-pact flocs with good settling velocities will improve the dewatering performance.Liss et al. [2004] observed that flocs from freshwater beds are more compact withlarger organic content than wastewater but the compactness of flocs is enhanced bychemicals in wastewater treatment plants to settle them more quickly.

Compactness is a dimensionless characteristic and is expressed by the followingequation;

Compactness =

√4×Area

π

Major axis(2.9)

10


2.2.5 Solidity

Solidity is a morphological characteristic that indicates the ability of floc particles tooccupy the free room (Amaral [2003]). Compactness and solidity have a relationshipwith each other while compact flocs have better solidity. The solidity of flocs hasspecial importance in wastewater treatment plant as it influences the transportation,dewatering, reduction in sludge volume and storage (Ludovico Spinosa [2008]).

Figure 2.2: Solidity and convex hull; actual area of floc (blue line) and convex hullarea of floc (red line)

Solidity is a function of convex hull area of flocs (figure 2.2 where convex hullarea is the overestimation of the actual size of the floc (Olson [2011]). Solidity isa dimensionless characteristics ranging between 0 and 1 where 1 shows more solidflocs. The particle having smoother outline give value close to 1 and rough outlinewill have lower solidity value (Instruments [2012]).

Solidity =Area

Convex hull area(2.10)

2.2.6 Settling velocity

Settling velocity is an important factor which governs the transport of flocs or sed-iments in freshwater bodies. In addition, the removal of flocs in the form of sludgefrom the water and wastewater treatment plants with respect to their predictedfloc density is also controlled by the settling velocity (Liss et al. [2004]). Settlingvelocity depends not only on floc size but on shape, compactness and the chemicalcomposition of flocs.

The idea behind measuring the floc settling velocity using the MATLAB imageprocessing code is to measure the distance covered by a floc in a floc settling chamber(cuvette) with a known time using a sequence of images. Horizontal as well asvertical velocity is measured in the floc settling chamber, the horizontal velocity offloc is negligible due to the fact that flocs are settled in a vertical cuvette/column.

11

2.3. Chemical properties

2.3 Chemical properties

2.3.1 Extracellular polymeric substances

The extracellular polymeric substances (EPS) molecules are regarded as the majorfactor influencing the microbial biofilm formation process by consuming the organicmatter in aquatic water bodies. EPS production depends upon the availability andcomposition of nutrients. EPS consists of proteins, sugars, fatty acids, lipids, andnucleic acids etc (Nouha et al. [2016]). EPS in flocs or biofilms represent the struc-ture components of flocs (size, shape and compactness) and are also accountablefor the sorption process (Liss et al. [2004]).

Various physical and chemical methods such as centrifugation, heating andblending are used to extract EPS so as to determine the concentration of variouscomponents of EPS. Here, the chemical method with centrifugation is used to ex-tract the EPS. Protein and carbohydrate content of EPS are selected to representthe chemical properties of flocs which are measured by colorimetry (spectropho-tometer) as they are major constituents of EPS.

Proteins

Proteins are one of the dominating factors and represent the largest fraction of EPSin biofilms of freshwater bodies and activated sludge from wastewater (Wuertz et al.[2003]). In natural water bodies, proteins are also part of organic matters producedby the degradation of phytoplankton, bacteria, among others. Proteins are mainlyresponsible for the initial attachments of microbial cells to form the biofilms and itsstability. In other words, proteins contribute to the adhesive properties of biofilmswhich makes their study important.

Carbohydrates (sugar)

Carbohydrates (sugar) are the source of energy for biofilms. EPS carbohydratealso have an influence on the structural properties of biofilms/flocs as they canform complex network (Gerbersdorf and Wieprecht [2015]). According to Donlan[2002], the concentration of carbohydrate content varies from biofilm to biofilmwhich makes the study of carbohydrate more relevant.

2.3.2 Chlorophyll

Chlorophyll present in a biofilm/floc represents the organism (microalgae) that cantrigger photosynthesis. In other words, chlorophyll is an indicator of the biomasspresent in the natural water bodies or engineered water bodies (Taguchi et al.[1993]). So, higher values of chlorophyll a suggest the larger surface area withlarger floc particles, which allows them to settle better (Meyer-Reil [2005]).

12

Chapter 3

Materials and methods

This chapter describes the sample sites (lake water and wastewater treatment plant),the experimental setup to grow biofilms, the instruments used to acquire the proper-ties of the flocs and the procedure to determine the physical and chemical propertiesof flocs.

3.1 Sample site

To understand floc physical and chemical properties in different aquatic bodies,two different water bodies are selected; lake water and wastewater. Moreover, adescription of these water bodies and their respective locations for sample analysisare discussed in the following sections.

3.1.1 Lake water

(a) Location on google map (b) Map of Lake Altmuhlsee

Figure 3.1: Location of site (Google maps [2017] and Wasserwirtschaftsamter [2017])

13

3.1. Sample site

Freshwater water flocs are obtained by eroding the fully grown biofilms using lakewater from Lake Altmuhlsee for this study. Lake Altmuhlsee is an artificial lakesituated about 50 km southwest of Nurnberg in the region of Bavaria (Germany)(figure 3.1). The lake acts as a buffer lake due to the flooding water which it re-ceives from the area Altmuhl and this lake area is also used for leisure activities(sailing, swimming and fishing etc.) (Wasserwirtschaftsamter [2017]). This eu-trophic shallow lake has a depth of 2.5m and a storage capacity of 13.8 million m3

(Wasserwirtschaftsamter [2017]). The input content which causes eutrophicationgenerally are fertilizers used in farming activity and wastewater discharge.

3.1.2 Wastewater

Due to the exclusive environment conditions and different influent types in eachcomponent within a wastewater treatment plant (WWTP), different types of bac-teria are present with a variety of organic and inorganic contents (Environmen-talLeverageInc. [n.a]). Floc characteristics are therefore also different in each partwithin a Wastewater treatment plant due to these different populations of bacteriaas well as organic and inorganic matters. So, several components within a wastew-ater treatment plant are chosen to acquire the information on flocs.

The samples to determine the properties of flocs from different part of a wastewa-ter treatment plant are provided by the Institut fur Siedlungswasserbau Wassergute-und Abfallwirtschaft (ISWA). An overview diagram of ISWA is shown below (figure3.2).

Figure 3.2: Schematic diagram of wastewater treatment plant,ISWA

14

3.1. Sample site

Flocs from the following parts of the WWTP are taken to understand theirproperties:

1. Primary sedimentation

2. Trickling filter

3. Main aerator (8 days retention time)

4. Surface aerator (approximately 21 days retention time)

5. Secondary sedimentation

Primary sedimentation

It is the final step during the mechanical treatment of wastewater. The main pur-pose of primary sedimentation is the removal of organic solids to reduce the load onfollowing treatment steps (Steinmetz and Krampe [2009]). The number of colloidalparticles is higher due to the larger amount of organic content. This increases thechances of forming bigger flocs. External coagulants are added to help the process.The velocity of flow, shape and size of particles are important factors which controlthe settling and formation of flocs. Flocs collected from primary sedimentation arediluted in order to introduce a handful amount of flocs in the floc settling chamber.

Trickling filter

It is one of the steps of biological treatment. During the slow percolation throughthe trickling filter, wastewater comes in contact with microorganisms growing onthe media, so that pollutants can be degraded (Steinmetz and Krampe [2009]). Theinorganic part is higher due to the consumption of organic part by bacteria due tohigher microbial activity in the trickling filter. So, flocs can be found in a varietyof sizes. The flocs are collected from the effluents of the trickling filter.

Aeration system - Main aerator and surface aerator

The aeration system provides oxygen from atmosphere to all microorganisms. Ingeneral, the size of flocs in aeration tanks is relatively smaller as constant mixingin the aerator tank breaks the floc structure (weaker bond between microorganismsin flocs).

Two different types of aerators are chosen to study the properties of floc whichare: surface aerator and main aerator. The main aeration tank has a high sludgeload with 8 days of retention time. On the other hand, the surface aerator hasa low sludge load with a retention time of 21 days. Both tanks work as part ofthe activated sludge process and have their own secondary sedimentation tank toobserve different floc behavior.

15

3.2. Experimental setup to grow biofilms using lake water

Secondary sedimentation

It is the one of biological treatment step which determines the effluent quality ofwastewater by separation of the solid remaining after passing through the activatedsludge tank. The size of the secondary sedimentation tank is largely dependent onthe suspended solid. Sludge retention time is kept as low as possible in the sec-ondary sedimentation in order to avoid unwanted denitrification process which willdissolve the phosphorus in wastewater and leads to bulking of the sludge (Steinmetzand Krampe [2009]).

3.2 Experimental setup to grow biofilms using

lake water

The experiment is set up in order to grow the biofilms using lake water in a labo-ratory for the purpose of biostabilization potential and floc characterization. Thebiostabilization potential is explored by M.Sc. Abhishek Pandey who has measuredthe adhesive properties of biofilms over the period of the experiment and the chem-ical analysis of biofilm is performed by M.Sc. Hasibur Rahman Lemon and M.Sc.Hajrah.

Figure 3.3: Experimental setup to grow biofilms

For the experimental setup (figure 3.3), 1200 liters of water are collected fromthe Lake Altmuhlsee. Simultaneously, six flumes are set up to divide 200 liter ofwater into each flume. Each flume contains 16 cartridges of 9 cm × 6 cm (length ×width). This makes a total count of 96 cartridges for six flumes. Each cartridge iscompacted with non-cohesive glass beads (<150 µE). Glass beads act as substratethat allows the biofilm to attach on. There is constant circulation of water with thehelp of a pump for 24 hours. Importantly, two different flow conditions are selected

16

3.3. Instrumentation and operation

for three of each flume.

The following predefined conditions are kept for flumes to grow biofilm usinglake water:

• All the flumes are kept under certain light conditions of 8 hours of light and 16hours of darkness, as it influences the biomass of biofilms. Lights are installedaround 28.5 cm above the flumes.

• The temperature of the water is maintained around 15◦C as it is an averagetemperature in freshwater systems.

• Initially, the flow of water is maintained at 0.51 L/s for all flumes. Afterward,different flow conditions are kept for flumes 1, 3, 5 and flumes 2, 4, 6 tounderstand the influence of different flow velocities on biofilm. Flumes 1, 3,5 are maintained under low flow conditions (0.5 L/s) while flumes 2, 4, 6 areunder high flow conditions (2.2 L/s).

• The growth period for biofilms is kept to 8 weeks.

Within a few weeks, biofilm start growing due to nutrient availability in thelake water. Technical reasons resulted in power being switched off in the laboratoryduring the 7th week, thus negatively affecting the growth of biofilm by a hindrancein water circulation (sudden stopping of pump), causing biofilms to dry. The oneextra week is given to recover the potential damage on the biofilms. After 8 weeks,matured biofilms are taken for measuring surface adhesiveness (stickiness) with thehelp of MagPI.

3.3 Instrumentation and operation

This section illustrates the various instruments and their operation which are:MagPI to measure the surface adhesiveness of biofilms, a floc erosion chamberto erode the flocs from biofilms grown using lake water,a floc settling chamber toobtain the data for the physical properties which will be evaluated with the MAT-LAB image processing code and spectrophotometer to obtain the concentrations ofEPS-proteins, carbohydrate and chlorophyll by measuring the absorbance.

3.3.1 MagPI-IP – To measure the adhesive properties

The biofilm displays a firm adherence to the surface in each and every step of itsformation because of glue like structure produced by EPS. This adhesiveness of thebiofilm surface is measured using MagPI (Magnetic particle induction). It workson the principle of electromagnetic field (Larson et al. [2009]) in which the forcerequired to attract the ferrous particles can be expressed as the surface adhesion.

