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

32 32 P a g e

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

35 Figure 10: Installation Schematization During the normal filtration in the pilot plant, the influent water comes in from the pipe in the middle and the water passes through the sand to be collected at the clean-water storage tank. Figure 11: During Normal Filtration Process Currently the backwashing procedure in the Nieuwegein Treatment Plant is as described in Table 1. This is used as the reference backwashing procedure for our pilot plant experiments. 35 P a g e

36 Table 1: Reference Backwashing Procedure Step Method Duration (s) 1 Low velocity of Water and High velocity of Air Low velocity of Water only % bed expansion by water 338 During backwashing, water level is lowered, so we can see from Figure 12 that raw influent water goes directly into the waste-water line while backwashing is taking place. Figure 12: Backwashing Step 1 It is observed that at the end of step 3, the backwash water is much cleaner than at the beginning of backwashing. Figure 13: Backwashing Step 2 Figure 14: Backwashing Step 3 36 P a g e

37 3.3 Analysis Equipment Throughout the experiments, effluent turbidity measurements were taken to serve as an indicator for the filter performance. Generally, turbidity is defined as a measure of the light transmitting properties of water and used to indicate the quality of waste discharges and natural waters with respect to colloidal and residual suspended matter. The measurement is based on the comparison of the intensity of light scattered by a sample to the light scattered by a reference suspension under the same conditions (Miska-Markusch, 2009). The turbidity meter used in this pilot plant is using the 90 infrared scattered light method according to DIN EN ISO Its brand is Dr Lange (Hach), type is Utraturb. Other than turbidity measurements, there are 6 pressure gauges vertically throughout the depth of the filter to reflect the pressure difference in each horizontal layer. For filter 1, the first pressure gauge is inside the supernatant water, and the second pressure gauge is about 10 centimeters below the top of the sand bed. The other pressure gauges are 30cm apart from each other while the last pressure gauge is about 20 centimeters into the gravel layer. Figure 15: Filter 1 Layers with Pressure Gauge Positions Delay in Re-starting of Turbidity Measurement It has been observed that there was often a delay of several minutes for the turbidity meter to restart measurement after each backwash. This was because the effluent outlet valve is automatically controlled by computer and the degree of opening depends on the level of supernatant water. Immediately after the backwashing, the level of supernatant water was low, so the effluent valve was only slightly or partially opened. During this time, the monitoring line did not measure. It was only until the effluent valve opened to a certain degree that the turbidity measurement re-started. 37 P a g e

38 Table 2: Filter 1, Turbidity Measurement Delay Time after Backwash * Residence Time here means the time taken for one filter bed volume of water to pass through the filter Week Number Influent Velocity (m 3 /h) Residence Time (min) Delay time (min) Therefore, the effluent flow rates in the first few minutes were not yet at the full flow rate (3.5m 3 /h). Still, if we were missing the first few minutes, we do not see the first stage of ripening and maybe even the beginning of second stage which was mentioned in the ripening conceptualization in Figure 8. It is possible that the ripening peak that we have observed was due to the dirt detached in the upper layer of the bed or even the dirt already in the supernatant water above the filter at the end of the backwash. That is why most of the graphs show that the turbidity is already at a higher level when it is measured after each backwashing, i.e. there is no or only a partial rising stage captured. In addition, as this actually equals to the situation of slow start of filtration after backwash, the ripening peak might not be as significant as that when the flow rate goes at full-speed immediately at the re-start of filtration (Amburgey, 2005). However, as the delay time is consistent for the same flow rate, the results can still be compared. 3.4 Experimental Set-ups The experimental sets with varying flow rates, with different expansions, with pre-wash and with collapse pulsing were conducted on Filter 1 while the last experimental set with draining of supernatant water was conducted on Filter 2. Computer controls the automatic backwash of the filters. The criterion of filter going into backwashing is that the pressure difference between pressure gauge 1 and 6 reaches a certain set value. This value for the experimental set with varying velocities was 100cm and for other experimental sets was 70cm. 38 P a g e

39 3.4.1 Influence of Velocity on Breakthrough Three influent velocities were chosen to see the difference in filter performance: 3.5m 3 /h, 2.5m 3 /h and 1.5m 3 /h. The backwashing procedures were kept the same. As the filter surface area is m 2, the flow rates in units of m/h is 7m/h, 5m/h and 3m/h respectively. Table 3: Experimental Setup for Experiments with Different Velocities Week Experiment on Filter 1 Number of Runs m 3 /h flow rate (7 m/h) m 3 /h flow rate (5 m/h), other things being equal, except for incoming turbidity and temperature m 3 /h flow rate (3 m/h), other things being equal, except for incoming turbidity and temperature 3 3 The variation of incoming water characteristics is inevitable but there will be normalization of results later with regard to incoming water turbidity for better comparison Different Expansions and Expansion Durations The second set of experiment was designed to have the same flow rate but different backwash procedures. The flow rate was kept at 3.5m 3 /h (7m/h). The difference in the backwash procedures is the expansion degree and duration. Table 4: Experimental Setup for Experiments with Different Expansions Week Backwash Procedure on Filter 1 Number of Runs 5 5% expansion with duration 338sec % expansion with duration 210sec, keeping the total water usage the same, other things being equal, except for influent turbidity and temperature 7 20% expansion with duration 338sec, other things being equal, except for influent turbidity and temperature Pre-Wash with Water Backwash Effect In the current backwash practise, a large amount of air with a small amount of water is the first step of backwash. This experiment tried to use a pre-wash with water-only before adding in air scour. The flow rate was kept at 3.5m 3 /h (7m/h). Table 5: Experimental Setup for Experiments of Pre-wash Week Backwash Procedure on Filter 1 Number of Runs 10 Reference backwash 4 39 P a g e

