The Effect of Backwashing Procedures on Filter Ripening and General Effluent Quality

Transcription

1 The Effect of Backwashing Procedures on Filter Ripening and General Effluent Quality Feng Xue For NUS TUD Double M.Sc. Degree Program Hydraulics Engineering and Water Resources Management Date of Submission: 23 June 2011 Date of defence: 30 June 2011 Examination Committee: Prof. dr. ir. L. C. Rietveld Dr. ir. P. J. Visser Drs Petra Scholte ir. Petra S. Ross Assistant Prof. Zhou Zhi Delft University of Technology Section Sanitary Engineering Delft University of Technology Section Hydraulics Engineering Waternet Research Department Delft University of Technology Section Sanitary Engineering National University of Singapore Environmental Engineering Division Section of Sanitary Engineering, Department of Water Management Faculty of Civil Engineering and Geosciences Delft University of Technology Delft, the Netherlands 1 P a g e

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3 Abstract Almost all water treatment plants use sand filters to purify water. The sand grains in the filter capture and remove the suspended solids and other impurities from water. Water companies have been investing on research to optimize the performance of filters. Thus, the project FilterXpert was initiated by a group of Dutch water companies to gather expert knowledge and understand more about filter operations. As part of the FilterXpert activity, my thesis research was to investigate the effect of different backwashing procedures on the magnitude and duration of the ripening period, as well as on the general effluent quality from rapid sand filters. It has been observed in these experiments that higher flow velocities could shear off already-attached particles and induce early breakthrough. There was also evidence supporting the additional collectors theory. Moreover, pre-wash with water before the existing backwash procedure prevented dirt from accumulating in the lower part of the sand column, thus gave better effluent quality. On the other hand, the advantage of the collapse-pulsing theory was not observed from the experiments, while there was also no evidence to support the theory which assumes the supernatant water after backwashing gives the highest peak in ripening. Stimela software was used to match the experimental results and theoretically-predicted results, and the change of values in the parameters has reasonable explanations. An evaluation tool based on cost analysis has also been proposed to compare the different backwashing methods. Recommendations for further research has been given in this report too to have a better understanding of the effectiveness of the backwashing methods in order to find the optimal filter operation regime. 3 P a g e

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5 Preface This thesis research experiments have been conducted in the beautiful Waternet Cornelis Biemond treatment plant in Nieuwegein, for a duration of 3 months. The rest of the analysis work took place in Delft. I would like to firstly thank Prof Luuk Rietveld for his patience in helping me with so many questions. I am really grateful of him taking time off his busy schedule to have meetings with me. He is such a fatherly figure and so kind. Also, I would like to thank Petra Scholte for her guidance and assistance during the filter experiments. Thank you for changing your schedule for me so we could finish the experiments on time. In addition, thank Rene van der Aa for coming to the pilot plant when Petra could not be there. My gratitude to Waternet for giving me the chance to do experiments on their test filters. Moreover, thank Petra Ross for her help with Matlab and Stimela. Without her Matlab script, I would never be able to obtain decent results from Stimela. Thank TU Delft for giving me the opportunity to do this master thesis so that I am able to learn so much about filter operations! Also thank all the members of my examination committee for their time and support! 5 P a g e

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7 Contents Abstract... 3 Preface... 5 Contents... 7 List of Figures List of Tables : Introduction Background Definition and Objectives Approach and Report Layout : Theoretical Background of Filters Introduction Filtration Mechanism Design Options Filter Operation Set-up Filtration Theory Backwashing of a Rapid Sand Filter Filter Troubles General Problems Troubles Related to Ripening : Materials and Methods Hypotheses for Experiments Hypothesis Hypothesis Hypothesis Hypothesis Hypothesis General Information of the Pilot Plant Filters Analysis Equipment Delay in Re-starting of Turbidity Measurement Experimental Set-ups Influence of Velocity on Breakthrough Different Expansions and Expansion Durations Pre-Wash with Water Backwash Effect P a g e

8 3.4.4 Collapse-Pulsing Backwash Effect Draining of Supernatant Water : Results and Discussion Influence of Varying Flow Rates on Breakthrough General Observations Ripening Durations Amount of Dirt Coming Out During Ripening Peak Discussion Effect of Different Expansions during Backwash General Observations Amount of Dirt Coming Out During Ripening Peak Pressure Drop Changes in Horizontal Layers Discussion Experiments With and Without Pre-wash General Observations Amount of Dirt Coming Out During Ripening Peak Discussion Experiments With and Without Collapse-Pulsing General Observations Amount of Dirt Coming Out During Ripening Peak Discussion Experiments With and Without Draining of Supernatant Water General Observations Discussion Observations and Recommendations Sectional Conclusion : Stimela Model Purpose General Information Compromises Made Parameter Values found for Experiments Experiments with Different Expansions Experiments with and without Pre-Wash Experiments With and Without CP : Evaluation Tool of Backwash Procedures Parameter Determination Example Calculation P a g e

9 7: Conclusion : Reference : Appendix Stimela graphs for optimal parameters Lindquist Diagrams P a g e