In this master’s thesis, the MagPI system is integrated with the MagPI imageprocessing (MagPI IP) setup to interpret the voltage consumed during different

17


excitation states of ferrous particles via a MATLAB code to get more accurateresults and avoid errors due to human error. However, the technology on its own isnot completely reliable as sometimes poor resolution of images affects the calculationof ferrous particle retrieval. Therefore, manual measurements are also done toincrease the reliability of the results. Comparison of both measurements enablesthe verification of the results which is explained in chapter 4.

Figure 3.4: Magnetic Particle Induction (modified figure Larson et al. [2009])

The surface adhesiveness of biofilms is only measured for the lake water. It isdifficult to measure the surface adhesiveness for floc samples due to samller size.

First, the area of interest (1 cm2) is defined to provide the boundary conditionand also not to consider the particles which are not in that area of interest. Theelectromagnet is kept 4 mm above the area of interest. Then, a background picture istaken which is identified as reference image from which the results will be interpretedwhen images are evaluated using the MATLAB code. Afterward, ferrous particles(approximately 50 particles) are evenly distributed with a pipette on the pre-definedarea of interest. A power source is initiated, which results in a gradual increase of themagnetic field. Ferrous particles on the biofilm are retrieved on the electromagnetdue to an increase in the magnetic field; meanwhile a series of pictures (27 images)is taken by the computer which is used for further evaluation (figure 3.5).

(a) Electromagnet is kept 4 mm above thebiofilm under lightning conditions

(b) Multimeter is measuring the current requiredto attract the ferrous particle (Manual measure-ment) and subsequently computer is taking im-ages for automatic measurement

Figure 3.5: Actual images of MagPI

18


Two cartridges from each flume are used for measuring the adhesiveness proper-ties of biofilms. Moreover, 3 spots (replicate) on each cartridge (6 results per flume)are measured and the results are then averaged to summarize the results and toreduce the disparity. Surface adhesiveness is measured for two times of cartridgeduring the whole experiment phase, one at the beginning of the experiment andother at the end of the experiment.

The values from the multimeter (measures the current in ampere) at 50% exci-tation state (50% of ferrous particles are retrieved) are manually noted. Similarly,results of 50% (T50%) of retrieved particles are also obtained from the evaluation ofMagPI code through image processing. Once the stickiness of the biofilm is mea-sured, flocs to represent the biofilm experiments need to be eroded from the biofilmswhich are obtained by the floc erosion chamber described below.

3.3.2 Floc erosion chamber - To obtain the flocs from biofilms

A floc erosion chamber is a device which is used to erode the sediments. For thisthesis, flocs are eroded from biofilm grown from lake water attached to glass beadsand the eroded flocs are proceed to understand the physical and chemical propertiesof flocs.

Figure 3.6: Assembly of floc erosion chamber

A floc erosion chamber (figure 3.6) works on the principle of critical shear stress.It consists of a circular shape chamber (radius of 40.3 cm) with 7 rectangular groves(9 × 6 × 3 cm3; length × width × height) and the chamber is filled with water.Rotating members (shaft and impeller) are mounted on the chamber that are easilyseparable with the help of a screw and an Allen key (hex key) (figure 3.7). Addi-tionally, a motor is also connected to the rotating member. Motor setting allow the

19


rotating members to rotate at pre-defined speed. The speed at which the motor isrunning can be observed by tachometer (measures the speed).

Figure 3.7: De-attachment of shaft and impeller from empty erosion chamber

When the shaft and the impeller are rotating, the biofilms are subjected to ashear stress caused by the currents of water. At certain rotating speeds, the particlesof biofilms are eroded and form the flocs. The motor setting and rotating speed of1 particle erosion and mass erosion are being noted by the observer. All the erodedflocs are collected by a big spoon very carefully without destroying the structureand further proceed for acquiring the properties of flocs.

3.3.3 Floc settling chamber - To obtain the data for physicalcharacteristics

The floc settling chamber is integrated with the MATLAB image processing codewhich acquire all the physical properties of flocs mentioned in chapter 2.2 by usingimages taken by the camera system.

Figure 3.8: Arrangement of floc settling chamber

20


The experimental setup of a floc settling chamber is shown in figure 3.8.There isa UI-2250-M-BG camera system connected to the computer which takes a sequenceof 242 images with a resolution of 1600 × 1200 pixels for 30 seconds via the programcalled InPMS - vermessung von schwebeteilchen , when the extracted flocs from lakewater and the wastewater are introduced into a cuvette/ vertical column filled withwater (6 × 6 × 33 cm3; length × width × height). The flocs are introduced withthe help of a big spoon or a pipette into the cuvette. Lighting (LED – 67MW ofwavelength 470 nm) is an important factor to take successful images by ensuringthe constant image quality of homogeneous object (flocs) because MATLAB imagesprocessing code does not investigate the object itself instead it examines the visualimages of the object (StemmerImagingLtd [n.a]).

Six runs are performed for each of the floc samples in order to summarize theresults during the evaluation of images. In other words, flocs of same samples areintroduced into the cuvette six times to increase the precision. Each run takes 242images in 30 seconds which are used for evaluation. All saved images of floc fromcomputer are evaluated using the MATLAB image processing code developed byM.Sc. Daniela Pavia Santolamazza to get the physical properties of the flocs. Af-terwards, the results of each of the obtained floc samples of the biofilm experimentsand wastewater treatment plant are averaged to summarize and to avoid the ran-dom error. The remaining extra flocs of wastewater and the biofilm experimentsare used for chemical properties.

3.3.4 Spectrophotometer - To measure the absorbance forchemical properties of flocs

Spectrophotometer is used to measure the chemical characteristics (EPS, chloro-phyll) of flocs. It is a device which measures light absorbance at different wave-length.

Figure 3.9: Working principle of spectrophotometer (ByjusClasses [2018])

Figure 3.9 shows the working principle of the spectrophotometer, where a lightbeam strikes a prism/grating which separates the light into the components’ wave-

21

3.4. Procedure

lengths. The wavelength selector/slit allows only specific wavelength of light toreach the exit. Then, light passes through the sample in a cuvette and the trans-mission (amount of light that completely passes through sample) and absorbanceof the sample are measured by the detector.

Figure 3.10: Spectrophotometer

First, samples are prepared to measure EPS – proteins, carbohydrates andchlorophyll. The methodologies to prepare samples for all chemical characteris-tics are mentioned in the next section. The prepared samples are then inserted inthe slit of the spectrophotometer so as to measure the light intensity according towavelength. Afterward, the absorbance value is used to determine the concentra-tion.

3.4 Procedure

This section describes the process of the evaluation of the data (images) obtainedfrom the floc settling chamber in order to retrieve the physical properties of the flocsand further procedure to determine the concentration of the chemical properties.

3.4.1 Evaluation of physical properties

To evaluate the physical properties with the help of the MATLAB image processingcode, a sequence of the images (242 images per run/30 second) taken by the camerasystem is used, when flocs are introduced into the cuvette/column (floc settlingchamber).

Object tracking is the main purpose of the MATLAB code to evaluate the phys-ical properties of flocs. This is done by identifying them in a series of pictures, andthus assess their properties; area, perimeter, equivalent diameter, volume, all shapefactors (form factor, roundness, aspect ratio and Corey shape factor), compactness,solidity and settling velocity, used to analyze floc behavior. Before running thecode, the following procedure is followed to successfully run the code:

1. 242 images taken by the camera system are inserted into the subfolder (pic-tures) of the main folder. The first image of 242 images is pasted into thebackgroundpicts subfolder of FloculaMazza v1.0 use main folder because

22

3.4. Procedure

it is considered as the reference image on which basis the following imagesare evaluated. All the folders are needed to be linked in MATLAB code forsuccessful evaluation of the code.

Figure 3.11: Subfolders of FloculaMazza v1.0 use

2. There is one excel file called ”InputParameters.xlsx” in the Results subfolder(figure 3.11) which is used to change the various parameters such as theamount of pictures, the file names, conversion from pixels to millimeters,tolerances for the floc recognition, and desired output plots according to re-quirement. For this thesis, no parameters are changed.

Figure 3.12: Inputparameters.xlsx file to change the parameters

3. The physical properties of flocs are obtained by running the code (file name -floc properties) by linking all the folders into the MATLAB code. The entirecode for obtaining the physical characteristics is provided in the appendix.

4. When the code is successfully finished, file name - mean values in Resultssubfolder is received which contains values for all the properties.

3.4.2 Chemical properties

To measure the chemical properties, flocs samples need to be prepared first for fur-ther analysis. The procedure to prepare samples of proteins, carbohydrate (sugar)and chlorophyll contents are provided by Dr. rer.nat. Sabine Ulrike Gerbersdorf.Afterward, concentrations are calculated with the help of absorbance values of sam-ples measured with the spectrophotometer.

23

3.4. Procedure

(A) Protein analysis

The determination of proteins is important as it contributes to the “stickiness” ofthe biofilms and flocs.

For protein as well as carbohydrate analysis, a supernatant with EPS from flocsof the biofilm experiments and wastewater needs to be extracted. So, 0.5 cm3 offlocs and 1.5 ml Milli-Q water are added in a 2.0 ml eppendorf tube and mixed wellon a roller mixer (company name - CAT) for 1 hour. Supernatant with EPS areextracted from floc samples after 5-10 minutes centrifugation (14500rpm with 7522gacceleration). After carefully retrieving the centrifuged samples, they are separatedfrom underlying pellets. The supernatant formed water is then subdivided into 250µl in 3 different eppendorf tubes (triplicating) in order to have better precision andto avoid the random error.

To determine the protein content of the floc samples, first a standard curve(calibration curve) with known concentrations has to be created. Therefore, dif-ferent concentrations of BSA (Bovine serum albumin) are used. Quantifying ofthe proteins samples (floc samples) is done by comparing the unknown quantity(concentration) of protein samples with known concentration of BSA (RocklandInc[n.a]). With the known BSA concentrations used during the test, the spectropho-tometer absorption results are linked with them. The resulting standard curve(calibration curve) is used to calculate the protein content in floc samples.

The process to determine the absorbance rate of proteins is described below:

1. Different reagents which are necessary for protein analysis and their compo-sitions are listed in the table below (Table 3.1).

Reagent Composition

Reagent 1 0.572 g Sodium hydroxide (NaOH) + 2.8617 g Sodium carbonate(Na2CO3) in 100 ml distilled water

Reagent 2 0.1424 g Copper (II) sulfate pentahydrate with 10 ml distilled water

Reagent 3 0.285 g Di Sodium (Na) Tatrate with 10 ml distilled water

Reagent 4 Reagent 1,2,3 in ratio 100:1:1 (V:V:V)

Reagent 5 Folin Reagent (5 ml Folin/6ml Milli-Q water)

SDS 0.576 g Dodecyl sulfate sodium salt (2% solution) with 50 ml distilledwater to denaturate the proteins (NationalDiagnostics [n.a])

Table 3.1: Chemical composition required to prepare the protein sample

2. The standard solutions (BSA) are prepared for the standard curve. To set upthe standard stock solution, 20 mg BSA in 100 mL-Erlenmeyer flasks, is filled

24

3.4. Procedure

up with Milli-Q water. To acquire a more distinct standard curve, 6 differ-ent standard solutions (Table 3.2) are prepared with known concentration bydilution (range: 40 to 160 mg/l) in triplicates.

Concentration Quality of mixture200 mg/l Pure 250µl (standard)160 mg/l 200 : 50 µl (standard : water)100 mg/l 125 : 125 µl (standard : water)80 mg/l 100 : 150 µl (standard : water)40 mg/l 50 : 200 µl (standard : water)4 mg/l 5 : 245 µl (standard : water)

Table 3.2: Concentration of standard solution (BSA)

3. A blind sample of 250 µl Milli-Q water is prepared in triplicates which areconsidered as reference in the spectrophotometer.