40 11 Adding one more step before the normal backwash procedure. The first step becomes water-only 5% expansion wash with a duration of 180sec. Other things being equal Collapse-Pulsing Backwash Effect A velocity combination of air and water that suits the equation + % = was used to give collapse pulsing (CP) condition to see if CP gives an advantage in filter operation. Due to time constraint, not all combinations of velocities could be tried to find the optimal, so V/V mf = 40% has been selected. The reason for this selection was that the research paper by Amirtharajah stated that the optimal particle removal for his filter occurred when V/V mf = 39.4%. The flow rate was kept at 3.5m 3 /h (7m/h). Table 6: Experimental Setup for Experiments with Collapse-Pulsing Week Backwash Procedure on Filter 1 Number of Runs 12 Reference backwash 5 13 Collapse Pulsing, use water V = 40% of V mf,, other things being equal Draining of Supernatant Water An experiment has been designed to see if the ripening peak is lower when we remove the supernatant water left at the end of backwash. This experiment set was repeated twice. The flow rate was kept at 3.5m 3 /h (7m/h). Table 7: Experimental Setup for Experiments of Draining of Supernatant Water Experiment 1 Reference backwash 2 Draining of supernatant water immediately after backwashing, before re-starting filtration. Other things being equal This experiment was done on filter 2, which has a shorter sand column than filter 1. The pressure gauge 2 is about the same level as the top of sand column. Due to experimental constraint, a hose has been installed half-way between pressure gauge 1 and pressure gauge 2 to drain the supernatant water on top. Although most of the supernatant water could be drained (about 70cm), there was still about 15cm of water left below the hose level which could not be drained. While draining, the influent water went directly into the waste-line. The whole draining process took about minutes. Below is the illustration of filter 2 configuration. 40 P a g e

41 Figure 16: Filter 2 Layers with Pressure Gauge Positions 41 P a g e

42 42 P a g e

43 4: Results and Discussion 4.1 Influence of Varying Flow Rates on Breakthrough General Observations In order to eliminate the influence of varying incoming turbidity on the result analysis, the effluent turbidity has been normalized with respect to their respective influent turbidity. The method to normalize is to divide the effluent turbidity by influent turbidity at every time step. One representative filter run per each flow rate is plotted for direct comparison. It is observed that both 3.5 m 3 /h (7m/h) and 2.5m 3 /h (5 m/h) case give similar turbidity baseline, but the ripening peak (0.018 FTU/FTU) for 3.5m 3 /h is approximately FTU/FTU higher than that from 2.5m 3 /h (0.011FTU/FTU). However, the ripening peak for 1.5m 3 /h (3 m/h) is the highest and it takes much longer for the turbidity to lower to baseline value. On the other hand, filter breakthrough starts faster and with greater magnitude when the influent flow rate was higher. The 3.5 m 3 /h case has a very high breakthrough of 0.03 FTU/FTU just prior to backwashing, while for 2.5m 3 /h, the turbidity has risen from to FTU/FTU before the criteria of backwashing is reached. For the lowest flow rate, there is no breakthrough yet. Normalized Turbidity (FTU/FTU) Bed Volume of Water Passed 7m/h 5m/h 3m/h Figure 17: Normalized Effluent Turbidity for Experiments with Varying Velocities Ripening Durations The ripening peak duration is defined to be the time taken for the obvious peak to disappear. Table 8: Estimated Average Ripening Peak Duration for different Flow Rates Flow Rate (m 3 /h) Average Ripening Peak Duration 3.5 1hr 2.5 1hr20min 1.5 2hr 43 P a g e

44 Apparently the ripening peak takes longer to disappear with lower flow rate. This might be because of the lower velocity of the water takes dirt out slower than higher velocity. Also, lower velocity allows time for turbulent mixing which might delay the loose dirt from coming out to effluent Amount of Dirt Coming Out During Ripening Peak One representative filter run per flow rate has been chosen to calculate the amount of dirt coming out during the ripening peaks. These curves are not normalized against the influent turbidity in order to show the real amount of dirt coming out during ripening. Representative of 3.5m 3 /h flow rate case: Turbidity (FTU) Time (hr) 3500l/h (7m/h) outgoing turbidity Figure 18: 3500l/h flow (7m/h), effluent turbidity Estimation of amount of dirt coming out during the obvious peak area = FTU. m (.. ) Representative of 2.5m 3 /h flow rate case: 44 P a g e

45 Turbidity (FTU) Time (hr) 2500l/h (5m/h) outgoing turbidity Figure 19: 2500l/h flow (5m/h), effluent turbidity Estimation of amount of dirt coming out during the obvious peak area = 1.4h FTU. m (.. ) Representative of 1.5m 3 /h flow rate case: Turbidity (FTU) Time (hr) 1500l/h (3m/h) outgoing turbidity Figure 20: 1500l/h flow (3m/h), effluent turbidity Estimation of amount of dirt coming out during the obvious peak area 2h FTU. m (.. ) The amounts of dirt for 2.5 m 3 /h and 1.5 m 3 /h are close, but the 3.5 m 3 /h flow rate gives significantly higher amount of dirt. The highest amount of dirt from 3.5 m 3 /h can be explained as follows. As the higher interstitial velocities at the end of the filter run has loosened some dirt and brought them into the lower part of the filter, the backwash with the same duration as for other flow rates has the responsibility to take this greater amount of loosened dirt out to the gutter. If the backwash duration 45 P a g e

46 is not able to take out all the detached dirt, then the loose dirt will be flushed down into effluent when the new filtration process starts. It is also possible that the first few minutes of the turbidity measurement when filtration re-started was influenced by the high turbidity at the end of the previous filter run (the high breakthrough for 3.5 m 3 /h flow case), so the recorded turbidity values were not very accurate at the start for that case Discussion The high breakthrough at the later stage of 3.5 m 3 /h filter run agrees with our hypothesis that when the interstitial velocities was high enough due to pore clogging, this high-velocity-flow might pick up already-settled solids and carry them into effluent. The other flow rates do not give the same extent of breakthrough because the velocity is not high enough. Also, because of the less shearing force when the flow rate is lower, the filter can process more bedvolume of water before the pressure drop reaches the criteria for backwashing. 4.2 Effect of Different Expansions during Backwash General Observations From here on, the water velocity has been kept at 3.5m 3 /h = 7m/h. Due to the maintenance of pipelines in the Nieuwegein Cornelis Biemond Treatment Plant, there is some disturbance in the turbidity measuring in the pilot plant, so the effluent turbidity has been firstly normalized according to the turbidity trend of Nieuwegein Treatment Plant and secondly normalized with respect to influent turbidity for better comparison. After normalization, the 5% reference backwash gives a ripening peak of roughly FTU/FTU, while the ripening peak for 20% expansion is almost FTU/FTU, almost three times of the reference case. Something odd also can be seen from the 20% expansion with longer duration. The turbidity baseline is higher than the other two situations. The filter run time for the reference case is 17.7h, for the short 20% expansion is 19.7h while for the long 20% expansion is 21.7h to reach the same value of headloss. Normalized Turbidity (FTU/FTU) Time in Hour Reference 5% expansion Shorter 20% expansion Longer 20% expansion Figure 21: Normalized Effluent Turbidity for Experiments with Different Expansions 46 P a g e

