Paper no. 3124777
APCChE 2015 Congress incorporating Chemeca 2015 27 Sept – 01 Oct 2015, Melbourne, Victoria
Sewage sludge dewatering properties for predicting performance of industrial thickeners, centrifuges and filters Samuel J. Skinner1*, Anthony D. Stickland1, Raul G. Cavalida1, Shane P. Usher1 and Peter J. Scales1 1 Department
of Chemical and Biomolecular Engineering, The University of Melbourne, Melbourne
*Corresponding
author. Email:
[email protected]
Abstract: Solid-liquid separation involving suspensions is important in a large range of industrial applications, including mineral processing and wastewater treatment and disposal. The development of theoretical descriptions of solid-liquid separation, or dewatering, has allowed modelling of different dewatering behaviour and optimisation of dewatering device design. Buscall and White [1] introduced a fundamentally rigorous dewatering theory for suspensions that is able to account for material compressibility. The theory uses two key parameters; the extent of dewatering or compressive yield stress, Py(ϕ), and the rate of dewatering or hindered settling function, R(ϕ). R(ϕ) is a material property dependent on the solids volume fraction, ϕ, that quantifies the interphase drag or hydrodynamic force on suspensions. This can be applied to a solid moving through liquid during settling or liquid moving through solids in the case of cake consolidation. There are existing methods capable of determining R(ϕ) from gravitational settling, centrifugal settling and filtration tests that have been shown to accurately model dewatering behaviour. Sewage sludge, the suspension studied herein, is typically dewatered using operations such as high-speed centrifugation and plateand-frame filter presses. To predict the expected behaviour in such devices, dewatering properties were extracted from a sewage sludge over a large range of solids concentrations and were validated using comparison of model predictions versus experimental data for settling, centrifugation and filtration. These results enable more rigorous characterisation of material dewaterability, thus allowing prediction and optimisation of behaviour in industrial thickeners, centrifuges and filtration devices. Keywords: Sewage sludge, dewatering equipment, thickening, centrifugation, filtration
1 Introduction The wastewater treatment process produces vast quantities of a solids rich by-product termed sewage sludge. The reduction in volume of this sludge (dewatering) is an important and expensive industrial process. The ultimate objective of sewage sludge dewatering is to minimise the volume of sludge in the most economic manner in order to reduce transport, disposal or reuse costs. The final product of this process is a highly concentrated sludge that can be disposed of in stockpiles or by incineration, or reused beneficially in land applications. The selection of dewatering equipment is generally determined by the economics and end-user application. Simple disposal practices, including stockpiling, typically use low cost methods such as gravity settling in thickeners that only achieve low solids concentration, followed by solar drying. Applications that require highly concentrated sludge, such as incineration or pelletisation (for reuse), require more energy intensive and higher cost processes including centrifugation or filtration. Examples of these types of dewatering equipment are solid-bowl decanter centrifuges, gravity belt filter presses and plate-and-frame filter presses. The final solids concentration that can be achieved, hence the final volume of sludge, depends significantly on the feed concentration as well as the type of sludge and extent to which it has been digested [2, 3]. For example, operating data suggests that aerobically digested sludge may only achieve 12 wt% off a belt filter compared to a thermally hydrolysed sludge that may reach 50 wt% from the same dewatering device [4]. In order to give a general context for sludge dewatering, the typical range of achievable solids concentrations for each type of dewatering device is presented in Table 1, where cake solids refers to the suspended solids mass fraction, x (wt%). 1