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11 List of Figures Figure 1: Physical Removal of Impurities by Filter Grains (van Dijk, 2010) Figure 2: Typical Rapid Sand Filter ( 2010) Figure 3: Sieve Curve of Filter Medium (van Dijk, 2010) Figure 4: Progress of Filter Bed Resistance in Time (van Dijk, 2010) Figure 5: When to Backwash (Bose, 2010) Figure 6: Basic Backwashing (Van Dijk, 2010) Figure 7: Schematic Diagram for Backwash Water Quality Theory Figure 8: Filter Ripening Conceptualization (Amburgey, 2005) Figure 9: Nieuwegein Pilot Plant Figure 10: Installation Schematization Figure 11: During Normal Filtration Process Figure 12: Backwashing Step 1 Figure 13: Backwashing Step 2 Figure 14: Backwashing Step 3 Figure 15: Filter 1 Layers with Pressure Gauge Positions Figure 16: Filter 2 Layers with Pressure Gauge Positions Figure 17: Normalized Effluent Turbidity for Experiments with Varying Velocities Figure 18: 3500l/h flow (7m/h), effluent turbidity Figure 19: 2500l/h flow (5m/h), effluent turbidity Figure 20: 1500l/h flow (3m/h), effluent turbidity Figure 21: Normalized Effluent Turbidity for Experiments with Different Expansions Figure 22: Pressure Drop for Experiments with Different Expansions Figure 23: Reference Effluent Turbidity for Expansion Difference Experiment Figure 24: Effluent Turbidity for 20% Expansion Backwash with 210sec of Expansion Duration Figure 25: Effluent Turbidity for 20% Expansion Backwash with 338sec of Expansion Duration Figure 26: Pressure Drop for Lowest Layer for Experiments with Different Expansions Figure 27: Pressure Drop for Top Layer for Experiments with Different Expansions Figure 28: Pressure Drop in Upper 40cm of Sand Column, Before and After 20% Expansion Figure 29: Pressure Drop in Upper 70cm of Sand Column, Before and After 20% Expansion 11 P a g e

12 Figure 30: Lindquist Diagram for Reference of 5% expansion Figure 31: Lindquist Diagram for Long 20% expansion Figure 32: Normalized Effluent Turbidity for Experiments with and without Pre-wash Figure 33: Pressure Drop for Experiments with and without Pre-wash Figure 34: Pressure drop on Top Layer of Sand bed for Experiments with and without Pre-wash Figure 35: Effluent Turbidity for Experiments With and Without Pre-Wash Figure 36: Difference brought by Pre-wash with Water Figure 37: Normalized Effluent Turbidity for Experiments with and without CP Figure 38: Pressure Drop for a filter run from Experiments with and without CP Figure 39: Effluent Turbidity for Experiments with and without CP Figure 40: Ripening Peaks for 1st time of Experiments with and without Draining of Supernatant Figure 41: Ripening Peaks for 2nd time of Experiments with and without Draining of Supernatant Figure 42: Lambda Shift Explanation Figure 43: Stimela Graph for Reference run without CP Figure 44: Stimela graph for backwash with CP Figure 45: Optimal Parameters for Reference case of Experiments with Different Expansions Figure 46: Optimal Parameters for short 20% expansion Figure 47: Optimal Parameters for long 20% expansion Figure 48: Optimal Parameters for Reference case of Experiments with and without Pre-Wash Figure 49: Optimal Parameters for pre-wash with water Figure 50: 3500l/h flow rate (7m/h) Lindquist Diagram Figure 51: 2500l/h flow rate (5m/h) Lindquist Diagram Figure 52: 1500l/h flow rate (3m/h) Lindquist Diagram Figure 53: Reference Case for Experiments with and without Pre-Wash Lindquist Diagram Figure 54: Pre-Wash with Water Lindquist Diagram Figure 55: Reference Case for Experiments with and Without CP Lindquist Diagram Figure 56: Collapse-Pulsing backwash Lindquist Diagram 12 P a g e

13 List of Tables Table 1: Reference Backwashing Procedure Table 2: Filter 1, Turbidity Measurement Delay Time after Backwash Table 3: Experimental Setup for Experiments with Different Velocities Table 4: Experimental Setup for Experiments with Different Expansions Table 5: Experimental Setup for Experiments of Pre-wash Table 6: Experimental Setup for Experiments with Collapse-Pulsing Table 7: Experimental Setup for Experiments of Draining of Supernatant Water Table 8: Estimated Average Ripening Peak Duration for different Flow Rates Table 9: Constant-Value parameters used in Stimela Table 10: Experiments with Different Expansions Optimal Parameters Table 11: Experiments with and without Pre-Wash Optimal Parameters Table 12: Experiments with and without CP Optimal Parameters Table 13: Parameters for Evaluation Table 14: Cost Function of Parameters Table 15: Reference backwash for Experiments with Different Expansions Cost Calculation Table 16: 20% Short Expansion cost calculation Table 17: 20% Long Expansion cost calculation 13 P a g e