4. Floc samples (supernatant form water with EPS), standard samples and blindsamples (0.25 ml) are all mixed (by lab dancer) with the SDS mixture (0.25ml)and reagent 4 (0.7ml) with the help of a pipette. Afterward, mixed samplesare vortexed and kept in a water bath for 15 min at 30◦C.

5. 0.1 ml of reagent 5 is also added into each samples and again kept in a waterbath for 45 min at 30 ◦C.

6. The absorbance values of each sample are measured with the spectropho-tometer at a wavelength of 750nm. Zero compensation of spectrophotometeris done by Milli-Q water before measuring the absorbance values.

A standard curve equation is obtained from averaged values of each standardsample with known concentration. This equation is used to calculate the contentof proteins from measured mean absorbance values of each floc sample.

(B) Carbohydrate (sugar) analysis

Carbohydrate is one of the most essential parameter in flocs and biofilms as it pro-vides the source of energy. Additionally, carbohydrate is one of the dominant partof EPS and it can have positive influences on the floc stability.

Supernatant with EPS are required to determine the content of carbohydrate inflocs. Supernatant are extracted with the same process as that described in proteinanalysis and subdivided in 200 µl into 3 different eppendorf tubes in triplicates (inorder to have accuracy). Afterward, with the same procedure as carried in the pro-tein analysis, a standard curve (calibration curve) needs to be set up for reference.Therefore, glucose standard solution (D-Glucose) is used for setting up the standardcurve. The content of carbohydrate is calculated from the resulting standard curve

25

3.4. Procedure

with known concentration (calibration curve).

The process to prepare carbohydrate samples to determine the absorbance rateis as follows:

1. The following chemicals for carbohydrate analysis are used:

• Phenol 5 %

• Sulfuric acid: 95-98 %

2. Standard stock solution (D-Glucose) is set up for calibration curve. 20 mg D-Glucose solution in 100 mL-Erlenmeyer flasks, is filled up with Milli-Q water.It is diluted further to come to the required concentration range.

Concentration Quality of mixture200 mg/l Pure 200µl (standard)150 mg/l 150 : 50 µl (standard : water)100 mg/l 100 : 100 µl (standard : water)50 mg/l 50 : 150 µl (standard : water)25 mg/l 25 : 200 µl (standard : water)5 mg/l 5 : 195 µl (standard : water)

Table 3.3: Concentration of standard solution (D-glucose)

3. A blind sample of 200 µl Milli-Q is prepared in triplicates which are consideredas reference in spectrophotometer.

4. 200 µl of 5 % phenol and 1 ml Sulfuric acid are added carefully with the helpof a dispenser tube in 200 µl of floc samples (supernatant form water withEPS), standard samples and blind samples. Afterward, mixed samples arevortexed and kept in water bath for 35 min at 30 ◦C (samples turned yellow).

5. The spectrophotometer is adjusted to 488 nm wavelength and zero compen-sation of it is done by pure sulfuric acid before measurement of absorbancevalues. The samples are removed from the water bath and mixed carefullyto avoid the formation of bubbles. Later, absorbance values are measured foreach sample.

The carbohydrate content from a sample, where the concentration is so far unknown,is determined from the equation obtained from the standard curve (D-Glucose) withknown concentration.

26

3.4. Procedure

(C) Chlorophyll analysis

Chlorophyll a is one of the indicators of the biomass in biofilms and biomass illus-trates the growth of biofilms. On the other hand, pheopigment a is used to estimatethe degradation products, which is originally chlorophyll a (Taguchi et al. [1993]).

The extraction of supernatant in chlorophyll and pheopigment analysis is dif-ferent from protein and carbohydrate analysis. Here, 0.5 cm3 samples of flocs areinserted in a 15 ml centrifuge tube. The samples are then kept in darkness whenthey are not in use. 10 ml ethanol is added into the collected floc samples andmixed with a roller mixer (low shaking rate) for 24 hours in complete darkness.Afterward, 6 ml of the supernatant are taken out with a pipette for further analysis.

The process of measuring the concentration of chlorophyll a and pheopigmentsa in floc samples is illustrated below:

1. Before acidifying the samples, the absorbances of the supernatant are mea-sured with a spectrophotometer at a wavelength of 665 nm and 750 nm intriplicate. The reasons for two different wavelengths are that the maximumof pigment adsorption is done at 665 nm and to subtract out the influenceof turbidity which is at 750 nm. Zero compensation of spectrophotometer isdone by ethanol.

2. After measurement, supernatants are put back into the centrifuge tube. Sub-sequently, 120 µl of 1N Hydrochloric acid (HCl) are mixed well with super-natant to acidify the samples.

3. All samples (supernatant of floc samples) are measured again in the photome-ter in triplicates with the same wavelengths as before.

4. The concentration of chlorophyll a and pheopigment a is calculated with thefollowing formulas:

chla =(E665(korr) − Eacid

665(korr))×RR−1× VExtr

ec × l × Vp(3.1)

pheaoa =(R× Eacid

665(korr) − E665(korr.))× RR−1× VExtr

ec × l × Vp(3.2)

while,Eacid

665(korr) = Eacid665 − Eacid

750 (3.3)

andE665(korr) = E665 − E750 (3.4)

where,chla = content of chlorophyll a of a floc sample,pheoa = content of pheopigment a of a floc sample,

27

3.4. Procedure

E665(korr) = Corrected extinction at the maximum of adsorption before acidi-fication,Eacid665 (korr) = Corrected extinction at the maximum of adsorption after acidifi-

cation,ec= Specific extinction coefficient of chlorophyll a at maximum of adsorptionin red spectral range. For 96% ethanol, ec = 83× 10−3 cm µm-1 cm-1,Vextr = Amount of extraction agent (cm3),l = Length of cuvette (cm),R = Ratio of extinction (1.7),Vp = Volume of sediment sample (cm3).

28

Chapter 4

Results and discussion

4.1 Adhesive properties of the biofilm grown from

lake water

Cartridges of the biofilm (grown from lake Altmuhlsee) from different flumes aresubjected to the instrument MagPI IP based on magnetic attraction in order to ac-quire the adhesive properties. To observe the growth of biofilm over a time periodof 8 weeks with 2 different flow conditions (mentioned in chapter 3), two samplingperiods are selected which are at the time of beginning and end of the experiment.The measurements are executed in two different way: manual measurement andautomatic measurement

This work only includes the two sampling periods (beginning and end of theexperiment) for two cartridges. The reason for measuring the adhesive propertiesof the biofilms instead of flocs is that it is difficult to measure surface adhesive-ness of the small flocs with the help of MagPI IP. The detailed observation whichinclude several sampling periods within the 8 weeks time period is performed byM.Sc. Abhishek pandey.

The 50% excitement or attraction of ferrous particle kept on surface of thebiofilms with different flow conditions is observed using both measurement methods.Additionally, a comparison of these measurement methods as small part of the thesisare also discussed here. The adhesion strength of biofilms is obtained in Ampere.

4.1.1 Manual measurements

The ferrous particles on the biofilms are subjected to a gradual increase in voltageand the observer has to judge and note the 50% retrieval of ferrous particle frombiofilms through MagPI. The results are interpreted as the surface adhesiveness ofthe biofilms in Ampere, which are shown in the following figure 4.1.

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4.1. Adhesive properties of the biofilm grown from lake water

Figure 4.1: Surface adhesiveness by manual measurement

It is observed that surface adhesiveness “stickiness” is increased over the periodof the experiment in all flume except flume 5 in which algae is observed. The man-ual removal of the algae growth and negative impact due to a sudden stopping ofpump during week 7 can be the reasons for decreased surface adhesiveness.

The impact of flow conditions on the surface adhesion of biofilms can easily benotice in the above figure. Flumes 1,3 and 5 are run at low flow condition whichshows the significant variation of surface adhesion of biofilms at the end of the exper-iment when they are compared with flumes 2,4 and 6 (high flow condition). Higherflow conditions encourage the biofilm structure to increase the tensile strength whichresults in an increase in adhesiveness compared to low flow conditions (Stapf andHan [2006]).

4.1.2 Automated measurements

The automated measurement results are obtained by integrating the MATLABimage processing code with MagPI. When ferrous particles are retrieved due toelectromagnetism, subsequently a series of images is also taken by the computersoftware and later all the images are evaluated by MATLAB code to acquire theresults of surface adhesiveness of the biofilm. Here, 50% excitement of ferrous parti-cles computed through image analysis in MATLAB is used to represent the biofilmstrength. The following figure 4.2 shows the surface adhesiveness in Ampere byautomatic measurement.

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4.1. Adhesive properties of the biofilm grown from lake water

Figure 4.2: Surface adhesiveness by automated measurement

It is quite evident that biofilm adhesive strength is increased at the end of theexperiment. Flumes with high flow conditions (flume 2,4 and 6) have higher sur-face adhesiveness approximately around 0.83 A and flumes with low flow conditions(flume 1,3 and 5) shows results around 0.60 A except flume 3. Even though flume 3is maintained at low flow conditions, it has shown the highest surface adhesivenesswith 0.84 similar to the flumes running at high flow conditions. Additionally, flume5 has not shown significant increase between the beginning and end of the experi-ment due to accidental biofilm erosion when algae on biofilm surface was removedmanually.

4.1.3 Comparison of the manual measurement and auto-mated measurement

Both methods are compared in order to have a better perspective about the surfaceadhesion of biofilms. The results of 50% attraction of ferrous particle at the end ofexperiment for both methods are shown in the following figure 4.3.

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4.2. Floc erosion

Figure 4.3: Comparison of the manual measurement and automated measurement

The pattern of results for both methods is almost similar but there is a quitedifference in terms of the values of surface adhesion (A) between the methods. Inmanual measurement the observer see the retrieval of ferrous particle with nakedeyes, the underestimation of the results is also possible which may be the maincause of difference in both the measurements.

4.2 Floc erosion

Once the surface adhesiveness of biofilms is measured with MagPI IP, the flocsis needed to be obtain to represent the biofilms experiments to investigate flocproperties. It is achieved by the floc erosion chamber. The flocs are eroded by therotating members (shaft and impellers) in floc erosion chamber when the biofilmsare subjected to shaer stress. The speed of impeller at which biofilms are erodedis noticed by the observer. 1 particle erosion and mass erosion in terms of rpm isrepresented in the following chart which also indicate the strength of biofilms.

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Figure 4.4: Erosion of flocs at certain rotating speed indicating the strength of thebiofilms

It is quite evident that biofilms from flumes (2,4,6) maintained at high flow con-ditions indicate higher speed is required to erode the flocs compared to the biofilmsfrom flumes (1,3,5) maintained at low flow conditions. This depict higher biofilmstrength. While flume 5 (low flow conditions) shows least strength with low speedrequired to erode confirming the results of MagPI IP. Once the flocs are eroded,they are carefully collected by a big spoon which be used to study the properties offlocs from the biofilm experiments.

The limitation with this observation is that results are subjective to the observerwhen the mass erosion is considered, but in genral it gives the overview regardingthe strength of the biofilms.

4.3 Physical properties

Once flocs are eroded in the floc erosion chamber, the eroded flocs are carefullyintroduced in a floc settling chamber which is integrated with the MATLAB imageprocessing code. Six runs per sample are performed for flocs from the biofilmexperiment and the wastewater treatment plant in the floc settling chamber. Theevaluated results are averaged for each sample in order to have better precision andreliability (to reduce anomalous results). The averaged evaluated results of flocsof the biofilm experiment and the wastewater treatment plant are discussed in thefollowing sections.