47 There is also a change in the pressure drop lines. The clean bed resistance decreased from 39cm (reference case) to 32cm (the 20% expansion cases). Pressure Difference (cm) Time in Hour Pressure Drop for Reference case Pressure Drop for 20% Expansion Shorter Duration Pressure Drop for 20% Expansion Longer Duration Figure 22: Pressure Drop for Experiments with Different Expansions Amount of Dirt Coming Out During Ripening Peak One representative filter run per each backwash regime has been chosen to calculate the amount of dirt coming out during the ripening peaks. Representative from reference case 5% expansion backwash: Turbidity (FTU) Time (hr) Reference case Figure 23: Reference Effluent Turbidity for Expansion Difference Experiment Estimation of amount of dirt coming out during the obvious peak area = FTU. m (.. ) 47 P a g e

48 Representative from shorter duration of 20% expansion backwash: 0.3 Effluent Turbidity (FTU) Time in Hour short 20% expansion Figure 24: Effluent Turbidity for 20% Expansion Backwash with 210sec of Expansion Duration Estimation of amount of dirt coming out during the obvious peak area = FTU. m (.. ) Representative from longer duration of 20% expansion backwash: Effluent Turbidity (FTU) Time in Hour Longer 20% expansion Figure 25: Effluent Turbidity for 20% Expansion Backwash with 338sec of Expansion Duration Estimation of amount of dirt coming out during the obvious peak area = 1h FTU. m (.. ) The 20% expansion backwashing process shears out a significantly higher amount of dirt than the 5% backwashing procedure, so there is more loosened particles (6 times in amount) being flushed out during ripening from the 20% expansion case. When the expansion duration is longer, there is more 48 P a g e

49 dirt being washed away into the gutter, so less loose dirt left to contribute to ripening. The amount 0.14 FTU. m from the longer expansion wash is still bigger than the 5% expansion ( FTU. m ) because 20% expansion does shear off much more dirt and not all the extra dirt is able to go into the gutter during the backwash Pressure Drop Changes in Horizontal Layers There is a big difference in the pressure changes in horizontal layers between 5% expansion and 20% expansion cases. The lower layers (between pressure gauge 3 and 6) all registered smaller pressure drop after 20% expansion. For example in Figure 26, the bottom layer of the sand column has less resistance for 20% expansion case. Pressure Difference (cm) Time in Hour Reference 5% expansion Short 20% expansion Long 20% expansion Figure 26: Pressure Drop for Lowest Layer for Experiments with Different Expansions At first glance, this is probably because the 20% expansion backwash cleaned the bed better than 5% backwash so there is less resistance; less resistance means less pressure drop. However, the less resistance phenomenon only occurs in the lower layers. Between the first and the second pressure gauges, the pressure drop for 20% expansion is much higher than the reference case, see Figure 27 below. The short 20% expansion gives a huge increase in pressure drop during filtration process on the top layer of the sand compared to the 5% reference case, and long 20% expansion gives even higher pressure drop. 49 P a g e

50 30 Pressure Difference (cm) Time in Hour Reference 5% expansion Short 20% expansion Long 20% expansion Figure 27: Pressure Drop for Top Layer for Experiments with Different Expansions This sudden high pressure drop on the top layer of the sand bed can be explained by two possibilities. One important explanation is that the 20% expansion backwash caused some stratification of the bed whereby the finer grains become on top while the coarser ones are below. As those sand grains are the finest among all the grains in the filter bed, the clogging is most significant in the first few centimeters. This resembles more like cake-filtration where the majority of the pressure drop occurs on the top layer. This explanation is supported by the fact that the pressure drop in the lower layers of the sand in the later experimental sets showed less resistance than before this 20% expansion. There are pressure gauge measurements before and after the 20% expansion experiment to prove that the filter is more stratified after the 20% expansion. Figure 28 shows the upper layer change and Figure 29 shows the lower layers. Apparently the headloss during the reference filter runs after the 20% expansion experiment has higher headloss in the upper part of the sand column and lower headloss in the lower part of the sand column compared to the reference filter runs before this experiment. 50 P a g e

51 60 Pressure Drop (cm) Time in Hour Reference runs before 20% expansion Reference runs after 20% Expansion Figure 28: Pressure Drop in Upper 40cm of Sand Column, Before and After 20% Expansion Pressure Drop (cm) Time in Hour Reference Runs before 20% Expansion Reference runs after 20% Expansion Figure 29: Pressure Drop in Lower 70cm of Sand Column, Before and After 20% Expansion Another contributing factor is that, for 20% expansion case, there is a lot more dirt dislodged from the filter to go into the backwashed water, so the supernatant water left after backwashing is filled with suspended solids. When filtration re-starts, all these solids become a huge loading impact onto the top layer of the sand, blocking the pores and giving a very fast pressure drop for the top of sand column. From the Lindquist Diagrams drawn for the reference and the long 20% expansion case, it can also be seen that the top layer of sand column is taking more dirt for the later case. 51 P a g e

52 Filter Height (cm) Filter Height (cm) Absolute Pressure in cm of Water Column Absolute Pressure in cm of Water Column Hydrostatic Pressure Hydrostatic Pressure Clean Bed Resistance Clean Bed Resistance End of Run Resistance End of Run Resistance Sand Column Top Sand Column Top Bottom of Sand Column Bottom of Sand Column Figure 30: Lindquist Diagram for Reference of 5% expansion Figure 31: Lindquist Diagram for Long 20% expansion Although the top layer get very much more clogged for the 20% expansion cases, the filter run time is 2 4 hours longer than the reference case, probably because when the filter starts out with less attached particles, then it takes longer time for the clogging to reach the set criteria for backwashing Discussion The observation agrees with the first part of my hypothesis 2. From the observation that the clean bed resistance is lower for 20% expansion, it can be deduced that 20% expansion sheared off more dirt from the sand grains than the 5% expansion. It is possible that when the bed is cleaned too well by the 20% expansion, there is a lack of additional collectors at the start of the subsequent filter run, so the ripening turbidity is high as a lot of suspended solid are not trapped by the grains yet. As for the hypothesis that when the expansion time is longer, the more detached particles can have the chance to escape, so the less dirty the bed is at the end of the backwash, and thus higher ripening peak, it does not seem to agree with our observation because the ripening peak for both short and long 20% expansion is the same in Figure 21. It is possible that the short 20% expansion has already sheared off whatever particles that this backwash velocity can shear off, so longer duration does not make a difference to the amount of dirt left, and thus no different in ripening peak. 52 P a g e