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15 1: Introduction 1.1 Background Under the big umbrella of two-year research project named FilterXpert, participated by various water treatment experts from Dutch educational institutions and water companies, my thesis is just a small part of all the research activities in order to understand more about filter operations. The rationale behind the formation of this FilterXpert study is that although most of the Dutch water companies use sand filters in their water purification facilities, there is a lack of genuine scientific knowledge about how they actually work. Water companies operate their sand filters on the basis of traditional know-how and historical assumptions (TUD, 2010). Therefore this FilterXpert initiative is trying to develop a pool of knowledge about filter operations to make available to the participating companies. The idea of investigating ripening phenomenon in this thesis was inspired by the problem encountered by Waternet Leiduin water treatment plant whereby the ripening values are quite high, affecting the effluent quality. 1.2 Definition and Objectives It has been observed that there is a filter ripening period immediately after backwashing. Ripening period refers to the duration where there is a higher than desired turbidity level in the treated effluent after backwashing. The filter ripening process is still not completely understood, and the increased passage of particles into the finished water supply is not always well-managed (Amburgey, 2005). This thesis research was to investigate the effect of different backwashing procedures (varying the duration, flow rate and amount of air/water combination) on the magnitude and duration of the ripening period, as well as on general effluent quality, in order to find an optimal backwashing approach. 1.3 Approach and Report Layout To achieve this objective, I have firstly read scientific journals and articles to understand the theory about filters. Then I have defined 5 hypotheses for each set of experiment. The 5 sets of experiments were conducted on pilot plant filters for three months. After that, comprehensive analysis of results were carried out, using excel, Stimela and Matlab. In this report, a summary of the theoretical background of filter operations is presented first. Then, you will find a description of the experiments, followed by analysis of experimental results. Stimela modelling results will also be shown later, as well as my proposed cost evaluation method. 15 P a g e

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17 2: Theoretical Background of Filters 2.1 Introduction Filtration is an important step in water and wastewater processes. This process involves passing water through fine granular materials, such as sand, to remove the fine suspended particles and other impurities in the water. The review below will be concentrating on the various characteristics of deep bed filters where particulates are captured within a porous body of material. 2.2 Filtration Mechanism Conventional granular filters consist of beds of granular materials such as sand and anthracite which retains solid particles as they pass by. The removal mechanisms can be physical, chemical or biological processes. There are mainly four kinds of basic physical mechanisms involved in the working of filters: Sedimentation, interception, Brownian diffusion and inertial impaction (Bose, 2010). Sedimentation refers to the situation that gravity and the associated settling velocity of the particle causes it to deviate from the streamline and bump into the filter media. If a particle that is following a streamline comes very close to the filter media surface, it will hit the media grain and be captured. This is called interception. Brownian diffusion happens mainly for small particles with a diameter less than 1 micron. These small particles move randomly in the fluid due to thermal gradients and thus hits and sticks to the filter media grain. Inertia impaction occurs when a particle is so large that it is unable to adjust quickly enough to the abrupt changes of streamlines direction near a filter media particle. Due to inertia, this particle will continue along its original path and hit the filter media, thus being removed from the water. Figure 1: Physical Removal of Impurities by Filter Grains (van Dijk, 2010) Chemical processes could involve adsorption and oxidation. Adsorption forces extract the impurities from the flowing water onto sand grains. An example for oxidation is that in the presence of oxygen, a dissolve form of iron can be converted into precipitated form, thus precipitating out of the water stream (Michalakos, 1997). Biological processes are very important in the functioning of filters as well, especially for slow sand filters. The filter materials give bacteria and other biomass a living place where they are able to consume the undesirable elements in the water. For example, the bacteria genera Nitrosomonas and Nitrobactor convert ammonium to nitrate to improve water quality (Vayenas et al, 1997). 17 P a g e