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4.3.1 Quantity of flocs

Figure 4.5: Quantity of flocs introduced in the floc settling chamber

Unknown amount of flocs are introduced carefully with the help of a big spoon intothe floc settling chamber and the software capture images for further image anal-ysis. During floc settling, not all recorded flocs can be considered in the analysisby the image processing code of MATLAB; some reasons for not identifying theflocs are: bad resolution of images and flocs moving sideways of the camera. TheMATLAB code procures the physical properties of identified flocs only. Unidentifiedflocs properties are ignored by MATLAB. The final results given by MATLAB codeare averaged values of all identified flocs of particular sample. Still, MATLAB finalresults show the number of identified and unidentified flocs which is representedabove (figure 4.5);

It is evident that the number of unidentified flocs is really high compared to thenumber of identified flocs in all floc samples, but the amount of identified flocs issufficient enough to represent the physical properties of flocs when compared withother methods of floc characterization. For example, using microscopic method tounderstand the physical properties of flocs, few flocs (maybe 2-3) are observed un-der a microscope to represent all flocs of that particular sample.

The biofilms are eroded by a floc erosion chamber in order to obtain the flocs.The flocs are eroded in a controlled situation, which leads to fewer flocs from thebiofilm being observed. Additionally, the reason for the higher ratio of unidentifiedflocs to identified flocs from the biofilm experiments is that, the loose glass bead(substratum of the lake biofilm) also get settled when flocs are introduced in thefloc settling chamber. The loose glass beads of the flocs are recognized by the image

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processing code. On the other hand, the number of flocs identified in the wastewatertreatment plant is fair enough to represent the floc properties of wastewater asalmost 30% of flocs are considered as identified flocs for all wastewater treatmentplant floc samples except flocs from the trickling filter effluent.

4.3.2 Floc size

Floc size distribution is important for understanding the settling of flocs in fresh-water and wastewater. It is a key parameter that affects the settling velocity ofthe flocs. Floc size is mostly controlled by the availability of colloidal particles andmicrobial activity. So, various parameters representing the floc size are discussedin the following sections.

(A) Area

The area of flocs is measured based on the pixel size of images as it is a twodimensional property and results of area of flocs are shown in the following figure 4.6.Due to a large difference value in terms of area between flocs from the wastewatertreatment plant and the biofilm experiments, it is difficult to differentiate the resultsof individual floc sample of the biofilm experiment. The separate chart of the flocsamples from the biofilm experiment is represented in figure 4.7.

Figure 4.6: Area of flocs samples

As it is quite evident that the area of flocs from the wastewater treatment plantis significantly higher than flocs from the biofilm experiment. This is explained

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by the enhanced flocculation process in wastewater treatment plant by adding thechemicals in order to settle them faster to increase the treatment efficiency. Also,colloidal particles and microbial activity necessary to form flocs are also higher inwastewater which can result in higher surface area of flocs.

As wastewater treatment goes further, the amount of colloid particles and micro-bial activity reduce and floc size also reduces which is also noticed in the figure 4.6.In addition to that, every wastewater treatment process in a wastewater treatmentplant has a different amount of organic and inorganic content with different micro-bial population. So, a large range of area is observed between the flocs from differentcomponents within a wastewater treatment plants. Due to substantial amounts ofcolloids particles (organic and inorganic) in primary sedimentation, chances of form-ing wide range of flocs with large surface area are high with high standard deviationand it is noticed in the graph also. The flocs from the trickling filter effluent havea smaller area compared to the flocs from the primary sedimentation. Flocs fromtrickling filter effluent have high diversity (large span of standard deviation) whenit comes to the area of flocs. This is due to higher inorganic content present inwastewater while organic parts are being consumed by microbial activity. Largerretention time of wastewater leads to better floc formation which can be easily no-ticed in aerator systems. So, flocs from the surface aerator (21 days retention time)have considerable area compared to flocs from the main aerator (7 days retentiontime) even though the function of the aerator is same. Secondary sedimentation isthe final step of the activated sludge process (next step after aeration tank). Theflocs from secondary sedimentation have almost the same floc area as the mainaerator due to less organic content available to form bigger flocs.

Figure 4.7: Area of flocs samples of biofilm experiment

Specifically regarding the flocs from the biofilm experiments, it is observed from

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figure 4.7 that flocs from the high flow conditioned biofilms have a larger areacompared to the flocs from the low flow conditioned biofilms. It is because flocs withhigh flow conditions have more stability and better attachment of microorganismwhich binds the floc particles together when biofilms are eroded in a floc erosionchamber. But flocs from flume 5 are scrutinized due to almost having the samearea as high flow conditions flumes and further detailed investigation is needed tounderstand this difference in floc sizes.

(B) Perimeter

The perimeter is the boundary of the floc based on a 2D image. The morpholog-ical parameters such as porosity and density are a function of fraction properties’perimeter’. The resulting perimeter of different floc samples are shown in the figurebelow;

Figure 4.8: Perimeter of each flocs sample

The results are quite similar to the areas, whereby the perimeter of flocs fromthe wastewater treatment plant is considerably higher than that of flocs from thebiofilm experiments. It is due to higher colloidal particles that are available to formflocs and higher microbial activity. Also, floc formation in a wastewater treatmentplant is enhanced to separate solid phase from liquid phase to increase treatment ef-ficiency. But the average perimeter of flocs from flume 2 of the biofilm experimentsis quite comparable with flocs from the main aerator, but the standard deviationof flocs from flume 2 is high.

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The perimeter results of flocs from the wastewater treatment plant follow thesame trend as the results of area confirming the observation that as the wastew-ater passes through the sequences of treatment, the floc size (perimeter) also de-creases with the reduction of colloidal particles and microbial activity. Additionally,wastewater contains filamentous bacteria which generally increase the perimeter offlocs. While, the perimeter difference between flocs from both aeration systems isdue to the difference between the retention time. The longer retention time allowsthe flocs to form in bigger size (chances of forming excessive filamentous bacteria)which increase the perimeter.

Figure 4.9: Perimeter of flocs from the biofilm experiments

As it is observed from the figure 4.9, the perimeter of flocs from the high flowconditioned biofilm except flume 2 are slightly higher compared to the flocs fromthe low flow conditioned biofilm because of better floc stability (due to EPS) athigh flow conditions.

(C) Equivalent diameter

Equivalent diameter represents the one-dimensional characterization of floc particlesfrom a two dimensional image analysis. Equivalent diameter is a function of area(measured earlier), and is calculated from the equation 2.3. Equivalent diameterhas an influence on the settling velocity of flocs, which is desired in aquatic waterbodies in order to have cleaner water. The following charts (figure 4.10 and 4.11)show the average equivalent diameter for flocs from the biofilm experiments as wellas from the wastewater treatment plant.

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Figure 4.10: Equivalent diameter of flocs from floc samples

As equivalent diameter is calculated based on the area measured earlier, theresults are similar. The flocs found in the wastewater treatment plant exhibit aswide range of diameters between 150 µm - 550 µm. Whereas, the diameters of theflocs eroded from the biofilm experiments are smaller compared to the flocs from thewastewater treatment plant due to the reasons mentioned earlier, with an averageof 100 µm.

In wastewater, the large amount of colloidal particles (organic and inorganiccontent) in different sizes are available in primary sedimentation. Hence, it is easierto bond them together and make them bigger for effective settling by adding anexternal coagulant. So, flocs from primary sedimentation (560.40 ± 104.25 µm)have yielded the highest diameter of flocs.Generally, there is a general trend of decreasing equivalent diameter as wastewatergoes through the wastewater treatment plant processes with reduced solid content.So, flocs from the trickling filter effluent have shown smaller equivalent diametersthan flocs from the primary sedimentation. The breakage of flocs due to mixingand higher inorganic content in wastewater resulted in a wide range of diameterswith a large standard deviation in the trickling filter (404.44 ± 203.64 µm). Thereis little increase in the diameter between the main aerator (165.72 ± 18.84 µm) and secondary sedimentation (228.72 ± 97.86 µm) flocs. This may be due toretention time and favorable conditions to form bigger flocs even though they aresequential processes of the activated sludge process. As mentioned earlier, due tothe retention time, the surface aerator flocs is observed with greater floc diameters

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than main aerator flocs and secondary sedimentation flocs.

Figure 4.11: Equivalent diameter of flocs from the biofilm experiments

The average equivalent diameter of the flocs from the high flow conditionedbiofilms (average diameter of 110 µm) are 1.6 times greater than the flocs fromthe low flow conditioned biofilms (average diameter of 66 µm) confirming that flocsfrom high flow conditioned biofilms have better stability.

(D) Equivalent volume

The equivalent volume is the function of the equivalent diameter which calculatedby equation 2.4. Due to the very large difference between the various floc samples,it is difficult to represent equivalent volume in general scale. To avoid this difficulty,the equivalent volume is represented in the logarithm scale which is shown in thefigure 4.12. Due to higher influence of the flocs larger size on equivalent volume,standard deviation is not represented in the logarithm graph.

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Figure 4.12: Equivalent volume of flocs from floc samples on logarithm scale

It is quite evident from the logarithm graph that the flocs from the wastewatertreatment plants is observed with really high values compared to the flocs from thebiofilm experiments same as the equivalent diameters of the floc samples. Floc sizein terms of equivalent volume is not preferred due to the few larger size flocs canhave huge influences on equivalent volume.

The equivalent volumes of the flocs from the wastewater treatment plant are fol-lowing the same trend as the equivalent diameter. The highest equivalent volumeis observed in the flocs from the primary sedimentation, followed by flocs from thetrickling filter effluent. There is difference in terms of equivalent volume betweenboth aeration system’s flocs due to the difference in the equivalent diameter. Thelowest equivalent volume is noticed in flocs from the secondary sedimentation asthe amount of flocs and equivalent diameter are reduced with increase in treatmentefficiency in secondary sedimentation.

The flocs from the high flow conditioned biofilms are observed with slightlyhigher equivalent volume than the flocs from the low conditioned biofilms followingthe similar pattern as the equivalent diameter with similar reasons.

4.3.3 Major and minor axis

Measurements of the major and minor axes are important, when an image process-ing code is used, to determine the shape parameters. Based on the major and minor

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axes, various shape parameters of flocs such as aspect ratio, form factor, roundnessand the corey shape factor indicate the deviation from a circular shape. The resultsof the major and minor axes are shown in figure 4.13 and 4.14 respectively.

Figure 4.13: Major axis length of floc samples

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Figure 4.14: Minor axis length of floc samples

The flocs from the wastewater treatment plant have a higher length and widththan the flocs from the biofilm experiments due to larger flocs, which can be dis-tinguished from the lengths of the major and minor axes.

The major and minor axis length of wastewater treatment plant’s flocs follow thesame trend as the floc size where the length of major and minor axis is decreased asthe wastewater follows the sequence of the treatment. Flocs from the trickling filtereffluent have a large variation in the major and minor axis, with a larger standarddeviation compared to other parts of the wastewater treatment plant. The tricklingfilter effluent might contain loose eroded biofilm of different sizes from rotating thedisc might be the reason for the huge standard deviation.

The flocs from the high flow conditioned biofilms yielded higher value thanthe flocs from the low flow conditioned biofilms indicating more floc stability andelongated flocs erosion. The flocs from flume 2 (figure 4.13 and 4.14) show a greateraveraged length of major and minor axes but standard deviation is large, whichplaces it in the same range as other flumes under high flow conditions.