Table 1: Typical final cake solids concentrations achieved by dewatering equipment for sewage sludges of differing origins and makeup Equipment type Gravity Thickener Gravity Table (Belt Thickener) Rotary Drum Thickener Belt Filter Press Solid-Bowl Decanter Centrifuge Plate-and-Frame Filter Press Diaphragm Press Rotary Screw Thickener Electro-dewatering (Pilot-scale)
Cake solids (wt%) 3–8 4–7 4 – 12 12 – 35 17 – 41 29 – 49 31 – 51 35 – 55 42 – 52
Ref. [5] [6] [7] [8] [8, 9] [8] [8] [7, 10] [11, 12]
The ability to model these processes is important for direct quantitative comparison and optimisation. The main difficulties with modelling sewage sludge dewatering are the low concentration at which a network forms, termed the gel point, and the extremely compressible nature of the material. Many biological sludges, which contain high molecular weight and cross-linked biomolecules such as extracellular polymeric substances (EPS), show very different filtration profiles from typical mineral slurries [13, 14]. Conventional models of filtration proposed by Ruth [15] and Tiller and Shirato [16] are unable to account for this extremely compressible behaviour because they describe filtration in terms of an average specific cake and membrane resistance. More accurate modelling was achieved when these parameters were redefined from average values to consider parameters that are dependent on the local solids concentration [17]. Compressive rheology, which is based on the work of Landman, White and Buscall [1, 18, 19], is another approach that contains parameters related to those from conventional filtration modelling. This theoretical framework for dewatering implicitly accounts for material compressibility by considering only local solids volume fractions [1]. The two main parameters are the compressive yield stress, Py(ϕ), and the hindered settling function, R(ϕ). These parameters quantify the extent and rate of dewatering respectively. The relationship between the compressive yield stress curve and the inputsoutputs of typical dewatering operations is shown in Figure 1. Note that with the aid of flocculants and use of shear force, concentrations higher than the presented compressive yield stress curve can be achieved.
Compressive Yield Stress, Py(ϕ) (kPa)
1000
Gel point, ϕg Filtration
100 Centrifugation 10 Sedimentation 1 Thickening 0.1 Clarification 0.01 0.001 0.001
Feed 0.01
0.1
1
Final
Solids Weight Fraction, x (w/w) Figure 1: Typical feed and final solids concentration ranges for industrial dewatering equipment superimposed on a compressive yield stress curve, Py(ϕ), for a typical sewage sludge with a low gel point (adapted from de Kretser et al [20])
2
Laboratory scale experiments involving gravity settling, centrifugation and filtration are able to characterise the dewaterability of a sludge over the range of concentrations observed in industry [21]. Extraction of the parameters from experimental results contains numerous assumptions and approximations that require validation. This validation involves using the material properties as inputs to one-dimensional models of sedimentation, centrifugation and filtration, in which the governing equations are solved to produce a numerical output for comparison to the original results. These results enable a more rigorous characterisation of material dewaterability and greater confidence in further modelling results. Although method validation has been completed for pressure filtration, gravity and centrifugal settling of sewage sludges, these three methods have not been verified together for the same sludge sample. Once validated, these material parameters can be used to predict the operation of thickeners, centrifuges and filters to allow for quantitative comparison of design options and design optimisation. The example used in this study is a sewage sludge provided by City West WaterTM from Altona Treatment Plant, Melbourne. The method of characterisation and validation of dewatering properties are outlined and the predictions of performance in thickening, centrifugation and filtration are presented to demonstrate how each of these operations can be optimised and compared.
