Coagulation Pre-Treatment for Microfiltration with Ceramic ... · Colofon Title Coagulation...

Coagulation Pre-Treatment for Microfiltration with Ceramic Membranes

September 2007

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Coagulation Pre-Treatment for Microfiltration with Ceramic Membranes

September 2007

Colofon

Title Coagulation Pre-Treatment for Microfiltration with Ceramic Membranes Author(s) Siegfried Gaulinger Quality Assurance By Bas Heijman Deliverable number D 2.3.2.1.

Coagulation Pretreatment for Microfiltration with Ceramic Membtranes © TECHNEAU September 2007

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Summary

Ceramic microfiltration membranes need to operate at high flux, with very little pre-treatment chemicals, in order to compete with polymeric membranes in surface water treatment applications. In-line coagulation pre-treatment prior to MF/UF membrane filtration is reported to reduce membrane fouling therefore, allow higher operational fluxes. The advantages of (in-line) coagulation are attributed to reduced pore penetration due to agglomeration of colloids, and a more porous filter cake. Particular conditions which lead to improvement of filterability, and reduced colloidal fouling, and its alleviation in ceramic membrane systems, are sparsely elaborated in literature. In this study, filtration behaviour employing various coagulation conditions: coagulant dose (ferric chloride), Gt induction, and feed water pH were investigated. As an intermediate step prior to employing ceramic membranes, filtration tests were performed with an Amicon (Un)stirred Cell in dead end and constant pressure mode (TMP=0.1 MPa), with prefiltered (122µm) surface water. A hydrophilic PVDF 0.1µm microfiltration membrane was employed in the (un)stirred cell filtration tests. The effect of coagulation conditions on filterability were assessed by processing time and weight data from permeate collected via an electronic balance. Subsequently, the MFI was calculated from the slope of the ?t/?V versus V plots. The calculated MFI values were utilized to compare the fouling behaviour of various feed water after coagulation pre-treatment. The organic carbon content of the feed and permeate water was analyzed and compared. In the presence of coagulant, cake filtration was the dominant filtration mechanism observed with a Gt ranging from 9,400 to 140,100. A coag ulant dose of 1mg/l effective Fe 3+, at pH 8, employing a Gt of 140,100 gave the lowest MFI observed. This dose (1mg/l) was lower and the optimum pH (pH=8) higher than suggested in the classical ferric chloride solubility diagram for sweep coagulation and charge neutralisation. Increasing and decreasing the coagulant dose from this local minimum for a range of Gt values (9,400 to 140,100) increased the MFI. The variation in MFI was very high (BBB) at a low coagulant doses. For example, the MFI decreased by 66% when the Gt (flocculation time) was increased from 9,400 to 140,100 for a coagulant dose of 0.5 mg/l, suggesting that the Gt employed for (in-line) coagulation was very critical at low coagulant doses (<0.5 mg/l) Fe3+. On the contrary, the Gt (flocculation time) was much less critical at higher coagulant doses (> 15 mg/l), as the reduction in MFI was only 10% when the Gt was increased from 9,400 to 140,100 for a coagulant dose of 15 mg/l.


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Varying the pH between 7 and 8 had marginal impact on MFI. It is much less influential than the dose and Gt induced. Nevertheless, higher pH consistently gave lower MFI, and vice versa in the rabge pH 6-8. The higher MFI values at pH of 6 were attributed to a lower permeability of the fouling layer due to more denser and less porous flocs. Computation of MFI via the slope of the relation (?t/?V)*0.5/?V improved the accuracy of the MFI. This was concluded based on R-squared data from regression analysis. The R square value from regression analysis of MFI computation could be regarded as an index of accuracy when cake filtration is present. Accuracy of results in terms of high R square values was found mostly depending on the method of time series data processing. This study focussed on the impact of (in-line) coagulation on the filterability of surface water. However, this is only one aspect in the optimization of in-line coagulation of ceramic membrane filtration. To assess the overall rate of fouling, the backwashability of the fouling layer and the efficiency of the chemically enhanced backwash and chemical cleaning to remove membrane foulants need to be assessed in a follow up study. Keywords: Ceramic, Microfiltration, Coagulation, Gt, shear rate, MFI, Fouling


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Contents

Summary i

Contents iii

1 Introduction 1 1.1 General 1

1.2 Problem Identification 1 1.3 Goal and Objectives 2

2 Background 3 2.1 Surface water 3

2.2 Conventional Surface Water Treatment 4

2.3 Low Pressure Membranes in Surface Water Treatment 4 2.4 Classification of Microfiltration Membranes 5 2.4.1 Physical Morphology 6 2.4.2 Membrane Geometry 6 2.4.3 Physical Chemical Membrane Characteristics 6 2.4.4 MF Membrane Operation Modes 8 2.5 Membrane fouling 8 2.5.1 Membrane Fouling Categories 8 2.5.2 Reversible and Irreversible Fouling 9 2.5.3 Factors influencing fouling 10 2.5.4 Membrane Fouling Mechanisms 11 2.6 Modeling of Filtration and Prediction of Fouling 12 2.6.1 Fouling Prediction 12 2.6.2 Pore Blocking and Cake Filtration 14 2.6.3 Cake Compression 15

2.7 Natural Organic Matter (NOM) in Aquatic Environment 16 2.7.1 NOM characterisation 16 2.7.2 NOM removal in Low Pressure Hybrid Membrane Systems 17 2.8 Coagulation 17 2.8.1 Principles of Coagulation 17 2.8.2 Coagulation in Low Pressure Membrane Filtration 18 2.9 Theory for Dead End Filtration 20 2.9.1 Flux through Porous Media 20 2.9.2 Temperature Effects on Flux 21

3 Materials and Methods 22

3.1 Materials 22 3.1.1 Stirred Cell Setup 22 3.1.2 Membrane Conditioning Procedure 26 3.1.3 Jar Test Unit 26


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3.2 Analytical methods 26 3.2.1 Specific Ultra Violet Adsorption SUVA 26 3.2.2 Liquid Chromatograph Organic Carbon Detection 27 3.3 Computational Methods 27 3.3.1 Determination of Initial Clean Membrane Resistance Rm 27 3.3.2 Computation of the MFI 29 3.3.3 Spreadsheet Computations 31 3.3.4 Data smoothening 31 3.3.5 Curve Fitting and Application of First Derivative Model 32 3.4 Experimental Procedure 34 3.4.1 Selection of Independent Process Variables for Coagulation 34 3.4.2 Filtration Procedure 34 3.4.3 Point of Diminishing Return Coagulant Dose 35

4 Results and Discussion 36 4.1 Membrane Filtration Experiments 36

4.2 Effect of Gt on MFI 36

4.3 Effect of Coagulant, Gt (Slow Mixing t) on MFI 39 4.4 Effect of Gt (RM G) and Coagulant Dose on the MFI 42

4.5 Effect of Gt (SM t) and Coagulant Dose at Constant pH on MFI 44

4.6 Effect of Gt (SM t), Coagulant Dose and pH Variation on the MFI 47

4.7 LC OCD Samples Analysis 50 4.8 Removal Efficiency of Coagulation 52

4.9 Point of Diminishing Return Coagulant Dose 53

4.10 Determination of Initial Membrane Resistance Rm 54

5 Conclusions and Recommendations 56

5.1 Conclusions 56 5.2 Recommendations 58

6 Literature References 60

7 Annexe 63

7.1 Rpm – Gt conversion diagram for the Jar test unit 63 7.2 Water quality analysis 63 7.2.1 Permeate Quality for 4.1.1 63 7.2.2 Permeate Quality for 4.1.2 64 7.2.3 Permeate Quality for 4.1.3 (variation of RM G and dose) 64 7.2.4 Permeate Quality for 4.1 .4 (variation of SM t and dose) 65 7.3 Sample Spreadsheet Computation (4.1.2) 66

7.4 Sample plots of (4.1.2) 67

7.5 Table of LC OCD Sample Analysis 68 7.6 Point of Diminishing Return Coagulant Dose: Water Quality Analysis 69


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7.7 Point of Diminishing Return Coagulant Dose: Model computation 70

7.8 Effect of Feed Reservoir on Gt 71


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1 Introduction

1.1 General

The application of micro filtration (MF) and ultra filtration (UF) for drinking water purification became a standard during the past two decades. The first full scale applications in this field were reported in 1988(Amy, 2006). In the meantime MF and UF have developed into widely established methods of primary treatment, with a steady increase in installed production capacity in the recent decades (Furukawa, 2002) both in the European Union and the United States. By replacing conventional treatment steps (coagulation, sedimentation, and rapid filtration) with microfiltration a more reliable, robust, effective, and cheaper treatment method is introduced (Mallevialle et al., 1996). Compared to traditional treatment methods further advantages are (a) stable process under varying feed water quality, (b) smaller footprint, and (c) highly automatic operation. Most full-scale treatment plants are designed with polymeric MF/UF membranes. On the technical lifetime basis for the purchase of membranes for an entire drinking water treatment plant, the employment of ceramic membranes compared to polymeric ones was not a competitive alternative due to higher cost. Just recently studies in pilot scale (Loi-Brügger et al., 2006a), (Lerch et al., 2005) (Lerch et al., 2006) suggested that the process of purifying coagulated surface water with monolithic ceramic microfiltration membranes in constant flux and dead end mode can be optimised to such an extend, that their employment is competitive to the application of polymeric hollow fibre membranes. Higher flux and less frequent cleaning of ceramic membranes, but also considering the longer membrane lifetime is the basis for this recent leap in productivity.

1.2 Problem Identification

Membrane fouling reduces productivity, increases downtime, chemical use, and backwashing and membrane replacement (AWWA, 2005). Fouling is defined as adsorption of colloidal particles and dissolved solids, which are smaller than the membrane pore size, into and on the membrane (Mallevialle et al., 1996). NOM and colloidal material have been stated in numerous scientific studies as the main contributor to membrane fouling (Carroll et al., 2000), (Zularisam et al., 2006). (Mallevialle et al., 1996) pointed out the fact that parameters for membrane fouling are related to the feed water composition, its conditioning in terms of pre -treatment, the properties of a particular membrane, and the interaction between these two components. In the highly interdependent reactions taking place between the feed water and


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the membrane properties, conditioning feed water is set as the target of coagulation prior to MF in many studies. Suitable conditions for feed water to achieve delays in reversible and irreversible fouling, improve permeate flux and obtain higher permeate quality have been subject of many studies. For instance (Dong et al., 2006) investigated the effect of pH on membrane fouling. (Guigui et al., 2002), (Jang et al., 2006) and (Judd and Hillis, 2001) elaborated the impact of coagulation on MF under different conditions. The unit process of coagulation prior a MF or UF membrane targets removal of dissolved NOM by adsorption onto iron salts and precipitation. That process is also reported to result in higher permeate flux and reduce membrane fouling (Carroll et al., 2000), (Judd and Hillis, 2001) Mallevialle, 1996 #18}. These advantages are attributed to reduced pore penetration from bigger flocs not able to penetrate membrane pores and a more porous filter cake. The mechanisms of fouling and the substances causing membrane fouling are closely monitored by numerous studies (Pikkarainen et al., 2004), (Judd and Hillis, 2001), (Zularisam et al., 2006). Conventional sand filtration utilizes charge neutralization (at low pH, and low doses) or enmeshment/sweep coagulation (higher pH and dose) conditions to overcome electrostatic forces of colloidal particles. (Guigui et al., 2002) (Pikkarainen et al., 2004) suggested that optimisation of the pre-coagulation-membrane filtration process ferric salts are superior for a particular feedwater matrix studied on the basis of DOC removal, filter cake specific resistance values compared to aluminium salts. These mechanisms for hydrolysis of ferric chloride metal salt preferably take place in the range of pH and dose as displayed in Figure 1.1.

NOM fouling of ceramic MF membranes and its alleviation with coagulation pre-treatment in hybrid systems is sparsely elaborated in literature. (Konieczny et al., 2006) compared filtration of blank feed water with those of different coagulants (Aluminium sulphate, and ferric chloride) and found that primary coagulation pre-treatment was responsible for maintenance of high flux. In their study, (Lerch et al., 2005), (Loi-Brügger et al., 2006a) and (Lerch et al., 2006) thoroughly present high fluxes with ceramic microfiltration membranes over long time, in combination with coagulation pre -treatment at pilot scale. Though, their objectives are set in a wider context and not primarily

Figure 1.1: Hydrolysis of ferric chloride (Source: Johnson and Amirtharajah 1983)


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concerned with optimization of pre-treatment conditions. With these facts a need has been identified to examine coagulation pre-treatment prior microfiltration with ceramic membranes more closely. For the same type of monolith ceramic membrane (Yonekawa et al., 2004) identified unique characteristics for pre-coagulation of micro-particles behaviour. They discovered that after coagulant dispersion the floc size increased rapidly inside membrane tubes, despite short hydraulic residence time in the monolith membrane. Flocculation was not necessary because these characteristics promoted aggregation sufficiently.

1.3 Goal and Objectives

The goal of this study is to assess the effect of various coagulation pre-treatment process parameters on the filterability of surface water with a (ceramic) microfiltration membrane. The objectives are:

• To investigate the impact of (a) pH, (b) coagulant dose (c) G and t induction, on the filterability of surface water over a microfiltration membrane.

• To analyse NOM fractions fouling the membrane and their quantification and qualification.


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2 Background

2.1 Surface water

Surface water is exposed to a wide range of influences in the hydrological cycle and from human interaction. Its quality fluctuates widely over time. Apart from other contaminations, the intake of Organic Carbon as Natural Organic Matter (NOM) is a major issue influencing treatment methods of such waters for drinking water purposes. For rivers and small lakes the contribution of NOM is generally considered as allochthonous. NOM sources are originating mainly from non point sources, outside of the aquatic system, and can only gradually be influenced by human measures. (Hendricks, 2006) Surface water quality is determined by various interactions of natural and human origin. Such are further categorized into point and non point sources. Natural factors that impact on water quality include chemical formation and humification, climate, watershed topography and geology, nutrients, and lake or reservoir density stratification. Typical human factors include wastewater discharges and hazardous waste facilities, spills, agricultural and urban runoff, and land development. Water quality parameters of concern for human consumption include suspended solids, chemical and biological parameters. These appear in dissolved, colloidal and suspended state, according to their division in size. These natural and artificially occurring contaminations of sur face water may contain the following material, impurities and contaminants, according to the division in size. (Letterman, 1999)

Ionic range Molecular range

Macro- molecular

range

Micro-particle range

Macro-particle range

0.001 0.01 0.1 1.0 10 100 1000

Size µm dissolve

d colloidal suspended

Relative size of various material

s in water

Separati

on process

Figure 2.1: Size distribution of adopted from (Hendricks, 2006)

Viruses Bacteria Sand

Algae

Dissolved organics Clays Silt

Humic acids

Metal ions

Salt

UF

RO

NF

MF

Conventional filtration processes


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Solids and Turbidity: (> 10µm) high concentration of solids is causing turbidity in raw water sources, which require more coagulant chemicals. Sources of solids include domestic sewage, urban and agricultural runoff, stream bank erosion, and construction activity. The composition of solids may be organic, like residuals of plants and animals, or inorganic as clay and silts. Nutrients (including nitrogen and phosphorus): Nutrients can increase microbial activity, which may cause increase in taste and odour problems in the finished water. Nutrient sources include septic system and landfill leachate, wastewater plant discharges, surface runoff, and rainfall. Organic Carbon: originations from a variety of organic debris taken into water Microbial contaminants: Pathogenic organisms like bacteria and viruses are associated with risks to the human health and ubiquitous in aquatic systems. Further parameters of significance for determination of water quality include the content of metals, presence of algae, and dissolved oxygen content. Other substances like oil, grease, heavy metals, and toxic chemicals degrade the quality of water and impact on treatment for human consumption. Many of these are affected by multiple environmental factors involving both natural and human inputs.(Letterman, 1999)

2.2 Conventional Surface Water Treatment

In most systems, the configuration of the treatment plant depends on the quality of the raw water. Surface water treatment comprises a sequence of unit processes in order to produce potable water in accordance to governing regulations. Unit processes are defined as an engineered step to change a state (physical and chemical) of the media water (Hendricks, 2006). The most common unit processes are disinfection, coagulation, rapid mixing, flocculation, sedimentation, filtration and a final disinfection before distribution. There is a variety of different combinations of such unit processes to achieve this target, and for each unit process different treatment technologies are in practice.

2.3 Low Pressure Membranes in Surface Water Treatment

By replacing the unit processes sedimentation and sand filtration from the conventional treatment train with microfiltration, substantial advantages in efficiency can be achieved. As for the reasons stated in the introduction membrane filtration has gained in popularity. A classification of membrane types is also drawn on different separation mechanisms. For UF/MF porous membranes are used and the pore dimensions mainly determine the separation characteristics. Non-porous membranes, which are considered as dense media, are used in reverse osmosis. In this case, the separation is based on differences in solubility and diffusivity of materials in the membrane. NF membranes can be classified as an intermediate class between porous and non-porous (Mallevialle et al., 1996).


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Although not an absolute measure, distinction is based on the pore size. For MF units are given in micro meter (micron) and for UF in Molecular Weight Cut Off (MWCO). By referring to Figure 2.1, MF membranes are able to remove suspended solids and larger sized colloidal particles. This process is based on a sieving mechanism with pore sizes relatively smaller than that of the particulate matter. An indication of distinction between MF and UF based upon their different parameters is given by (Mallevialle et al., 1996) in Table 2.1. However, they too state that these sizes are not an absolute measure and overlaps between adjacent processes have been found in literature.

* for surface water applications

Low pressure membranes are considered as an excellent choice for removal of suspended matter and bacteria. Nonetheless they have no effect in removing other dissolved substances, such present in molecular bases like salts. Also, as displayed in the arrangement of Table 2.2, Microfiltration, due to its pore size does not guarantee an absolute barrier against all kinds of microorganisms. Since the smallest pore size of MF membranes is bigger than viruses, their removal is not guaranteed in a sufficient Log scale for most regulations. Though, with appropriate pre-treatment increased removal capacity of MF for viruses in combination with coagulation is reported from several sources (Amy, 2006). Where else Ultrafiltration membranes, depended on their pore size have the capacity to remove viruses in most cases. Both MF and UF remove just an insignificant part of NOM due to their pore size.

Membrane type

Suspended Matter

Bacteria Viruses NOM

MF + + +/- - UF + + + +/-

2.4 Classification of Microfiltration Membranes

Other common classification than the separation mechanism of membranes in general, as given by (Mallevialle et al., 1996) is structured into physical morphology, membrane geometry and module configuration, chemical nature, and operation conditions

Table 2.1, adopted from (Amy, 2006)

Membrane class

Pore Size

Pressure [bar]

Flux* [litre/ m2. h]

MF 0.1 – 0.5 0.1 - 1 100 - 200 UF 1 – 100 kD 0.5 - 5 50 - 100

Table 2.2: Low Pressure Membrane Performance adopted from (Kennedy, 2006)


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2.4.1 Physical Morphology

As flux for pressure driven membrane is inversely proportional to the membrane thickness, asymmetric membranes are of advantage. Asymmetric membranes have a thin skin at the filtration surface of the same material as the more porous substructure. This skin typically has a thickness of 0.1 to 1.0 µm. The skin is highly permeable to water, retains suspended and dissolved solids by size exclusion. The minimum diameter of pores is at the skin, so that, once a solute enters a pore, it will permeate with the filtrate and not be trapped in the membrane where it can cause fouling, therefore it is easy to clean. Symmetric membranes have a constant pore size over the whole cross section of the membrane, and have a thickness of 10 to 200 µm. Composite membranes are asymmetric membranes with a very thin and dense top layer. The top layer and sub layer originate from different materials, which can be optimised independently. The porous layer is generally an asymmetric membrane.