18 2.3 Design Options There are several categories of sand filters. The water flow direction could be either upwards or downwards, but the downwards direction is generally used, most probably due to its lower operating cost. Among the downward flowing filters, the water can flow under gravity or under pressurized condition. When the water flows under gravity only, there are also two types of filters: rapid gravity sand filters and slow sand filters. The main differences of rapid sand filters and slow sand filters include: Firstly, the effective sizes of the grains are larger in rapid sand filters than slow sand filters. Thus, the flow rate is much higher in rapid sand filters than slow sand filters. Due to the higher loading rate, rapid sand filters normally occupy less land areas and are also less sensitive to variations in water demand or upstream loading rate (Ogbonnaya, 2010). Secondly, although rapid sand filters are fast, they normally cannot remove bacteria or other harmful micro-organisms, thus producing water which definitely needs post-treatment to reach potable water standard. Also, rapid sand filters are designed as a part of a treatment train used by large municipalities, so the cost of construction, maintenance and operation of the whole treatment train is higher than that of slow sand filters only. Slow sand filters are also easier to operate and maintain, so they are very popular in some developing countries where skilled labour is not readily available or drinking water demand is lower. For developed countries and large municipalities where water demand is high or seasonal variation is large, rapid sand filters are more often used. Thirdly, while slow sand filters use a high level of biological processes to remove impurities in the water, rapid sand filters use mainly physical-chemical processes. Rapid sand filters do not remove pathogens, taste and odour as effectively as slow sand filters. Lastly, the methods of periodic cleaning are also different for the two types of filters. Slow sand filters capture particles near the surface of the bed, so cleaning involves scraping off the top few millimeters of the fine sand layer (Bose, 2010). Water is then re-circulated for a few hours to allow the formation of the new biomass layer before bringing it back to service. Another method of cleaning is by lowering the water level to just above the biomass layer, stirring the sand and thereby suspending the solids in that layer and then draining the water to waste. The filter is then filled to full depth and start functioning again. On the other hand, rapid sand filters need frequent backwashing as cleaning method. Water direction is reversed and the bed is thus fluidized to get rid of the accumulated particles. Compressed air is sometimes added as well. More details will be given later. As mentioned earlier, a rapid sand filter is part of a water treatment train, so the design and operation of the pre-treatment and post-treatment are relevant to the filter operation too. Before going through the filter, the water often has already undergone flocculation, coagulation and sedimentation so the water is relatively clear when it reaches the filter. Chemical aids are usually used in conjunction with the filters to produce a better effluent. Indeed, passing flocculated water through a rapid sand filter strains out the floc and the particles trapped within it, reducing the numbers of bacteria and removing most of the solids. After leaving the filter, there has to be at least some form of disinfection to reach an acceptable water quality. Often it becomes necessary that two or more layers of different granular materials combine together to treat the water to the desired quality. Anthracite, sand and gravel can be used in the multimedia filters and the different densities of the materials allow clear separation after backwashing. The coarser materials takes out the larger particles while the below finer materials trap the other particles, so the filters do not clog as fast as if all the particles are entrained by the top layer of the singlematerial filters (Ogbonnaya, 2010). 18 P a g e

19 Another design variation is the choice of filter media. The granular media should have the following characteristics (van Dijk, 2010): Resistant to abrasion Free from impurities Uniform grain size distribution While sand is a popular candidate, other materials are often used depending on the availability and price. In the Netherlands, water and wastewater treatment plants generally choose to use the more efficient rapid sand filters while slow sand filters are only used as a final polishing step, so the paragraphs below will only focus on rapid sand filters. 2.4 Filter Operation Set-up A typical rapid sand filter is shown in Figure 2: Figure 2: Typical Rapid Sand Filter ( 2010) The filter box is usually made of concrete. The influent flows down to the filter media from the wash troughs and flows through the sand and gravel and then finally captured by the underdrains which direct the water for post-treatment. During backwashing, the underdrains can also evenly distribute the backwash water (Ogbonnaya, 2010). The gravel is used to support the sand and also prevent the sand from being lost into the underdrains. Before a filter medium is chosen, a grain size distribution analysis should be performed and the uniformity of the grains can be represented by the Uniformity Coefficient (UC): 19 P a g e

20 = where = the size of sieves that let pass 10% of the sand mixture (mm) and = the size of sieves that let pass 60% of the sand mixture (mm), as shown in Figure 3. Figure 3: Sieve Curve of Filter Medium (van Dijk, 2010) The higher the UC, the larger the variation of the grain sizes. For rapid filtration, the value of the uniformity coefficient should be between 1.3 and 1.5 to avoid stratification of the filter bed during backwashing (van Dijk, 2010). The size of the granular materials in the filter is an important parameter. If the sand size is too large, the voids between the sand will be too large to trap tiny particles. On the other hand, if the sand size is too fine, not only more energy is required to pump the water through (if it is pressured filters), but also the pores can be quickly clogged, thus resulting in a too fast head loss and too frequent backwashing. When mixed media with different specific gravities are used, for example, anthracite on top of sand, to have the same settling velocities after backwashing, the particle sizes can be computed by = ( ) / where, = the diameter of medium 1 and 2 (mm), and = specific gravity of medium 1, 2 and water respectively (Lin, 2001) Filtration Theory During the filtration process, as particles accumulate on the filter grains, the pore volume is reduced while the grain sizes increase. The concentration of suspended solids decreases with increasing filter bed depth. An equation has been formulated to find the concentration of suspended particles (van Dijk, 2010): 20 P a g e

21 = λ Together with mass balance equation: = Where = concentration of suspended and colloidal solids (g/m 3 ) = depth of filter bed (m) = filtration rate (m/s) = pore velocity (m/s) ( = where P is porosity) λ = filtration coefficient (m -1 ) σ = accumulated solids (g/m 3 ) The filtration coefficient depends on filtration velocity, viscosity, grain size, quality of raw water, clogging of the bed and other factors. As the pore velocity increases due to pore clogging, the filtration coefficient decreases. Researchers have given several formulas to determine clean bed filtration coefficient and the relationship between λ and σ (van Dijk, 2010). Lerk: = Maroudas: = (1 Where = grain size (m) = initial porosity (%) = density of flocs (kg/m 3 ), = constants = kinematic viscosity (m 2 /s) ) The value of the constant is often assumed to be 9 10 and the constant is the reciprocal value of the maximum pore filling n (0< n <1). The ratio between the accumulated solids σ and the density is the reduction in pore volume ( )(m 3 /m 3 ). = Assuming stationary situation, so = 0. With the boundary conditions y=0, c=c 0 and the initial condition t=0, =0 and =, the solution of the system of equation can be obtained. Thus, the effluent quality has an equation of = = 21 P a g e