53 4.3 Experiments With and Without Pre-wash General Observations Two kinds of backwash procedure, one reference and one with pre-wash with water were tested. The effluent turbidity has been normalized with respect to their respective influent turbidity. There are in total 10 filter runs, one representative run per each backwash method is plotted for direct comparison. The effluent turbidity including the ripening peak values after a pre-wash with water backwashing process is lower than reference backwash process. Normalized Turbidity (FTU/FTU) Time in Hour Reference Pre-Wash with Water only Figure 32: Normalized Effluent Turbidity for Experiments with and without Pre-wash Looking at the pressure drop graph, it is seen that the clean bed resistance for Pre-Wash with Water is 3cm less than the reference case. The filter run time for both cases is the same (19.7h). Pressure Drop (cm) Time in Hour Reference pressure drop Pre-wash with water pressure drop Figure 33: Pressure Drop for Experiments with and without Pre-wash 53 P a g e

54 However, from the pressure drop graphs of different layers and also from the Lindquist Diagrams drawn, it is found that on the top layer of the sand bed, pressure drop is higher for the pre-wash with water backwash. Figure 34 below gives the pressure drop on top layer of sand for both cases. It can be seen that the pressure drop on the top layer of the sand column increased from 13 to about 20cm when the pre-wash with water was used during the backwash procedure Pressure Drop (cm) Time in Hour Reference Pre-Wash with Water Figure 34: Pressure drop on Top Layer of Sand bed for Experiments with and without Pre-wash Different from the top layer, all the lower layers of the sand registered slightly lower pressure drop in the pre-wash with water case Amount of Dirt Coming Out During Ripening Peak One representative filter run per each backwash regime has been chosen to calculate the amount of dirt coming out during the ripening peaks. The two curves are shown on the same figure. The turbidity values here are not normalized in order to show the real amount of dirt coming out. Effluent Turbidity (FTU) Time in Hour Effluent Turbidity for Reference backwash Effluent turbidity for Pre-wash with water backwash Figure 35: Effluent Turbidity for Experiments With and Without Pre-Wash 54 P a g e

55 Reference case: estimation of amount of dirt coming out during the obvious peak area = 2hr. (.. ) 0.07 FTU. m Pre-wash with water (dotted line): estimation of amount of dirt coming out during the obvious peak area 2h. (.. ) 0.07 FTU. m The shapes of the two curves are quite similar, and although the turbidity baselines are different, the amount of dirt coming out during the ripening peak for their respective baseline is approximately the same. This could mean that the dirt brought deeper into the bed does not come out immediately after backwash, but take their time coming out gradually during the entire filter run Discussion The lower effluent turbidity of the pre-wash with water case compared to reference case agrees with our hypothesis 3 that adding a pre-wash with water step in front of the normal backwash procedure can improve filter performance. It is possible that the Pre-Wash with water step prevented any dirt being brought down deeper into the filter bed, so it helped preventing dirt from accumulating in the lower layers. Thus, the top layer is able to take more dirt before the pressure drop criteria reaches the value for automatic backwashing. Figure 36 shows the difference in depth that the dirt-trapping layer can go with and without the dirt at the bottom of the sand column. One thing to note is that our filter at that moment has already been stratified, so there is more cake-filtration behaviour than deep-bed filtration characteristics. Figure 36: Difference brought by Pre-wash with Water When there are solids brought down by normal reference backwashing procedure into the lower part of the sand column, the progression of pore-clogging layer goes to a certain depth, let say 20cm, just before total pressure drop reaches criteria 70cm for backwashing. When the pre-wash is used, the 55 P a g e

56 dirt attached on top is no longer brought down, so the initial resistance is lower. The pore-clogging layer is able to progress deeper into the sand column before the pre-set headloss criterion for backwashing is reached. Therefore, more solids can be removed from the influent in this case, producing better effluent quality. 4.4 Experiments With and Without Collapse-Pulsing General Observations Two kinds of backwash procedures, one reference and one with collapse-pulsing (CP) were tested. In this reference backwash case, the duration of backwash step 1 has been extended to 300s in order to have the same duration as the collapse-pulsing backwashing case. This means that the reference backwash for this experimental set is a bit different from the reference backwash for previous experimental sets. The effluent turbidity has been normalized with respect to their respective influent turbidity. One representative run per each flow rate is plotted for direct comparison. There is no obvious difference in the ripening peak values or in the initial resistance, and even the filter run time is the same. The ripening duration is very similar, and the baseline turbidity values too. Normalized Turbidity (FTU/FTU) Reference Time in Hour Collapse-Pulsing Figure 37: Normalized Effluent Turbidity for Experiments with and without CP 56 P a g e

57 Pressure Drop (cm) Time in Hour Pressure drop for a reference filter run a filter run from Collapse-Pulsing backwash Figure 38: Pressure Drop for a filter run from Experiments with and without CP Amount of Dirt Coming Out During Ripening Peak One representative filter run per each backwash regime has been chosen to calculate the amount of dirt coming out during the ripening peaks. Turbidity (FTU) Time in hr Reference backwash Collapse Pulsing backwash Figure 39: Effluent Turbidity for Experiments with and without CP Reference case: estimation of amount of dirt coming out during the obvious peak area = 2hr. (.. ) FTU. m Collapse-pulsing (dotted line): estimation of amount of dirt coming out during the obvious peak area = 2hr. (.. ) FTU. m 57 P a g e

58 There is thus negligible difference in terms of the amount of dirt coming out during ripening peaks, thus no ground to say that collapse pulsing creates a very different scouring environment for the grains compared to existing reference backwash Discussion It seems that there is no evidence to support our Hypothesis 4 that CP will give greater ripening. One explanation is that this collapse pulsing condition used in the experiment is not the optimal condition to give the maximum scouring effect on the grains. Different filters have different optimal conditions, so maybe this V/V mf = 40% (that does well for Amirtharajah s filter) does not apply to this pilot plant filter. Due to time constraint, other percentage values were not tried to find the optimal condition for this filter. Another possibility is that the current backwash procedure is already efficient in removing attached particles. Maybe the abrasion between sand grains is already quite violent in the existing backwash procedure, so collapse-pulsing does not create much extra abrasion. 4.5 Experiments With and Without Draining of Supernatant Water General Observations This is a special experiment among all the experiments performed, so the way I analyze the results is also different from the previous four experimental sets. The same experiment (draining supernatant water immediately after backwash, before filtration) has been repeated twice. From both times, it seems that there is not much difference in ripening peak values between reference backwash procedure and draining of supernatant water experiment. Average temperature for that two runs are quite similar. Turbidity (FTU) Time in Hour Reference Backwash the 1st time Draining of Supernatant water Figure 40: Ripening Peaks for 1st time of Experiments with and without Draining of Supernatant In the second time, there is some slight lowering of turbidity between minutes after restarting of the filtration process. This could be caused by the variation of incoming turbidity because the average incoming turbidity between the first minute to the 12 th minute immediately after 58 P a g e