2.4.2 Membrane Geometry

MF module geometries for drinking water treatment applications are generally Hollow Fibre Capillary or Tubular Membranes. These relates to the ease of cleaning, the high package density of membrane surface unit per volume. Hollow Fibre (Capillary) Membranes consist of up to several thousand from a diameter of <1 to 5mm. They have the advantages of large surface area to volume ratio and simplicity of construction, and the disadvantage of a high susceptibility to fouling. They can be easily cleaned by backwashing, which compensates for their propensity for fouling. Tubular Membranes are characterized by a low packaging density, but good ability to filter high turbid and viscous fluids, and are easy to clean. Other geometries not relevant for industrial drinking water production are flat sheet and spiral wound membranes. For their low package density (m2/m3) flat sheet membranes have limited application, and are e.g. used for lab scale experiments. Spiral wound membranes can not be used on turbid feed water without pre-treatment due to their vulnerability to clogging and limited ability for backwashing.(Mallevialle et al., 1996), (AWWA, 2005).

2.4.3 Physical Chemical Membrane Characteristics

Membrane material Membranes can be of organic e.g. polymeric or inorganic e.g. Metal or ceramic substances. Organic Membranes are most common and show a great degree of flexibility with respect to rejection characteristics. Their advantages are the easy processing, low cost, availability of wide structure variations, and the possibility to realise any configuration. Their disadvantages are reduced chemical resistance against chlorine and cleaning agents. As well as inferior


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stability at high temperature, high pressure, and time dependent relaxation phenomena are further the most often stated disadvantages. Most commonly used materials for of organic membranes are Cellulose acetate, polyamide and polysulfone. (Mallevialle et al., 1996), Inorganic membranes possess considerable advantages over polymeric membranes in material property and operational terms, making them an overall more reliable choice. The downside is their high cost which explains their application currently is limited to aggressive/high temperature fluids treatment. Though several studies focus on the application of ceramic membranes in surface water treatment fro drinking water(Lerch et al., 2005), (Loi-Brügger et al., 2006), and conclude in promising result for use in drinking water. These attributes, based on information obtained from these studies are summarized in Table 2.1. Materials most commonly used for ceramic membranes are metal oxides, like Aluminium Oxide, Zirconium Oxide, Titanium Oxide and Silicon Dioxide and combinations of them. (Guizard et al., 2002), (Hsieh, 1996).

Ceramic membranes Polymeric Membranes Mechanical strength Monolithic unit, with high

resistance against pressure, Generally less pressure compliant; permanent integrity testing required

Chemical resistance Resistant to high chemical concentrations and less degradation from of cleaning agents

Low doses and careful application of cleaning agents is essential for integrity and long lifetime

Energy Consumption Higher Permeability (Higher flux at lesser Trans membrane pressure increase) reflects in less energy consumption

Lower flux and faster TMP increase result in more frequent cleaning and less recovery

Life time expectation Life time expectation is stated to be at least twice the one of polymeric membranes

The lifetime of hollow fibre membranes is estimated with 5 to 7 years

Surface charge Another important aspect of membranes is their surface charge. Hydrophilic means the surface chemistry allows membranes to be wetted forming a water film or coating on their surfaces. Hydrophobic materials have little or no tendency to adsorb water, which tends to form “beads” on their surface. Hydrophilic materials possess an ability to form hydrogen-bonds with water (Mallevialle et al., 1996). Hydrophobicity and hydrophilicity of the surface of membranes strongly influence fouling (Jung et al., 2006). Many fractions of NOM are negatively charged, or have partial negative charge due to dipole or multiple chemical bonds in their structure.

Table 2.3: comparison of ceramic and polymeric membrane properties according to (Mallevialle et al., 1996)


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(Cho et al., 1999) therefore points out that membrane surface should have a negative charge to repel NOM , thus inhibiting its adsorption and helping to maintain a high flux.

2.4.4 MF Membrane Operation Modes Two operation modes are used in MF membrane processes: dead-end mode and cross-flow mode. In dead-end mode, all the feed water is forced to flow through the membrane so that retained particles accumulate and form a type of cake or gel layer on the membrane surface. The thickness of the cake layer increases with the filtered volume. In cross-flow mode, the feed flow is parallel to the membrane surface and perpendicular to the filtrate flow. A part of the feedwater (concentrate) is recycled. Dead-end systems are more susceptible to fouling than cross-flow systems; however systems operated in dead-end have lower energy consumption. A further distinction is made into constant pressure and constant flux operation. For constant pressure mode, where constant pressure is applied, flux decreases over filtration time. In constant flux mode the driving force of pressure is subsequently increased to overcome the increasing resistance of the deposit formed on the membrane surface and for keeping the permeate flux constant. Generally in pilot and full scale applications for potable water treatment constant flux mode is employed. Only for lab scale applications constant presser mode is applied. (Davis and Grant, 1992)

2.5 Membrane fouling

(Zularisam et al., 2006) summarized several studies on Natural Organic Mater. Fouling is the process of physical and chemical retention and/or adsorption of colloids, multivalent ions and organics in the feed water on the membrane surface and inside the pores. Fouling is the effect that increases the resistance of water passing the membrane and leads to flux decline or trans membrane pressure increase (Mallevialle et al., 1996). Thus, limiting fouling plays a vital part in efficient utilization of membrane filtration. Fouling is considered as a complicated phenomenon, of which causes of fouling are multiple and poorly understood. (Kennedy, 2006) identified the main contributors to fouling in MF and UF of surface water as particulate fouling/scaling, and organic fouling. Therein it is also stated that hardly one type of fouling occurs alone, but always a combination of them.

2.5.1 Membrane Fouling Categories

Fouling is classified into four categories, distinguished by their main cause, as (a) inorganic fouling/scaling, (b) particulate fouling/scaling, (c) microbial fouling, and (d) organic fouling. Combinations of these fouling types can occur at the same time. (Liu et al., 2001) Particulate fouling is caused by organic and inorganic suspended and colloidal particles. Their presence in natural surface water is ubiquitous and inevitable. Many times they are electrically charged, due to the presence of


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charged functional groups on the surface of the colloid itself or through the adsorption of ions from the surrounding water. Particulate fouling was found to be the main contributor to flux decline. Accumulation in the form of retained particles, colloids, macromolecules and other matter contained in the feed water depositing on the membrane surface and/or within membrane pores by constricting the pore diameter leads to flux decline. (Lee et al., 2004) Inorganic fouling (scaling) occurs when ion concentration of inorganic precipitates and metals exceed the saturation index and precipitate on the membrane surface and inside the pores. Since MF and UF do not have a sufficiently narrow pore size to mechanically sieve metals and ions from the bulk water concentration polarization of them on the surface of the membrane does not occur (Kennedy, 2006). However organic fouling of MF membranes is promoted by chemical bonding between cations and negatively charged organic material. Frequently multivalent cations are mentioned e.g. (Zularisam et al., 2006) chemically bind between negatively charged membrane surface and negatively charged NOM fractions determining extend of fouling. Calcium (Ca 2+) is mentioned repeatedly as a major inorganic foulant (Mo and Huang, 2003). Microbial fouling is a result of the multiplication of bacteria attached on the membrane surfaces. Their production of extracellular polymeric substances leads to the formation of biofilms, which protect them from biocides. The severity is said to be greatly related with feed water conditions such as abundance of microbes, nutrient availability and condi tions for microbial growth. (Liu et al., 2001) Organic fouling Organic fouling is profound with source water containing relatively high natural organic matters (NOM) and is believed to be the most significant factor contributed to flux decline. (Mallevialle et al., 1996).

2.5.2 Reversible and Irreversible Fouling

Contrary to the prior paragraph which was focused at the different causes of fouling this paragraph is concerned with the effects of fouling in MF/UF systems, and operational aspects of fouling remediation. (AWWA, 2005) defines fouling as the gradual accumulation of contaminants on the membrane surface or within a porous mem brane structure that inhibits the passage of water, thus decreases productivity. To counter this development cleaning via backwashing (BW) is done. This typically involves periodic reverse flow of clean water and/ or air from the filtrate side to remove accumulated foulants. For chemically enhanced backwashing (CEB) chemicals are added to permeate. Flux and pressure for backwashing are usually higher that in filtration cycles. The impact of these measures in case of constant flux filtration is summarized in the following figure:


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Reversible Membrane Fouling (backwashable fouling) is referred to the restoration level of TMP by removing the depo sited gel cake layer largely outside the membrane pores via frequent backwashing . Irreversible Membrane Fouling (Non Backwashable) is defined by the TMP increase (in case of constant flux applications, otherwise flux decline) which is not removed by frequent backwashing between filtration cycles. Irreversible fouling is referred to as precipitates, particulate and/or organic matter, entering the pores and attaching/adsorbing to the membrane. Those can not be removed with backwashing procedures, even after chemically enhanced backwashing. Particular cleaning procedures are required in order to restore the initiation TMP. The rate of fouling is defined by the decline of flux for constant pressure mode or the increase in trans-membrane pressure for constant flux mode over filtered volume or time.

2.5.3 Factors influencing fouling

Related to membrane materials, based on a review of several researches (Zularisam et al., 2006) stated, that membrane properties such as MWCO (pore size), surface charge and roughness, porosity, and hydrophilicity/hydrophobicty contribute to determination of membrane fouling types. They concluded that fouling is higher with negatively charged membranes when they are exposed to neutral (hydrophilic) materials and protein (bases) compared to highly negatively charged material (humic substances), though they possessed high adsorptive behaviour due to their high hydrophobicity.

Figure 2.2: Scheme for rate of fouling

TMP

Volume, Time

Cycle 1

Backwash 1

Cycle 2 Cycle n

CEB

Irreversible fouling

Reversible Backwashable

fouling

Backwash 2

Fouling


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This is contradicted by (Jung et al., 2006), who observed that the effect of membrane properties on the adsorption of organic fractions was greater for the hydrophobic membrane than for the hydrophilic membrane regardless of the kind of organic fractions. A tendency of electrostatic repulsion between membranes with negative surface charge and NOM constituents with a high charge density is observed (Liu et al., 2001). This usually happens in nature pH conditions of water. Therein the electrostatic charge of the membrane surface is conditioned to be negatively charged, where else colloids, particles and dissolved organic matter carries a negative charge too. pH conditions of the source water are also reported to have influence on fouling. For instance (Dong et al., 2006) reported variation of total resistance in filtration of raw water at different pH, where the resistance for lower pH increased more rapidly. At neutral pH, the membrane rejected less NOM, but with the decrease of pH, the removal efficiency could be increased markedly. Fouling is furthermore influenced by chemical and physical factors of foulants. Foulants are characterized according to their molecular structure, molecular size, surface charge, charge density, and the content in terms of functional groups (Zularisam et al., 2006). They also state that the presence of divalent ions and the pH of the water are of influence to fouling. (Jung et al., 2006) mentioned that hydrophobic organics adsorbed much more quickly than hydrophilic organics, by using regenerated cellulose and polysulfone UF membranes with different levels of hydrophilicity in a stirred cell arrangement. (Lee et al., 2004) found that high hydrophilic (HPI) fraction content of NOM resulted in significant flux decline. In their experiments macromolecules of a relatively hydrophilic character (e.g. polysaccharides) were effectively rejected by low-pressure membranes, suggesting that macromolecular compounds and/or colloidal organic matter in the hydrophilic NOM fraction may i s a problematic foulant of low-pressure membranes. They further concluded that MF membrane fouling may be caused by pore blockage associated with large (macromolecular) hydrophilic molecules and/or organic colloids. Another factor responsible for the extend of fouling are operation conditions. Applied fluxes, the frequency of backwashing, their intensity, and cleaning are crucial to fouling. Several sources (Jang et al., 2006) are mentioning all these factors and especially their interaction responsible for membrane fouli ng

2.5.4 Membrane Fouling Mechanisms

Bowen et al. (1995) cited by (Lee et al., 2004), has elucidated the consecutive steps of membrane blocking in flux decline during protein MF as follows: (i) the smallest pores are blocked by all particles arriving to the membrane, (ii)


12

the inner surfaces of bigger pores are covered, (iii) some particles arriving to the membrane cover other already arrived particles while others directly block some of the pores, and (iv) a cake starts to be built.

The four fouling mechanisms shown in Figure 2.3 are governed by the following principles: Complete blocking, or called pore blocking phenomenon, represents that each particle arriving to the membrane participates in blocking some pore or pores with no superposition of particles. Standard blocking, called internal adsorption, is described in case that each particle arriving to the membrane is deposited onto the internal pore walls leading to a decrease in the pore volume. In intermediate blocking, or long-term blocking phenomenon, each particle can settle on other particle previously arrived and already blocking some pore or it can also directly block some membrane area Cake filtration phenomenon represents that each particle locates on others already arrived and already blocking some pores and there is no room for directly obstructing some membrane area.

2.6 Modeling of Filtration and Prediction of Fouling

2.6.1 Fouling Prediction

As mentioned, particulate fouling mainly causes decline in permeate flux in MF and UF. Particulate fouling referrers to deposit and adsorption of colloids, suspended solids, and microbial cells on and inside the membrane pores. Water quality parameters such as turbidity, suspended matter and particle counting cannot be used to estimate the particulate fouling potential of a

Figure 2.3 Membrane Fouling Mechanisms adopted from (Mallevialle et al., 1996)


13

feedwater. Nevertheless, from design, economical and operational points of view it is important to predict and control membrane fouling. Cleaning frequencies, pre-treatment requirement, operating condition, cost and performance are affected by membrane fouling. (Mallevialle et al., 1996). For the purpose of fouling prediction methods have been developed using the basic resistance model to predict particulate fouling. This process in MF and UF is described by pore blocking and cake filtration mechanism. s Silt Density Index The Silt Density Index (SDI) as a method designed to measure the particulate fouling potential of feedwater is employed with special equipment. A specific 0.45 micron microfiltration membrane with a diameter of 45 mm and appliance of a pressure of 3 psig is used to determine the SDI. One shortcoming of the SDI is that it does not measure the rate of change of resistance during duration of the test, but only at view preset intervals throughout the procedure. Modified Fouling Index The Modified Fouling Index, derived by (Schippers and Verdouw, 1980) form the SDI, was aimed to predict the rate of fouling for RO membranes. For determination of the MFI the same equipment and procedure as the SDI is applied. Hence, the volume is recorded in an interval of 30 seconds over a 15 minutes filtration period. The MFI is based on the physical laws of filtration theory derived by Darcy and Hagen-Poiseulle. The thick ness of the cake layer formed at the membrane is in direct proportion with the filtered volume, as a homogenous distribution of particle in the feed water is assumed. The total resistance is the sum of the initial membrane resistance and the cake resistance. (Boerlage, 2001) pointed out that the MFI is based only on cake filtration mechanism, though depth filtration and pore blocking is happening during the initial stage of the filtration cycle. The total resistance building up is dependent on the particle size through the Carmen- Kozeny equation for specific cake resistance. The linear relation between the index and the concentration of colloidal and suspended matter leads to the conclusion that smaller particles present in the cake result in higher MFI values.

)(* cm RRP

AQ

AdtdV

+∆==

η

The permeability of a clean membrane is a function of the filter properties, such as filter thickness, surface porosity, pore radius, and tortuosity, and can be defined by Poisseulle’s law (see paragraph filtration theory). The resistance in series model The resistance of the cake formed during constant pressure filtration is proportional to the amount of cake deposited at the filter medium, provided that the retention of particles and ? is constant (Ruth et al. 1933).

Equ. 1


14

CbR AV

c**α=

By rearranging and substitution of Cb with the coagulant dose in g/m3 the specific cake resistance over the filtration cycle is calculated.

VPAC

PAR bm

Vt

*22 ∆+

∆=

ηαη

One reported shortcoming of both of these test is that fouling of colloidal matter is not taken sufficiently care off due to the wide pore size (0.45 um) of the filter used. (Boerlage, 2001). It was therefore that a more accurate measure to predict particulate fouling in RO membranes was developed (Boerlage et al., 2003).

2.6.2 Pore Blocking and Cake Filtration

Hermans and Bredee in 1936 first distinguished the previously mentioned fouling and filtration mechanisms retaining particles on porous media during constant pressure filtration in dead end mode. They found a way to mathematically describe those four mechanisms, based on parallel capill ary filter tubes as describe by Poiseulle, thus leading to the general equation for the rate of blocking (second derivative model)

β)(2

2

dVdt

dVtd

K b=

The coefficients ? is a constant depending on the initial flow rate (except for intermediate blocking). The exponent ß describes the prevailing filtration mechanism. Values for ß of 0 define cake, for 1 intermediate blocking, for 1.5 standard and for 2 complete blocking filtration. The rate of blocking increases in the order of: complete > standard > intermediate > cake blocking filtration. Hermia reanalyzed the filtration laws of Hermans and Bredee in 1982, and developed physical models for the transition states between these mechanisms. He confirmed the previously established proof for the respective laws, when linearity is given for certain plots. With constant pressure filtration complete blocking is leading to a linear decrease of flux over time, hence a linear relation can be obtained when plotting exp (t) over filtered volume.

))exp(1(0

0 tKVK bb dtdV −−=

Equ. 2

Equ. 3

Equ. 4

Equ. 5


15

To proof standard blocking a linear relation can be obtained when plotting dt/dV0.5 over filtered volume.

dVdt

Vt

tK s0

022−=

Intermediate blocking, gives a linear relation between dt/dV (resistance of the filter) over filtration time t.

)1ln( 0 tQKVK ii +=

Cake filtration is given when a linear relation between t/V plotted against V is obtained.

0

22QV

tVK c −=

2.6.3 Cake Compression

At the beginning of filtration the membrane resistance controls the rate of flow, and the whole of the pressure drop occurs in the membrane. As filtration continues and a cake builds up a proportion of the pressure drop is absorbed by the cake. At a given point the membrane resistance becomes negligible. A hydraulic pressure gradient over the cake depth develops resulting in an increase in the viscous drag at the particle surfaces over which the fluid flows. If the shape of the particles or their physical strength is such that the packing arrangement in the cake formed on the membrane can sustain this drag force without significant deformation, the cake is regarded as incompressible. The porosity of the cake and its specific resistance are then independent of imposed pressure (Boerlage, 2001) Practically few cakes are incompressible. Those composed of clays and microbial cells which are highly compressible. As the pressure drop increases over the cake during filtration (or for a test conducted at higher pressure) the cake porosity reduces as particles compress (creating a non-uniform porosity distribution in the direction of flow). In addition, the filtration of fine particles in the cake structure may also be responsible for an increase in the specific cake resistance over time. Fine particles will block or narrow the voids present in the cake (cake clogging), hence increasing the specific cake resistance. As a consequence the specific cake resistance increases (Boerlage, 2001). (Lee et al., 2003) concludes that co mpressibility is strongly influenced by trans-membrane pressure (TMP), particularly for smaller floc sizes. The deposition of highly porous aggregates onto the membrane results in the

Equ. 6

Equ. 7

Equ. 8


16

formation and maintenance of a highly porous cake layer, provided a low TMP (<10 kPa) is applied. Rapid compression of the cake occurs at higher TMP’s (>60 kPa) as shown by the significantly lower porosity of the cake.