4.3.4 Shape parameters

All shape parameters except the form factor are a function of the major and minoraxis measured by evaluating images with the MATLAB code. Shape parametersindicate the variation from a circle where circular flocs are desirable in aquatic

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bodies in order to have better settling velocity. The results of shape parameters aredescribed below.

(A) Form factor

Form factor (FF) is calculated from the equation 2.5 based on the areas and perime-ters where 1 with a circle and 0 with a non-circle.

Figure 4.15: Form factor of floc samples (1 is a circular floc and 0 is a non-circular)

Flocs from the biofilm experiments show less deviation from the circle comparedto the flocs from the wastewater treatment plant. The reason for more circular flocsfrom the biofilm experiment is not completely clear, but there are less filamentousbacteria living on the flocs of the biofilm experiments compared to wastewatertreatment plant. This can influence the shape of the flocs (EnvironmentalSystems[2014b]). Additionally, the flows in the biofilm experiments are more uniform andless turbulent than the wastewater treatment plant which can be another reasonfor more circular flocs.

Primary sedimentation flocs (FF = 0.47) are observed with high irregularity inshape (greater perimeter) because of hindrance (friction and resistance) caused bya large number of colloidal particles in wastewater. These colloidal particles causeproblem in the formation of the regular shape flocs. The flocs from the trickling filtereffluent have a form factor of 0.68 which suggests flocs with more circular shapesas compared to the flocs from the primary sedimentation due to less hindrance,more stability and increase in microbial activity without filamentous bacteria. The

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aeration system flocs have a lower form factor, especially in the surface aerator (FF= 0.38) and it is due to the excessive growth of filamentous bacteria in aerationsystems (Koivuranta et al. [2013]), which influence perimeter (roughness of flocs).Secondary sedimentation flocs (FF = 0.75) indicate less deviation from a circlecompared to other parts of the wastewater treatment plant.

It is clear that flocs from the high flow conditioned biofilms (average FF = 0.73)show more deviation from circular flocs compared to the flocs from the low flowconditioned biofilms (average FF = 0.85) than can be due to an influence of flowvelocity on the roughness. At higher longitudinal velocities, the biofilms formingtend to become longer in the direction of flow but the clear reason is still unknown.

(B) Roundness

Roundness is a function of area and of the major axis. The MATLAB code usesthe equation 2.6 to calculate it. Roundness considers the elongation of flocs anddoes not influenced by the roughness of floc particles. The results of roundness areshown below (figure 4.16);

Figure 4.16: Roundness of floc samples (1 with a circular floc and 0 with a non-circular)

The roundness of flocs does not give a clear picture regarding the differencebetween the flocs from the biofilm experiments and from the wastewater treatmentplant. Still, the average roundness of flocs from the biofilm experiments have a

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minor edge in higher values than flocs from wastewater.

The results of roundness of flocs from wastewater treatment plant follow thesame pattern as the form factor but values are different, whereby flocs from sec-ondary sedimentation have highest circularity (0.68), followed by trickling filter ef-fluent (0.62), primary sedimentation (0.56) and both aeration systems (main aeratorand surface aerator, 0.52 and 0.47 respectively). The variation in the roundness offlocs in the wastewater treatment plant is different due to different organic/inorganicactivity with different flow conditions.

The roundness of flocs from the low flow conditioned biofilm have minor highervalue than flocs from the high flow conditioned biofilm suggesting that flocs fromthe high flow conditioned biofilm are little bit more elongated due to different flowconditions. This assumption is supported by Manning and Dyer [1999] where heobserved that high turbulent shear stress caused the formation of more elongatedflocs and more circular flocs are observed at low shear stress. But there is also apaper published by Zhu et al. [2015] which totally contradict this assumption.

The values of roundness are lower than form factor values in every floc samplesimplying the influence of the perimeter in form factor.

(C) Aspect ratio

Aspect ratio (AR) depends on the major and minor axes. Aspect ratio indicatesthe elongation of the flocs, where 1 is a circular floc. Aspect ratio does not have anyinfluence on the roughness of the flocs like form factor. The results of the aspectratio for all different floc sample are shown in the figure 4.17 below.

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Figure 4.17: Aspect ratio of floc samples (1 is a circular floc, otherwise the floc iselongated)

The average aspect ratio of flocs from the wastewater treatment plant is slightlyhigher than for the flocs from the biofilm experiments, indicating that the flocs fromthe biofilm experiments are closer to a circle and show less elongation. Similar re-sults as for the roundness of the flocs are observed (figure 4.16) where the differenceis not that high between all floc samples.

Flocs from the main aerator and surface aerator indicate large deviations froma circle, indicating more elongated flocs present with aspect ratios of 1.89 and 1.88due to filamentous bacteria, respectively. Flocs from secondary sedimentation showthe lowest elongation (more circularity), with aspect ratio of 1.50 among all com-ponents of a wastewater treatment plant. Flocs from the trickling filter effluentand the primary sedimentation have almost similar aspect ratios of 1.68 and 1.70respectively.

The aspect ratio of flocs from the low flow conditioned biofilm indicate highervalue with high standard deviation than flocs from the high flow conditioned biofilm.Thus, flocs form of high flow conditioned biofilm are elongated and vary from circu-lar flocs. Even though the flocs from flume 6 (high flow conditioned biofilm) havethe lowest aspect ratio (AR = 1.55), the standard deviation is large. The resultsof aspect ratios are quite comparable with the roundness confirming the shape offlocs briefly.

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(D) Corey shape factor

The Corey shape factor can be described as the particle’s thickness (minor axis)divided by the other axes (median and major axes) of the particle (Santolamazza[2013]). This factor assumes that all three axes affect the shape of floc particle andcalculated by equation 2.8 where the value of 1 suggest a circle. The Corey shapefactor is also generally associated with the flatness of the particle (Albar [2000]),but it is difficult to judge flatness from two dimensional images. The results ofCorey shape factor are shown in the figure 4.18.

Figure 4.18: Corey shape factor of floc samples (1 with a circular floc and othersbeing deviation from circle)

The Corey shape factor did not give a clear picture regarding the shape dif-ference between the flocs from the biofilm experiments and from the wastewatertreatment plant due to several limitations associated with the Corey shape factor.The Corey shape factor does not consider the roundness of the flocs. Still, theaverage CSF of flocs from the biofilm experiments (0.80) is slightly higher than forthe flocs from the wastewater treatment plant (0.78).

As for all previous shape factors mentioned above, secondary sedimentation flocs(0.85) are more circular followed by primary sedimentation flocs (0.78). The CSFvalues (0.76) for the trickling filter effluent, main aerator and surface aerator arethe almost the same.

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Flocs from low flow conditioned biofilms have better circularity than flocs fromhigh flow conditioned biofilms similar to all the shape factors.

Summary of all shape parameters

All the shape parameters provide a general overview of the deviation from a circularshape, and every parameter has its own advantages and disadvantages. Form factoris least suitable as it is very sensitive to the roughness of flocs where perimeter isinfluenced by the roughness. The Corey shape factor also has its own drawbacks,such as not considering the roundness of flocs. For example, the CSF of a sphereand a cube is 1, but it is used as it allows to reduce tedious work to calculate thepixel maps of perimeter (Walsh [1988]). Often, aspect ratio sometimes gives highvalues to very symmetrical shapes such as square, equilateral triangle which is thedisadvantage of aspect ratio (Olson [2011]). Still, the aspect ratio and roundnessgives adequate results which are also quite comparable.

Water ID Form Round Aspect Coreybodies factor -ness ratio shape factor

Flume 1 0.82 ± 0.18 0.65 ± 0.11 1.67 ± 0.12 0.81 ± 0.66

Flume 2 0.73 ± 0.22 0.64 ± 0.16 1.68 ± 0.33 0.77 ± 0.06

Biofilm Flume 3 0.94 ± 0.09 0.67 ± 0.04 1.59 ± 0.14 0.85 ± 0.01

exper Flume 4 0.76 ± 0.21 0.58 ± 0.13 1.67 ± 0.30 0.77 ± 0.06

-iment Flume 5 0.88 ± 0.11 0.61 ± 0.11 1.62 ± 0.22 0.82 ± 0.06

Flume 6 0.71 ± 0.20 0.62 ± 0.19 1.55 ± 0.41 0.80 ± 0.09

Primary

sedimentation0.47 ± 0.11 0.56 ± 0.05 1.70 ± 0.16 0.78 ± 0.02

Trickling filter 0.68 ± 0.13 0.62 ± 0.03 1.68 ± 0.10 0.76 ± 0.10

Waste Main aerator 0.61 ± 0.08 0.52 ± 0.02 1.89 ± 0.15 0.76 ± 0.03

water Surface aerator 0.38 ± 0.05 0.47 ± 0.06 1.88 ± 0.22 0.76 ± 0.03

Secondary0.75 ± 0.11 0.68 ± 0.15 1.50 ± 0.30 0.85 ± 0.08

sedimentation

Table 4.1: Summary of floc shape parameters for all floc samples

The table 4.1 shows all the shape parameter values. All the shape parameterresults indicate that flocs from the biofilm experiment are more circular than flocsfrom the wastewater treatment plant. Flocs from secondary sedimentation are morecircular flocs followed by flocs from the trickling filter effluent and from primarysedimentation. The least circular flocs are from both aeration systems. Addition-ally, flocs of the low flow conditioned biofilms are best when it comes to shape,

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followed by the flocs of high flow conditioned biofilms in the biofilm experiment.

4.3.5 Compactness

Compactness indicates the biostabilization and self-unification of flocs resultingin denser flocs which helps them settle quickly (EnvironmentalSystems [2014a]).Van der Lee [2000] observed that larger flocs with excessive growth of filamentousbacteria are more porous and less compact, with lighter organic materials whichreduces the density of flocs that affect their settling velocity. Thus, it is possiblethat smaller flocs are more compact due to their resistance to transfer the oxy-gen (Mueller et al. [1968]). The results of compactness of flocs from the biofilmexperiments and from the wastewater treatment plant are shown in figure 4.19.

Figure 4.19: Compactness of the floc samples (1 with a highest compactness andother with decreased compactness)

Having a certain amount of filamentous bacteria is good as it helps floc for-mation, but excessive growth of filament bacteria reduces the compactness whichdirectly affects the settling velocity due to bulking (LenntechBV [n.a]). The growthof filamentous flocs depends upon the nutrient content available in the water bodies.Generally wastewater contains filamentous bacteria. So, the values of compactnessof flocs from the biofilm experiments have a slight edge over the values of flocs fromthe wastewater treatment plant mainly because of filamentous bacteria presence(due to nutrient) in the respective water bodies.

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The flocs from the biofilm experiments are eroded from biofilms in the floc ero-sion chamber. They are not formed by natural flocculation process. So, the chancesof having porous flocs are reduced which affects the compactness.

The trickling filter effluent flocs have a higher microbial activity, which consumethe nutrient and might produce more stable and compact flocs. Secondary sedi-mentation is almost the final step of the wastewater treatment plant, meaning lessnutrient is available and chances of forming flocs with excessive filamentous bacteriais very less, indicating more compact flocs. So, it is observed from the graph thattrickling filter effluent and secondary sedimentation flocs have high compactness(0.81). Primary sedimentation has an abundance of organic and inorganic matter(solid content) and the chances of forming large, porous flocs is high, showing lesscompactness of flocs (0.74) than the flocs from trickling filter effluent. The aerationtanks have favorable conditions (temperature) for the growth of flocs with excessivefilamentous bacteria (Pal et al. [2014]). The reduction of compactness in the aera-tion system (main aerator and surface aerator) is observed due to forming certaintype of flocs with excessive filamentous bacteria.