2 Theory 2.1 Dewatering Theories Kynch’s theory of sedimentation [22], proposed in 1952, was the first major step towards a complete description of dewaterability. He stated that the settling velocity was related to the material volume fraction, ϕ (vol%). The suspended solids volume fraction, ϕ, and suspended solids weight fraction, x, are related by the following equation
=ݔ
ଵା
ଵ
ഐ
(1)
భ ቀ ିଵቁ ഐೞೞ ഝ
where ρliq and ρsolids are the densities of the liquid and solid phases. This theory was further developed to account for material compressibility, as well as to extend its application to centrifugation and filtration. Complete dewaterability theories, which use different parameters but are all related, have been developed by Tiller and Hsyung [23], Landman and White [18] and Garrido et al [24]. The compressive rheology approach based on the work of Buscall, White and Landman is used herein, as it implicitly accounts for extreme compressibility and has been proven to accurately model dewatering behaviour of sewage sludges [14, 25]. In this theory, the governing equation for compressional dewatering in one-dimension is a secondorder hyperbolic partial differential equation డథ డ௧
డ
డథ
= డ௭ ቂܦሺ߶ሻ డ௭ + ߶ݍሺݐሻ + ߶∆ߩ݃
ሺଵିథሻమ ோሺథሻ
ቃ
(2)
The height of the sample is given by z, the bulk flow is q(t), the density difference between the liquid and solid phase is Δρ = ρsolids - ρliq. The solids diffusivity, D(ϕ), is a function of both the extent of dewatering and material permeability. It can be substituted into Equation 2 to solve the governing equation if Py(ϕ) and R(ϕ) are known for a particular suspension
ܦሺ߶ሻ =
ௗ ሺథሻ ሺଵିథሻమ ௗథ
ோሺథሻ
(3)
The physical meaning of each of these parameters will be briefly discussed in the following sections.
2.2 Extent of Dewatering The compressive yield stress, Py(ϕ), is a material dependent parameter that is non-linear with respect to ϕ. The pressure applied to a suspension must exceed Py(ϕ) for the structure to undergo compression and dewater. For wastewater treatment sludges, a networked gel forms at very low solids concentrations (around 1-2 vol%), and below this value there is no measureable network strength. As the concentration approaches the particulate close packing limit, then Py(ϕ) asymptotes to infinity. Py(ϕ) is a property of the material and can be used to model and predict all dewatering applications in which shear effects are negligible.
3
2.3 Rate of Dewatering The hindered settling function, R(ϕ), is the other material dependent parameter that is highly nonlinear with respect to ϕ. R(ϕ) describes the rate of dewatering and is equivalent to the inverse of Darcian permeability and is proportional to the specific cake resistance [26]. Below the gel point, R(ϕ) represents the increased drag, or hindered settling, due to the presence of neighbouring particles and approaches Stokes’ drag as the solids concentration approaches zero. Above the gel point, R(ϕ) represents the resistance to flow through the porous network structure.
2.4 Dewaterability Characterisation Techniques Methods have been developed for extracting material parameters over a large range of volume fractions using transient gravity batch settling [27], transient and equilibrium centrifugal settling [21] and constant pressure filtration [28]. A stepped-pressure procedure developed by de Kretser et al [29] allowed rapid analysis of mineral slurries. Unfortunately, this method was unable to accurately extract sewage sludge material properties due to the short cake formation behaviour [13, 25]. Hence, single pressure, constant pressure filtration tests are required over a range of applied pressures from 5 kPa to 500 kPa.
3 Materials and Methods 3.1 Material The sample characterised was a sewage sludge from the Altona Treatment Plant (ATP), Melbourne, Victoria. ATP treats approximately 1.3 million litres of sewage per day using an intermittent decant extended aeration (IDEA) reactor. The sludge samples underwent initial screening and grit removal, followed by an extended aeration process. They were collected prior to the belt filter press, without the addition of polymeric flocculants. The particle density was measured gravimetrically to be 1630 kg/m3 with a liquor density of 1002 kg/m3 at an initial concentration of 0.87 wt% (0.54 vol%) as received. The volatile suspended solids content was determined according to standard methods to be 61% of the total solids.