59 backwashing was 1.65FTU lower than the influent turbidity between 12 th to 24 th second. As the water takes time to flow down to the effluent, the first 12 minutes significantly lower incoming turbidity contributed to the lower effluent turbidity between minutes. Therefore, it should not have anything to do with the draining of supernatant water. Turbidity (FTU) Time in Hour Reference backwash of the 2nd time Draining of supernatant water Figure 41: Ripening Peaks for 2nd time of Experiments with and without Draining of Supernatant Discussion Hypothesis 5 assumed that the supernatant water is the dirtiest and removing the supernatant water is able to remove the highest peak of the ripening peak and thus reduce the ripening duration. However, the reality is that there is no evidence to support our hypothesis. One possible reason might be that the draining of supernatant water took 30 minutes, so during this time, the particles inside the water re-deposited on top of the sand bed or concentrated into the lower 15cm of water that cannot be drained, so the draining did not remove enough of the particles to show any obvious difference. It is also likely that the dirty supernatant water s suspended solids concentration has been diluted on the way down through the filter due to turbulent mixing so that the high peak cannot be detected. Maybe our pilot plant s filter has a higher mixing and less plug-flow than the filters used by Amirtharajah, so the results observed are different. 4.6 Observations and Recommendations There could be a few inaccuracies involved in the experiments. Firstly, it has to be noted that the turbidity measurement accuracy can be affected by various influent compositions. For example, the presence of light absorbing particles, such as biological material like algaes or pathogenic micro organisms, could influence the light signal and give non-accurate measure of suspended solids inside the water. From February to April, the environmental temperature rose from 4 o C to 16 o C so there are different microorganisms present in the water, as well in the pipelines. Therefore, inaccuracy in turbidity measurement might be a possibility. For future research, it is 59 P a g e

60 recommended to keep influent characteristics the same, although it will require higher investment in facility. Secondly, due to facility constraint, there is only one effluent turbidity meter for these three filters. It would be the best if there are three turbidity meters for each filter so that a reference filter (filter 3) effluent turbidity can be measured simultaneously while the experiment is conducted on filter 1 to facilitate better comparison. However, as this ideal situation is not possible, the reference condition is measured first before measuring the effluent turbidity from the modified backwash procedure. This introduces possibility of inaccuracy due to the variation of influent characteristics. In future research, it is recommended to have an identical filter along with the experimental filter and measure both at the same time to ensure good comparison of results. Thirdly, due to time constraint of this thesis research, not all intended experiments could be done. For example, more trials can be done to find the optimal collapse-pulsing condition for these filters. Or, for instance, many different degrees of expansion (10%, 15%, 25%, 30%...) can be tested to gain a more comprehensive understanding. Moreover, for the last experimental set, the draining time was relatively long due to equipment constraint. It is recommended that a faster draining method should be employed in further research to reduce the chances of solids concentrating at the bottom part of the supernatant water or even depositing on top of sand bed. In addition, it will be more accurate if the turbidity measurement re-starts immediately after the end of backwash, so the entire ripening progression can be registered. Lastly, this analysis results only apply to surface water treatment. Wastewater, with its totally different water compositions, might not give the same results as these experiments. 4.7 Sectional Conclusion To conclude what has been said in this section, it has been observed that different flow rates do give different degree of breakthrough, which corresponds to our prediction. Also, additional collectors theory has been supported in many occasions from the experimental results. The suggestion of using pre-wash with water before reference backwash method has also proven to be a good suggestion. On the other hand, the experimental results did not show support to Amirtharajah s theory on collapsepulsing or ripening stages, but it does not necessarily mean that his theory is not valid. There are many factors that could influence the accuracy of these experiments, so recommendations have been provided for further research. 60 P a g e

61 5: Stimela Model 5.1 Purpose Stimela software was developed by Delft University of Technology, Kiwa and DHV, especially designed for water quality modelling. It calculates the change of water quality parameters during water treatment process to enable the users to design and simulate the drinking water treatment processes. This software has been used in this thesis research to aid our understanding of the effect of different backwash procedures on ripening peak and effluent quality. 5.2 General Information The theoretical basis of this software is the Maroudas equation mentioned in Filtration theory. In this Maroudas theory, it is assumed that the filtration coefficient decreases linearly as clogging increases. Therefore, a small modification of the Matlab code was made to incorporate the term called lambda shift. Lambda shift value is a value that determines when the filtration coefficient will decrease from the initial rising trend. It is assumed that immediately after backwashing, the filtration coefficient increases as there are more and more additional collectors on the grains. After ripening is completed, it starts to decrease due to pore clogging. The smaller the lambda shift value, the smaller the ripening peak. Figure 42: Lambda Shift Explanation Calibration has been used first to determine the range of values of the parameters, while fine-tuning of parameters by trial-and-error on Stimela was used later to determine the exact values. Some parameters have always been fixed in value during the simulation runs: Table 9: Constant-Value parameters used in Stimela Input Parameter Value Unit Filter surface area m 2 Water level above filter bed 1.26 m Bed height 1.1 m 61 P a g e