Flocs from the high flow conditioned biofilm exhibit higher compactness com-pared to the flocs from the low flow conditioned biofilms. This observation is con-firmed by Ashton [1986] where it is mentioned that the more turbulent the flow,the higher the compactness

4.3.6 Solidity

Solidity is a good parameter to represent the ability of flocs to occupy space, whichis an important property for the wastewater treatment plant. The compactnesshave influence on the solidity of the flocs (Amaral [2003]). The results of solidityare presented below figure 4.20;

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Figure 4.20: Solidity of the floc samples (1 with a solid flocs and other with de-creased solidity)

It is difficult to distinguish the flocs from the biofilm experiments and from thewastewater treatment plant when it comes to the solidity. They are observed withalmost similar values, except the solidity of flocs from both aeration systems andfrom primary sedimentation. Solidity is affected by the roughness of the edges ofthe flocs, leading to an overestimation of the area during the identification of thefloc.

Same as the compactness, the solidity of flocs from the trickling filter and fromthe secondary sedimentation is high and followed by flocs from the primary sed-imentation. The flocs from the main aerator and from the surface aerator havelow solidity. The difference between flocs from the main aerator and from surfaceaerator is high and it might be due to the higher roughness of flocs in surface aerator.

The results of flocs from the high flow conditioned biofilms is low comparedto the flocs from the low flow conditioned biofilms contradicting the results of thecompactness.

4.3.7 Settling velocity

Floc settling velocity under gravitational force is a behavioral characteristic whichis affected by the number of parameters such as size, shape, compactness, solidity,density, porosity, specific weight and the composition of flocs (Liss et al. [2004]).

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The settling velocities of flocs are measured with the MATLAB image processingcode using images taken by a floc settling chamber, when flocs are introduced. Thefollowing graph (figure 4.21) represents the settling velocities of flocs.

Figure 4.21: Settling velocity of the floc samples

Flocs from the biofilm experiments maintained at high flow conditions (flumes2,4,6) show a high settling velocity around 2.75 mm/s among all other floc samples.Theoretically, flocs from the wastewater treatment plant should have a higher set-tling velocity than flocs from the biofilm experiments due to their larger size. Butother factors such as shape, compactness, solidity, specific weight and density alsoaffect the settling velocity of flocs.

Flocs from the trickling filter effluent indicate prominent settling velocity (1.88mm/s) because they are large, stable, compact, solid and more circular comparedto all other components of the wastewater treatment plant. Even though primarysedimentation flocs are larger, they are unstable and highly irregular. Additionally,dilution in water during floc settling chamber process might have also affected theirsettling velocity (1.12 mm/s). Flocs from the main aerator demonstrate the low-est settling velocity among all the wastewater treatment components (0.88 mm/s)because of smaller flocs, less compactness, and high deviation from circle. Addition-ally, according to organic matter data of the main aerator (4.8 g/l) provided by theISWA on the day the floc samples are collected, lower organic matter might haveresulted in the low settling velocity. Flocs from the surface aerator (1.46 mm/s)show higher settling velocity than flocs from the main aerator, but with a largestandard deviation, the reason for which being unknown even though main aerator

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flocs are better shaped, compact and solid. Flocs from secondary sedimentationhave a settling velocity of 1.21 mm/s. In general, secondary sedimentation flocs arenon-settleable due to their light specific weight (Steinmetz and Krampe [2009]).

Flocs from the high flow conditioned biofilms are larger sized, fair shape (lesscircular than flocs from low flow conditioned biofilms) and more compact thanflocs from the low flow conditioned biofilms. So, flocs from biofilms grown in highflow conditions show greater settling velocity than flocs from biofilms at low flowconditions.

4.3.8 Summary of the physical properties

The area and equivalent diameter are found to be quite adequate parameters torepresent the floc size as it give better picture regarding the floc behavior com-pared to the perimeter and equivalent volume. Perimeter is highly influenced fromroughness of particle, whereas equivalent volume has an impact due to prescenceof few larger flocs. In terms of shape factor, roundness and aspect ratio providea better representation of circularity of flocs. Compactness is a function of thebiostablization. Solidity indicates the quality of being solid and ability of floc parti-cles to acquire less space which impacted by compactness. If compactness is higher,then solidity tends to be higher. Eventually all above mentioned parameters havesomehow impact on the settling velocity.

4.4 Chemical properties

The floc samples are prepared according to the lab protocol provided by Dr. rer.Nat Sabine Gerbersdorf (mentioned in chapter 3.4.2) to determine the contents ofthe EPS- proteins, carbohydrates and chlorophyll. The content is determined fromthe principle of colorimetry, which is measured with spectrophotometer.

4.4.1 Protein analysis

The absorbance values of standard sample solutions with known concentrations aremeasured with the help of a spectrophotometer and a standard curve for proteinanalysis is prepared. The following graph (figure 4.22) represents the standardcurve and its equation, used to calculate the gravimetric concentration of proteinsper dry weight (DW) in all flocs samples. The content of proteins are representedas micro-gram per gram of dry weight.

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Figure 4.22: Standard calibration curve for protein analysis

As it is evident, the standard curve is in an acceptable range with a coefficientof determination R2 = 0.9779. Now, the samples from all the flocs are extractedand absorbance is measured. The resulting protein content is calculated from thestandard curve equation (y = 278.68x). The logarithm graph (due to large variationin values) representing the content of proteins in floc samples calculated from theabsorbance values is shown below and the table indicating the protein content arein appendix (5.1).

Figure 4.23: Protein content on a logarithm scale

55


Gravimetric protein content (µg/g DW) of flocs from the wastewater treatmentplant have a higher value than flocs from the biofilm experiments especially flocsof primary sedimentation because microbial activity in wastewater is significantlyhigher compared to freshwater, resulting in the secretion of EPS (higher proteincontents). Higher EPS value contributes to the stickiness of the flocs.

Flocs from various components of a wastewater treatment plant shows a varietyof protein content, where flocs from primary sedimentation have the highest content(3289 ± 1.92 µg/g DW), due to a large amount of organic content available for mi-crobes, indicating a higher adhesion capacity to form the larger flocs. Meanwhile,flocs from trickling filter effluent (568.81 ± 53.39 µg/g DW) have low protein con-tent compared to other wastewater components, as the flocs from the trickling filtereffluent have a low organic content compared to inorganic content (organic contentis consumed by microorganism), which results in decreased protein content. Theflocs from the main aerator (657.99 ± 29.88 µg/g DW) have lower protein contentthan flocs from the surface aerator (930.88 ± 7.88 µg/g DW), because longer re-tention times indicate better stability and adhesion capacity. Flocs from secondarysedimentation have a protein content of 584.50 ± 25.75 µg/g DW, almost similar toflocs from the trickling filter effluent due to lower contents of organic and inorganicmatter, and low flocculating ability.

Figure 4.24: Protein analysis of the biofilm experiments

Flocs from the biofilm experiments (figure 4.24) have a protein content range of80 µg/g DW - 100 µg/g DW, where flocs obtained from the high flow conditioned

56


biofilms have slightly higher values than flocs obtained from the low flow conditionedbiofilms, indicating better surface adhesiveness. This is confirmed from the surfaceadhesiveness measured earlier by MagPI IP (figure 4.2)

4.4.2 Carbohydrate analysis

To obtain the carbohydrate content per dry weight (DW) in the various floc samples,a standard curve with known concentration of a standard solution (D-glucose) isprepared, which is shown in the following figure.

Figure 4.25: Standard calibration curve for carbohydrate analysis

The regression model value (R2 = 0.99) fits very well with the measured ab-sorbance of standard solution with known concentration. So, the concentration ofcarbohydrates is determined from the standard curve equation (y = 375.54x). Thetable of content of carbohydrates in floc samples are presented in appendix 5.1 andthe graphical representation of gravimetric content of carbohydrates is done on alogarithmic scale (due to large value differences) which is below (figure 4.26);

57


Figure 4.26: Carbohydrate analysis of the floc samples on a logarithm scale

The results of the gravimetric content of carbohydrates follows the same trendas the proteins content where the content of carbohydrates in wastewater treatmentplant flocs is much higher than flocs from the biofilm experiments. The carbohy-drate content values in all floc samples are lower than protein content, which ispreferred because higher carbohydrate contents lead to the bulking of flocs (growthof filamentous bacteria), which renders in floc settling (Shin et al. [2001]).

The carbohydrate content is highest in the primary sedimentation flocs with2087 ± 122.41 µg/g DW because of higher contents of organic and inorganic mat-ter to consume for microorganism, followed by flocs from the surface aerator with(402.36 ± 0.00 µg/g DW) due to longer retention time. The flocs from the tricklingfilter effluent and from the main aerator do not show a large difference in carbohy-drate content. The microbial activity in the secondary sedimentation flocs is lowdue to less organic content available for microorganisms, which leads to lower con-tent of carbohydrate.

58


Figure 4.27: Carbohydrate analysis of the biofilm experiments

Flocs from the high flow conditioned biofilms have slightly higher value of car-bohydrate content than flocs from the low flow conditioned biofilms, except flocsfrom flume 3. Flocs from flume 3 maintained at low flow conditions have highestcarbohydrate content among all the flumes (74.50 ± 10.26 µg/g DW), while otherflumes’ carbohydrate content range between 40 - 65 µg/g DW. The reason for flocsfrom flume 3 having a higher value is unknown.

4.4.3 Chlorophyll analysis

Chlorophyll a and pheopigment a have an inverse relationship with each other aspheopigment is the degradation product of the chlorophyll. Chlorophyll indicatethe biomass which represents the presence of photosynthetic microorganism (mi-croalgae) in aquatic bodies. They are expressed in microgram per gram dry weight.The calculated content of chlorophyll and pheopigment from the equations 3.1 and3.2 are represented in the following charts (figure 4.28 and 4.29) and their valuesare presented in appendix 5.1.

59


Figure 4.28: Chlorophyll a content in floc samples

Figure 4.29: Pheopigment a content in floc samples

60


The results of the chlorophyll analysis are unexpected as chlorophyll contentis higher in flocs from the primary sedimentation and from the main aerator thanflocs from the biofilm experiments. Generally, chlorophyll content in the wastewateris lower due to insufficient sunlight and lack of active microorganism that can dophotosynthesis. The reason for having high values of chlorophyll in wasterwater isthat certain species of microorganism act as biomass. (Norweco [n.a]).

The similar trend is observed for the pheopigment a content as it is the degra-dation product of the chlorophyll a.

The chlorophyll a and pheopigment a content are observed to be higher in flocsfrom the primary sedimentation and main aerator which is unexpected due to defi-ciency in sunlight and photosynthetic microorganism. The flocs from the tricklingfilter effluent depicts less chlorophyll a and pheopigment a contents, while the flocsfrom the surface aerator and from secondary sedimentation indicate 7.39 ± 3.19µg/g DW and 10.82 ± 3.64 µg/g DW respectively. On the other hand, their degra-dation product indicate the values of 8.06 ± 5.70 µg/g DW and 1.71 ± 2.02 µg/gDW respectively.

The higher chlorophyll a and pheopigment a content is observed in the flocsfrom the low flow conditioned biofilms compared to the flocs from the high flowconditioned biofilms. These results suggest presences of biomass in the form ofmicroalgae. Similar results also are observed by Straughan [1972], where it is men-tioned that higher algae production is observed at low flow conditions.

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Chapter 5

Conclusion

The transportation and deposition of flocs in freshwater bodies and the requirementto separate the solid phase from the liquid phase in various engineered systems, givereasons to study the properties of flocs. These reasons provide the aim to analysethe physical and chemical properties of the respective flocs, which are covered underthe scope of this thesis.