22 When clean water flows through a clean granular filter, the loss of head can be estimated by Carmen- Kozeny equation which is derived from the fundamental Darcy-Waeisback equation for head loss in circular pipes (van Dijk, 2010). Where = initial resistance gradient L = total depth of filter bed (m) This equation is only valid when (van Dijk, 2010). = = 180 (1 ) = 5 Another equation to estimate head loss through a filter medium was also developed experimentally by Rose in This equation can be used for rapid sand filters with a uniform near spherical or spherical medium (Lin, 2001). where h = head loss (m) = shape factor (round sand 0.82, angular sand 0.73 ) = Drag Coefficient Only clean bed head loss is not enough. When clogging occurs during filtration, the resistance formula, from the Carmen-Kozeny equation, becomes: Where I = resistance gradient = = ( ) By integrating the resistance gradients along the height of the filter bed, the total resistance over the filter bed can be calculated. The largest resistance is normally built up in the upper layers where most of the solids are trapped. In the lower layers, the resistance is similar to clean bed gradient, as shown in Figure 4 (van Dijk, 2010). 22 P a g e

23 Figure 4: Progress of Filter Bed Resistance in Time (van Dijk, 2010) Backwashing of a Rapid Sand Filter For a filter to work efficiently during a filter run, regular backwashing needs to be done. There are a few ways to determine when backwashing is needed. One easy way is to schedule a reasonable length of filter run based on experience or observation. Once a filter run reaches that fixed hour of operation, it should be cleaned. This method might not be able to cope with sudden variation of influent load quality or quantity. Therefore, a more accurate way is to measure the turbidity of the effluent or the head loss through the filter and then determine when to backwash. When most of the grains have been attached with particles from the water, the sites available to collect more particles become limited, that is when flocs formed previously start to break through the filter into the effluent. A turbidity meter could be used to detect that high level of turbidity. That is the time that backwashing is necessary to free the collection sites again. Moreover, when water flows through a clogged filter, the friction causes the water to lose energy, so the water leaving the filter is under less pressure (head) than the influent water (Ogbonnaya, 2010). When that pressure (head) loss reaches a certain value, it means the filter is clogged and a cleaning is necessary. A head loss gauge can be used to measure the head loss of water through the filter. 23 P a g e

24 Figure 5: When to Backwash (Bose, 2010) Basic Backwashing Procedure The effectiveness of backwashing plays a crucial role in the proper operation of the filters. During backwashing, the inlet valve is closed and the waste line is opened. Treated water which was stored in a storage tank is pumped in from the underdrains to the media, in the reverse direction as that of the downward water flow direction during filtration process. The backwashing rate needs to be controlled in such a way that it is large enough to expand the bed from its undisturbed state but not too large that the media is destructed and washed away (Bose, 2010). The expansion and agitation of the bed cause the sand grains to rub against each other, thus dislodging the adhered floc. The rising wash water carries away the materials and discharges them to gutters. Figure 6: Basic Backwashing (Van Dijk, 2010) In addition, the upper part of the filter bed is the dirtiest. To ensure adequate cleaning, surface washers spray water over the top layer of the sand for an extra boost of agitation effect. 24 P a g e

25 Improvement/Modification from Basic Backwashing - Air Scour In most cases, water alone cannot agitate the media enough, so compressed air is forced through the underdrains until the sand is thoroughly agitated, then the desired expansion of sand media and complete removal of flocs can be achieved. The air may be forced through the underdrains before the wash-water is introduced or through a separate piping system placed between the gravel and the sand layer (The Water Treatments, 2010). However, it is not recommended to use air scour before backwash with water. This is because usually air is introduced by a limited ited number of openings only, so due to the large difference in specific gravity compared with the surrounding water, the air rises more or less vertically to above, entraining the neighbouring water in the same way as an air-lift pump does. With no supply from below, the water thus displaced has to flow back in the space between the jets of air, thus taking solids from the surface of the filter to below. (Van Dijk & Huisman, 1998) ) Therefore, a certain amount of water has to be used simultaneously with the air scour. - Collapse-Pulsing Backwash In the 1990 s, filter backwash research was conducted by Amirtharajah, et al, at Georgia Tech in the US. It was thus discovered that backwashing filters with simultaneous air plus water at sub-fluidized rates provides the best cleaning of the filter media. It was also found that using higher simultaneous rates of air and water (near fluidization rates) did not significantly increase the amount of debris released from the bed (Davis, 2007). Amirtharajah has identified a "'collapse pulse" mechanism where small voids are created within the media as the air passes through. The air bubble exits the air delivery device (orifice) and expands under the weight of the media. When the air bubble expands, the media expands slightly within the vicinity of the bubble, and the bubble collapses and reforms just above its original location. This collapsing is due to the weight of the media. The bubble reforms above its original location because the media is only partially expanded. Just prior to collapsing, high local water velocities occur at the perimeter of the bubble. Simultaneous to bubble collapse, media particles rush together and collide in a violent scouring action. This creates a pulsation in the bed. The bubble travels on upward, expands, collapses and re-forms again, and repeats the process several times as it passes through the bed. Eventually the bubble reaches the surface and bursts to atmosphere (Davis, 2007). The overall effect produces strong abrasion between the grains with negligible bed expansion. A general equation for the collapse-pulsing condition is proposed as: % (Amirtharajah, 1993) where V = backwash water velocity V = Minimum fluidization backwash water velocity = Backwash air flow rate and b = constants for a particular system, depending on various parameters such as media depth, height of water above media and so on 25 P a g e