2.7 Natural Organic Matter (NOM) in Aquatic Environment

In their review (Zularisam et al., 2006) defined NOM in water as a mix of particulate and dissolved components of inorganic and organic origin that vary for each source. It source is from the breakdown of organic material from vegetation and animals. The heterogeneously composition contains of a wide range of molecular weights and functional groups. They also state that that Dissolved Organic Matter (DOM) gives the main contribution to organic fouling of membranes. From NOM especially Humic and Fulvic substances are responsible for the colour in water. Those fractions of NOM are considered as a minor contribution to fouling (Carroll et al., 2000).

2.7.1 NOM characterisation

(Hendricks, 2006), by citing Edzwald (1993) gives a classification of NOM in two major groups of hydrophobic and hydrophilic substances with a further subdivision into acids, bases and neutrals. They also state that the main groups responsible for being disinfection by-products precursors are Humic and Fulvic Acids. Category Acid/Base Chemical Group Hydrophobic Strong acids Humic acid Fulvic acid High MW alkyl monocarboxylic and

dicarboxylic acids Aromatic acids Weak acids Phenols Tannins Intermediate MW alkyl monocarboxylic and

dicarboxylic acids Bases Proteins Aromatic amines High MW alkyl amines Neutrals Hydrocarbons Aldehydes High MW methyl ketones Hydrophilic Acids Hydroxyl acids, sugars, sulfonics Low MW alkyl monocarboxyclic and

dicarboxyclic acids Bases Amino acids, purines, pyrimidines Low MW amines Neutrals Polysaccarides Low MW alkyl alcohols, aldehyeds, ketones

Table 2.4: NOM fractions and Groups Source: (Hendricks, 2006)/ Edzwald (1993)


17

According to (Thurman, 1985) the average composition of NOM in Surface water DOC contains about 40 % Fulvic Acid, 10 % Humic Acid, 25 % low molecular weight (LMW) acids, 5 % neutral compounds, 5 % bases and 5 % contaminants.

2.7.2 NOM removal in Low Pressure Hybrid Membrane Systems

Notwithstanding that improvement of NOM removal by coagulation prior membrane filtration has been mentioned repeatedly (Carroll et al., 2000), low pressure hybrid membrane filtration alone is not effective to remove all NOM fractions and alleviate fouling. Their study identified that the major contribution to fouling were attributed to the NOM fractions comprising small, neutral, hydrophilic compounds. Residual NOM after coagulation was mainly composed of small molecular weight, neutral hydrophilic substances which are poorly bound by coagulation. Residual NOM played an important role in determining the rate of fouling. The substantial removal of the hydrophobic and charged fractions by alum had little impact on the rate of fouling from their feed water.

2.8 Coagulation

In classical treatment trains coagulation was targeted on colour removal, which is a manifestation of NOM (Fulvic Acid and Humic Acid) in water. Colour has bad aesthetics and causes taste in drinking water. Furthermore it increases demand in chlorine and provides nutrients for algae and bacteria (Hendricks, 2006). Suspended solids remaining from any prior treatment step like screening or settling basins are of colloidal nature (<10um). Due to their small size and large surface they have negligible settling velocities. Also their mainly negative charge prevents them from aggregation and repulsion from identically charged particles keeps them in suspension. Dispersion of metal salts (Al, Fe) into the solution is destabilizing the suspension by neutralization of their charge. Colloids are adsorbed and enmeshed into the coagulant matrix. The induced energy while mixing the suspension and supporting the collision of particle is of great importance for this unit process.

2.8.1 Principles of Coagulation

During coagulation specific constituents of NOM are agglomerated, where else others are hardly entrapped. (Hendricks, 2006) states that coagulants should be more effective in removing hydrophobic and high molecular weight material. This is confirmed by (Carroll et al., 2000), who observed from literature review that coagulation pre-treatment selectively removes certain types of dissolved NOM. Aluminium-based and iron-based coagulants are known to preferentially remove hydrophobic rather than hydrophilic substances, charged rather than neutral substances and larger -sized rather than smaller sized substances. Residual NOM after coagulation was mainly composed of small molecular weight, neutral hydrophilic substances which are poorly bound by coagulation.


18

Certain parameters are characterizing the flocs created in different source water and under altering coagulation conditions. Thus, those have an impact on filterability and fouling characteristic. Among others (Cho et al., 2005) have pointed out that floc characteristics plays an important role in membrane fouling. They stated that physical and chemical properties of flocs are sensitive to flocculation conditions including mixing intensity and flocculation time. In their experiments they concluded that floc size is only secondary influential to specific cake resistance. With increasi ng flocculation time no increase in floc size was found (coagulant Polyaluminum Chloride). It therefore could not be related to decreasing specific cake resistance. Instead, they said that this might be attributed to the changes in floc structure represented by fractal dimension, confirmed by observations where longer flocculation time resulted in the formation of flocs having lower fractal dimension (looser flocs). They assumed that this lead to an increase in floc permeability because flocs with low fractal dimension are generally porous and large. They concluded that flocs generated at longer flocculation time show lower value of specific cake resistance. These assumptions hold only under the condition that compression of the filter cake did not take place. Furthermore in this study (Cho et al., 2005) mentioned that the reduction of the number colloidal particles with increasing flocculation time has been found to influence filterability positively. This was also found to be in accordance with to the Carmen-Kozeny equation where the specific cake resistance decreases with its composing particle size. Another parameter describing properties of particle in aqueous solutions is zeta potential. For organisms and other particles they seem to be in general strongly negative (Hendricks, 2006). They respond to the effect of increasing coagulant dose with increasing zeta potential. (Mo and Huang, 2003) mentioned that coagulation conditions which produced particles with a zeta potential near zero were found to minimize the fouling.

2.8.2 Coagulation in Low Pressure Membrane Filtration

In conventional water treatment coagulation has the objective of turbidity and colour removal, both attributed to colloidal particles of organic and inorganic origin, like clays, and micro organisms. For coagulation and flocculation followed by sedimentation andfiltration it is essential to create flocs with properties supporting settling in reasonable time and attachment on granular media filters. In microfiltration suspended solids and bacteria are primarily removed by the sieving process. Coagulation prior low pressure membrane filtration follows different objectives than in conventional processes. (Jang et al., 2006) cites several sources stating that a combination of membrane filtration and the physicochemical process of coagulation (so called Hybrid Systems) can improve the quality of the water produced and membrane permeability.


19

In other instances sources where cited (Mallevialle et al., 1996) where coagulation reduced the frequency of cleaning procedures. According to them this holds especially for ceramic MF, which is also confirmed by (Bichii, 2006). For instance (Carroll et al., 2000) reported that fouling was caused by colloidal material when the raw water was filtered untreated and by NOM when the raw water was coagulated before filtration. Though, (Mallevialle et al., 1996) mentioned cases where coagulation in combination with polymeric MF leads to detrimental results. Despite various studies confirming the positive effect of coagulation pre -treatment with metal ions, others show increase in NOM adsorption of metal ions onto the membrane and worse irreversible fouling mechanisms. Referring to coagulation conditions (Lee et al., 2003) conducted a study on the effect of floc size and structure on specific cake resistance and compressibility in dead-end microfiltration showing that there is interplay between floc size and structure. They provide evidence that larger flocs form cakes with large inter-floc permeability, which resul ts in a significantly lower resistance than achieved for smaller flocs. Concomitantly, looser flocs (of low fractal dimension) are likely to form cakes with higher intra-floc porosity and lower resistance than a cake made of compact flocs of similar size. Cakes formed of smaller flocs are more sensitive to floc structure effects than cakes of larger flocs. In the same study (Lee et al., 2003) pointed out that cake compressibility is strongly influenced by trans-membrane pressure (TMP). This holds particularly for smaller floc sizes. Rapid compression of the cake occurs at higher TMPs (> 0.60 kPa) as shown by the significantly lower porosity of the cake. Under high TMP conditions, the cake-averaged porosity exhibits strong size dependence with larger floc sizes yielding higher porosities. This result may indicate formation of relatively impermeable assemblages (as a result of significant compaction) with resistance controlled by interaggregate permeability (i.e., flow around compressed flocs). (Amy, 2006) summarized that coagulation prior membrane filtration can improve permeate flux by 3 mechanisms with vary ing efficiency: Reduction of foulants penetration into the membrane: Adsorption onto flocs of precipitated metal hydroxides and aggregation to larger particle may prevent particles from entering pores and deposit on the membrane surface. (Judd and Hillis, 2001) and (Guigui et al., 2002) are suggesting that either a more permeable cake is formed or else greater membrane protection from foulants takes place at higher doses. Conditioning of the material deposited on the membrane: With increasing particle sizes through aggregation of colloid the specific resistance of a cake deposited on a membrane decreases. Next to the particle size compressibility of them is of significant influence. This is confirmed by (Pikkarainen et al., 2004) how reports that specific cake resistance has been shown to decrease with the increasing coagulant dose. Improved particle transport: In tubular and hollow fibre membranes, larger particles are more easily transported along the membrane walls with the feed stream.


20

(Amy, 2006) further mentions that permeate quality improvement stems from Enhanced removal of Microbial contaminants: due to the limit of pore diameter MF does not build a barrier against viruses. Though, significant removal of viruses is reported in combination with coagulation prior to MF.

2.9 Theory for Dead End Filtration

In this paragraph only theory related to dead end filtration under constant pressure mode is elaborated. The explanations given here are based on the explanations given in (Davis and Grant, 1992).

2.9.1 Flux through Porous Media

Flux is defined as the volume through a unit area per unit time. Generally, the pure water flux, J, through a membrane without material deposited on it s surface is often described by Darcy’s law, where J is directly proportional to the applied pressure:

RP

AQ

dtAdV

m

J** η∆===

[m3/m2.s]

where: J linear fluid velocity ? P applied pressure ? viscosity of the solvent Rm the intrinsic clean membrane resistance

Darcy’s law is a simplification of the model most frequently used for describing flow through pores is based on the Hagen-Poiseuille model for streamline (laminar) flow in channels, where parameters of the porous media are described more in detail:

δη Θ

∆=

m

prfJ 8

2

where f fraction of open pore area on the membrane surface r pore radius T pore tortuosity factor dm effective thickness of the membrane

A simplification of the expression representing the porous media can be made by substituting f, r, T and dm for total membrane resistance, RT, following resistances according to the resistance-in-series model. This fact applies when filtering a solution

Equ. 9

Equ. 10


21

containing particles depositing at the membrane. Therein the diameter and porosity of the membrane mainly determined this initial resistance of the membrane.

Rm initial membrane resistance Rb resistance due to pore blocking Rc resistance due to cake layer formation Rad resistance as a result of adsorption

Thus, RT = Rm + Rb + Rc + Rad For a clean membrane, Rb, Rad and Rc are 0 at the beginning of filtration.

2.9.2 Temperature Effects on Flux

The viscosity of water increases with decreas ing temperature. As observed in Equ. 9, and Equ. 10, increase in viscosity will lead to a reduction in flux. Since water temperature can have a significant impact on flux, it is common practice to ‘normalize’ the flux to a reference temperature for the purposes of monitoring system productivity independent of temperature-related effects. Reference a temperature of 20oC is typically used (AWWA, 2005). The following equation cited by (Farahbakhsh and Smith, 2002) is used for temperature correction of flux:

eJJ T

mC

)20(239.0

20

−−=

Where: J20C adjusted flux for media at 20C Jm measured permeate flux at actual temperature T actual fluid temperature

They also reported that Equ. 11 may introduce an error of about 3 to 10% in the temperature range of 0 to 10 C. They recommended the use of the following equations to correct for the effects of temperature on water viscosity: For t = 20C µ T = e-0:0282(T- 2 0 )

For t > 20C µ T = e-0:021 (T- 2 0 )

where µ water viscosity at the test temperature T .

Equ. 11


22

3 Materials and Methods

On laboratory scale, using a Stirred Cell in constant pressure and dead end mode tests were carried out by using Delft Westvest canal water prefiltered through a 122 um sieve (furthermore called prefiltered canal water). Ferric chloride was used as coagulant throughout the whole study. The configuration of the filtration equipment, computational methods for determination of filtration parameters and analytical methods of water quality parameters are explained in this chapter.

3.1 Materials

3.1.1 Stirred Cell Setup

A scheme of the experimental setup for the Stirred Cell is provided in Figure 3.1 and in Picture 3.1. It includes the main items utilized in this study, which are explained in this section. For all the tests procedures performed a Stirred Cell (Millipore Series 8000, model 8200) was used (Picture 3.2). According to data given on the web page of the manufacturer this model can take a volume of up to 200ml solution, uses a membrane of 62mm in diameter and its effective membrane area is 27.8cm2. The cell is resistant to a range of solvents; therefore the coagulant solution should not be affected.

Figure 3.1: Experimental setup and procedure; Stirred Cell, Feed Reservoir, digital balance

Feed Reservoir with Deionised water with a resistance greater than 18.2

MO water

Stirred Cell with 0.1 um membrane Digital

Balance

PC with data acquisition software

Nitrogen gas at 0.1 MPa

Membrane flush

Filtrate

Step 1 Step 2

Stirred Cell with Coagulant solution


23

The magnetic stirring mechanism, available to control the concentration polarization or accumulation of macromolecules on the membrane surface, was not used for the test runs and removed (non stirred tests).

The reference membrane was also purchased from Millipore. According to the manufactures statement on their webpage in the internet it has the following characteristics:

Durapore Membrane Catalogue Number: VVLP29325

Thickness, µm 125 Porosity % 70 Filter Material Hydrophilic PVDF Filter Pore Size, µm 0.1 Wettability Hydrophilic Water Flow Rate, L/m2h 1500 Bubble Point at 23 C =4.8 bar

Each membrane with its original diameter of 90mm was reshaped by using a razor blade cutter to the cell diameter of 63mm. During that procedure the surface of the membrane has not been touched, the separation sheets on each side of the membrane were also reshaped. Pressure was directly applied onto the solution in the Stirred Cell by means of nitrogen gas. Nitrogen is recommended due to the high reactivity which oxygen gas would have had with the feed water. For most of the experiments the transmembrane pressure (TMP) was chosen with 0.1MPa. This selection is

Picture 3.1: Filtration setup with Feed Reservoir Stirred Cell, and balance Picture 3.2: Stirred Cell in holder

Table 3.1: Reference membrane characteristics


24

based on its appropriateness to show a quick cake layer build-up, and result in reasonable filtration time for a 180ml sample. Picture 3.4 shows a fouled membrane in the base plate after filtration. The TMP was kept at a constant level of 0.1MPa by means of a manually adjustable pressure sustaining valve (Picture 3.3). This valve is manually adjustable by means of a turning wheel and shows a disadvantage when the pressure is changed. The accuracy of adjustment is depending on readings from the scale. For this reason the setting of the gauge for 1 bar was never changed throughout one testing procedure, which compares single filtration runs and the readings from the scale always compared with the target pressure. Applying a TMP of 0.1MPa flux is unusually high compared to industrial microfiltration membranes. This is the reason why in all of the filtration runs a relatively high flux decline occurs during the filtration, hence admitti ng rapid blocking, cake filtration and perhaps cake compression quickly to establish within the cell volume of 180ml. Another reason why a TMP of 0.1MPa was chosen stems from the fact that the MFI45 employs the same pressure on a membrane with wider pore size (0.45µm).

Parameters influencing flux are feed water composition, membrane properties, operational conditions (dead end or cross flow) as well as applied pressure. For polymeric MF membranes a rough estimate is in the magnitude of 50 to 90 L/m2.h in full scale applications. Filtrations of 180ml coagulant solution for some test runs take from 146 to 220 seconds, depending on coagulant dose and Gt induction. Without considering further flux decline or any backwashing time, then by averaging the flux over time and extrapolation it equals to a flux of 945 to 1445 L/m2.h at a TMP of 0.1MPa. By considering the prior stated simplifications, the usually observed fluxes for polymeric MF membrane would be exceeded for about the tenfold. In literature (Lee et al., 2003) there is reference that filter cakes exposed to a TMP higher than 60kPa show rapid compression. This particularly applies to porous flocs, forming a porous layer on the membrane surface.

Picture 3.3: pressure sustaining valve Picture 3.4: Fouled Membrane after filtration


25

For easy operation a manual switch valve has been used to open and close the gas supply quickly. The system was examined on any gas leaks by means of a soap solution

For some of the test runs a Feed Reservoir with a volume of 3.875litre was used. This would allow filtration of higher quantities of coagulant solution, probably enabling observation of cake the compression mechanism. As it turned out, evidenced by later test runs, utilizing the Feed Reservoir had an adverse effect on flocs created during flocculation. It is assumed the hose 0.125 inch hose leading for the vessel to the Cell, and its sharp inlet edge is breaking up flocs. This lead to the practice, that the Feed Reservoir has only been used for flushing of the membrane. Filtrate has been collected in a beaker placed on a digital scale (Mettler Toledo, Model PB 602-S, Picture 3.8). The scale had an RS 232 interface with a PC. Data sets of filtrate weight collected over time were recorded and imported into an MS Excel spread sheet; by data acquisition software (WinWedge, www.taltech.com). The sampling frequency was set to the order of magnitude of 2 and 4 seconds. A major disadvantage of the digital scale is that only a quantity of up to 600g can be measured with it.

Picture 3.5: Switch Picture 3.6: Feed Reservoir

Picture 3.7: Jar test unit Picture 3.8: digital balance


26

3.1.2 Membrane Conditioning Procedure

For each test run a new membrane was soaked in deionised water with a resistance greater than 18.2 MO before flushing it with 1 litre of deionised water at a TMP of 0.1MPa. As determined by previous tests, after flushing the membrane with 600ml no substantial change in membrane resistance has been detected. This was therefore seen as sufficient also remove the protection layer Before preparing the membrane for the coagulant solution most of the remaining deionised water from the flushing procedure has been removed by means of an air flush.

3.1.3 Jar Test Unit

The jar test unit used for coagulation was provided by TU-Delft. It is equipped with a continuously adjustable speed regulator (Picture 3.9). The jar test unit consists of 6 round containers with 4 baffles in each container. A flat-bladed stainless steel turbine impeller is used for Gt induction. The solution containers, made of glass, have got double walls, where inside a cooling liquid can be circulated in order to simulate coagulation a low temperature. The rpm applied to the samples were measured by a digital contact tachometer, (Typ Extech Instruments, Model 461891, Picture 3.10), which was directly applied to the axis propelling the blades of the stirrer. Trough a conversion diagram from rpm to G, which is attached in the Annex Section of this report the corresponding G value to rpm was obtained.

3.2 Analytical methods

3.2.1 Specific Ultra Violet Adsorption SUVA

Specific Ultraviolet Absorption (SUVA) at 254 nanometers (nm), is an indicator of the humic (Natural organic matter- NOM) content of water. The

Picture 3.9: rotation adjustment for jar test unit

Picture 3.10: digital contact tachometer


27

presence of humic acid in water intended for potable or industrial use can have a significant impact on the treatability of that water and the success of chemical disinfection processes. Accurate methods of establishing humic acid concentrations is therefore essential in maintaining water supplies Specific UV absorbance (SUVA or SUVA 254) is defined as the sample’s UV absorbance at 254nm divided by the DOC concentration of the sample.