The flocs to study are obtained from the biofilm experiments on lake water andfrom some units of the wastewater treatment plant of ISWA. The physical proper-ties of these flocs are analysed by evaluation of images of flocs in MATLAB. Theseimages are taken by the camera system connected to a floc settling chamber inwhich flocs are introduced from top. On the other hand, the chemical contents offlocs are calculated from the absorbance values measured using spectrophotometer.

Floc size is one of the major physical properties which have an impact on set-tling velocity. The larger amount of colloidal particle available to form the flocsalong with the enhanced flocculation process in the wastewater treatment plant (toaccelerate the settling) have led to indicate the greater floc size compared to theflocs obtained from biofilm experiments. In a wastewater treatment plant, as thetreatment efficiency is increased along sequence of the treatment process, the re-duction in organic/inorganic activity leads to smaller diameter of flocs. Here, largerflocs are observed in the primary sedimentation as compared to other componentsof the wastewater treatment plant. On the other hand, small flocs are found in thesecondary sedimentation stage. The better adhesive properties of high flow condi-tioned biofilms result in slightly higher flocs size compared to flocs from the lowflow conditioned biofilms.

The shape parameter is a morphological property which gives a better picture ofthe physical properties of floc. The presence of filamentous bacteria and turbulencein the flow negatively affects the shape of flocs from the wastewater treatment plantas compared to the flocs from the biofilm experiments. Flocs from surface aeratordepicts least circular shape due to excessive growth of the filamentous microor-ganism while secondary sedimentation flocs have shown the most circular shapecompared to other components of a wastewater treatment plant due to less pres-

62

ence of microorganisms. The flocs from the low flow conditioned biofilms are morecircular in shape than flocs from the high flow conditioned biofilms.

In addition to the shape factor, compactness is used to describe the floc prop-erties more briefly. Compactness represents the biostabilization of flocs. Due tothe presence of the filamentous bacteria in wastewater, compactness is little lowerthan flocs from the biofilm experiments. The flocs from the trickling filter and thesecondary sedimentation indicate more compactness compared to other parts of thewastewater treatment plant, whereas compactness of aeration system is the lowerdue to excessive growth of filamentous bacteria. The higher turbulent flow leadsto more compactness. This leads to better compactness of flocs from high flowconditioned biofilms than flocs from the low flow conditioned biofilms.

Solidity is a parameter which shows the quality of being solid. It is difficult todifferentiate the solidity in between flocs from the biofilm experiments and wastew-ater treatment plant. The solidity of flocs is high in the secondary sedimentationtank and trickling filter, whereas the aeration systems have lower solidity due toroughness of flocs. The flocs from the high flow conditioned biofilms show slightlylower solidity than the flocs from the low flow conditioned biofilms, which is incontrast to the compactness.

The settling velocity is the most important property as all other properties affectit in some way. Flocs from the high flow conditioned biofilm have exhibited the bestsettling velocity among all other floc samples. Flocs of the wastewater treatmentplant have a wide range of settling velocities but all are little less than flocs of highflow conditioned. This indicates floc size does not influence the settling velocityalone but other factors such as shape, organic content, specific weight and chemicalproperties also affect the settling properties.

To measure the chemical properties, EPS - protein and carbohydrate contentsare measured as EPS have a positive influence on the stability of the flocs. Thehigher contents of EPS- protein and carbohydrate found in the flocs from thewastewater treatment plant indicate higher secretion of EPS due to high microbialactivity in wastewater compared to the flocs from the biofilm experiments. Theprimary sedimentation flocs demonstrate higher protein and carbohydrate contentsthan other components of the wastewater treatment plant due to availability ofhigher organic content for microbes, while flocs from the trickling filter effluent andsecondary sedimentation demonstrate low contents of proteins and carbohydratesdue to less microbial activity.

Chlorophyll content is used as the proxy for the biomass. The higher chloro-phyll a content is found in the flocs from the primary sedimentation and from themain aerator which is quite unanticipated. The flocs from the low flow conditionedbiofilms have higher values of chlorophyll a content compared to the flocs from thehigh flow conditioned biofilms due to the built up of high algae at low flow condi-tions.

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5.1. Future scope

The understanding of the floc structure and EPS dominated chemical propertiesalong with microbial activity in natural system helps in the modeling of sedimenttransport associated with contaminant uptake. These can have an influence onthe surrounding environment. On the other hand, the settling of the flocs withhigh organic/inorganic activity are desired in the wastewater treatment plant toincrease the treatment efficiency with minimum cost. So, understanding of the flocproperties enables the wastewater treatment plant to enhance the flocculation inminimum time and cost.

5.1 Future scope

It is rather difficult to accurately characterize the three dimensional flocs from twodimensional images even if it is enough for an overview of the physical properties.Also, a camera with better resolution can be used to reduce the number of uniden-tified flocs in order to get the physical properties more accurately. These measuredproperties give a good overview regarding their flocs characterization but some otherphysical properties such as density, specific weight of flocs, organic and inorganiccontent in the flocs can be also measured to define them more accurately which arenot included in the scope of this work. Other than physical properties, several otherchemical properties such as microbial activity, constituents of flocs and biologicalproperties such as bacteria count, bacteria type can be explored in order to have adetailed characterization of the flocs. Additionally, the flocs behavior in differentwater bodies such as marine waters, rivers should be investigated in detail in orderto understand them better.

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Appendix

Water ID Protein Carbohydrate Chloro- Pheo-analysis analysis phyll a pigment

bodies (µg/g DW) (µg/g DW) (µg/g DW) (µg/g DW)

Flume 1 80.99 ± 2.04 49.06 ± 4.63 8.81 ± 3.02 4.91 ± 1.72

Flume 2 83.07 ± 7.67 51.31 ± 3.57 6.47 ± 1.15 3.69 ± 0.73

Biofilm Flume 3 87.27 ± 4.88 74.50 ± 10.26 9.71 ± 4.12 5.55 ± 2.36

exper Flume 4 92.42 ± 3.82 63.34 ± 5.91 9.05 ± 2.81 5.14 ± 1.63

-iment Flume 5 85.71 ± 18.49 42.45 ± 10.48 13.24 ± 7.01 8.89 ± 5.46

Flume 6 100.67 ± 11.43 47.87 ± 10.13 9.10 ± 2.08 5.30 ± 1.20

Primary 3289.10 ± 1.92 2087.91 ± 122.41 20.57 ± 3.35 20.60 ± 0.96sedimentation

Trickling filter 568.81 ± 53.19 295.80 ± 3.64 4.11 ± 0.77 0.68± 0.58

Waste Main aerator 657.99 ± 29.88 233.85 ± 22.03 21.03 ± 3.50 18.32 ± 0.83

water Surface aerator 930.88 ± 7.81 402.36 ± 0.00 7.39 ± 3.19 8.06 ± 5.70

Secondary 584.50 ± 25.75 124.44 ± 5.68 10.82 ± 3.64 1.71 ± 2.02sedimentation

Table 5.1: Summary of content of chemical properties

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MATLAB code to evaluate the physical properties of the flocs

%This is the main file to be run with Matlab %Version 1- Daniela %Version 1.01- Moritz (20150423)- rows referencing the excel table were %incorrectly (rows: 20-23) fixed %added: MeanValues=[a,18]; as MeanValues was not defined before

function [] = FlocProperties() %% Reading the File and extracting the info tic; javaaddpath(which('MatlabGarbageCollector.jar'));%for cleaning the java heap

memory

%% Reading the File and extracting the info

File = 'Pictures/InputParameters.xlsx';

AmountOfPictures = xlsread(File,1,'b2'); %OK mmpixel = xlsread(File,1,'c2'); %OK FrameFrequency = xlsread(File,1,'d2'); %OK minpixel = xlsread(File,1,'e2'); %OK flowVelocity = xlsread(File,1,'h2'); %OK TArea = xlsread(File,1,'i3'); %OK TCSF = xlsread(File,1,'l3'); %OK Tdx = xlsread(File,1,'o3'); %OK Tdy = xlsread(File,1,'p3'); %OK [~,Back]= xlsread(File,1,'b4'); %OK plotId= xlsread(File,1,'c7'); %OK plotCentroid= xlsread(File,1,'c8');%OK plotMajor= xlsread(File,1,'c9');%OK plotMinor= xlsread(File,1,'c10');%OK plotConvex= xlsread(File,1,'c11');%OK [~,Text] = xlsread(File,1,strcat('a1:a',num2str(AmountOfPictures+1))); TotalFlocs=0;

%% Reading the Pictures and getting the properties

for i=1 : AmountOfPictures Picture = strcat(char(Text(i+1,1)),'.bmp'); Images = genvarname(['Image' num2str(i)]);%create variable Image(i) that

will contain the information of each Picture. eval([Images '=imread(Picture);']); Properties = genvarname(['Properties' num2str(i)]);%Get the matrix of

flocs properties for each Picture

eval([Properties

'=ExtractImageProperties(minpixel,i,Text,Back,plotId,plotCentroid,plotMajor,p

lotMinor,plotConvex);']); jheapcl();%clear java heap memory [a,~]=size(eval(['Properties' num2str(i)])); TotalFlocs=TotalFlocs+a;

end

clear functions

%% Comparing the Pictures to track the flocs % x=1; y=1;

Flocs = [];%where the columns represent the number of the picture and the

number of the floc. Each pair of columns are for one Picture

for i=1 : AmountOfPictures-1 ProptPict1 = eval(['Properties' num2str(i)]);% Take properties of picture

i [AmountOfFlocs,~] = size(ProptPict1);

for j=1 : AmountOfFlocs

Area1 = ProptPict1(j,2);% Take properties of picture i MajorAxis1 = ProptPict1(j,3); CoorX1 = ProptPict1(j,5); CoorY1 = ProptPict1(j,6); CSF1 = ProptPict1(j,15);

Flocs(x,y)= i;%number of the picture where the floc was detected Flocs(x,y+1)= j;%number of the floc detected y=3;

for k=i+1 : AmountOfPictures ProptPict2 = eval(['Properties' num2str(k)]);

floc = find((ProptPict2(:,2)>=(1-TArea)*Area1) &

(ProptPict2(:,2)<=(1+TArea)*Area1)); %Find flocs with the same area within a tolerance value

if isempty(floc)%No floc with the same area detected in the 2

Pictures

break%No floc detected then it should go to the next floc of

picture i else possibles=0; for l=1 : size(floc)

CoorX2 = ProptPict2(floc(l),5); CoorY2 = ProptPict2(floc(l),6); CSF2 = ProptPict2(floc(l),15);

if (abs(CoorX2-CoorX1)<=(Tdx*MajorAxis1)) & ((CoorY2-

CoorY1)>2) & ((CoorY2-CoorY1)<(Tdy*MajorAxis1)) & (CSF2>=((1-TCSF)*CSF1)) &

(CSF2<=((1+TCSF)*CSF1)) possibles=possibles+1

samefloc=floc(l); end end if possibles==1

Flocs(x,y) = k;%Generate Matrix with the flocs detected.