3.2 Material Dewaterability Characterisation Gravity batch settling, centrifugal batch settling and pressure filtration tests were performed in order to characterise the dewatering properties of the sludge over a large range of solids concentrations. Gravity Batch settling The gravity batch settling test involved recording the rate of settling of the solid-liquid interface in a 500 mL straight-walled measuring cylinder as a uniform sludge at initial solids fraction, ϕ0, settled under gravity. The sludge was diluted with its own liquor to 0.18 wt% to ensure that it was below the gel point and that a defined interface was observable. The recorded interface height data, h(t), was used for the analysis. The method presented by Lester et al [27] was used to extract a settling velocity profile, u(φ), from the h(t) curve. This was then converted to a R(ϕ) curve using Equation 4
ܴሺ߶ሻ =
∆ఘሺଵିథሻమ ௨ሺథሻ
(4)
The test was allowed to go to equilibrium to estimate Py(ϕ) at low concentrations (i.e. at concentrations close to the gel point). The bed height at equilibrium, hf, is important for determining the gel point and Py(ϕ) based on the observation that the bed exhibits a profile of solids concentrations ranging from the gel point at the top of the bed to the equilibrium concentration associated with the applied hydrostatic pressure head at the bottom of the bed. Py(ϕ) was determined from this test in the range between zero applied pressure to the compressive pressure at the base of the bed due to the self-weight. Centrifugal batch settling The centrifugal batch settling tests involved two different types of tests; a permeability test at a single rotation speed of 4000 rpm to determine R(ϕ) and a compressibility test at stepped rotation rates increasing from 500 rpm through to 4000 rpm to determine Py(ϕ). These tests were conducted on a LUMiFuge® Stability Analyser LF-110, which used light transmission to record the rate of settling of the solid liquid interface in 10 mm x 10 mm x 80 mm polycarbonate tubes while a sludge at a uniform initial solids concentration settled under the centrifugal force. The recorded interface height data, h(t), from the permeability tests were analysed using the graphical method presented by Usher et al [21]. The method is an extension of the Kynch method [22] adapted 4
to centrifugal coordinates to extract a settling velocity profile with solids volume fraction. This profile was again converted to a R(ϕ) at evenly distributed solids concentrations. The recorded interface equilibrium height data, heq, for each rotation rate were analysed using the method presented by Usher et al [21] based on Buscall and White [1]. A curve fit of the heq versus rotation rate was used to calculate Py(ϕ) over the relevant range of solids volume fraction. Constant pressure filtration The constant pressure filtration tests involved the application of a constant pressure to a sludge sample of known initial height and initial solids volume fraction, and recording the specific volume of filtrate with time, V(t). This test was performed using laboratory scale filtration rigs. The pressure was controlled by a pressure transducer and the filtrate volume was measured using a linear encoder to determine the height of the piston. The tests were performed at applied pressures of 20, 50, 100, 200 and 500 kPa, and allowed to approach equilibrium (filtration time of between 24 to 72 hours was ensured by varying the initial sample height). The recorded V(t) data were used to estimate the volume fraction at equilibrium, ϕ∞, and hence Py(ϕ∞) and D(ϕ∞) using the method presented in Stickland et al [14]. This method uses a mass balance to calculate an average volume fraction from the V(t) data and fits a first order logarithmic function to the compression region data to determine ϕ∞, and consequently D(ϕ∞). Once the functional form of Py(ϕ) was known, then the D(ϕ∞) data were converted to R(ϕ∞) using Equation 3.
3.3 Material Dewaterability Characterisation Validation As the extraction of Py(ϕ), R(ϕ) and D(ϕ) from experimental data required numerous assumptions and approximations, these parameters were validated through re-prediction of the original results. A full numerical approach is used to solve the governing equation (Equation 2) for one-dimensional gravity and centrifugal settling and dead-end filtration. Importantly, the only inputs to these simulations are the initial sample height, initial concentration, as well as the centrifuge outer radius plus the rotation rates for centrifugal settling and the applied pressure plus the membrane resistance for filtration. There are no fitting parameters used in this numerical analysis, therefore accurate re-prediction is assumed to be a verification of the material properties.