SUVA (L/mgC.m) = UV 254 (m-1) / DOC (mg/L) = 100*UV254 (cm -1) / DOC (mg/L).

SUVA indicates the level of aromaticity of compounds in the DOC and can be used to estimate the chemical nature of the DOC. A SUVA = 3 L/m-mgC indicates non-humic NOM while SUVA 4-5 L/m-mgC indicates humic NOM. High SUVA means high aromaticity or hydrophobicity of samples in the limited DOC. Permeate from membrane the filtration process with a high SUVA value conforms to most of the rejected compounds that are non-humic and that resulted in high values of UV254 in the permeate. (Hendricks, 2006).

3.2.2 Liquid Chromatograph Organic Carbon Detection

LC-OCD (Liquid Chromatography - Organic Carbon Detection) is a liquid chromatographic system especially designed for the analysis of hydrophilic compounds in natural and technically refined waters. It is equipped with an UV-detector and a high-sensitive mass detector for organic carbon (TOC, OCD) The OCD is based on a Gräntzel gravity-flow thin-film reactor. The organic and inorganic carbon species (OC, IC) are separated in the upper part of the reactor by means of continuous acidification and nitrogen strip ping. The quantitative oxidation of the organic bound carbon takes place in a nitrogen atmosphere via radiolytic dissociation (wavelength 185 nm) of small amounts of water into highly reactive oxygen radicals. The reaction product carbon dioxide is measured by non-dispersive infrared absorption. The detection limit for TOC is in the low -ppb concentration range (1 -10 ppb). Polymer filled columns are used in the chromatographic separation process. The dominating separation criterion is the molecular size (gel filtration chromatography, GFC). Secondary chromatographic effects including hydrophobic interaction and ionogenic interaction contribute to the characterization of chromatographic fractions. Samples are not treated; a small aliquot is directly injected. Suspended microparticles are removed before entering the column by in-line filtration. A full TOC characterization of a sample includes the partitioning of TOC (total OC) into DOC (dissolved OC), HOC (hydrophobic OC), POC (particulate OC) and CDOC (“chromatographable”, hence fractionated OC). (Huber, 2007)

3.3 Computational Methods

3.3.1 Determination of Initial Clean Membrane Resistance R m

The membrane resistance or permeability Rm is a function of filter properties such as filter thickness (?x), surface porosity (e), pore radius (rp) and tortuosity (t). It can be defined by Poiseulles law:


28

2

8

prx

mR ετ∆

= [m-1]

Most of these parameters are of rather theoretical value and can hardly be determined, nor are they provided by the membrane manufacturer/supplier. As mentioned in previous elaboration and in accordance with the law of Darcy and the resistance in series model the membrane resistance is determined by the relation

QPA

tR **

η∆

= [m-1]

By subtracting Rm from the total resistance Rt the cake resistance Rc can be calculated via the relation of resistance in series.

RRR cmt +=

By assuming at the start of a filtration cycle no filter cake has accumulated and therefore the no resistance, the total resistance is equivalent of the membrane resistance. Membrane resistance for the reference membrane (PVDF, hydrophilic, 0.1 um pore size), determined with deionised water with a resistance greater than 18.2 MO:

• Changes over filtered volume, until it reaches an asymptotic value, when flushed before use.

• Is different for each singular membrane out of a package. There is a margin of deviation for each membrane.

• Is closely similar for DI water, 122um prefiltered uncoagulated canal water, and 0.45um prefiltered uncoagulated canal water (elaborated in 4.10).

However, the definition of membrane resistance in literature is ambiguous, in terms of which media should be used. Nevertheless in literature some annotations to this fact were found. For nearly all of the filtration curves (visible in t/V over V or when computing Total resistance by rearranging (Equ. 1) explicit to Rm) the same behaviour is observed. According to observations done during the entire test series it takes about 4 to 10 seconds from start of the weighing procedure until a minimum value of the Total Resistance is observed. From that moment on the resistance constantly increases again. These artefacts might be due and influenced by three factors: (a) not all the air is purged from the membrane

Equ. 12

Equ. 13

Equ. 14


29

and the hose leading to the balance, which needs to stabilize, (b) inaccuracy of the balance in use, and (c) influence of the permeate stream hitting the scale randomly, and showing no linear increase in filtered weight. In any case, the initial membrane resistance based on these observations of extremes are highly likely to be influenced by one of the mentioned factors, and therefore considered as unreliable. This fact is supported by the by studies reporting that pore blocking mechanism without cake filtration is happening in the initial stages of filtration (Ye et al., 2004), (Zularisam et al., 2006). As pore blocking is happening during the described unstable conditions, the observed minimum has been exposed to another factor causing inaccuracy. Another approach, as mentioned by (Ye et al., 2004) and (Brauns et al., 2004) utilizes computation of the Membrane Resistance Rm via determination of the MFI and rearranging Equ. 15. This approach has proved as useful and practical. It provides a measure of reliance based on the R square value from the linear regression of the cake filtration mecha nism when plotting ?t/?V over V. (Ye et al., 2004) also introduced a ratio of Rm/Rm’, providing that Rm’ is determined by the initial measurement and Rm the value derived via the MFI. The ratio of Rm/Rm’ will be used throughout the study. Conclusively it can be noted that from theoretical point of view computations involving Rm should be avoided, given the inaccuracy of its determination theory and method.

3.3.2 Computation of the MFI

The MFI was developed by (Schippers and Verdouw, 1980), and regarded by (Boerlage et al., 2003) as a good indicator for water quality and fouling prediction for membrane filtration. The Modified Fouling Indices (MFI45, and MFI-UF) are based on cake filtration and are proportional with colloidal concentration. Furthermore, the MFI tests comprise the fouling index (I) which describes the fouling potential of a feedwater, and which is corrected to standard reference conditions of pressure, feedwater viscosity and the area of the microfiltration membrane. The MFI is also the basis of a model to predict flux decline or pressure increase. The model incorporates an experimentally determined pressure correction factor to account for differences in the applied (Boerlage et al., 2004). Throughout this study the MFI was considered as the main indicator evidencing the effect of coagulation conditions on filterability. By definition MFI is the slope of the t/V over V plot, in case cake filtration is present and 100% of the particle are retained the relation in Equ. 15, Equ. 16, apply.

VPAC

PAR bm

Vt

*22 ∆+

∆=

ηαη

MFIbPACb ==

∆ 22ηα

Equ. 15


30

Slope = 22.PAI

∆η

d(t/V)/dV where η

2**2 PAslopeI

∆=

In order to correct for Temperature, TMP and the difference in filtration area applied in the MFI45 Equ. 18 is yielded:

20200

202

0200

220

2 AP

Islope

AP

PAMFI

∆=

∆

∆=

η

η

η

This relationship can be presented graphically when plotting t/V over V or as ?t/?V over V shown in Figure 3.2.

The coefficients of I, the fouling index, specific cake resistance ? and particle concentration Cb (Equ. 19 and Equ. 20)are not computed explicitly in this study. They are variable coefficients composing the MFI, representing the TMP and filter area involved. By definition Cb is constant.

ICb=*α

32 **)1(*180

ερε

αss d

−=

Equ. 16

Equ. 17

Equ. 18

Figure 3.2: Ratio of differential filtration time and filtration volume as a function of total filtrate volume

Equ. 19

Equ. 20

Blocking Filtration

Cake filtration Cake filtration

with compression

? t / ? V

V MFI: ? (?t/? V)*0.5/ ? V


31

In the current mode of computation the MFI is in the magnitude of 10e9. The usual magnitude observed for UF membra nes are in the range of 10e3 to 10e5 is as observed in (Boerlage et al., 2003). There the MFI-(UF), is normalized to the pressure of 2 bar, the filtration area as well as to a temperature of 20°C, as displayed in Equ. 18. The magnitude of 10e9 relates to the fact that the filtered Volume is not normalized to 1m 2 of filtration area, and not adjusted 2 bar.

3.3.3 Spreadsheet Computations Raw data transmitted from the electronic balance and recorded by the data software installed at the PC were saved to Microsoft Excel Spreadsheets. Calculations of filtration parameters and compilation were then carried out with Microsoft Excel. From the weight/time series recordings filtration volume at time t was calculated by using the specific density [kg/m3] of the feed water at the temperature of the filtration time. Further the specific flux and normalized specific flux were computed, taking into account unification of temperature as discussed. The time series of the total membrane resistance was further calculated by plugging in the variable values into the relation described in Equ. 13. The initial membrane resistance was compute from a local minimum in the first ten recording sets, and the normalized flux by means of a local maximum of flux in the first 10 recording sets.

3.3.4 Data smoothening For most of the test series the recording interval from the data acquisition software was set to 4, some were set to 2 seconds. This should give suffi cient chance to observe the initial stage of pore blocking and intermediate blocking, which happens at the beginning of the filtration time (Ye et al., 2004) and does not last long. The downside of a short recording interval is that these recordings are more easily affected by random and systematical error. The recording error is determined by (i) the accuracy of the balance in use, and (ii) the noise from recording and the environment. For this reason comparison of different data processing methods have been evaluated at the beginning and later on applied for processing all the data. These methods are discussed in more detail in Chapter 4, Results and Discussions, in the context of general accuracy considerations.. One method considered and trialed for data processing was by means of a set of VBA macros for Microsoft Excel, downloaded from (Decisions et al., 2007). This function can perform all the standard smoothing methods of exploratory data analysis (Thurman, 1985) with a high degree of flexibility: As later on provided in Chapter 4, Results and Discussions the outcome was inferior to other methods in terms of increasing accuracy. The data smoothening method was also evaluated as not appropriated to for computation of MFI variation over filtration time. The main purpose of this trial to smoothen data series were the wildly fluctuations of data series when computing the variation of MFI over filtration time via the relation ?t/?V/ ?V. These were found by processing the original time and weight recordings


32

at different intervals (4, 8, and 12 seconds) and plotting them over filtration time (view Figure 2.1, a, b, c, and d). From these computations it was impossible to derive any trend line of these data sets to the variation of MFI during a filtration cycle. This observation is caused by the noise recorded along with the information and the inaccuracy of the balance. It is amplified by the fact that the slope between two data points does not steadily increase over all the recordings. Certainly it can happen that the slope between the points (t x+1 – t x)/ (V x+1 – V x) is less than (t x – t x-1)/ (V x – V x -1), thus leading to negative values when computing ?t/?V. This mechanism could not be fully eliminated, not even by setting the recording interval to reasonable length of 12 seconds. When subtracting for ?t/?V (t x – t x-1)/ (V x – V x-1) negative values occur randomly and can not be eliminated.

1.0E+08

1.0E+09

1.0E+10

1.0E+11

12 36 60 84 108 132 156filtration time [s]

MFI

[s/m

6]

dose 1mg, Gt 9400 dose 1mg, Gt 18300 dose 1mg, Gt 37200

dose 1mg, Gt 77100 dose 1mg, Gt 98100 dose 1mg, Gt 144100

1.0E+09

1.0E+10

1.0E+11

12 24 36 48 60 72 84 9610

812

013

2144

156

168

filtration time [s]

MFI

[s/m

6]

dose 1mg, Gt 9400 dose 1mg, Gt 18300



(a) (c)

1.0E+07

1.0E+08

1.0E+09

1.0E+10

1.0E+11

12 32 52 72 9211

213

2152

172

filtration time [s]

MFI

[s/m

6]




1.0E+08

1.0E+09

1.0E+10

1.0E+11

24 36 48 60 72 84 96108 120 132 144 15

6168filtration time [s]

MF

I [s

/m6]




(b) (d)

3.3.5 Curve Fitting and Application of First Derivative Model

One way to overcome the previously discussed shortcoming for computations of the MFI variation over filtration time via the relation of ?t/?V/ ?V is to apply curve fitting for the first derivative of dt/dV. This gives another opportunity to compute blocking mechanisms accurately.

Figure 3.3: comparison of different data possessing modes for computation of MFI fluctuation over filtration time; (a) smoothened data series of 4 seconds recording interval, (b) data series of 4 seconds recording interval, (c) data series of 8 seconds recording interval (d) data series of 12 seconds recording interval


33

Computation of the primary derivative model requires curve fitting of the time/weight data series via non linear regression (Newton approximation) due to their oscillation. According to (Boerlage, 2001) the model used for to describe t as a function of V is (Equ. 21)

eVdVcbVaVft **)( 2 +++==

By plugging in the coefficients b, c, d, and e in the first derivate (Equ. 22) and compute the over the time series values the derivative (dt/dV) time series was obtained.

)1(****2)( −++== eVedVcbVfdVdt

Further the d2t/dV2 vs. dt/dV can be computed and plotted to proof the existence of complete blocking.

12

12

2

2 )()(

VVdVdt

dVdt

dVtd

−

−=

However, by importing the recorded data series into a curve fitting software called Curve Expert. (Unregistered trial version of the curve fitting software is freely available via the internet), the proposed model could not be processed. The software suggested a best fit for the curves with (Equ. 24):

432 ***)( VeVdVcbVaVft ++++==

This yields the first derivate (Equ. 25):

32 *4**3***2)( VeVdVcbVfdVdt

+++==

The coefficients a, b, c, d, and e were then computed by the software. By plugging them into the first derivate (Equ. 25) and using time series data records from the original recordings, time series for the derivative (dt/dV) were obtained.

Equ. 21

Equ. 22

Equ. 23

Equ. 24

Equ. 25


34

Further with plotting the (dt/dV) vs. V curve and examining it upon linearity evidence for cake filtration shall be found. By plotting (dt/dV)^0.5 the vs. V the same can be done for proofing that standard block mechanism is present.

3.4 Experimental Procedure

3.4.1 Selection of Independent Process Variables for Coagulation

G and t values for rapid mixing (RM) and slow mixing (SM) were selected by the traditional approach to coagulation and flocculation, and on values citied by other studies under similar conditions Camp (1955) suggested for slow mixing G values from 20 to 74 s-1 and a flocculation time (SM) varied between 20 and 45 minutes resulting in Gt values in the range of 2×10E4 and 2×10E5. (EPA, 1999) for example recommends for rapid mixing a detention time of at least one minute. Further they state that in order to obtain instantaneous, uniform dispersion of the coagulant through the raw water, the most efficient use of the coagulant is achieved with instantaneous dispersion, citing velocity gradients in the range of 300 up to 1000 s-1 for rapid mixing. Several sources are citing similar G and t values for RM and SM as the ones used in this study. (Cho et al., 2005) were performing filtrations test with a dead-end and submerged polyethylene membrane (pore size 0.1 µm) at a rapid mix with a G of 150 s-1 and a slow mix G of 45 s-1. Their RM t was fixed to 3 minutes; where else their SM was ranging from 20 to 480 minutes resulting in a SM Gt range of 54,000 to 1,299,600. (Kim et al., 2006) stated similar values of (RM Gt of 320 for 30 seconds and SM Gt of 25 for 30minutes) for dead end constant pressure stirred cell filtration with UF membranes (100 kDa MWCO). The initial selection of coagulant dose had to cover a wide spectrum from under dosing to relative overdosing (0.5 to 15mg/l of effective Fe3+). With further evaluation of results the range of doses applied could be limited. Variation of pH was based to follow an approach which favoured naturally occurring pH ranges (6 to 8), thus a limitation in chemical use for pH adjustment.

3.4.2 Filtration Procedure

When preparing a test series an appropriate amount of uncoagulated prefiltered canal water was taken from the batch container in the cooling storage (4 degree C) the evening before the tests were performed. Over night the samples could adjust to room temperature, and in all of the cases it had 20 degree C with beginning of the tests. Before measuring the amount required for a single test the covered bucket with the uncoagulated prefiltered canal water at room temperature was thoroughly stirred in order to avoid inhomogenity in content of suspended solids for the single tests. Prior commencing the coagulation/flocculation procedure jar test vessel was cleaned from residuals from the previous test. It then was filled with 1 litre of


35

prefiltered (122um), uncoagulated canal water. Care has be given to dispersion of suspended solid The coagulant stock solution was prepared in a concentration of 1mg/l effective Fe 3+. Coagulant solution has been prepared freshly each week. Prior to dispersion of coagulant the feed water has been adjusted in pH to a value which expectedly suited the desired value after completion of the mixing process. Therefore HCl and NaOH in high concentrations were used. After completion of the targeted flocculation time the coagulant solution was immediately and gently transferred into the Stirred Cell by a tipper. The outlet was plugged, and just opened after the cell cover was closed, put into the frame and placed close to the scale. Then the hose leading from the cell into the permeate beaker has been connected and plugged again until the weight recording software was started. Thereupon the pressure has been applied to the cell and the filtration cycle started. Between each filtration run the Stirred Cell was cleaned and stored in DI water.

3.4.3 Point of Diminishing Return Coagulant Dose

For determination of the coagulant doses to be applied in these experiments a systematic approach in evaluation of results gathered from the experiment conducted in this reporting period, is based on the concept of Poi nt Of Diminishing Return of coagulant dose (PODR), published by the Environmental Protection Agency (Enhanced Coagulation and Enhanced Precipitative Softening Guidance Manual, 1999). Though this concept has a different aim and approach, it will serve as a baseline in limiting the number of experiment by indicating those most effective to TOC/NOM removal.


36

4 Results and Discussion

4.1 Membrane Filtration Experiments

The general objective was to define the coagulant dose that creates a minimum resistance with respect to dissolved and suspended matter present in prefiltered surface water. The raw water used for these tests was surface water from a canal in Delft, prefiltered through a 122um sieve. The raw water samples where taken in different batches, and stored at 4 degree C. For each of the tests series, consisting of separate filtration runs, but which are judged against each other, a common batch of raw water was used. The raw water was coagulated under specified conditions (independent variables: G and t, coagulant dose, and controlled pH), and sequentially filtered through a 0.1um hydrophilic PVDF microfiltration membrane, in an Amicon Stirred Cell under pressure. The filtration mode was dead end and a constant pressure of 0.1MPa. A new MF membrane was used for each test. Prior to use the membrane was soaked in DI water for 10 minutes, and then flushed with one litre of DI water. For each filtration run the permeate weight/time data series was recorded by means of an electronic scale. The recording interval of the data acquisition software was set to 4 seconds. Illustration of the filtration behaviour in terms of t/V over filtered volume, flux decline, and membrane resistance increase over filtered volume are computed but not shown.

4.2 Effect of Gt on MFI The impact of Gt on the MFI of un-coagulated pre-filtered Delft Canal was assessed. At the same time the influence of utilizing a Feed Reservoir (pressure vessel) in combination with a Stirred Cell compared to direct filtration in a Stirred Cell was compared. The recording interval for the permeate volume was 2 seconds. The following conditions applied for the experiments:

rapid mixing slow mixing dose

[mg/l] Vessel used pH G [s-1] t [s] Gt

RM G [s-1] t [s] Gt SM Gt total

- yes 7.72 470 60 28200 30 7200 216000 244000 - no 7.70 470 60 28200 30 7200 216000 244000 - yes 7.75 0 - no 7.70

direct filtration direct filtration 0

Table 4.1: Mixing conditions for uncoagulated, prefiltered canal water at a pH of 7.7


37

Raw Water Source: Delft Canal TOC 20.42 mg/l Fe 0.58 mg/l DOC 14.75 mg/l Fe 0.07 mg/l

pH 7.92 TSS 4.80 mg/L

Water quality analysis for the prefiltered, uncoagulated canal water, displayed in Table 4.2 resulted in 20.42 mg/l TOC, TSS of 4.8 mg/l, a pH of 7.92, and Fe of 0.58mg/l.