26 Backwash Theory It was stated that the major mechanism in cleaning is hydrodynamic shear from the liquid flowing past the grains (Amirtharajah, 1993). The scouring force of the washwater is equal to the mass of grains under water (van Dijk, 2010): = 6 ( ) So ( ) Where = shearing force (kg/m 2 ) = mass density of the filter material (kg/m 3 ) = mass density of the water (kg/m 3 ) From experiments, an empirical equation for the resistance during backwashing has been derived: 130. (1 )... The above empirical equation is valid until the upward flow rate becomes so high that the bed fluidizes. This is when the resistance is equal to the mass of the filter bed under water. (1 ) (1 ) ( ) If properly controlled, there should be minimum loss of filter materials during backwashing, which means the amount of filter materials remains constant. Therefore, porosity of the expanded bed can be calculated (van Dijk, 2010): Where = porosity of expanded bed (1 ) (1 ) As = In which = bed expansion = initial height of the filter bed (m) = height of the expanded filter bed (m) So porosity of expanded bed is = An equation has been derived to give the backwashing water velocity (m/s) which is needed to achieve a certain expansion (van Dijk, 2010):.. ( ) ( ).. The minimum fluidizing velocity V is important to determine the required minimum backwashing flow rate because it is the superficial fluid velocity needed to start fluidization. An equation has been proposed by some researchers (Lin, 2001): 26 P a g e

27 Where = Galileo number = ( ) ( ).. In practice, the grain diameter of spheres of equal volume is not available, so the sieve size is used instead. A safety factor of 1.3 is used to ensure adequate movement of the grains. One of the mathematical models proposed for predicting backwash water quality is based on Surface Renewal Theory. A relationship between the quality of backwash water and the volume of the backwash water passed is simplified and proposed to be (Amirtharajah, 1985): ( ) ( ) Where = concentration of particles in water (mg/m 3 ) = total mass transfer into volume V of backwash water (mg) = initial area across which the surface renewal mechanism will transport material (m 2 ) = volume of backwash water (m 3 /m 2 ) = superficial velocity (m/s) = time taken (s) = diameter of the collectors (media grains) (mm) According to the Surface Renewable Theory, during backwashing, as the clean water traverses the fluidized bed it would accumulate particles detached from the media surfaces. The total mass transfer into the volume of water will depend on the area across which the surface renewal mechanism will transport material. The rate of transfer into the volume declines with time. As particles are removed from the surfaces of the collectors, some of the collectors will reach the non erodible layer and will no longer supply particles into the volume. Thus a front at which no further particle detachment takes place will move up the filter bed as in Figure 7 (Amirtharajah, 1985). Figure 7: Schematic Diagram for Backwash Water Quality Theory 27 P a g e

28 When the duration of backwashing is short, the upper layers of the grains in the filter bed are not washed as clean as the lower ones. Plus the fact that the top layer always traps more dirt from the influent water than the lower layers, thus, the most of the pressure drop occurs in the top part of the sand. Lastly, the filter efficiency is defined as the effective filter rate divided by the operational filtration rate (Lin, 2001): Where = effective filtration rate (m 3 /m 2 /h) = operating filtration rate (m 3 /m 2 /h) = unit filter run volume, (L/m 2 ) = unit backwash volume (L/m 2 ) 2.5 Filter Troubles = = General Problems In reality, not all filters run smoothly and efficiently all the time. There are several universal common problems encountered by water and wastewater treatment plants. As mentioned earlier, the top layer of the filter is the dirtiest because dust accumulates on the top surface of the filter and may form mudballs. The mudballs are sticky, so they continue accumulating impurities and filter grains when filter cleaning is not done thoroughly. They grow larger and eventually sink into the filter media. This results in loss of filter capacity and shortened filter run time because water cannot flow through the mudballs, but must flow around them (Ogbonnaya, 2010). Air binding is another common problem. Headloss increases gradually as the filter run goes on, and if not backwashed in time, negative head might develop where the frictional resistance of the filter exceeds the static head of the water. As a result, the dissolved gases inside the filter and underdrains are released, preventing water from flowing through (Johnson et al, 2009). The bubbles formed stick to the sand grains and cause abnormally high head loss, disrupting the proper functioning of the filter. Therefore, backwashing should be done as soon as the head loss exceeds the allowable value. Furthermore, during winter, the treatment regime has to be changed to adapt to the low temperature. Chemical reactions take longer in colder water, so it might take a longer time for coagulants and flocculants to work. Another example would be that the viscosity of cold water is bigger than that of warm water, so if the backwashing rate is the same as the rate in summer, then the filter media will over-expand during winter backwashing and potential loss of media material become a problem. Therefore, it is better to adjust the backwash rate seasonally and to take temperature effects into consideration during design and operation (Beverly, 2005). Another problem that sometimes occurs in sand filters which have a higher emphasis on biological removal processes is that backwashing washes away a significant amount of biomass such as Nitrosomonas and Nitrobacter, so the nitrification rate and organic carbon removal rate immediately after backwashing is very slow. It takes some time for the biomass to be re-accumulated (Traenckner, 2008). Therefore, careful calculation of backwashing rate that can maximize cleaning effect while minimizing biomass washout should always be carried out to prevent this problem. 28 P a g e