3.4 Dewatering Equipment Operation Predictions The validated dewaterability properties, Py(ϕ), R(ϕ) and D(ϕ), were then input into models of dewatering equipment in order to predict and optimise operation. The first model was for a cylindrical steady state thickener with a diameter of 10 m. It is a one-dimensional model that makes the simplifying assumptions that the thickener is straight walled, that there is line settling, negligible shear forces and that no solids exit in the overflow [30]. The numerical output from the model presents the underflow solids concentration against the specific solids throughput, Qsolids [kg h-1 m-2]. A pseudo two-dimensional batch centrifugation model was used to predict operation of a solid-bowl decanter centrifuge. It is essentially a one-dimensional model of sedimentation that accounts for the centrifugal force and considers a conical centrifuge geometry by using a shape factor that gives the ratio of the diameters at each end. For an Apex Decanter Centrifuge (V400) with an effective bowl diameter of 480 mm and length of 2170 mm, a shape factor of 4.8 gives wall gradients of 5°. It was again assumed that that there is negligible shear forces and that no solids exit in the overflow. The numerical output from the model also presents the underflow solids concentration against Qsolids. In order to predict filtration performance, the material properties were used as inputs into a fixed-cavity plate and frame filter press [31]. This model used an iterative Runge-Kutta numerical algorithm to solve the governing equation for filtration [19] with appropriate boundary conditions. The numerical output gives the average final cake solids concentration variation with specific solids throughput of the batch filtration process. Qsolids is the mass of solids processed divided by the filter area and total cycle time as shown in Equation 5 ାబ
ܳ௦ௗ௦ = ߩ௦ௗ௦ ߶ ൬ ௧ ା௧ ൰
(5)
where the specific volume of filtrate is Vf [m3/m2], the double-sided cavity width is 2h0 [m], the fill time is tf [h] and the handling time is th [h].
5
4 Results and Discussion 4.1 Dewatering characterisation The equilibrium cake concentrations were extracted from the lab-scale stepped-rotation centrifugation test and a series of constant pressure filtration tests, then they were plotted against the equivalent applied pressure, ΔP ≈ Py(ϕ). A gravity settling test was used to estimate the gel point as 0.80 wt%. The sludge can be concentrated from this gel point to approximately 40 wt% by applying 500 kPa, this demonstrates the extreme compressibility of the sample. Stepped rotation rates lower than 500 rpm are required to obtain the experimental data for Py(ϕ) that is missing between 1.5 and 3 wt%. The available data was fitted using a composite power law function, as shown in Figure 2(a). (b)
Gel point
Hindered Settling Function, R(ϕ) (Pa s m-2)
Compressive Yield Stress, Py(ϕ) (kPa)
(a) 1000 100 10 1 0.1 0.01 0.001
0.0001 0.001
0.01
0.1
1
Gel point
1.E+19 1.E+17
Press filtrati
1.E+15 1.E+13 1.E+11 1.E+09 1.E+07 0.001
0.01
0.1
1
Solids Weight Fraction, x (w/w)
Solids Weight Fraction, x (w/w)
(c) Gel point
Solids Diffusivity, D(ϕ) (m2/s)
1.E-05 Gravity settling
1.E-06 1.E-07
Centrifugal settling
1.E-08
Pressure filtration
1.E-09 1.E-10 1.E-11 0.001
0.01
0.1
1
Solids Weight Fraction, x (w/w) Figure 2: (a) The compressive strength, Py(ϕ), (b) Hindered settling function, R(ϕ), or inverse permeability and (c) Solids diffusivity, D(ϕ), of a sewage sludge sample from Altona Treatment Plant from lab-scale gravity settling, centrifugal settling and pressure filtration tests [14, 21, 27]
6
Gravity and centrifugal batch settling tests were analysed using methods based on the Kynch theory of sedimentation [22] to determine low and intermediate concentration permeability data. Further permeability data were extracted through analysis of cake compression during filtration with the results shown in Figure 2(b). An interpolating function was used to fit the data to give a continuous function for R(ϕ). The permeability and compressibility parameters were combined using Equation 3 to give the solids diffusivity, as shown in Figure 2(c). This parameter is non-monotonic (peaked) and then decreasing over the concentration range of interest, which predicts the non-quadratic filtration behaviour, V(t), consistent with that observed during constant pressure filtration [14, 25]. The significant gaps between data points collected from gravity settling, centrifugal settling and filtration tests reinforce the need to validate the characterisation.