6.0E+05

7.0E+05

8.0E+05

9.0E+05

1.0E+06

1.1E+06

1.2E+06

1.3E+06

1.4E+06

1.5E+06

1.6E+06

1.7E+06

0.0E+

00

2.0E-05

4.0E-0

5

6.0E-0

5

8.0E-05

1.0E-04

1.2E-04

1.4E-0

4

1.6E-0

4

1.8E-04

Filtered Volume [m3]

?t/

?V

[s

/m3

]

Gt 244000,feed feservoir

Gt 244000,stirred cell

Gt 0, feedreservoir

Gt 0, stirredcell

Gt 244000

6.0E+05

7.0E+05

8.0E+05

9.0E+05

1.0E+06

1.1E+06

1.2E+06

1.3E+06

1.4E+06

1.5E+06

1.6E+06

1.7E+06

0.0E+

002.0

E-05

4.0E-05

6.0E-0

5

8.0E-05

1.0E-04

1.2E-04

1.4E-04

1.6E-04

1.8E-04


?t/?

V

[s/m

3]

Gt 244000,feed reservoir

Gt 244000,stirred cell

Gt 0, feedreservoir

Gt 0, stirredcell

Gt 244000

(a) (b)

An important observation from Figure 4.1 (a) and (b) is the linearity in ?t/?V over V plots for canal water where no Gt was induced and the non linear growth in MFI for samples exposed to a Gt of 244000. A linear plot of ?t/?V over V suggests that cake filtration was the dominant filtration mechanism during the entire filtration test. However, a wide range of particle sizes exist in canal water, and therefore particles smaller than 0.1 um are expected to block/plug the membrane at the start of filtration (complete and/or standard blocking) resulting in a sharp increase in the slope of ?t/?V v’s V at the start of filtration. Eventually a cake is produced on the membrane surface and a linear increase in ?t/?V v’s V is expected during the cake filtration period. However, no blocking filtration was observed in the ?t/?V over V plots for canal water without Gt induction. The absence of blocking filtration may be due to any of the following reasons (i) all particles in the canal water were larger than 0.1 um, and thus standard/complete blocking did not occur, (ii) blocking was not observed because the TMP was quite high (1 Bar), and consequently the blocking phase was completed within 4-8 seconds (first 2 points on the ?t/?V v’s V plot) and (iii) air bubbles may have produced artefacts in the first 4-10 seconds of the filtration test, thus making it impossible to observe blocking filtration.

Table 4.2: Water quality data of prefiltered, uncoagulated canal water at a pH of 7.9

Figure 4.1 a and b: Effect of Gt on prefiltered canal water (without coagulant) at a TMP of 0.1MPa and a pH of 7.7; comparison of raw data series (a) with smoothened data series (b)


38

0.0E+00

1.0E+05

2.0E+05

3.0E+05

4.0E+05

5.0E+05

6.0E+05

0 10 20 30 4 0 50 60

normalized volume [L/m2]ex

p (t

) [s]

Gt 244k, FR used

Gt 244k, no FR used

Gt 0, FR used

Gt 0, no FR used

7.5E+05

7.8E+05

8.0E+05

8.3E+05

8.5E+05

8.8E+05

9.0E+05

0 2 0 40 6 0 80 100 120 140 160

Filtration Time [s]

t/V [s

/m3]

Gt 244k, Feedreservoir used

Gt 244k, only cellused

Gt 0, Feedreservoir used

Gt 0, cell onlyused

(a) (b)

As mentioned earlier, the ?t/?V over V plot for canal water exposed to a Gt of 244000 was non linear. Non linearity is more easily distinguished when operating with data smoothening, as seen from Figure 4.1 (b) to the right. At approximately 80ml of filtered volume a bend in the plot is observed. From this stage on the linear relationship no longer exists between ?t/?V over V, but ?t/?V increases apparently with a different slope or even exponentially. According to (Boerlage, 2001), and based on the blocking laws by Hermia, linear relationship in plots of exp (t) over normalized volume is an indicator for complete blocking filtration, and for t/V over filtration time for standard blocking. Figure 4.2 (a) and (b) display these plots. As it can be observed from Figure 4.2 (a), the plots show complete blocking in the initial stage and then just with continuing filtered volume the relation tends to linearity for all tests. In the intermediate stage from approximately 10 to 45 l/m2 linearity can not be observed. For standard blocking Figure 4.2 (b), shows linearity over almost all of the filtration time after the system has stabilized. Together with these observations, and the initial low slope as well as the increase in incline just at a later state leads to the conclusion that initially cake filtration and later cake compression are the dominant filtration mechanisms. Blocking filtration is rather unlikely since the initial slope is fairly similar to the sample without Gt induction. From the plot of ?t/?V over V, displayed in Figure 4.1 it is visible that samples where no Gt was induced showed lower MFI than those exposed to a Gt of 244000. This fact is further illustrated in Table 4.3. For example, the MFI increased from 9.75E+08 to 3.06E+09 for canal water without Gt induction and canal water with an induced Gt of 244000, respectively. The higher MFI was most likely caused by disaggregating of particles, colloids and macromolecules in the feed water exposed to a Gt of 244000, which therefore formed a tighter and less porous cake deposit on the membrane surface .

Figure 4.2 a and b: Effect of Gt on prefiltered canal water (without coagulant) at a TMP of 0.1MPa and a pH of 7.7; blocking k law plots: (a) exp (t) over normalized volume for complete blocking filtration, and (b) t/V over filtration time for standard blocking


39

Adsorption of colloids and macromolecules into the membrane pores is more like to happen to microfiltration membranes, given the fact of their relative bigger nominal pore size, compared to ultrafiltration membranes. This is supported by the observation of the non linear behaviour for the entire filtration cycle in the ?t/ ?V over V plot for the canal water sampled with an induced Gt of 244,000. The occurrence of cake compression and/or depth filtration was not observed during short (15 mins) filtration tests carried out in this study. Moreover, (Schippers and Verdouw, 1980), claimed that the mechanism of cake compression/depth filtration could not be of significance for any length of time, in view of the structure and thinness of the membrane filters.

Feed reservoir Gt total R square MFI Rm Rm'

Rm/ Rm'

used [m-1] [m-1] yes 244000 # 0.91* 3.06E+09* 1.52E+11* 1.82E+11 0.84* no 244000 0.91* 3.18E+09* 1.50E+11* 1.83E+11 0.82*

yes 0 # 0.86 9.75E+08 2.01E+11 1.80E+11 1.12 no 0 0.87 1.00E+09 2.03E+11 1.68E+11 1.21

* Numbers processed from linear regression through curved line, # In this setup the additional Gt stemming from the Feed Reservoir and the piping to the Stirred Cell were not considered in the compilation in Table 4.3. Additional Gt induced by the Feed Reservoir is elaborated in the annex of this report. From Table 4.3 it can be observed that the utilization of a Feed Reservoir did not have a big impact on the MFI of non coagulated, prefiltered canal water. Contrary to what was expected, the MFI increased by 3 and 4% when the feed reservoir was not employed with a Gt of 0 and 244,000, respectively. However, the additional Gt of approximately 6000 induced through the additional piping between the feed reservoir and the cell was expected to lower the MFI. In Table 4.3 the two different ranges of MFI, but also the Rm derived from the MFI can be easily discriminated. The accuracy of these computations is comparable, since in the same magnitude. No distinct conclusion regarding the impact of Gt induction on TOC removing capacity can be observed (Table 7.1, Appendix).

4.3 Effect of Coagulant, Gt (Slow Mixing t) on MFI The effect of Gt (slow mixing conditions) on the MFI of coagulated canal water (5mg Fe 3+/l) was assessed. All samples were coagulated with the same coagulant dose (5mg Fe3+/l), at a temperature of 21C, and at a pH of approx. 7.7. (See Table 4.4 for experimental conditions). The recording interval of the digital balance was 2 seconds.

Table 4.3: Effect of Gt on prefiltered canal water (no coagulant) on Filtration parameters at a TMP of 0.1MPa and a pH of 7.7 using original, non smoothened data


40

Water quality analysis, for the prefiltered, uncoagulated canal water is displayed in Table 4.5. The TOC, TSS, pH and Fe was 20.03 mg/l, 4.8 mg/l, 7.92, and 0.58mg/l, respectively.


[mg/l]

Feed Reservoir used

pH after coagulation

G [s-1]

t [s]

Gt RM

G [s-1] t [s] Gt SM Gt total

5 Yes 7.72 470 60 28200 30 600 18000 46200 # 5 No 7.70 470 60 28200 30 600 18000 46200 5 Yes 7.75 470 60 28200 30 1800 54000 82200 # 5 No 7.70 470 60 28200 30 1800 54000 82200

# In this setup the additional Gt stemming from the Feed Reservoir and the piping to the Stirred Cell were not considered in the compilation in. Additional Gt induced by the Feed Reservoir is elaborated in the annex of this report.

Raw Water Source: Delft Canal, prefiltered TOC 20.03 mg/l Fe 0.58 mg/l DOC 14.75 mg/l Fe 0.07 mg/l

pH 7.92 TSS 4.80 mg/L

6.0E+05

7.0E+05

8.0E+05

9.0E+05

1.0E+06

1.1E+06

1.2E+06

1.3E+06

1.4E+06

1.5E+06

1.6E+06

1.7E+06

1.8E+06

0.0E+00

2.0E-05

4.0E-05

6.0E-05

8.0E-05

1.0E-04

1.2E-04

1.4E-04

1.6E-04

1.8E-0

4


?t/?

V [

s/m

3]

Gt 46000, Feed Reservoir usedGt 46000 Stirred Cell

Gt 82000, Feed Reservoir used

Gt 82000 Stirred Cell

7.0E+05

7.5E+05

8.0E+05

8.5E+05

9.0E+05

9.5E+05

1.0E+06

1.1E+06

1.1E+06

1.2E+06

1.2E+06

0.0E+

00

2.5E-05

5.0E-0

5

7.5E-05

1.0E-0

4

1.3E-04

1.5E-0

4

1.8E-04


t/V

[s/m

3]

Gt 46000, feed reservoir

Gt 46000, stirred cell



Table 4.4: Coagulation conditions for coagulated, prefiltered canal water at a pH of 7.7

Table 4.5: Water quality data for uncoagulated, prefiltered canal water

Figure 4.3: t/V over V plot for coagulated canal water (5mg/l effective Fe 3+), Gt and Feed Reservoir on filtration parameters at a TMP of 0.1MPa and a pH of 7.7

Figure 4.4: Effect of Feed Reservoir and variation in Gt on MFI of prefiltered canal water with a coagulant dose of 5mg/l of Fe3+ (TMP of 0.1MP and pH of 7.7)


41

From Figure 4.4 and Table 4.6 it can be observed that the MFI decreased from 3.64E+09 to 2.66E+09 (with the FR) and from 4.21E+09 to 1.80E+09 (without the FR) when the Gt increased from 46,200 to 82,200, respectively. The fact that the MFI was (1.80E+09 - 57% without FR, 2.66E+09 – 27% with the FR) lower at the higher Gt value of 82,000 compared to 46,000 may be attributed that the flocculation time was three times longer during slow mixing (1800 seconds at a Gt of 82,000 compared to 600 seconds at a Gt of 46,200). Consequently, the longer flocculation time of 1800 seconds (Gt of 82,000) probably resulted in larger flocs and a looser, mor e permeable cake on the membrane, and a lower MFI. (Gt of 46,200) The observation of decreasing MFI directly related to longer t (time) in flocculation in combination with constant G is in correlation with results reported from (Cho et al., 2005). They state that increasing flocculation time (originating from t) result in a lower specific cake resistance, or as displayed in their study in smaller rate of flux decline. Loose flocs were formed at longer flocculation time as indicated by their low fractal dimension were found responsible for that behaviour. Their test setup also utilized a stirred cell in dead end and constant pressure mode (TMP=40kPa). However, their feed water DOC content was considerably lower with 2–3 mg/L compared to 14.75mg/l and they were using Polyaluminum chloride (PACl) as a coagulant at 3.18 mg/L as Al 2O3 (corresponding to 30 mg/L as PACl dose)

coagulant feed Gt total R square MFI Rm Rm' [mg/l Fe

3+] used [m-1] [m-1] 5 yes 46200 # 0.96 3.64E+09 1.61E+11 1.77E+11 5 no 46200 0.96 4.21E+09 1.59E+11 1.56E+11 5 yes 82200 # 0.95 2.66E+09 1.65E+11 1.77E+11 5 no 82200 0.88 1.80E+09 1.81E+11 1.72E+11

# In this setup the additional Gt stemming from the Feed Reservoir and the piping to the Stirred Cell were not considered in this compilation. Additional Gt induced by the Feed Reservoir is elaborated in the annex of this report. The impact of the feed reservoir was more evident in the tests with coagulated water (3.64E+09 with FR, 4.21E+09 without FR at a Gt of 47000, 16%) compared with non coagulated water (3-4% difference). For example, coagulated water exposed to a Gt of 82,000 resulted in a 47% lower MFI when the coagulated water was fed directly to the stirred cell unit (no feed reservoir employed). This suggests that flocs created at a Gt of 82,000 are somewhat more affected by additional Gt (9 %), indicating that they probably break, creating a more dense cake on the membrane surface, resulting in a higher MFI. The opposite effect was observed when a lower Gt of 46,200 was employed, as the MFI was 16% higher when the coagulated water was filtered directly through the stirred cell (no feed reservoir). The observed facts indicate that coagulant flocs created under a Gt of 46,000 are positively

Table 4.6: Effect of prefiltered, coagulated canal water (5mg/l effective Fe 3+), Gt and Feed Reservoir on filtration parameters at a TMP of 0.1MPa and a pH of 7.7


42

affected by additional Gt induced by the feed reservoir as they probably agglomerated further and do not break up. From the regression lines computed from the ?t/ ?V over V plots show reasonable linearity in terms of R squared. From Figure 4.4, it can be observed that at the start of filtration, none of the typical pore blocking mechanisms was observed. On the contrary, the resi stance decreased rapidly in the initial stages of filtration (first 8 seconds), and reached a minimum at ca. 10 seconds. Thereafter, linear behaviour was observed suggesting that cake filtration occurred. No real explanation exists for the decreasing resistance at the start of filtration, and it is assumed that this is an artefact of the start-up process e.g. the presence of air in the membrane. No distinct conclusion regarding the impact of Gt on TOC removal was observed in Table 7.2– Appendix. The average DOPC removal was 16.2% with 5mg/l effective Fe 3+.

4.4 Effect of Gt (RM G) and Coagulant Dose on the MFI The effect of 2 different Rapid Mixing conditions (a t of 9,000 and 28,000) with the same SM Gt value (21,000), leading to a total Gt of 30,000and 49200, respectively was assessed. The coagulant dose ranged from 0, 2, and 5mg/l effective Fe3+ (see Table 4.7 for experimental conditions). All samples were filtered directly through a 0.1um PVDF microfiltration membrane in a stirred cell, without utilizing the Feed Reservoir . The pH of the feed water was adjusted prior to coagulation in order to result in a final pH of 7.0 (after coagulation). The recording interval of the dig ital balance was set to 4 seconds. The quality of the prefiltered, uncoagulated canal water is displayed in Table 4.8, where the TOC, TSS, pH & Fe3+ was 15.52 mg/l, 5.2 mg/l, 7.7, and 0.36mg/l, respectively.


[mg/l] pH G [s-1] t [s] Gt RM G [s-1] t [s] Gt SM Gt

total 0 7.02 150 60 9000 35 600 21000 30000 2 7.02 150 60 9000 35 600 21000 30000 5 7.02 150 60 9000 35 600 21000 30000

0 7.02 470 60 28200 30 600 21000 49200 2 7.02 470 60 28200 30 600 21000 49200 5 7.02 470 60 28200 30 600 21000 49200

Raw Water Source: Delft Canal, prefiltered TOC 15.52 mg/l Fe 0.36 mg/l DOC 15.19 mg/l Fe 0.01 mg/l

pH 7.7 TSS 5.2 mg/l

Table 4.7: Coagulation conditions for coagulated, prefiltered canal water at a pH of 7.7

Table 4.8 Water quality data for uncoagulated, prefiltered canal water


43

6.0E+05

8.0E+05

1.0E+06

1.2E+06

1.4E+06

1.6E+06

1.8E+06

0.0E+

002.0

E-05

4.0E-0

5

6.0E-0

58.0

E-05

1.0E-0

4

1.2E-0

4

1.4E-0

4

1.6E-0

41.8

E-04


?t/

?V

[s/

m3]

dose 5mg, Gt 49k

dose 5mg, Gt 30k

dose 2mg, Gt 30k

dose 2mg, Gt 49k

dose 0mg, Gt 30k

dose 0mg, Gt 49k

The results of varying Gt and coagulant dose in terms of MFI are displayed in Figure 4.5. The corresponding MFI values computed from the slope of the ?t/ ?V over V plots are presented in Table 4.9. From Figure 4.5, the linear plots suggest that cake filtration without cake compression is occurring for all coagulant doses and Gt values. Pore blocking could not be observed from any of the ?t/ ?V over V plots. From Figure 4.5 it can be seen that higher coagulant doses consistently resulted in a higher MFI. For example, the MFI increased from 5.93E+08 to 2.97E+09 (400 %) and from 5.51E+08 to 3.53 E+09 (540 %) as the coagulant dose increased from 0 to 5 mg Fe3+ at a Gt of 30,000 and 49.000, respectively. This may be attributed to the fact that the addition of 2-5 mg/l coagulant increased the resistance of the existing colloids and macromouecules in the water, irrespective of the Gt employed. In this case the optimum coagulant dose may have been lower than 2 mg/l. Increasing the Gt from 30,000 to 49,200 (difference in RM Gt 19200, increase in 213%) decreased the MFI by both by 7 % for a coagulant dose of 0 and 2mg/l, respectively. In the case of the 0mg/l coagulant dose, the lower MFI at the higher Gt value of 49,200 may be attributed to an increase in the size some particles and colloids in the canal water due to the additional energy in the system. Where else for the coagulant dose of 2 mg/l, the 7% lower MFI may be due to the better dispersion of the coagulant when the G was increased from 150 (Gt of 30,000) to 470 s-1 (Gt of 49,200) during rapid mixing. As a consequence, initial floc formation may have been promoted, resulting in

Figure 4.5: Effect of RM Gt and dose on MFI of prefiltered canal water with variation in coagulant dose (0, 2, and 5mg of Fe3+ per litre) and a TMP of 0.1MPa at a pH of 7.7


44

larger flocs and a more permeable cake l ayer. For the higher coagulant dose of 5 mg/l Fe3+, the higher G (470 s-1 v’s 150 s-1) may have resulted in faster dispersion of the coagulant, but prolonged Gt induction appeared to cause the flocs to collapse resulting in a higher MFI under these conditi ons.

coagulant dose

Gt total R square MFI Rm Rm' Rm/Rm'

[mg/l] Fe 3+ RM+SM [m-1] [m-1] 0 30000 0.79 5.93E+08 2.06E+11 1.80E+11 1.1 2 30000 0.95 2.42E+09 1.78E+11 1.71E+11 1.0 5 30000 0.76 2.97E+09 1.74E+11 1.85E+11 0.9 0 49200 0.77 5.51E+08 2.03E+11 1.68E+11 1.2 2 49200 0.94 2.24E+09 1.78E+11 1.84E+11 1.0 5 49200 0.97 3.53E+09 1.73E+11 1.73E+11 1.0

Gt Coag. Dose DOC res Fe+ TOC removal

Fe3+ [mg/l] [mg/l] [mg/l] [%] 30000 0.00 14.563 0.04 6.20 30000 2.00 14.135 0.02 8.95 30000 5.00 12.697 0.01 18.21 49000 0.00 14.348 0.00 7.58 49000 2.00 12.612 0.02 18.76 49000 5.00 13.908 0.00 10.41

Raw water and Permeate water quality in terms of DOC removal capacity and residual iron for these tests are displayed in Table 7.3 - Appendix. Results show that removal capacity for TOC is rather limited with a maximum of 18.76% for the sample at a Gt of 49000 and a coagulant dose of 2mg/l . Detection of residual Fe for all tests shows very low, indicating that the coagulant is retained in the membrane, since al so the Fe content in DOC of a blank sample was already at the lower detection limit.