29 2.5.2 Troubles Related to Ripening The increased passage of particles and microorganisms through granular media filters immediately following backwashing is a common problem known to the water treatment community as filter ripening. Ripening Conceptualization and Models A conceptualization of filter ripening by Amirtharajah is explained below and illustrated by Figure 8. At the end of the backwashing operation of a repeatedly used filter there would be remnants of the backwash water in the filter system. The backwash water remnants in the system can be subdivided into three types: (1) clean backwash water in the underdrain and connecting pipework from the backwash water supply system up to the bottom of the filter media, (2) backwash water remnants within the pores of the media and (3) backwash water remaining above the filter media up to the level of the wash water gutter. The three fractions of water have different characteristics. The first fraction of water is clean; the second fraction, which is the remnant within the pores of the media, would have a backwash water quality characterized by the last stages of the backwashing operation because the collapse of the fluidized bed does not typically dislodge a significant number of particles from the surfaces of the media into the backwash water remnants within the pores; the third fraction of the backwash water above the media would have a quality which is poorer than the second backwash remnant since it preceded this backwash water during backwashing and hence would be removing more particles from the filter media (Amburgey, 2005). As shown in Figure 8, after going back to service from backwashing, it is the clean backwash water that comes out first, so the turbidity is low. After that, the effluent quality rapidly degrades until the first turbidity peak due to the backwater remnant within the media. As the first peak is due to collisions at the end of the backwashing operation, factors such as the increased effectiveness and longer duration of backwash, slow closure of backwash valve and the increased strength of the adhesive forces between the filtered particles and the media may obscure the two independent peaks and a single plateau type response may be evident in the initial effluent quality. The backwash water remnant above the media has particles which were removed from the filter grains during backwashing. The concentration of particles will be highest at level T B since it was at an earlier instant during backwashing, and lower at level T M. After the effluent quality has reached the second peak the quality of the effluent slowly improves during the final filter media conditioning stage. The duration of the improving phase is affected by filtration rate, influent concentration, particle size and the physicochemical character of the influent particles (Amirtharajah, 1985). 29 P a g e

30 Figure 8: Filter Ripening Conceptualization (Amburgey, 2005) In addition, a filtration model on additional collectors was proposed to describe a filtration cycle consisting of ripening, working and breakthrough stages. The hypothesis is like this: Firstly, suspended particles start to deposit onto the surface of filter grains and deposited particles start to serve as additional collectors for the further attachment of suspended particles. The detachment of deposited particles does not occur during filtration at this period of time until the specific deposit reaches a certain value. Secondly, the detachment starts to occur, mainly due to the increase of hydraulic shear forces with increase of interstitial velocity within the free space of filter media. Deposition and detachment both take place at this stage (Han et al, 2009). It has been acknowledged that the additional collector effect was dominant only in the final stage (#5 in Figure 8) of a multi-stage filter ripening process, which refers to the time where newly attached particles become collectors of other particles within the filter and improve filtration performance (Amburgey, 2005). Methods to Limit Filter Ripening Period It is estimated that almost 40% of the total passage of particles during a filter run was passed during ripening period. A number of factors said to influence the extent of this phenomenon have been researched upon, such as influent quality, filtration rate and the rate of backwash valve closure (Colton, 1996). Various approaches were also proposed to mitigate this problem, although most of the approaches have their limitations. For example, a filter-to-waste line can be provided to waste the first few minutes of effluent water to eliminate the production of poor quality water. However, sometimes the ripening period can be pretty long, and putting this filter out of service for so long might not be feasible for small plants (Amburgey, 2005). Also, if the wasted water is recirculated back for filtration, then it means there is a sudden loading of water with a high concentration of pollutants being dumped on the filter again in a very short period of time (Pizzi, 2000). Adding coagulants or filter aid polymers into the filter during or after backwashing has also being researched on. When a polymer is used as a filter aid, the size of particles can be increased by interparticle bridging. The polymer can also neutralize the particle surface charge, so the attachment can be enhanced by a reduction in electrical repulsive forces. Thereby, both the transport and 30 P a g e