4.2 Dewatering characterisation validation The gravity settling, centrifugal settling and filtration data from lab-scale tests were re-predicted using one-dimensional compressional dewatering models. This was an important step as the analysis methods for extracting material properties required a number of approximations, and verification for biological sludges has previously proved difficult [14]. R(ϕ) and Py(ϕ) were used as inputs to numerical sedimentation models along with the initial sample height and initial concentration to re-predict the laboratory experiments in Figure 3(a). The simulation results provide reasonable agreement with the experimental data with increased error at longer settling time. This is a result of extremely slow settling and lack of data collection at the end of the fan region from 20,000 and 80,000 seconds. However, the accuracy is sufficient to validate the low concentration material properties for gravity settling. Centrifugal settling was then investigated using R(ϕ) and Py(ϕ) functional forms as inputs to numerical centrifugation models together with the initial sample height, initial concentration, centrifuge outer radius and rotation rates. The simulation and centrifugation experimental results are presented in Figure 3(b). In the case of centrifugation, the re-prediction shows good agreement with the available LUMiFuge® data indicating that the assumptions for extracting material properties are valid. Due to the operation of the LUMiFuge® with multiple samples, there were limited experimental data available at early settling times less than 10 seconds. More data can be collected by reducing the number of samples in the centrifuge. The current minimum time between measurements is 2 seconds, which is recommended for future work. Small errors in this early settling are assumed to have a negligible effect on subsequent modelling because the batch centrifugation time in any industrial process is significantly longer than 10 seconds. As the simulation predictions reproduce the centrifugal sedimentation behaviour from experiments at longer time with reasonable accuracy, this allows confidence in the model outputs. (a)
0.40
(b)
0.03
Experimental Data
Experimental Data
0.35
Simulation
Simulation
0.02 Height, h (m)
Height, h(t) (m)
0.30 0.25 0.20 0.15 0.10
0.02
0.01
0.01
0.05 0.00
0.00 0
30000
60000
Time, t (s)
90000
1
10
100
1000
10000 100000
Time, t (s)
Figure 3: (a) Laboratory gravity settling results for settling of a sewage sludge in a 500 mL measuring cylinder and re-prediction using extracted R(ϕ) and Py(ϕ) functional forms [27, 32] and (b) Laboratory centrifugal settling results for settling of a sewage sludge in a centrifugation device (LUMiFuge®) and re-prediction using extracted R(ϕ) and Py(ϕ) functional forms [21] 7
Verification of the material properties for filtration was also performed by simulating the original filtration experiments. R(ϕ) and Py(ϕ) functional forms were used as inputs into a numerical filtration model where the initial sample height, initial concentration, filtration time and a membrane resistance were also specified. The output of the model and original filtration experimental results, plotted in terms of the filtration time versus squared filtrate volume (t versus V2), are shown in Figure 4. 18000 20 kPa 15000
100 kPa 200 kPa
Time, t (s)
12000
500 kPa Prediction
9000
6000
3000
0 0.0000
0.0020
0.0040
Filtrate Volume,
V2
0.0060
(m2)
Figure 4: Dewaterability parameter integrity verification involving the re-prediction of laboratory filtration data using functional forms of Py(ϕ) and R(ϕ) for Altona Treatment Plant sludge [14] The comparison of simulation and experimental results shown in Figure 4 indicated that there was reasonable agreement, especially the 100, 200 and 500 kPa runs. The error increased significantly for the low pressure runs, indicating that the permeability data extracted from lab-scale centrifugation underestimated the hydrodynamic drag during filtration. The low pressure filtration simulations were particularly sensitive to errors in R(ϕ) at low solids concentrations. As a result of this sensitivity, the reprediction of experimental results for sewage sludges has previously proved difficult [14]. The full scale filtration predictions presented herein are made for pressures above 100 kPa, which were simulated accurately. Any modelling results at pressures below 100 kPa would not be considered valid. The data from Figures 3-4 provided a reasonable level of confidence in the validity of the dewatering material properties and the data analysis methods used. All fits were based on a full numerical analysis without the use of fitting parameters, as such the integrity of the Py(ϕ), R(ϕ) and D(ϕ) in terms of the ability to predict gravity settling, centrifugal settling and filtration are demonstrated.