4.5 Effect of Gt (SM t) and Coagulant Dose at Constant pH on MFI The influence of 5 different Slow Mixing Gt values ranging from 0 to 126000 with a RM Gt value of 14100 and/or 940), combined with a coagulant dose ranging from 0 to 10 mg/l of effective Fe 3+ on filterability and permeate constitution for prefiltered canal water was assessed (Table 4.11). A quantity of 180ml of prefiltered (coagulated and uncoagulated) canal water was filtered using a Stirred Cell under pressure (no Feed Reservoir was used). The pH of the feed water was adjusted prior to coagulation in order to result in a

Table 4.9: MFI; Effect of RM Gt and dose on MFI of prefiltered canal water with variation in coagulant dose (0, 2, and 5mg of Fe3+ per litre) and a TMP of 0.1MPa at a pH of 7.7

Table 4.10: Permeate Quality; Effect of RM Gt and dose on MFI of prefiltered canal water with variation in coagulant dose (0, 2, and 5mg of Fe3+ per litre) and a TMP of 0.1MPa at a pH of 7.7


45

final pH of 8 after coagulation. Tests were perfor med at ambient temperature (21°C), and a TMP of 0.1MPa. The recording interval during the filtration process was 4 seconds.

rapid mixing slow mixing Gt total G [s-1] t [s] Gt RM G [s-1] t [s] Gt SM 470 20 9400 0 0 0 9400 470 30 14100 35 120 4200 18300 470 30 14100 35 660 23100 37200 470 30 14100 35 1800 63000 77100 470 30 14100 35 2400 84000 98100 470 30 14100 35 3600 126000 140100

TOC 18.44 mg/l Fe 0.28 mg/l DOC 16.03 mg/l Fe 0.06 mg/l pH 7.98

Water quality analysis for the prefiltered, uncoagulated canal water, displayed in Table 4.12 resulted in 18.44 mg/l TOC, a pH of 7.98, and Fe of 0.28mg/l In Figure 4.7, the impact of coagulant dose on MFI for each Gt value is presented. A dose of 1mg/l (effective Fe3+) resulted in a minimum MFI (5.89E+08 - 2.28E+09) for all the Gt values evaluated. Reducing the coagulant dose to 0.5mg/l effective Fe3+ caused the MFI to increase by 169% – 224% at a Gt of 9400 and 140100, respectively. From Figure 4.7 it is also observed that the influence of Gt on coagulant dose is greatest at a low dose of 0.5mg/l effective Fe 3+, as the greatest variation in MFI can be observed. This variation reduced as the coagulant dose increases. For example, from Table 4.13 it can be observed that for a coagulant dose of 0.5 mg/l, a variation of 292% was observed in the MFI at a Gt of 9400 compared with a Gt of 140,000). At a coagulant dose of 15mg/l, the variation in MFI reduced to 112% at a Gt of 9400 compared with a Gt of 140,000). Thus, for Delft canal water the Gt imposed was very critical at low coagulant doses (0.5 1 mg/l). At a coagulant dose of 0.5 mg/l, the rapid mixing conditions were the same for all tests (and imposed Gt values). Thus, the large observed differences in the MFI were probably due to the slow mixing conditions, and in particular the slow mixing (flocculation) time which was as low as 0 at a Gt of 9,400 and as high as 3600s at a Gt of 140,100. The short flocculation times (at the low Gt values) may have resulted in smaller flocs and a less permeable cake compared to the flocs formed at longer flocculation times (e.g. 140,100). At the higher coagulant dose of 15 mg/l, the vari ations in the MFI as a function of

Table 4.11: Coagulation conditions with variation of Gt and coagulant dose at constant pH 8



46

the imposed Gt value was lower (292% at 0.5 mg/l versus 112% at 15mg/l). This may be attributed to the fact that the higher coagulant concentration (15 mg/l v’s 0.5 mg/l) compensated partly for the shorter flocculation time at the lower Gt values (0s at a Gt of 9,400 and 3600s at a Gt of 140,100). Thus, for Delft canal water the Gt (flocculation time) was less critical at a high coagulant dose (>15 mg/l).

5.0E+08

1.0E+09

1.5E+09

2.0E+09

2.5E+09

3.0E+09

3.5E+09

4.0E+09

0.1 1.0 10.0

100.0

effective dose Fe 3+ [mg/L]

MFI

[s/

m6]

9,40018,30037,20077,10098,100140,100

4.0E+08

9.0E+08

1.4E+09

1.9E+09

2.4E+09

2.9E+09

3.4E+09

3.9E+09

1,000

21,000

41,000

61,000

81,000

101,00

0121

,000

141,000

Gt

MFI

[s/

m6]

0.0mg/l Fe 3+ 0.51.0 2.05.0 15.0Linear (0.5) Linear (15.0)

Above a coagulant dose of 1mg/l, the MFI increased with increasing coagulant dose. (For some of the Gt equivalent lines a local maximum at the dose of 2mg/l can be observed (Gt 18300 and 9400), and for other Gt equivalents this increase in MFI was linear up to the maximum dose of 15mg/l effective Fe3+.). As mentioned earlier the optimum coagulant dose should result in the lowest MFI. High Coagulant doses (15gm/l) in combination with Gt values between 9400 and 140100 result in high MFI values. An explanation of the phenomenon of higher coagulant dose leading to higher MFI may be found in the fact that increasing the coagulant dose is related with an increasing cake resistance. This phenomenon was also observed by (Tabatabai, 2007) who brought Polyaluminum chloride (PACl) into solution with e.g. Delft tap water. For coagulant doses from 1ppm to 5ppm an increase in resistance for the coagulant solution was observed compared to uncoagulated tap water. This study used the same hydrophilic PVDF 0.1µm microfiltration membrane in a stirred cell with a constant TMP of 0.1MPa in dead end mode. In this study, it appeared that coagulant primarily added to the filtration resistance, with the exception of the 1 mg/l coagulant dose at a Gt of 140,000. In that case, the combination of coagulant

Figure 4.6: MFI as function of Gt value for variation of coagulant doses 0, 0.5, 1, 2, 5, 10m/l of effective Fe 3+ at a constant pH of 8 (TMP=0.1 MPa)

Figure 4.7: MFI as a function of coagulant dose for variation of SM Gt (9400-140100) and coagulant dose (0, 0.5, 1, 2, 5, 15) at constant pH of 8 (TMP=0.1MPa)


47

dose and flocculation conditions produced water similar in terms of MFI, to prefiltered canal water. Another possible explanation of high doses leading to higher MFI is that next to the agglomerations of dissolved and suspended solids, also precipitation of metal ions in the membrane occurs which adds to the resistance of the membrane. A further explanation could be that residual metal ions present in the filter cake and membrane foul the (Laîné et al., 2003) reported a case of (irreversible) inorganic matter fouling of UF membranes at low temperature and (<10C) and low turbidity and TOC attributed to a low permeable, silicon rich ferric gel adhesion to the membrane. The ferric gel adhesion could be related to the surface charge of the membrane. From Figure 4.6 and in Table 4.13 in general the MFI decreased as the Gt increased, and the biggest decrease in MFI was observed low coagulant doses of 0.5 to 5mg/l of effective Fe3+. For example the MFI decreased from 2.3E+09 to 5.9E+08, from 2.5E+09 to 1.2E+09 and from 3.0E+09 to 1.8E+09 for the coagulant dose of 1, 2, and 5 ppm Fe3+ respectively. For the high coagulant doses (> 15mg/l coagulant), almost no change in MFI was observed as a function of Gt (decrease in MFI was 112% from a Gt of 9,400 compared to a Gt of 140,100). The MFI of uncoagulated prefiltered canal water increased with increasing Gt (% increase in MFI was 228% from a Gt of 18,300 compared to a Gt of 98,100), probably as a result of shearing of small particles at the higher Gt values.

Dose in mg/l 0.0 0.5 1.0 2.0 5.0 15.0 9,400 3.85E+09 2.28E+09 2.49E+09 3.00E+09 3.65E+09 18,300 5.83E+08 3.49E+09 2.03E+09 2.75E+09 2.67E+09 3.51E+09 37,200 7.20E+08 2.78E+09 1.25E+09 2.20E+09 2.21E+09 3.35E+09 77,100 5.87E+08 2.12E+09 1.57E+09 1.65E+09 2.45E+09 3.22E+09 98,100 1.33E+09 8.45E+08 7.48E+08 1.56E+09 2.30E+09 3.31E+09

Gt

140,100 5.73E+08 1.32E+09 5.89E+08 1.25E+09 1.83E+09 3.26E+09 A summary of the chemical analysis of particular permeate samples in terms of OC removal capacity, UVA254 adsorption, SUVA content and residual iron a trend is attached in Table 7.4, Annex Section.

4.6 Effect of Gt (SM t), Coagulant Dose and pH Variation on the MFI The influence of 3 different (Slow Mixing) Gt values (from 63,000 to 126,000) with the same RM Gt value (14,100), combined with varying coagulant doses (0, 1, and 5mg/l effective Fe 3+) on the filterability and permeate constitution was determined ( Table 4.14). The feed water pH was adjusted prior to coagulation in order to result in a final pH of 6, 7, and 8 after coagulation experiments. The tests were performed at ambient temperature, (21° C) and a TMP of 0.1MPa. The recording interval was set to 4 seconds.

Table 4.13: MFI and increase in MFI for variation of Gt and coagulant dose at constant pH of 8


48

rapid mixing slow mixing Gt total G [s-1] t [s] Gt RM G [s-1] t [s] Gt SM

470 30 14100 0 0 0 14100 470 30 14100 35 1800 63000 77100 470 30 14100 35 3600 126000 140100

TOC 18.44 mg/l Fe 0.28 mg/l DOC 16.03 mg/l Fe 0.06 mg/l pH 7.98

TSS 12.62 mg/l Water quality analysis for the prefiltered, uncoagulated canal water, displayed in Table 4.15 resulted in 18.44 mg/l TOC, a pH of 7.98, TSS of 12.62mg/l, and Fe of 0.28mg/l In Figure 4.8, the MFI as a function of coagulant dose and Gt for different pH values is presented. Coagulated canal water at a pH of 8 showed slightly lower MFI (e.g. for a Gt of 140100 4.64E+08 and 1.49E+09 at a dose of 1 & 5 mg/l, respectively) compared to a pH 7 (for a Gt of 140100 4.71E+08 and 2.00E+09 at a dose of 1 & 5 mg/l, respectively) and much lower than at pH 6 (3.53E+08 and 4.14E+09 at a dose of 1 & 5 mg/l, respectively), for all imposed Gt values.

14,10077,100

140,100

0.01.0

5.00.0

1.05.0

0.01.0

5.0

1.00E+08

1.00E+09

1.00E+10

MFI

Gt

pH 8

effctive Fe3+ [mg/L] pH 7pH 6

From Figure 4.8 and Table 4.17, the MFI increased by 474% and 180% when the imposed Gt was decreased from 140,100 to 14,100 for a coagulant dose of 1 and 5 mg/l, respectively (at pH 8). As explained previously, to increase the Gt from 14,000 to 140,100, the flocculation time (slow mixing) was increased from 0 to 3600 seconds, probably resulting in larger flocs and a more permeable filter cake, and thus a lower MFI.

Table 4.14: Coagulation conditions with variation of Gt, coagulant dose, and pH


Figure 4.8: MFI as a function of coagulant dose at pH 8, 7, and 6


49

Figure 4.10 show MFI as function of coagulant dose at different Gt values and pH. Identical Gt values are displayed in the same line style. There it can also be observed that Gt variation has more impact on MFI than the variation in pH, when comparing pH 7 and 8. A rather marginal increase in MFI (112%) occurred when the pH decreased from 8 to 7 for an imposed Gt of 77,000 at a coagulant dose of 1mg/l. A similar trend was observed fat a Gt value of 140,100 – the increase in MFI was even more marginal (101%) when the pH decreased 8 to 7. The gap gradually widens for the dose of 5mg/L for a change in Gt from 77k to 140k – the increase in MFI was 125% and 134%, respectively.

(a) (b) (c )

Higher MFI is most probably related to the fact the floe structure (porosity, density, cohesion force) differs as a function of these physical-chemical conditions in coagulation pH: Floc formed at the lowest pH and dose are reported to be denser and less porous than those formed at higher pH and dose (sweep coagulation). (Guigui et al., 2002)

1.0E+08

1.0E+09

1.0E+10

0 1 2 3 4 5


MF

I [s

/m6

]

pH 8 Gt 14100pH 8 Gt 77100pH 8 Gt 140100

1.0E+08

1.0E+09

1.0E+10

0 1 2 3 4 5


MF

I [s/

m6]

pH 7 Gt 14100pH 7 Gt 77100

pH 7 Gt 140100

1.0E+08

1.0E+09

1.0E+10

0 1 2 3 4 5


MF

I [s/

m6]

pH 6 Gt 14100pH 6 Gt 77100pH 6 Gt 140100

(a) (b) (c)

1.0E+08

1.0E+09

1.0E+10

1000

2100

0

4100

0

6100

0

8100

0

101000

121000

141000

Gt

MFI

[s/m

6]

dose 0 pH 8dose 1 pH 8dose 5 pH 8

1.0E+08

1.0E+09

1.0E+10

1000

21000

4100

0610

0081

000

101000

1210

00

141000

GtM

FI [

s/m

6]

dose 0 pH 7

dose 1 pH 7

dose 5 pH 7

1.0E+08

1.0E+09

1.0E+10

1000

21000

4100

061

000

81000

1010

00

1210

00

1410

00

Gt

MF

I [s/

m6]

dose 0 pH 6dose 1 pH 6dose 5 pH 6

Figure 4.9: MFI as a function of Gt at pH 8 (a), at pH 7 (b), at pH 6 (c)

Figure 4.10: MFI as a function of coagulant dose at different Gt values: at pH 8 (a), at pH 7 (b), at pH 6 (c)


50

Comparing similarities in trends at for each pH a pattern appearing for pH 7 and 8 was observed. The MFI increased strongly as the coagulant dose was increased from 0 to 5 mg/l. For example, increasing the coagulant dose from 0 to 1 mg/l at pH 8 and a Gt of 77100, resulted in an 200% increase in the MFI. The increase in MFI was less as the coagulant dose increased for 1 to 5mg/l (175%). For example an increment of 123%, 175%, and 322% was observed as the dose increased from 1 to 5mg/l at a Gt of 14100, 77100, and 140100 respectively at pH=8. Similar overall trends were observed for the pH of 7 (Table 4.16) . However the magnitude in the change in MFI was very high at pH=6 compared to pH=8

pH 8

14100 77100 140100 0.0 4.85E+08 4.85E+08 4.85E+08

454% 200% 96% 1.0 2.20E+09 227% 9.68E+08 209% 4.64E+08 474%

123% 175% 322%

Dose

5.0 2.70E+09 159% 1.69E+09 113% 1.49E+09 180% 556% 349% 308%

pH 7

14100 77100 140100 0.0 3.82E+08 3.82E+08 3.82E+08

639% 285% 123% 1.0 2.44E+09 225% 1.09E+09 231% 4.71E+08 519%

123% 195% 425%

Dose

5.0 3.00E+09 142% 2.12E+09 106% 2.00E+09 150% 787% 555% 524%

4.7 LC OCD Samples Analysis

Five Samples from the test determining the Effect of SM t, coagulant dose and pH Variation on MFI were drawn for analysis of DOC content and constituents. Selection of the samples aimed at comparing between extremes. Therefore two samples with different pH and Gt, resulting in a wide distinction in MFI were selected (Figure 4.11). Further prefiltered, uncoagulated canal water as a benchmark to compare removal capacity and constituents.

• Dose 1mg/l, pH 6 Gt 14100 Permeate and Coagulant solution • Dose 1mg/l, pH 8 Gt 140100 Permeate and Coagulant solution

Table 4.16: General trend in change of MFI depending on Gt and coagulant dose at pH 7 and pH 8


51

The coagulant solution sample was drawn directly from the jar test after completion of the SM phase. The permeate was collected from the weighing container on the digital balance.

1.0E+08

1.0E+09

1.0E+10

10000 100000 1000000

GtM

FI [

s/m

6]

dose 1 pH 6 dose 1 pH 8

In comparison to the TOC and DOC previously measured the laboratory results show a deviation. Previous measured results were showi ng a TOC of 18.44mg/L and DOC of 16.03mg/L where else those from the analyzing laboratory indicated 15.10mg/L and 14.37mg/L respectively. This deviation might stem from storage of the feed water, or else from an error in measurement. In both selected cases the permeate shows a higher peak in the humic fraction, than the coagulant solution. This might be caused by leaching of organic carbon from the membrane

Test Chromatograms

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100

Retention Time in Minutes

rel.

Sig

nal R

espo

nse

P

P

P

Biopolymers

Humics (HS)

Building Blocks

LMW Acidsand HS

Neutrals

Project : Sampling date :

P

-- OCD-- UVD-- OND

Nitrate

Figure 4.11: LC OCD sample selection indicated in MFI over Gt plot

Figure 4.12: DOC fractions from LC OCD analysis (1)

Dose 1mg/l, pH 8 Gt 140100; Permeate

Dose 1mg/l, pH 6 Gt 14100; Coagulant solution

Dose 1mg/l, pH 6 Gt 14100 Permeate


52

Test Chromatograms

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100

Retention Time in Minutes

rel.

Sig

nal R

espo

nse P

P

P

Biopolymers

Humics (HS)

Building Blocks

LMW Acidsand HS

Neutrals

Project : Sampling date :

P

P

-- OCD-- UVD-- OND

Nitrate

As it can be observed from Figure 4.12 and Figure 4.13, no distinct variation in DOC constituents is present between the samples for a dose of 1mg/l, pH 6 Gt 14100 and for a dose of 1mg/l, pH 8 Gt 140100 for both the permeate and the coagulant solution. At the same time DOC removal is not substantial.