62 Grain size 0.97 mm Number of completely mixed reactors 10 - Maximum poreflling 70 % Stimela was employed for the 3 experimental sets with different backwashing procedures to see their effects. They are the experiments on different expansions, with and without pre-wash and with and without CP. 5.3 Compromises Made A lot of effort has been trying to match every aspect (both pressure drop and turbidity throughout the filter run), but perfect match for both pressure drop and turbidity is impossible for most of the cases. Only when the lambda shift is 0 that the pressure drop line from Stimela can be very similar to the straight pressure drop line from the experiments. However, when lambda shift is 0, the model is not able to match the ripening phenomenon because the simplification of Maroudas equation assumes a linearly decreasing filtration coefficient during a filter run. The decreasing filtration coefficient causes the turbidity predicted by model to rise all the time instead of having a ripening peak and then decrease. On the other hand, when lambda shift value is increased to match the turbidity, the pressure drop line becomes more curved. Thus, a compromise has been made to reach a reasonable match for both pressure drop and turbidity lines. Another compromise made to match the graphs better is that the filtration coefficient for the experiments about different expansions is much larger than the filtration coefficient of the later two experimental sets. There is a 2.5 weeks interval between the experiments on different expansions and the other two experimental sets. During the 2.5 weeks, the average temperature increased from 6 C to 12 C. The theoretical equation mentioned for filtration coefficient in Section shows that the filtration coefficient should be larger in the later two experimental sets because of lower kinematic viscosity. Instead, the filtration coefficient is smaller in the later two experimental sets. One possible explanation could be that warm spring had arrived during the 2.5 weeks of interval. The influent water composition has changed in a way that microorganisms such as tiny diatoms had an exponential growth due to the high temperature of that season. This change of water composition might not be reflected by turbidity meter because the presence of light-absorbing particles, such as biological material, could influence the light signal and give non-accurate measure of suspended solids inside the water. Stimela was thus trying to mimic the experimental results by giving a different range of filtration coefficient values to the later experimental sets. Therefore, two ranges of lambda values are used respectively, one is 7.6 m -1 for the experiments on different expansions and the other range is between 5 m -1 and 5.3 m Parameter Values found for Experiments Experiments with Different Expansions The optimal parameter values for this experimental set are in Table P a g e

63 The mass density of floc is not constant here and the lambda shift increased when 20% expansion is used. Week Backwash Procedure Table 10: Experiments with Different Expansions Optimal Parameters Filter Porosity (%) Mass density of floc (Kg/m 3 ) Clean bed filtration coefficient (/m) Lambda Shift (/m) 5 Reference case Short 20% expansion Long 20% expansion The variation of mass density of floc might be due to the variation of incoming water characteristics. The lambda shift values for the 20% expansion is higher than that for 5% expansion reference backwash, because the 20% expansion cleansed the bed better so additional collectors effect was not large enough at the beginning of filtration process. The higher lambda shift value is to mimic the higher ripening peaks. The slightly lower clean bed filtration coefficient for the long 20% expansion backwash case is to match the slightly higher effluent turbidity measured. The higher porosity for 20% expansion means that the large expansion makes the voids between the sand grains bigger. The resulting graphs from Stimela can be found in Appendix Experiments with and without Pre-Wash The optimal parameter values for this experimental set are in Table 11. Week Backwash Procedure Table 11: Experiments with and without Pre-Wash Optimal Parameters Filter Porosity (%) Mass density of floc (Kg/m 3 ) Clean bed filtration coefficient (/m) Lambda Shift (/m) 10 Reference case Pre-wash with water The smaller lambda shift value for the pre-wash with water case is smaller to reflect the better effluent quality. The ripening peak value for pre-wash with water is lower, so smaller lambda shift value. On the other hand, the earlier the filtration coefficient turns to the decreasing trend, the earlier the onset of breakthrough too. That is why it is observed in Figure 35 that the turbidity curve goes up a little bit at the end of the filter run for the pre-wash with water case. The clean bed filtration coefficient is slightly higher in the pre-wash case because the general effluent turbidity is lowered, implying a higher filtration coefficient and thus better removal efficiency. The resulting graphs from Stimela can be found in Appendix Experiments With and Without CP The optimal parameter values for this experimental set are in Table 12. The lambda shift for CP is the same as the reference case. 63 P a g e

64 Week Backwash Procedure Table 12: Experiments with and without CP Optimal Parameters Filter Porosity (%) Mass density of floc (Kg/m 3 ) Clean bed filtration coefficient (/m) 12 Reference case Collapse-Pulsing Lambda Shift (/m) There is not much difference in the parameter values because the experimental results do not show much difference in effluent quality for the two backwash procedures. The graphs from Stimela for this set of experiments matches very well, as you can see in Figure 43 and 44. The curves with x markers are the selected data points from the experiments while the normal curves are predicted by Stimela. Figure 43: Stimela Graph for Reference run without CP Figure 44: Stimela graph for backwash with CP 64 P a g e

65 6: Evaluation Tool of Backwash Procedures 6.1 Parameter Determination Looking at the results of various backwash procedures, it might be useful to have a standard tool to evaluate the effectiveness of the backwashing procedures. Firstly, important parameters have been identified. Parameter Units Reason Table 13: Parameters for Evaluation Water produced per unit filter surface area per hour during Filtration Filter run time compared to backwash duration Backwash water used Air used during backwashing m 3 /m 2.hr hr m 3 per backwash m 3 per backwash Higher clean water production per unit filter surface area means higher yield per land area and thus lower investment for the same amount of clean water produced The longer the filter run time is, the less frequent the backwashing, and the less backwash water is required, so higher production of clean water The less backwash water used for each backwashing, the less clean water used, so more production of clean water The less air used during backwashing, the less energy consumed by air pump, so more savings on production cost Ripening peak value FTU The lower the ripening peak, usually the faster that the turbidity lowers to good standard, so less water-to-waste amount needed, thus less wastage of water and more production of clean water Ripening duration min The shorter the ripening duration, the less water-to-waste amount needed, so less wastage of water and more production of clean water Average Percentage Removal Percentage of time where the effluent turbidity is below 0.1FTU % The higher the percentage removal of suspended solids, the better the quality of produced clean water. With less suspended solid in the effluent, the transportation pipelines can be flushed less frequently or less chemicals can be added, thus lower cost % 0.1FTU is the industrial limit for Dutch water treatment plant to reach. Effluents with higher turbidity level is not desirable, so the more time where the effluent turbidity is below 0.1FTU, the higher the quality of produced water and the less extra maintenance needed to increase plant efficiency to reach desirable level Clean bed resistance immediately after backwashing Represented as cm of water column A lower initial bed resistance immediately after backwashing usually translates to longer filter run time because it takes longer for the bed resistance to reach a pre-set value for backwash 65 P a g e