31 attachment mechanisms can be facilitated by the formed polymer-particle flocs (Zhu et al, 1996). This method does reduce turbidity level immediately after backwashing and reduce the time needed for ripening, but the use of polymers increases the development of headloss, and the potential flocformation in underdrains and clearwells might pose a problem. Moreover, determining an accurate amount of coagulant to be added during a very short period of time to every filter is going to be difficult, especially when there is constant variation of influent water quality (Amburgey, 2005). Furthermore, delayed start or slow start after backwashing could also be employed to reduce the turbidity problem (Colton, 1996). However, the plants must be able to have sufficient production capacity when one filter is out of service for a relatively long period, or the plants should be able to precisely control the effluent flow rate of its individual filters. In addition, experiments have been carried out by researchers on the effect of duration of collapsepulsing backwashing on the ripening period. A suitable duration could shorten the ripening period. A technique was developed, which is called extended terminal subfluidization wash (ETSW). It is a procedure that extends the normal backwash duration at a subfluidization flow rate for an amount of time sufficient to move one theoretical filter-volume of water through the filter box. This technique could wash away the already-detached particles during fluidization stage while do not give extra shear stress to detach more particles (Amburgey, 2005). It has been quite effective according to some researchers, although pilot studies have to be done to fine the optimal ETSW rate for each treatment plant due to the difference in influent water quality. It is a pity that I was not able to try ETSW experiments due to thesis time constraint. Due to the increasing stringent governmental water quality regulations, better control and operation of the filters are of paramount importance. The problem associated with filter ripening after backwashing has been a headache, so the aim of the following report will be to examine the effects of various backwashing procedures on the ripening duration and general effluent turbidity level, in the hope of finding the optimal procedure. 31 P a g e

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33 3: Materials and Methods 3.1 Hypotheses for Experiments After knowing the theory of filters from part 2, reasonable hypotheses can be formulated for the planning of experiments Hypothesis 1 The first hypothesis is that higher flow velocity leads to earlier onset of breakthrough. It is based on the theory proposed by Van Dijk and Huisman, According to them, when clogging has reached a certain degree, the water, with a higher velocity of downward movement, prevents further sedimentation and even picks up settled solids and carry it to greater depth in the filter bed. As the bed has a limited thickness only, the suspended matter will eventually appear in the effluent, facilitating breakthrough Hypothesis 2 The second hypothesis is that the greater the degree of bed expansion during backwash, the higher and longer the ripening peak. The additional collectors theory mentioned that the suspended solids in the influent has to take time to attach to sand grains first and then act as additional collectors to remove more suspended solids and thus lower the turbidity. It was assumed that if the backwash velocity gives greater bed expansion, then there is more space for attached particles to escape, thus the filter bed has less attached particles left at the end of backwash. According to the theory mentioned above, less deposited particles means the additional collectors effect is not obvious at the start, so the effluent quality takes longer to get better. Also, if the expansion duration is longer, the more detached particles have the chance to escape, so the less dirty the bed will be when the backwash stops, and thus for a cleaner bed, it takes longer for the particles to re-attach and to improve the filter removal efficiency Hypothesis 3 The hypothesis here is that adding a pre-wash with water step in front of the current pilot plant backwash procedure can prevent dirt from being taken deeper into the bed, thus improving the performance of the filter. It is mentioned by Van Dijk & Huisman (1998) that it is not recommended to start with air-scour-only in a backwash. This is because usually air rises more or less vertically to above, entraining the neighbouring water. With no supply from below, the water thus displaced has to flow back in the space between the jets of air, thus taking dirt from the surface of the filter to below. Starting the backwash first step with water-only is predicted to prevent this from happening Hypothesis 4 This hypothesis is that backwash with collapse-pulsing gives higher ripening peak than reference backwash due to collectors theory. Collapse pulse mechanism was theorized by Amirtharajah and it is supposed to give a better cleaning effect on the filter bed due to violent scouring action of the media particles. However, to be consistent with our hypothesis 2, it is believed that the better scouring action makes the bed cleaner, so the additional collectors effect takes longer to mature, resulting in worse ripening condition. 33 P a g e

34 3.1.5 Hypothesis 5 The last hypothesis is that the ripening peak will be significantly lower if we remove supernatant water immediately after backwashing and before the re-starting of filtration. Filter ripening theory proposed by Amirtharajah states that immediately after a backwash, the part of water with the highest concentration of dislodged particles is the water left on top of the sand, i.e. the supernatant water. It is proposed in his theory that this part of the water gives the highest ripening peak. If this is true, then we would expect to see a significant lowering of ripening peak when supernatant water is drained. 3.2 General Information of the Pilot Plant Filters The experiments were conducted in Waternet Nieuwegein pilot plant during the period of February 2011 to April Figure 9: Nieuwegein Pilot Plant This pilot plant is situated on the premise of the Nieuwegein Cornelis Biemond Water Treatment Plant. The plant takes in the surface water from Lek canal as its influent raw water. After flocculation and sedimentation, the incoming water taking in by the pilot plant has a turbidity fluctuating between 2 FTU to 9 FTU, but mainly varying in the 3 FTU to 6 FTU range. There are in total 3 filters running in this pilot plant. The diameter of the surface of the filters is 0.8 meter; Filter 1 and 3 has a sand bed height of 1.1 meters while filter 2 has a sand bed height of 0.95m. A simplified schematic presentation of the installation is shown in Figure 10: 34 P a g e