4.8 Removal Efficiency of Coagulation

Jar test experiments have been conducted with the objective to determine the effect of various coagulation conditions on (a) the amount of DOC/TOC and turbidity that can be removed per mg of coagulant, and (b) the residual coagulant in the supernatant. At the same time other parameters have been tracked (UVA254, pH after coagulation, and SUVA) The variables in the coagulation matrix were as follows: 6 different coagulant doses: 3, 5, 7, 9 and 11 mg Fe3+/L and a 0 mg sample 3 values of initial pH before commencement of jar test: 6.5, 8, 9.1 2 Temperatures: for each of the above stated initial pH values/steps a homogenous sample batch has been drawn from the canal and stored for max. 48hrs. Half of the sample was stored in closed co ntainers in the refrigerator (5°C), the other in laboratory environment (at 18°C). Jar test procedure for the chilled sample commenced within 30min after removal from the refrigerator. However, after completion of the coagulation procedure (51 minutes) the temperature was examined and a temperature rise of the chilled sample to 12°C has been observed. It is therefore reasonable that dispersion of the coagulant took place at 10°C Gt Value: for all the experiments Gt Value remained constant at 9k (in accordance with the diagram supplied for the jar test apparatus in use, see

Figure 4.13: DOC fractions from LC OCD analysis (2)

Dose 1mg/l, pH 8 Gt 140100; Coagulant solution

Uncoagulated, prefiltered canal water


53

Section Materials and Methods), by setting the rpm for rapid mixing to 100rpm for 60sec (=> Gt 9000). Flocculation comprised mixing at 30rpm for 20 minutes (=> Gt 26400) followed by a settling of 30 minutes.

removal efficiency: DOC at 10 C

0

2

4

6

8

10

12

6 7 7 8 8 9 9 10

pH after coagulation

Fe+

dose

[m

g/L]

37%

22%

5%

7%

8%

16%

17%

21%

25%

15% 6%

4%

0%

34%

28%

25%

10%

15%

20%

removal efficiency: DOC at 20 C

0

2

4

6

8

1 0

1 2

6 7 8 9pH after coagulation

Fe+

dose

[m

g/L]

32%

31%

19%

4%

13%

10%

13%

16%

19%

20%

9%

6%

5%

1%

20%

24%

10%

15%

Removal efficiency results for DOC at 10 and 20 degrees are displayed in Figure 4.15 and Figure 4.14 . A full set of results for all parameters is attached in ANNEX section . For all these comparison the restriction of different initial TOC levels applies, though it has been taken from one batch of feed water. From Figure 4.15 and Figure 4.14 it is visible that good removal rates of DOC is taking place at low pH and temperature. There are also less coagulant doses required with at a temperature of 10°C than at 20°C. For turbidity removal a relation to coagulant dose could only be found for low pH (6.5) from the obtained results. No specific improvement in Turbidity decrease by higher coagulant doses was visible for pH 8 and 9. Both show better removal of turbidity at higher pH.

4.9 Point of Diminishing Return Coagulant Dose

Coagulation in accordance with the Point of Diminishing Return coagulant dose concept (EPA, 1999) was performed. This was to determine the order of magnitude for ferric chloride dose required in conventional coagulation, flocculation and sedimentation to remove TOC up to the point of a diminishing level of return for added coagulant. Therefore Jar test with canal water and stepwise increasing doses were performed at 19°C on one batch of feed water.

Figure 4.14: DOC removal efficiency by coagulation at 20C

Figure 4.15: DOC removal efficiency from coagulation at 10C


54

56789

10111213

0 18 36 55 73 91 109

127

146 164 182

ferric chloride dose [mg/L]

TOC

res

idua

l [m

g/L]

Analytical resultsmodel

The PODR test concluded that a dose of 81mg/l of ferric chloride (16.7 mg/l Fe 3+) is reaching a diminishing return. Thus, from the modelled data from this dose the TOC content could be reduced from 12.18 mg/l to 8.71 mg/l, representing a removal capacity of 28%. Details of the testing conditio ns and result are attached in the Annex section.

4.10 Determination of Initial Membrane Resistance Rm

Referring to the discussion in Chapter 3 on initial membrane resistance, comparison of the initial membrane resistance from different feed water sources was evaluated. Two samples of DI water and 4 samples uncoagulated prefiltered canal water (from the same batch, 2 of them were pre-filtered with a 0.45 um filter) through each a freshly soaked and cleaned reference membrane (flat sheet Durapore Membrane, PVDF, Hydrophilic, nominal pore size 0.1µm)

3.5E+05

4.5E+05

5.5E+05

0.0E+

00

2.5E-0

5

5.0E-0

5

7.5E-0

5

1.0E-0

4

1.3E-0

4

1.5E-0

4

1.8E-0

4

2.0E-0

4

filtered volume [m3]

?t/

?V

[s/

m3]

unfiltered 1

prefiltered 2

prefiltered 1

unfiltered 2

MQ 1

MQ 2

2.0E+11

2.2E+11

2.4E+11

2.6E+11

2.8E+11

3.0E+11

3.2E+11

4 8 12 16 20 24 28 32 36

filtration time [s]

Rm

[1/

m]

prefiltered 1 prefiltered 2 unfiltered 1

unfiltered 2 MQ 1 MQ 2

The results revealed that deionised water had just a slightly lower Rm than feed water. This fact supports adhering to the approach of (Brauns et al., 2004) and (Ye et al., 2004), who do not consider clean membrane resistance from deionised water, but from an extrapolation of the cake filtration slope (? t / ? V

Figure 4.16: Result for PODR experiment done with prefiltered canal water at floating pH (from 7.91 for 0 dose to 6.7 at a dose of 182mg/l ferric chloride) and at a temperature of 19C

Figure 4.17: variation of initial membrane resistance Rm for different feed water computed from flux

Figure 4.18: initial membrane resistance Rm computed for different feed water from linear regression of cake filtration mechanism


55

plotted over V). These results are illustrated in Figure 4.17. For computation of the regression line the same amount of data points were considered for each of the samples. From Figure 4.17 the fluctuations of the recorded initial membrane resistance can be observed.

Vessel used R

square MFI Rm Rm' Rm/Rm'

[m-1] [m-1] 0.45 um filtered 0.57 1.38E+08 2.63E+11 2.48E+11 1.1 0.45 um filtered 0.22 1.34E+08 2.75E+11 2.14E+11 1.3

unfiltered 0.96 8.04E+08 2.60E+11 2.67E+11 1.0 unfiltered 0.62 1.38E+08 2.64E+11 2.51E+11 1.1

MQ 0.07 2.46E+07 2.31E+11 2.13E+11 1.1 MQ 0.00 2.49E+06 2.40E+11 2.25E+11 1.1

As prior stated the Rm’ is defined as the membrane resistance calculated from the least resistance, highest flux recording at the initi al filtration. Here also the ratio of both values is introduced. Table 4.17 gives evidence that those values calculated from flux are consistently lower than those from the linear regression of the ?t/?V over V plot. Conclusively it is noted that Rm’ is based on extremes, where else Rm is based on more reliable, averaged measurements.

Table 4.17: Comparison of results from different methods for computation of initial membrane resistance


56

5 Conclusions and Recommendations

In this study the effect of varying coagulation pre-treatment conditions on filtration behaviour through an unstirred cell were investigated. The coagulation was performed with ferric chloride, and conditions were determined by altering the coagulant dose, feed water pH and Gt induction. Filtration test were performed in an Amicon Stirred Cell in dead end and constant pressure mode (TMP=0.1 MPa). A hydrophilic PVDF 0.1 um microfiltration membrane was used for all the described tests. Prefiltered (122um) surface water from the Delft Westvest Canal was used as feed water source. The effect of coagulation conditions on membrane filtration conditions were assessed from collected permeates on an electronic balance. The time and weight data sets were further processed to yield the MFI. The MFI was utilized to observe filtration and fouling behaviour of modified feed water pre-treatment conditions. Comparison of results was based on objectives, and grouped in distinct sample batches. Feed and permeate water quality were analyzed upon organic carbon and removal capacity by pre coagulation and MF membrane filtration. Liquid Chromatograph Organic Carbon Detection sampling was used to analyse transient permeate samples upon NOM constituents for insight into the removal and fouling behaviour under certain pre-treatment conditions.

5.1 Conclusions

• The coagulant dose of 1mg/l Fe 3+ (equivalent to 4.84 mg/l FeCl3 6H2O), at pH8, by employing a Gt of 140100 results in the lowest MFI observed. That result was found lower in dose and higher in pH than suggested in the ferric chloride solubility diagram for sweep coagulation and charge neutralisation (Figure 5.1).^

• In the presence of coagulant, cake filtration was the dominant filtration mechanism observed with a Gt ranging from 9.400 to 140,100. In the absence of any coagulant, cake filtration was the dominant filtration mechanism observed when a very low Gt (~ 0) was employed with Delft canal water. Observation of cake compression was very likely in the absence of coagulant in combination with a very high Gt (~ 240,000), and this was most probably attributed to the shearing and disaggregating particles and/or colloids as a result of rapid mixing (G = 470 s-1) for 60 s.

• The additional Gt (maximum 7000, depended on flux) introduced by

the use of a feed reservoir in combination with the stirred cell did not have a major effect on the MFI of non-coagulated (3 - 4 % difference).

• In the presence of coagulant (5 mg/l), increasing Gt from 46,200 to

82,200 (by increasing the Slow Mixing (flocculation) time from 600 to


57

1800 s), reduced the MFI by 57%. Longer flocculation time (Gt) probably resulted in larger flocs, and a lower specific resistance of the filter cake, and a lower MFI.

• Increasing Gt from 30,000 to 49,200 (by increasing the G during Rapid Mixing from 150 to 470 s-1) reduced the MFI by 7% with a low coagulant dose of 2 mg/l, probably due to better dispersion of the coagulant, which may have promoted initial floc formation resulting in larger flocs and a more permeable cake. On the contrary, the MFI increased by 19%, when a higher coagulant dose of 5 mg/l was employed in combination with the G of 470 s-1 (increasing the Gt from 30,000 to 49,200). In this case, prolonged exposure of the (larger) flocs at this high G value probably resulted in floc breakage, and a higher MFI.

• Increasing and decreasing the coagulant dose from the local minimum

found at each of the tested Gt (from 9,400 to 140,100) increased the MFI consistently.

• The MFI decreased by 66% when the Gt was increased from 9,400 to

140,100 for a coagulant dose of 0.5 mg/l – this suggests that the Gt employed for (in-line) coagulation is very critical at low coagulant doses (<0.5 mg/l) Fe3+. The reduction in MFI was only 11% when the Gt was increased from 9,400 to 140,100 for a coagulant dose of 15 mg/l, suggesting that the flocculation time was much less critical at higher coagulant doses (> 15 mg/l).

Figure 5.1: Hydrolysis of Ferric chloride indicating favourable coagulation conditions found in terms of coagulant dose and pH

Range of Tested

Conditions

Favorable Conditions

Found


58

• Variation of pH between 7 and 8 has marginal impact on MFI. It is

much less influential than the dose and Gt. Nevertheless, higher pH consistently leads to lower MFI and a pH of 6 resulted in the highest MFI.

• Computation of MFI via the relation ? (? t/?V)*0.5/ ?V eliminates

ambiguity in data point selection and improves accuracy based on R square from regression analysis.

• The R square value from regression analysis of MFI computation can

be regarded as an index of accuracy when cake filtration is present. Accuracy of results in terms of high R square values was found mostly depending on the method of time series data processing.

5.2 Recommendations

• Evaluation of backwashable and non-backwashable components of fouling from particular coagulation and feed water conditions should be taken into account. A low MFI in the first filtration cycle does not necessarily lead to high rate of backwashable fouling. Neither does a low rate in fouling during the first cycle need to reflect similar behaviour in the following cycles, and an overall constant fouling rate.

• Most membrane filtration applications are equipped with inline

coagulation. Translation of Gt induction conditions from the Jar Test vessels to inline coagulation can be seen a priority target. Evaluation whether G and t are linear and or to which extend interchangeable might be one of the objectives.

Figure 5.2: Filtration and backwashing cycle in constant flux mode

TMP

Volume, Time

Cycle 1

Backwash 1

Cycle 2 Cycle n

CEB

Irreversible fouling

Reversible Backwashable

fouling

Backwash 2

Current issue: Single cycle

Further consideration: Multi cycles, backwashing


59

• An intermediate step to inline coagulation could be to experiment with rapid mix dispersion in the jar test machine, and slow (inline) mixing by different length of piping from the pressure vessel to the filtration cell.

• Shortcomings from reproducing hollow fibre lik e membranes with flat sheet are deviation in results as elaborated by (Howe et al., 2007). They reported that different fouling behaviour might be caused by difference in geometry.


60

6 Literature References

Amy, G. (2006) Hybrid low pressure membranes (LPM). UNESCO IHE

Institute for Water Education. AWWA (2005) Microfiltration and Ultrafiltration Membranes (M53). Bichii, A. (2006) Application of Ceramic Membranes for Surface Water treatment,

MSc Thesis , UNESCO - IHE, Delft. Boerlage, S. F. E. (2001) Scaling and Particulate Fouling in membrane systems ,

Wageningen. Boerlage, S. F. E., Kennedy, M., Tarawneh, Z., Faber, R. D., and b, J. C. S.

(2004) Development of the MFI-UF in constant flux filtration Desalination 161(2), 103-113.

Boerlage, S. F. E., Kennedy, M. D., Aniye, M. P., Abogrean, E., Tarawneh, Z. S., and Schippers, J. C. (2003) The MFI-UF as a water quality test and monitor Journal of Membrane Science 211(2), 271-289.

Brauns, E., Teunckens, D., Dotremont, C., HooP, E. V., Doyen, W., and Vanhecke, D. (2004) Dead-end filtration experiments on model dispersions: comparison of VFM data and the Kozeny-Carman model. Desalination 177, 303 - 315.

Carroll, T., King, S., Gray, S. R., Boltom, B. A., and Booker, N. A. (2000) The Fouling Of Microfiltration Membranes By NOM After Coagulation Treatment. Wat. Res. , 34(11), 2861-2866.

Cho, J., Amy, G., and Pellegrino, J. (1999) Membrane Filtration Of Natural Organic Matter: Initial Comparison Of Rejection And Flux Decline Characteristics With Ultrafiltration And Nanofiltration Membranes. Water Research., 33(11), 2517-2526.

Cho, M.-H., Lee, C.-H., and Lee, S. (2005) Effect of flocculation conditions on membrane permeability in coagulation–microfiltration. Desalination, 191, 386–396.

Davis, R. H., and Grant, D. C. (1992) Theory for dead end microfiltration. In: Membrane Handbook, W. S. Ho and K. K. Sirkar, eds., Van Nostrand, Reinhold, NY.

Decisions, A. Q., 1235 Wendover Road, S., and Rosemont, P. (2007) www.. Dong, B. Z., Chena, Y., Gao, N. Y., and Fana, J. C. (2006) Effect of pH on UF

membrane fouling Desalination 195(1-3), 201-208. EPA, U. S. E. P. A. (1999) Enhanced Coagulation and Enhanced Precipitative

Softening Guidance Manual. Disinfectants and Disinfection Byproducts Rule (DBPR), 237.

Farahbakhsh, K., and Smith, D. W. (2002) Performance comparison and pretreatment evaluation of three water treatment membrane pilot plants treating low turbidity water. Journal of Environmental Engineering and Science 1: 113–122 (2002), 1, 113-122.

Furukawa, D. H. (2002) Global Status of Microfiltration and Ultrafiltration Membrane Technology. National Water Research Institue Briefing, 8.


61

Guigui, C., Rouch, J. C., Durand-Bourlier, L., Bonnelye, V., and Aptel, P. (2002) Impact of coagulation conditions on the in-line coagulation/UF process for drinking water production. Desalination 147(1-3), 95-100.

Guizard, C., Ayral, A., and Julbe, A. (2002) Potentiality of organic solvents filtration with ceramic membranes. A comparison with polymer membranes. . Desalination, 147, 275 - 280

Hendricks, D. (2006) Water Treatment Unit ProcessesPhysical and Chemical, CRC Press, Taylor & Francis Group,

Howe, K. J., Marwah, A., Chiu, K.-P., and Adham, S. S. (2007) Effect of membrane configuration on bench-scale MF and UF fouling experiments. Water Research, (2007), doi:10.1016/j.watres.2007.05.025 in press.

Hsieh, H. P. (1996) Inorganic Membranes For Separation And Reaction. Membrane Science and Technology, M. Wessling and K. Sirkar, eds., Elsevier, Alcoa Technical Center, Alcoa Center, PA, USA.

Jang, H.-N., Lee, D.-S., Park, M.-K., Moon, S.-Y., Cho, S.-Y., Kim, C.-H., and Kim, H.-S. (2006) Effects of the Filtration Flux and Pre-treatments on the performance of a Microfiltration Drinking Water Treatment System.

Judd, S. J., and Hillis, P. (2001) Optimisation of combined coagulation and microfiltration for water treatment Water Research 35(12), 2895-2904.

Jung, C.-W., Son, H.-J., and Kang, L.-S. (2006) Effects of membrane material and pretreatment coagulation on membrane fouling : fouling mechanism and NOM removal. Desalination , 197, 154–164.

Kennedy, M. (2006) Fouling in MF/UF systems. Membrane techology in Drinking and industrial water treatment, Delft, 300.

Kim, H.-C., Hong, J.-H., and Lee, S. (2006) Fouling of microfiltration membranes by natural organic matter after coagulation treatment: A comparison of different initial mixing conditions. Journal of Membrane Science , 283(1-2 ), 266-272

Konieczny, K., Bodzek, M., and Rajca, M. (2006) A coagulation –MF system for water treatment using ceramic membranes. Desalination 198 92-101.

Laîné, J.-M., Campos, C., Baudin, I., and Janex, M.-L. (2003) Understanding Membrane Fouling: a review of over a decade of research Water Science and Technology: Water Supply, 3(5-6), 155-164.

Lee, N., Amy, G., Croué, J.-P., and Buisson, H. (2004) Identification and understanding of fouling in low-pressure membrane (MF/UF) filtration by natural organic matter (NOM) Water Research 38(20), 4511-4523.

Lee, S. A., Fane, A. G., Amal, R., and Waite, T. D. (2003) The Effect of Floc Size and Structure on Specific Cake Resistance and Compressibility in Dead-End Microfiltration. Separation Science and Technology, 38(4), 869 - 887.

Lerch, A., Loi-Brügger, A., and Gimbel, R. (2006) R&D in the field of water supply and waste water treatment under regional conditions Part I: Drinking water, Vol. 2: Recommendations . Bundesministerium für Bildung und Forschung (BMBF), 73170 Bonn.


62

Lerch, A., Panglisch, S., Buchta, P., Tomita, Y., Yonekawa, H., Hattori, K., and

Gimbel, R. (2005) Direct river water treatment using coagulation/ceramic membrane microfiltration. Desalination 179(1-3), 41-50.

Letterman, R. D. (1999) Water Quality and Treatment. A. W. W. Association, ed., McGraw Hill, Inc., NY.

Liu, C., Caothien, S., J. Hayes, Caohuy, T., and Otoyo, T. (2001) Membrane Cleaning: from Art to Science. Proceedings of Membrane Technology Conference, San Antonio, TX, USA, March 4-7 2001.