Every row of the matrix represents a floc detected in a pair of successive

pictures y=y+1; Flocs(x,y) = samefloc; y=y+1; Area1 = ProptPict2(samefloc,2); MajorAxis1 = ProptPict2(samefloc,3); CoorX1 = ProptPict2(samefloc,5); CoorY1 = ProptPict2(samefloc,6); CSF1 = ProptPict2(samefloc,15); else break %When more than one floc is detected with the same

characteristics ignore it and take next floc of picture i end end end x = x+1; y = 1; end end

% Flocs % floc %Eliminate the rows where the floc was detected only in one picture. Because

it would not be possible to get the characteristics (Velocity). [x,~]=size(Flocs); i=1;

while (i<=x) y = Flocs(i,3); if y<0.1 Flocs(i,:) = []; if i==1 %in case the first floc of the first image is not detected on

the next picture. i=1; else i=i-1; end else i=i+1; end [x,~]=size(Flocs); end

%To eliminate the repetition of the floc in the rows

[x,y] = size(Flocs);

for i=1 : x %Take every row of the matrix "Flocs" look if they are in another

row. If they are repeated eliminate them for j=3 :2: y same = find(Flocs(:,1) == Flocs(i,j)); for k=1 : size(same) if Flocs(same(k),2) == Flocs(i,j+1); Flocs (same(k),:) = []; break end end end [a,~] = size(Flocs); if i==a break end

end [a,b]=size(Flocs);

cont=0; for i=1:a for j=1:2:b if Flocs(i,j)>0 cont=cont+1; end end end

FlocsFound=cont; FlocsNotFound= (TotalFlocs-cont);

csvwrite('Results/Flocs.dat',Flocs);

%% Getting the Floc Characteristics

[AmountOfFlocs,columns] = size(Flocs);

%Initialice all the matrices to save computational time. CoordX = zeros(AmountOfFlocs,columns/2-1); CoordY = zeros(AmountOfFlocs,columns/2-1); Area = zeros(AmountOfFlocs,columns/2-1); Perimeter = zeros(AmountOfFlocs,columns/2-1); MajorAxis = zeros(AmountOfFlocs,columns/2-1); MinorAxis = zeros(AmountOfFlocs,columns/2-1); EquivDiameter = zeros(AmountOfFlocs,columns/2-1); FormFactor = zeros(AmountOfFlocs,columns/2-1); Roundness = zeros(AmountOfFlocs,columns/2-1); AspectRatio = zeros(AmountOfFlocs,columns/2-1); Solidity = zeros(AmountOfFlocs,columns/2-1); Compactness = zeros(AmountOfFlocs,columns/2-1); CSF = zeros(AmountOfFlocs,columns/2-1); Volume = zeros(AmountOfFlocs,columns/2-1); dn = zeros(AmountOfFlocs,columns/2-1);

da = zeros(AmountOfFlocs,columns/2-1);

%Getting a matrix containing the information of alla flocs for each

characteristics. Each rw is a floc and each column is a picture where the

floc is repeated for i=1 : AmountOfFlocs x=1; for j=1 :2: columns a = Flocs(i,j);%Select the image to get the characteristics if a==0 break else %Build the matrixes PropertiesA = eval(['Properties' num2str(a)]); CoordX(i,x) = (PropertiesA(Flocs(i,j+1),5)); CoordY(i,x) = (PropertiesA(Flocs(i,j+1),6)); Area(i,x) = PropertiesA(Flocs(i,j+1),2)*mmpixel^2*1000^2;%units:

um2 Perimeter(i,x) = PropertiesA(Flocs(i,j+1),8)*mmpixel*1000;%units:

um MajorAxis(i,x) = PropertiesA(Flocs(i,j+1),3)*mmpixel*1000;%units:

um MinorAxis(i,x) = PropertiesA(Flocs(i,j+1),4)*mmpixel*1000;%units:

um EquivDiameter(i,x) =

PropertiesA(Flocs(i,j+1),9)*mmpixel*1000;%units: um FormFactor(i,x) = PropertiesA(Flocs(i,j+1),10); Roundness(i,x) = PropertiesA(Flocs(i,j+1),11); AspectRatio(i,x) = PropertiesA(Flocs(i,j+1),12); Solidity(i,x) = PropertiesA(Flocs(i,j+1),13); Compactness(i,x) = PropertiesA(Flocs(i,j+1),14); CSF(i,x) = PropertiesA(Flocs(i,j+1),15); Volume(i,x) =

PropertiesA(Flocs(i,j+1),16)*mmpixel^3*1000^3;%units: um3 dn(i,x) = PropertiesA(Flocs(i,j+1),17); da(i,x) = PropertiesA(Flocs(i,j+1),18);

x=x+1;

end end end

%Each of the following matrix will contain the velocities of the flocs. Been

each row one floc, and %each Colum the comparison between two consequtive frames. %Generate matrixes filled with zeros so the progrma is faster, avoiding

resizing at each cicle. VelocityY = zeros(AmountOfFlocs,columns/2-1); VelocityX = zeros(AmountOfFlocs,columns/2-1); Velocity = zeros(AmountOfFlocs,columns/2-1);

for i=1 : AmountOfFlocs

x=1; for j=1 :2: columns-2 a = Flocs(i,j);%Select first image b = Flocs(i,j+2);%Select second image if or(a==0,b==0) break else %Calculate the velocity in both axis, the resultant velocty and

the average for the Area and %Perimeter in 2 Pictures

PropertiesA = eval(['Properties' num2str(a)]);%get matrix with

properties or the first image PropertiesB = eval(['Properties' num2str(b)]);%get matrix with

properties or the second image VelocityY(i,x) = (-

PropertiesA(Flocs(i,j+1),6)+PropertiesB(Flocs(i,j+3),6))*mmpixel/FrameFrequen

cy+flowVelocity;%units: mm/s VelocityX(i,x) = (PropertiesA(Flocs(i,j+1),5)-

PropertiesB(Flocs(i,j+3),5))*mmpixel/FrameFrequency;%units: mm/s Velocity(i,x) = sqrt(VelocityX(i,x)^2 +

VelocityY(i,x)^2);%Resultant velocity, calculated with xVelocity and

yVelocity. units: mm/s

x=x+1; end end end

%Acceleration

[x,y]=size(VelocityY); AccelerationY = zeros(x,y-1);

for i=1 :x for j=1 :y-1 if VelocityY(i,j+1)~=0 AccelerationY(i,j)=(VelocityY(i,j+1)-

VelocityY(i,j))/(FrameFrequency);%units: mm/s2 else break end end end

[x,y]=size(VelocityX); AccelerationX = zeros(x,y-1);

for i=1 :x for j=1 :y-1 if VelocityX(i,j+1)~=0 AccelerationX(i,j)=(VelocityX(i,j+1)-

VelocityX(i,j))/(FrameFrequency);%units: mm/s2 else

break end end end

[x,y]=size(Velocity); Acceleration = zeros(x,y-1);

for i=1 :x for j=1 :y-1 if Velocity(i,j+1)~=0 Acceleration(i,j)=(Velocity(i,j+1)-

Velocity(i,j))/(FrameFrequency);%units: mm/s2 else break end end end

%Saving the Data of the flocs csvwrite('Results/Data/CoordX.dat',CoordX); csvwrite('Results/Data/CoordY.dat',CoordY); csvwrite('Results/Data/Area.dat',Area); csvwrite('Results/Data/Perimeter.dat',Perimeter); csvwrite('Results/Data/MajorAxis.dat',MajorAxis); csvwrite('Results/Data/MinoAxis.dat',MinorAxis); csvwrite('Results/Data/EquivDiamaeter.dat',EquivDiameter); csvwrite('Results/Data/FormFactor.dat',FormFactor); csvwrite('Results/Data/Roundness.dat',Roundness); csvwrite('Results/Data/AspectRatio.dat',AspectRatio); csvwrite('Results/Data/Solidity.dat',Solidity); csvwrite('Results/Data/Compactness.dat',Compactness); csvwrite('Results/Data/CSF.dat',CSF); csvwrite('Results/Data/Volume.dat',Volume); csvwrite('Results/Data/dn.dat',dn); csvwrite('Results/Data/da.dat',da); csvwrite('Results/Data/VelocityY.dat',VelocityY); csvwrite('Results/Data/VelocityX.dat',VelocityX); csvwrite('Results/Data/Veocity.dat',Velocity); csvwrite('Results/Data/AccelerationY.dat',AccelerationY); csvwrite('Results/Data/AccelerationX.dat',AccelerationX); csvwrite('Results/Data/Acceleration.dat',Acceleration);

[a,b]=size(Area); % Matrix containing the final values for each floc. The rows are different

flocs and the columns are each properties ordered as follows: Area,

Perimeter, MajorAxis, MinorAxis, EquivDiameter, FormFactor, Roundness,

AspecRatio, Solidity, Compactness, CSF, Volume, dn, da, VelocityY, VelocityX,

Velocity, Re

MeanValues=zeros(a,18);

for i=1 : a

x=0; for j=1 : b if Area (i,j) ~= 0 MeanValues(i,1) = MeanValues(i,1)+Area(i,j); MeanValues(i,2) = MeanValues(i,2)+Perimeter(i,j); MeanValues(i,3) = MeanValues(i,3)+MajorAxis(i,j); MeanValues(i,4) = MeanValues(i,4)+MinorAxis(i,j); MeanValues(i,5) = MeanValues(i,5)+EquivDiameter(i,j); MeanValues(i,6) = MeanValues(i,6)+FormFactor(i,j); MeanValues(i,7) = MeanValues(i,7)+Roundness(i,j); MeanValues(i,8) = MeanValues(i,8)+AspectRatio(i,j); MeanValues(i,9) = MeanValues(i,9)+Solidity(i,j); MeanValues(i,10) = MeanValues(i,10)+Compactness(i,j); MeanValues(i,11) = MeanValues(i,11)+CSF(i,j); MeanValues(i,12) = MeanValues(i,12)+Volume(i,j); MeanValues(i,13) = MeanValues(i,13)+dn(i,j); MeanValues(i,14) = MeanValues(i,14)+da(i,j);

x=x+1; end end MeanValues(i,1) = MeanValues(i,1)/x; MeanValues(i,2) = MeanValues(i,2)/x; MeanValues(i,3) = MeanValues(i,3)/x; MeanValues(i,4) = MeanValues(i,4)/x; MeanValues(i,5) = MeanValues(i,5)/x; MeanValues(i,6) = MeanValues(i,6)/x; MeanValues(i,7) = MeanValues(i,7)/x; MeanValues(i,8) = MeanValues(i,8)/x; MeanValues(i,9) = MeanValues(i,9)/x; MeanValues(i,10) = MeanValues(i,10)/x; MeanValues(i,11) = MeanValues(i,11)/x; MeanValues(i,12) = MeanValues(i,12)/x; MeanValues(i,13) = MeanValues(i,13)/x; MeanValues(i,14) = MeanValues(i,14)/x; end

[a,b]=size(VelocityY);

for i=1 : a x=0; for j=1 : b if Velocity (i,j) ~= 0 MeanValues(i,15) = MeanValues(i,15)+VelocityY(i,j); MeanValues(i,16) = MeanValues(i,16)+VelocityX(i,j); MeanValues(i,17) = MeanValues(i,17)+Velocity(i,j); x=x+1; end end if x==0 x=1; end MeanValues(i,15) = MeanValues(i,15)/x; MeanValues(i,16) = MeanValues(i,16)/x; MeanValues(i,17) = MeanValues(i,17)/x; end

for i=1 : a MeanValues(i,18) =

abs(MeanValues(i,15)*MeanValues(i,5));%ReynoldsNumber calculated with the

Equivalent Diameter end

csvwrite('Results/MeanValues.dat',MeanValues);

clearvars -global -except Properties* Amount* Image* Flocs TotalFlocs

%% Figure comparing 2 Pictures

[x,y] = size(Flocs);

for i=1 : AmountOfPictures-1

PropertiesA = eval(['Properties' num2str(i)]);%get matrix with properties

of the first image PropertiesB = eval(['Properties' num2str(i+1)]); Picture1=eval(['Image' num2str(i)]); Picture2=eval(['Image' num2str(i+1)]);

DrawVelocityVectors(x,y,i,Flocs,PropertiesA,PropertiesB,Picture1,Picture2);%e

nter subroutine for plotting final velocity vectors

jheapcl(); %claer java heap memory end

FlocsFound FlocsNotFound TotalFlocs

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