66 Secondly, for practical applications, it is the best to translate the parameters into quantitative numbers, such as cost, for easier comparison. Table 14 shows the process of translating the parameters into cost, often combining or modifying the parameters. Table 14: Cost Function of Parameters Basic Parameter Sub-parameters Relates to cost function (Euro/m 3 water produced in one run) Backwash water usage Amount of water used Treatment cost m 3 Euro/m 3 Cost of water used per backwash: Backwash water used Treatment cost Flow Rate ilter run time Filter run time h Flow Rate m 3 /h Air used during backwashing Air used duration Pump power h kw Cost of pumping air through filter during backwash: Air used duration pump power Electricity cost Flow Rate ilter run time Electricity cost Euro/kWh Filter run time h Ripening peak and duration Flow Rate m 3 /h Ripening h duration Flow rate m 3 /h Assuming water-to-waste diverts away the ripening duration effluent, then the values of ripening peak does not make a difference in terms of cost. Only the duration matters. Treatment cost Euro/m 3 Cost incurred during water-to-waste process: Filter run time h Ripening duration Flow rate treatment cost Flow Rate ilter run time Ripening duration Treatment cost = ilter run time Percentage of time where the effluent turbidity is Duration whereby the effluent turbidity is above h Profit loss when the effluent turbidity is higher than limit: 66 P a g e

67 below 0.1FTU 0.1FTU Flow rate m 3 /h Duration above 0.1FTU Flow rate Treatment cost Flow Rate ilter run time Duration above 0.1FTU Treatment cost = Filter run time Treatment cost Filter run time Euro/m 3 h Effluent turbidity Reference FTU or Effluent g/m 3 Turbidity New Effluent FTU or Turbidity g/m 3 from the modified backwashing procedure Flow rate m 3 /h Treatment Euro/m 3 cost Filter run h time Average /month flushing frequency Assuming that the effluent transportation pipelines have to be cleaned or flushed periodically. If the effluent turbidity is low, less cleaning is required. Assuming flushing is used n times per month for the effluent turbidity from the reference backwash procedure. Each time 10m 3 /h of water is flushed for 15 minutes, then the cost of each flushing/cleaning is 10 treatment cost = x euro/month Assuming the reference effluent turbidity is y FTU. Then the cost of cleaning of pipelines per month from the new backwash regime = x (n ) = cost of cleaning per month Divide this cost by the amount of water produced from each filter run in the whole treatment plant gives the cost in the same dimension as other items. However, in Nieuwegien, flushing is not done, so the cost for this item is zero. However, in other plants and in different situations, it is possible that this cost item will be used. 6.2 Example Calculation I will use the example turbidity from the experiments on different expansions and approximate total flow and cost for full-scale water treatment plant to illustrate the use of this evaluation tool. For easier calculation, it is assumed that the total flow rate in a full scale plant is 100 times of our pilot plant, so the flow rate is 350m 3 /h. The total amount of backwash water used is thus also 100 times more. The last item was not calculated in this case because no flushing of lines is used in this case. Values can be put into the table to form part of the calculation when deemed necessary for other plants. Table 15, 16 and 17 are the Excel calculations of various cost per unit amount of water produced per filter run. If we look at the sum of cost at the end of each table, we can see that the cost of reference 67 P a g e

68 backwash procedure in this case is better than the other two cases, which corresponds to the actual turbidity observation of the experimental set with different expansions. Table 15: Reference backwash for Experiments with Different Expansions Cost Calculation Basic Parameter sub-parameter needed subparameter values cost per m 3 of water produced per run 1 Backwash water used backwash water used (m 3 ) 137 treatment cost (Euro/m 3 ) 0.3 Filter run time (h) 17.7 Flow rate (m 3 /h) Air used during backwash Air used duration (h) 5 Pump power (kw) 2.2 Electricity cost (Euro/kWh) Filter run time (h) 17.7 Flow rate (m 3 /h) Ripening duration Ripening duration (h) 1 Flow rate (m 3 /h) 350 treatment cost (Euro/m 3 ) 0.3 Filter run time (h) Percentage of time where the effluent turbidity is below 0.1FTU Duration whereby the effluent turbidity is above 0.1FTU (h) 0 Flow rate (m 3 /h) 350 treatment cost (Euro/m 3 ) 0.3 Filter run time (h) Effluent turbidity Reference effluent turbidity (FTU) New Effluent Turbidity (FTU) Flow rate (m 3 /h) Treatment cost (Euro/m 3 ) Filter run time (h) Average flushing frequency (/month) 0 Sum of cost: P a g e

69 Table 16: 20% Short Expansion cost calculation Basic Parameter sub-parameter needed subparameter values cost per m3 of water produced per run 1 Backwash water used backwash water used (m 3 ) 137 treatment cost (Euro/ m 3 ) 0.3 Filter run time (h) 19.7 Flow rate (m 3 /h) Air used during backwash Air used duration (h) 5 Pump power (kw) 2.2 Electricity cost (Euro/kWh) Filter run time (h) 19.7 Flow rate (m 3 /h) Ripening duration Ripening duration (h) 1 Flow rate (m 3 /h) 350 treatment cost (Euro/ m 3 ) 0.3 Filter run time (h) Percentage of time where the effluent turbidity is below 0.1FTU Duration where the effluent turbidity is above 0.1FTU (h) 0.5 Flow rate (m 3 /h) 350 treatment cost (Euro/ m 3 ) 0.3 Filter run time (h) Effluent turbidity Reference effluent turbidity (FTU) New Effluent Turbidity (FTU) Flow rate (m 3 /h) Treatment cost (Euro/ m 3 ) Filter run time (h) Average flushing frequency (/month) Sum of cost: P a g e

70 Table 17: 20% Long Expansion cost calculation Basic Parameter sub-parameter needed subparameter values cost per m3 of water produced per run 1 Backwash water used backwash water used (m3) 211 treatment cost (Euro/m3) 0.3 Filter run time (h) 21.9 Flow rate (m3/h) Air used during backwash Air used duration (h) 5 Pump power (kw) 2.2 Electricity cost (Euro/kWh) Filter run time (h) 21.9 Flow rate (m3/h) Ripening duration Ripening duration (h) 1 Flow rate (m3/h) 350 treatment cost (Euro/m3) 0.3 Filter run time (h) Percentage of time where the effluent turbidity is below 0.1FTU Duration where the effluent turbidity is above 0.1FTU (h) Flow rate (m3/h) treatment cost (Euro/m3) Filter run time (h) Effluent turbidity Reference effluent turbidity (FTU) New Effluent Turbidity (FTU) Flow rate (m3/h) Treatment cost (Euro/m3) Filter run time (h) Average flushing frequency (/month) 0 Sum of cost: Therefore, from a cost perspective, the ranking (from good to bad) of backwash procedure is: 70 P a g e