Loi-Brügger, A., Panglisch, S., Buchta, P., Hattori, K., Yonekawa, H., Tomita, Y., and Gimbel, R. (2006) Ceramic membranes for direct river water treatment applying coagulation and microfiltration. Water Science & Technology: Water Supply, 6(4), 89-98.

Loi-Brügger, A., Panglisch, S., Buchta, P., Hattori, K., Yonekawa, H., Tomita, Y., and Gimbel, R. (2006a) Ceramic Membranes for Direct River Water Treatment Applying Coagulation and Microfiltration. IWA World Water Congress and Exhibition , Beijing, China

Mallevialle, J., Odendaal, P. E., and Wiesner, M. R. (1996) Water Treatment Membrane Processes. AWWA and AWWA Research Foundation

Mo, L., and Huang, X. (2003) Fouling characteristics and cleaning strategies in a coagulation-microfiltration combination process for water purification Desalination 159(1), 1-9.

Pikkarainen, A. T., Judd, S. J., Jokela, J., and Gillberg, L. (2004) Pre-coagulation for microfiltration of an upland surface water Water Research 38(2), 455-465

Schippers, J. C., and Verdouw, J. (1980) The modified fouling index, a method of determining the fouling Characteristics of water. Desalination, 32, 131-148.

Tabatabai, S. A. A. (2007) Effect of process conditions on the performance of in -line coagulation pretreatment of ultrafiltration, MSc Thesis , UNESCO IHE, Delft.

Thurman, E. M. (1985) Organic Geochemistry of Natural Waters, Martinus Nijhoff/Dr Junk W Publishers., Dordrecht.,

Ye, Y., Clech, E. L., Chen, V., Fane, A. G., and Jefferson, B. (2004) Fouling mechanisms of alginate solutions as model extracellular polymeric substances. Desalination , 175, 7-20.

Yonekawa, H., Tomita, Y., and Watanabe, Y. (2004) Behavior of micro-particles in monolith ceramic membrane filtration with pre-coagulation. Water Science and Technology 50(12 ), 317-325.

Zularisam, A. W., Ismail, A. F., and Salim, R. (2006) Behaviours of natural organic matter in membrane filtration for surface water treatment - a review Desalination 194(1-3), 211-231.


63

7 Annexe

7.1 Rpm – Gt conversion diagram for the Jar test unit

7.2 Water quality analysis

7.2.1 Permeate Quality for 4.1.1

Table 7.1: permeate water quality from the effect of Gt on prefiltered canal water (no coagulant) on Filtration parameters at a TMP of 0.1MPa and a pH of 7.7


64

feed reservoir Gt

Coag. Dose DOC

TOC removal

res Fe+

used Fe3+

[mg/l] [mg/l] [%] [mg/l] yes 244k # 0.00 16.593 18.76 0.06 no 244k # 0.00 17.553 14.06 0.04

yes 0 0.00 17.076 16.39 0.05 no 0 0.00 17.248 15.55 0.04

# In this setup the additional Gt stemming from the Feed Reservoir and the piping to the Stirred Cell were not considered in the compilation in Table 7.1. Additional Gt induced by the Feed Reservoir is elaborated in the annex of this report.

7.2.2 Permeate Quality for 4.1.2

Permeate Quality Feed

reservoir Gt Coag.

Dose DOC TOC

removal res Fe+

used Fe3+

[mg/l] [mg/l] [%] [mg/l] yes 46200 # 5.00 15.355 23.33 0.06 no 46200 5.00 16.502 17.61 0.02

yes 82200 # 5.00 16.003 20.10 0.04 no 82200 5.00 15.927 20.48 0.04

# In this setup the additional Gt stemming from the Feed Reservoir and the piping to the Stirred Cell were not considered in the compilation in Table 7.2. Additional Gt induced by the Feed Reservoir is elaborated in the annex of this report.

7.2.3 Permeate Quality for 4.1.3 (variation of RM G and dose)

Gt Coag. Dose DOC res Fe+ OC removal

Fe3+ [mg/l] [mg/l] [mg/l] [%] %

increment 30000 0.00 14.563 0.04 6.20 30000 2.00 14.135 0.02 8.95 8.95 30000 5.00 12.697 0.01 18.21 9.27

49000 0.00 14.348 0.00 7.58 49000 2.00 12.612 -0.02 18.76 18.76 49000 5.00 13.908 0.00 10.41 -8.35

Table 7.2: permeate water quality coagulated canal water (5mg/l effective Fe 3+), Gt and Feed Reservoir on filtration parameters at a TMP of 0.1MPa and a pH of 7.7

Table 7.3: permeate water quality coagulated canal water (0, 2, 5mg/l effective Fe 3+), variation of RM G with constant t on filtration parameters at a TMP of 0.1MPa and a pH of 7.7


65

7.2.4 Permeate Quality for 4.1.4 (variation of SM t and dose)

Gt Coag. Dose pH

UVA 254 DOC SUVA res Fe+

OC removal

Fe3+

[mg/l] [cm-

1] [mg/l] [L/mg.m] [mg/l] [%] 9,400 0.00 0.00

18,300 0.00 8.01 0.443 16.252 2.73 0.05 11.85 37,200 0.00 8.02 0.438 15.180 2.89 0.05 17.66 77,100 0.00 8.02 0.436 16.166 2.70 0.03 12.32 98,100 0.00 8.04 0.436 16.506 2.64 0.09 10.47 140,100 0.00 8.06 0.437 16.073 2.72 0.04 12.82

Gt Coag. Dose pH


OC removal

Fe3+

[mg/l] [cm-

1] [mg/l] [L/mg.m] [mg/l] [%] 9,400 5.00 8.00 0.372 13.882 2.68 0.03 24.70

18,300 5.00 8.10 0.375 14.422 2.60 0.02 21.77 37,200 5.00 8.08 0.372 14.360 2.59 0.02 22.11 77,100 5.00 8.10 0.369 14.340 2.57 0.02 22.22 98,100 5.00 8.11 0.370 14.470 2.56 0.02 21.52 140,100 5.00 8.08 0.383 14.306 2.68 0.01 22.41

Gt Coag. Dose pH


OC removal

Fe3+

[mg/l] [cm-

1] [mg/l] [L/mg.m] [mg/l] [%] 9,400 15.00 8.45 0.324 13.464 2.41 0.03 26.97

18,300 15.00 8.13 0.306 13.108 2.33 0.02 28.90 37,200 15.00 8.04 0.309 13.272 2.33 0.00 28.01 77,100 15.00 8.08 0.313 13.751 2.28 0.01 25.42 98,100 15.00 8.02 0.303 13.349 2.27 0.01 27.59 140,100 15.00 8.07 0.311 13.773 2.26 0.01 25.30

Table 7.4: permeate water quality coagulated canal water (0, 2, 5mg/l effective Fe 3+), variation of RM G with constant t on filtration parameters at a TMP of 0.1MPa and a pH of 7.7

© TECHNEAU September 2007

66

7.3 Sample Spreadsheet Computation (4.1.2)

t Time Weight V [m3]

dQ [m3/s]

J25 [l/m2]

dJ25 [m3/m2.s]

dJ25 [L/m2.h] J/Jo Rt Rc dt/dV

[s/m3] t/V

[s/m3]

6/19/07 15:53:37 0.010 2 6/19/07 15:53:39 0.280 2.81E-07 1.35E-07 1.08E-01 5.19E-05 1.87E+02 0.09 1.97E+12 1.79E+12 7.1E+06 7.1E+06

4 6/19/07 15:53:41 2.690 2.70E-06 1.21E-06 1.03E+00 4.63E-04 1.67E+03 0.80 2.21E+11 4.40E+10 8.3E+05 1.5E+06

6 6/19/07 15:53:43 5.350 5.36E-06 1.33E-06 2.06E+00 5.11E-04 1.84E+03 0.88 2.00E+11 2.32E+10 7.5E+05 1.1E+06 8 6/19/07 15:53:45 8.100 8.12E-06 1.38E-06 3.11E+00 5.28E-04 1.90E+03 0.91 1.93E+11 1.67E+10 7.3E+05 9.9E+05

10 6/19/07 15:53:47 11.110 1.11E-05 1.51E-06 4.27E+00 5.78E-04 2.08E+03 1.00 1.77E+11 0.00E+00 6.6E+05 9.0E+05

Rm’[1/m] Jo SUMMARY OUTPUT

1.77E+11 2.08E+03 Regression Statistics Multiple R 0.980687 MFI specific A R Square 0.961746

3.636E+09 1.22E+15 Adjusted R Square 0.961352 Computed from regression Standard Error 68338.78 a Rm[1/m] Observations 99

6.03E+05 1.61E+11 ANOVA df 5.50E+05 Regression 1

delta a delta Rm Residual 97 5.24E+04 Total 98

R square Coefficients 0.96 Intercept 550312.5

Table 7.5: Sample calculation for filtration parameters

Sample spreadsheet calculation for 4.1.2

tttWeightWeight

dQ∆−

−=

**1000*)()(

12

12

ρ [m3/s]


67

7.4 Sample plots of (4.1.2)

1000

1200

1400

1600

1800

2000

2200

0 10 20 30 40 50 60

Filtered Volume [L/m2]

J25

[L/m

2.h

]

Gt 46000, vessel used

Gt 46000, no vesselusedGt 82000, vessel used

Gt 82000, no vesselused

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 10 20 30 40 50 60

Filtered Volume [L/m2]

J/Jo

Gt 46000, vesselused

Gt 46000, novessel used

Gt 82000, vesselused

Gt 82000, novessel used

(a) (b)

7.0E+05

7.5E+05

8.0E+05

8.5E+05

9.0E+05

9.5E+05

1.0E+06

1.1E+06

1.1E+06

1.2E+06

1.2E+06

0.0E+

00

2.5E-0

5

5.0E-0

5

7.5E-0

5

1.0E-0

4

1.3E-0

4

1.5E-0

4

1.8E-0

4


t/V

[s/m

3]





0.0E+00

2.5E-01

5.0E-01

7.5E-01

1.0E+00

1.3E+00

1.5E+00

1.8E+00

2.0E+00

0.00E

+00

2.00E+

01

4.00E

+01

6.00E

+01

8.00E+

01

1.00E

+02

1.20E+

02

1.40E

+02

1.60E

+02

1.80E+

02

2.00E

+02

filtration time [t]

Rt/

Rm

[ ]

Gt 46000, vesselusedGt 46000, novessel usedGt 82000, vesselusedGt 82000, novessel used

(c ) (d)

Figure 7.1: Sample plots for 4.1.2 (a) Flux over filtered volume (b) Normalized flux over filtered volume (c) t/V over V (d) Rt/Rm over filtration time


68

7.5 Table of LC OCD Sample Analysis

Project: Siegfried Partitioning of Organic Carbon (OC) Chromatographic Fractionation of Organic Carbon (CDOC) (UV@254 nm)

sampl.date 1/0/1900 Approx. Molecular Weights in g/mol: >>20.000 ~1000 (see separate HS-Diagram) 300-500 <350 <350TOC=DOC+POC DOC=CDOC+HOC

Note: POC, hence TOC may be too low Bio- Humic Building Neutrals Acids Inorg. SUVA

TOC DOC POC HOC CDOC Polymers DON Subst. DON Aromaticity Mol-Weight Blocks Colloid.total OC dissolved particul. hydrophob. hydrophil. (Norg) (HS) (Norg) (SUVA-HS) (Mn) SAC (SAC/OC)

ppb-C ppb-C ppb-C ppb-C ppb-C ppb-C ppb-N ppb-C ppb-N L/(mg*m) g/mol ppb-C ppb-C ppb-C (m -1 ) L/(mg*m)

% TOC % TOC % TOC % TOC % TOC % TOC -- % TOC -- -- -- % TOC % TOC % TOC -- --

8.2.1 CS 13689 13650 39 1597 12054 425 4 8654 223 3.77 715 1701 1274 0 0.05 3.59

100 99.7 0.3 11.7 88.1 3.1 -- 63.2 -- -- -- 12.4 9.3 0.0 -- --

8.2.3 CS 13689 13602 87 1507 12095 398 14 8790 191 3.71 719 1674 1233 0 0.04 3.56 100 99.4 0.6 11.0 88.4 2.9 -- 64.2 -- -- -- 12.2 9.0 0.0 -- --

8.8.3 CS 14209 14238 -29 1909 12329 486 51 8998 327 3.72 720 1706 1138 0 0.08 3.55

100 100.2 -0.2 13.4 86.8 3.4 -- 63.3 -- -- -- 12.0 8.0 0.0 -- --

Blank 15097 14374 723 1949 12425 469 30 8886 270 3.93 716 1791 1279 0 0.16 3.23

No Label 100 95.2 4.8 12.9 82.3 3.1 -- 58.9 -- -- -- 11.9 8.5 0.0 -- --

8.2.1 F 12635 12320 315 436 11885 414 9 8627 244 3.63 685 1550 1294 0 0.13 3.53

100 97.5 2.5 3.4 94.1 3.3 -- 68.3 -- -- -- 12.3 10.2 0.0 -- --

8.2.3 F 13117 12924 192 923 12001 433 13 8641 236 3.77 684 1626 1302 0 0.04 3.51 100 98.5 1.5 7.0 91.5 3.3 -- 65.9 -- -- -- 12.4 9.9 0.0 -- --

8.8.3 F 13604 13610 -6 1193 12417 458 16 9042 275 3.70 697 1524 1393 0 0.03 3.43

100 100.0 0.0 8.8 91.3 3.4 -- 66.5 -- -- -- 11.2 10.2 0.0 -- --

8.8.1 CS 13840 13578 262 1289 12288 428 16 8663 236 3.59 678 1736 1461 0 0.12 3.45

100 98.1 1.9 9.3 88.8 3.1 -- 62.6 -- -- -- 12.5 10.6 0.0 -- --

8.8.1 F 12885 12862 23 929 11933 433 17 8289 253 3.75 726 1873 1339 0 0.03 3.51

100 99.8 0.2 7.2 92.6 3.4 -- 64.3 -- -- -- 14.5 10.4 0.0 -- --

Sample 10 n.n. n.n. n.n. n.n. n.n. n.n. n.n. n.n. n.n. -- n.n. n.n. n.n. n.n. n.n. n.n. 100 -- -- -- -- -- -- -- -- -- -- -- -- -- -- --

}

pH 8, 1mg, Gt 140k Supernatent

Uncoagulated Prefiltered canal water

pH 8, 1mg, Gt 140k permeatet

pH 6, 1mg, Gt 14k Supernatant Permeate


69

7.6 Point of Diminishing Return Coagulant Dose: Water Quality Analysis

COAGULANT MIXING CONDITIONS G Gt Value 4.84

Coagulant tested FeCl3 * 6H2O Rapid mix rpm 170 320 19200

Stock solution concentration 13.65 mg/ml Rapid mix duration (sec.) 60 Flocculation rpm 40 40 72000 Flocculation duration (min.) 30 Settling duration (min.) 40 91200 temperature 19

Coag. Dose Dose pH NTU TOC TOC removal and increment NTU improvement FeCl3 * 6H2O

[mg/l] Fe3+

[mg/l] after jar test (supernatant) [mg/l] [mg/l] improvement

[%] [mg TOC

/mg Fe3+] improvement

[%] increment

0.0 0.0 7.91 7.6 12.18 0.00 0.00 18.2 3.8 7.71 2.7 10.70 1.48 12.16 -0.081382 64.47 64.47 36.4 7.5 7.53 2.5 10.60 0.10 12.98 -0.005459 67.11 2.63 54.6 11.3 7.39 2.7 9.78 0.82 19.68 -0.044893 64.47 -2.63 72.8 15.0 7.24 2.4 8.91 0.88 26.88 -0.048184 68.42 3.95 91.0 18.8 7.19 2.2 8.25 0.65 32.23 -0.035764 71.05 2.63

109.2 22.6 7.04 2.4 7.87 0.38 35.35 -0.020865 68.42 -2.63 127.4 26.3 6.99 2.3 7.76 0.11 36.25 -0.006044 69.74 1.32 145.6 30.1 6.90 2.1 7.13 0.63 41.44 -0.034717 72.37 2.63 163.8 33.8 6.81 2.1 7.29 -0.16 40.13 0.008725 72.37 0.00 182.0 37.6 6.70 1.9 6.41 0.88 47.35 -0.048291 75 2.63 81.0 16.7 8.73 28.32


70

7.7 Point of Diminishing Return Coagulant Dose: Model computation

The following chart shows the computation of the PODR coagulant dose model computation in accordance with the Enhanced Coagulation and Enhanced Precipitative Softening Guidance Manual issued by (EPA, 1999).

56789

10111213

0 18 36 55 73 91 109

127

146

164

182

ferric chloride dose [mg/L]

TOC

res

idua

l [m

g/L]

Analytical results

model

slope coagulant dose

-0.03 81.0 [mg/l] FeCl3 6H2O

16.7 [mg/l] Fe 3+ f (x) a b C squared

error

12.18 7.0981 0.0082 5.0818 1E-12 80.97 11.19 2E-01 10.34 7E-02 9.61 3E-02 8.98 6E-03 8.44 3E-02 7.97 9E-03 7.57 4E-02 7.22 8E-03 6.93 1E-01 Sum of Error 6.67 7E-02 0.634

8.73 condition a + C = 12.18

Figure 7.2: PODR coagulant dose for prefiltered uncoagulated canal water


71

7.8 Effect of Feed Reservoir on Gt The additional Gt introduced from employing the feed reservoir was computed by (Tabatabai, 2007), based on the Hagen-Poiseuille law for the calculation of flow of incompressible uniform viscous liquids through a cylindrical tube with constant circular cross-section. Gt induction from the feed reservoir is caused by a steel tube inside the reservoir, a plastic tube from the reservoir to the stillerd cell and an orifce inside the stirred cell, which the feed solution has to pass.

G-value ~ Time in Different Sections of the Experimental Setup

0.00

20.00

40.00

60.00

80.00

100.00

120.00

5.00

26.00

47.00

68.00

89.00

110.0

013

1.00

152.0

017

3.00

194.0

021

5.00

236.0

025

7.00

278.0

029

9.00

Time [s]

G [

s-1]

Plastic Tube Orifice Steel Cylinder

As can be seen from the Figure 7.3, the G-value for each of the components is computed separately. G decresases with continued filtration time due to the build up in resistance and decreasing flux.

Gt ~ Time throughout the Length of a Filtration Run

01000

2000300040005000600070008000

9000

5.00

26.00

47.00

68.00

89.00

110.0

0131

.0015

2.00

173.0

019

4.00

215.00

236.0

0257

.0027

8.00

299.0

0

Time [s]

Gt

[]

Gt values based on flux (flux not given here) throughout the length of the filtration from all three components are added and integratted over time. As it can be observed from Figure 7.4 , the Gt is depending on flux and after an increase to the maximum value of Gt of approximately 7000 and occurs approximately 5 minutes after the start of the filtration run.

Figure 7.3: Gt induction as a function of filtration time stemming from use of the feed reservoir at TMP=0.1MPa (with courtesy and adopted from (Tabatabai, 2007))

Figure 7.4:Gt induction as a function of filtration time stemming from use of the feed reservoir at TMP=0.1MPa (with courtesy, and adopted from (Tabatabai, 2007))

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