4.5 Dewatering equipment predictions The gravity thickener, batch centrifugation and fixed-cavity plate-and-frame filter press models were all used to compare the sludge solids concentration outputs for given specific solids throughputs. The sludge bed height was varied in the gravity thickener model in order to observe the effect of increased bed height on the achievable underflow solids. This also provided perspective on the upper limits of the technology. It must be stressed that the material properties are for an unflocculated sludge, so the addition of flocculant will increase the throughputs compared to the results presented in Figure 5. As the feed to thickening operations is generally at low concentration, the feed solids was specified as 0.18 wt% (as per gravity batch settling tests) and a range of scenarios with different sludge bed heights were explored. The data in Figure 5 show that even for large bed heights and low throughputs, the achievable solids concentration was limited to 6.5 wt% with an approximate range of 2 – 6.5 wt%, which is similar to general observation in industry of 3 – 8 wt%. The main differences between industrial operation and the model are that the effect of shear and aggregate densification are not accounted for in the model [33, 34]. Flocculant addition and complex thickener geometry are also not considered in the modelling. These factors would increase the throughput compared to Figure 5, but similar limits to the underflow concentration are expected to be observed. 8
Specific Solids Throughput, Qsolids (kg h-1 m-2)
Feed concentration Gel point x0 xg 1 Sludge bed height 1m 2m 5m 10 m Perm. Limit
0.1
Diluted feed, x0 = 0.18 wt%
0.01
0.001
0.0001 0
0.02
0.04
0.06
0.08
0.1
Underflow Solids Concentration, x (w/w) Figure 5: Typical steady state thickener model prediction of specific solids throughput as a function of underflow solids concentration a sewage sludge from Altona Treatment Plant with a feed solids of 0.18 wt% for a range of sludge bed heights (1, 2, 5, 10 m) and the permeability limit [30]
Specific Solids Throughput, Qsolids (kg h-1 m-2)
The rotation rate was varied in the batch solid-bowl decanter centrifuge model to investigate the effect centrifugal force on the achievable underflow solids, as well as indicating the upper limits of the technology. The specific solids throughput against underflow solids concentration is shown in Figure 6. The model indicates that the solid-liquid separation during centrifugation is relatively quick (400 s to reach 6 wt% at 6000 rpm) and that close to the maximum solids concentration can be achieved at high throughputs. This final solids concentration at the highest rotation rate of 6000 rpm is 13 wt%, which is much lower than the expected values that are achieved in industry of 28 – 30 wt%. This discrepancy may be related to the low feed concentration in the simulation. The effect of polymer conditioning and the shear forces experienced during the decanting process are also not incorporated into the modelling, which would increase the predicted achievable final solids concentration. Feed concentration x0
10
2000 rpm 4000 rpm 6000 rpm 1
Batch centrifugation As received x0
0.1
0.01
0.001 0
0.05 0.1 Average Final Cake Solids, xF (w/w)
0.15
Figure 6: The predicted throughput in a batch solid-bowl decanter centrifuge with underflow solids concentration for feed solids of 0.87 wt% at various rotation rates (r0 = 0.48m, ri = 0.10m) 9
Specific Solids Throughput, Qsolids (kg h-1 m-2)
The final model that was considered for comparison and optimisation was the fixed-cavity plate-andframe filter press. This model can be used to optimise variables such as cavity width and fill time but these factors have not been considered for this study. The parameter that was investigated in this work was the applied pressure, with specific solids throughput against average final cake solids for a range of applied pressures presented in Figure 7.