71 Reference Backwash (0.024 Euro per m 3 ) 20% Long Expansion Backwash (0.028 Euro per m 3 ) 20% Short Expansion Backwash (0.029 Euro per m 3 ) The difference in cost per m 3 of water is tiny, but if we think about the yearly amount of water produced by the whole plant, then a better backwashing method will lead to a significant saving for the water company. Of course, when this evaluation tool is applied to other water treatment plants, the prices/cost has to be adjusted to suite the particular situation. 71 P a g e

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73 7: Conclusion In these experiments from February to April, it has been observed that higher flow velocities created higher interstitial velocities which when high enough, could shear off already-attached particles and induce early breakthrough. There was also evidence supporting the additional collectors theory that if the bed is cleansed too well during backwash, then there will be a lack of additional collectors at the start of the subsequent filter run, so the ripening turbidity is high. 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. Furthermore, 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. There are results that agree to the theories but there are also results that are not as what is expected by theory. There are possible explanations for the observations but there are also assumptions that will have to be investigated by further and more comprehensive research. The Stimela software generally gives reasonable values of parameters to give matching results between modelled and measured turbidity values. The proposed evaluation tool using cost factor is also in a preliminary form which should be adapted to individual cases if it is to be applied in different treatment plants. 73 P a g e

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75 8: Reference A. Amirtharajah, Optimum Backwashing of Filters with Air Scour: A Review, Water Science & Technology, Vol 27, No. 10, Page , 1993 A. Amirtharajah, The Interface between Filtration and Backwashing, Water Res. Vol. 19, No. 5, pp , 1985 C. Ogbonnaya, Lesson 6, Filtration, Water / Wastewater Courses, Mountain Empire Community College, 2010 D. V. Vayenas, S. Pavlou & G. Lyberatos Development of a Dynamic Model Describing Nitrification and Nitrafication in Trickling Filters, Wat. Res. Vol. 31, No. 5, pp , 1997 G. D. Michalakos, J. M. Nieva, D. V. Vayenas & G. Lyberatos, Removal of Iron from Potable Water Using a Trickling Filter, Wat. Res. Vol. 31, No. 5, pp , 1997 H. Zhu, D. W. Smith, H. D. Zhou & S. J. Stanley, Improving Removal of Turbidity Causing Materials by Using Polymers as a Filter Aid, Wat. Res. Vol. 30, No. 1, pp , 1996 J.C. van Dijk, Granular Filtration Drinking Water Treatment 1, TUD Open Courseware website J. E. Amburgey, Optimization of the Extended Terminal Subfluidization Wash (ETSW) Filter Backwashing Procedure, Water Research 39, , 2005 J. F. Colton, P. Hillis & C. S. B. Fitzpatrick, Filter Backwash and Start-up Strategies for Enhanced Particulate Removal, Wat. Res. Vol. 30, No. 10, pp , 1996 J. Traenckner, B. Wricke & P. Krebs, Estimating Nitrifying Biomass in Drinking Water Filters for Surface Water Treatment, Water Research 42, , 2008 L. Huisman & J. C. van Dijk, Rapid Filtration, CTgz4470, TUDelft, August 1998 M. Johnson, D. D. Ratnayaka & M. J. Brandt, Chapter 8 - Water Filtration Granular Media filtration, Twort s Water Supply, 6 th Edition, Published by Elsevier Ltd, 2009 N. Pizzi, Optimizing Your Plant s Filter Performance, University of Washington, American Water Works Association, 2000 P. Bose, Filtration - Water and Wastewater Engineering, National Program on Technology Enhanced Learning, 2010 R. P. Beverly, Filter Troubleshooting and Design Handbook, American Water Works Association, 2005 S. D. Lin, Water and Wastewater Calculations Manual, McGraw-Hill, 2001 S. Han, C. S. B. Fitzpatrick & Andrew Wetherill, The Impact of Flow Surges on Rapid Gravity Filtration, Water Research 43, page , 2009 T. D. Davis, Filter Backwashing Minimize Media Loss, Maximize Water Conservation, IDS-Water White Paper website, 2007 The Water Treatments, Rapid Sand Filters, P a g e

76 TU Delft, Civil Engineering and Geosciences, Monitoring the Operation of Sand Filters Online, V. Miska-Markusch, Effluent Filtration for More than Particle Removal, TUD Thesis, P a g e

77 9: Appendix 9.1 Stimela graphs for optimal parameters Figure 45: Optimal Parameters for Reference case of Experiments with Different Expansions 77 P a g e

78 Figure 46: Optimal Parameters for short 20% expansion Figure 47: Optimal Parameters for long 20% expansion 78 P a g e

79 Figure 48: Optimal Parameters for Reference case of Experiments with and without Pre-Wash Figure 49: Optimal Parameters for pre-wash with water 79 P a g e

80 9.2 Lindquist Diagrams Filter Height (cm) Absolute Pressure in cm of Water Column Hydrostatic Pressure Clean Bed Resistance End of Run Resistance Sand Column Top Bottom of Sand Column Figure 50: 3500l/h flow rate (7m/h) Lindquist Diagram Filter Height (cm) Absolute Pressure in cm Water Column Hydrostatic Pressure Clean Bed Resistance End of Run Resistance Sand Column Top Bottom of Sand Column Figure 51: 2500l/h flow rate (5m/h) Lindquist Diagram 80 P a g e

81 Filter Height (cm) Absolute Pressure in cm of Water Column Hydrostatic Pressure Clean Bed Resistance End of Run Resistance Sand Column Top Bottom of Sand Column Figure 52: 1500l/h flow rate (3m/h) Lindquist Diagram Filter Height (cm) Absolute Pressure in cm of Water Column Hydrostatic Pressure Clean Bed Resistance End of Run Resistance Sand Column Top Bottom of Sand Column Figure 53: Reference Case for Experiments with and without Pre-Wash Lindquist Diagram 81 P a g e

82 Filter Height (cm) Absolute Pressure in cm of Water Column Hydrostatic Pressure Clean Bed Resistance End of Run Resistance Top of Sand Column Sand Column Bottom Figure 54: Pre-Wash with Water Lindquist Diagram Filter Height (cm) Absolute Pressure in cm of Water Column Hydrostatic Pressure Clean Bed Resistance End of Run Resistance Top of Sand Bottom Sand Column Bottom Figure 55: Reference Case for Experiments with and Without CP Lindquist Diagram 82 P a g e

83 Filter Height (cm) Absolute Pressure in cm of Water Column Hydrostatic Pressure Clean Bed Resistance End of Run Resistance Sand Column Top Bottom of Sand Column Figure 56: Collapse-Pulsing backwash Lindquist Diagram 83 P a g e