10
x0,1 x0,2
400 kPa 500 kPa 600 kPa 700 kPa 700 kPa, 4 wt% feed
1
As received, x0,1 = 0.87wt% Pre-thickened, x0,2 = 4 wt%
0.1
0.01
0.001 0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Average Final Cake Solids, xF (w/w) Figure 7: The predicted throughput for a sewage sludge in a fixed cavity plate-and-frame filter press compared to output cake solids for a fixed cavity width of 1cm for a range of fill pressures [31] The applied pressure had a negliglible effect at high specific solids throughputs and a minor effect at low throughputs due to the thin and highly impermeable layer that forms at the filter membrane [14, 35]. This supports previous studies that suggest that high pressure operation is not energy efficient as it does not result in a significant increase to filtration rates or final cake solids concentration [36]. There was an optimum in the solids throughput that was related to the handling time. As the sludge is very impermeable, the handling time was small compared to the overall filtration time, so this optimum occurs at low solids concentration. The data in Figure 8 show that the average final cake solids achievable was 34 wt% at low throughputs, which is within the generally observed range industry of 29 – 49 wt% and close to the typical output of 29 – 33 wt%. Although thickening is limited in its capacity to achieve high concentrations, it offers significant advantages for very dilute feeds as a stand-alone unit and as part of a process train. It is a continuous process compared to the two other batch processes presented and, as such, has no handling time resulting in low operating and maintenance costs. Thus, it is a competitive option for simple end-user applications, such as stockpiling. It can also be used to improve other dewatering operations as part of a series of processes. For example, from Figure 6 at a bed height of 5 m and Qsolids of 0.01 kg m-2 s-1, the underflow solids are at 4 wt%, which was then used as feed to the plate-and-frame filter press simulation. This pre-thickening improved the final concentration by up to 10 wt%, as shown in Figure 8. As the dewatering requirements of each plant are different, these models together with the validated characterisation, can be used in this manner in the design and optimisation of dewatering equipment.
5 Conclusions and recommendations Reduction in the volume, or dewatering, of sewage sludges is an important industrial process and optimisation of dewatering equipment operation can have significant economic and environmental benefits. Compressive rheology theory was used to completely characterise the dewaterability of a sewage sludge from Altona Treatment Plant using two key parameters; the extent of dewatering or compressive yield stress, Py(ϕ), and the rate of dewatering or hindered settling function, R(ϕ). These parameters were extracted using lab-scale gravity settling, centrifugal settling and pressure filtration. The extracted material properties were then verified using models of gravity settling, centrifugal settling and pressure filtration through re-prediction of the original experimental results. 10
The validated material properties were used to predict the operation of an industrial thickener, decanter centrifuge and plate-and-frame filter press. This provided insights into device optimisation and indicated the limits for the achievable cake solids from each unit. The gravity thickener is predicted to achieve solids up to 6.5 wt%, decanter centrifuge up to 13 wt% and plate-and-frame filter press up to 34 wt%, although this can be improved with the application of shear and sludge conditioning with polymer. The material properties and models of dewatering equipment can be used to further optimise device design for improved sewage sludge processing.
Nomenclature D(ϕ) hi Py(ϕ) ΔP q(t) Q R(ϕ) t V u(ϕ)
solids diffusivity, m2 s-1 interfacial sediment height, m compressive yield stress, Pa applied pressure, Pa bulk flow, kg s-1 specific solids throughput, kg h-1 m-2 hindered settling function, Pa s m-2 time, h specific filtrate volume, m settling velocity profile, m s-1
Greek letters ρsol solids density, kg m-3 φ solids volume fraction, v/v φg gel point solids volume fraction, v/v Subscripts 0 initial f final ∞ equilibrium solids solids liq liquor
Acknowledgements Samuel J. Skinner acknowledges receipt of the Elizabeth and Vernon Puzey Bequest Scholarship for PhD study. Infrastructure support is acknowledged from the Particulate Fluids Processing Centre, a Special Research Centre of the Australian Research Council. The authors acknowledge Theo Vlachos and City West Water for support and providing the Altona Treatment Plant sludge sample.
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