Example study for granular bioreactor stratification: three. dimensional evaluation of a sulfate reducing granular bioreactor

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Supplementary Information for Example study for granular bioreactor stratification: three dimensional evaluation of a sulfate reducing granular bioreactor Tian-wei Hao 1, Jing-hai Luo 1, Kui-zu Su 2, Li Wei 1*, Hamish R. Mackey 3, Kun Chi 1, and Guang-Hao Chen 1,4,5* 1 Department of Civil & Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. 2 School of Civil Engineering and Water Conservancy, Hefei University of Technology, Hefei, China 3 College of Science and Engineering, Hamad bin Khalifa University, Education City, Doha, Qatar 4 Water Technology Lab, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. 5 Hong Kong Branch of Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, HKUST, Clear Water Bay, China *corresponding author: ceghchen@ust.hk Number of pages: 10 Number of tables: 4 Number of figures: 8

Methods of biological analysis Barcoded universal bacteria primers Forward primer 515F and reverse primer 926R (F515:5 -CCATCTCATCCCTGCGTGTCTCCGACTCAGCAGAGTCTGTGCCAG CMGCCGCGGTAA -3 926R:5 -CCTATCCCCTGTGTGCCTTGGCAGTCTCAGCCGTCAATTYYTTTRA GTTT -3 ), Polymerase chain reaction (PCR) amplification and pyrosequencing Fragments of the 16S rdna gene were amplified by PCR using barcoded universal bacteria primers 515F and 926R targeting the V4 and V5 hypervariable regions (Quince et al., 2011). The average length of PCR product is 423 bp. The 100-µl PCR reaction mixture contained 5 U of Pfu Turbo DNA polymerase (Stratagene, La Jolla, CA, USA), 1X Pfu reaction buffer, 0.2 mm of dntps (TaKaRa, Dalin, China), 0.1 µm of each barcoded primer, and 20 ng of genomic DNA template. PCR was performed under the following thermocycle: 94 ºC for 5 min followed by 30 cycles of 94 ºC for 30 s, 53 ºC for 30 s and 72 ºC for 45 s, and a final extension at 72 ºC for 10 min. Pyrosequencing reads with ambiguous nucleotides, shorter than 200-nucleotides or without a complete barcode and primer at one end were removed and excluded from further analysis. The quality filtered reads were denoised by flowgram clustering to remove the homopolymer errors (Reeder and Knight, 2010). Each sample was sequenced three times and the results with high quantity and

relatively even fragments were chosen for further analysis. Total of eight samples were pooled for pyrosequencing analysis at each time. Sequence analysis Raw sequence data was firstly processed by trimming barcode tags and primer sequences. FASTA files were generated from the resultant sequences according to the barcodes of individual samples. The sequences were then aligned using the software Mothur ver. 1.17.0 (Schloss et al., 2009) and the distance matrix was produced. Operational taxonomic unit (OTU) was determined at the 90, 95 and 97% similarity levels (Mothur v. 1.17.0). Rarefaction curves were determined based on the calculated OTUs. For the taxonomy-based analysis, the representative sequences from each OTU were subjected to the RDP-II Classifier of the Ribosomal Database Project (RDP) (Cole et al., 2009).The relative abundance and occurrence of tags assigned to different taxonomies were visualized as a heatmap using the software MeV 4.8.1.

COD (mg/l) Removal Efficiency (%) (a) 100 350 90 300 80 250 70 200 60 150 50 100 40 50 30 0 165 170 175 180 185 190 195 day (d) (b) Figure S1. (a) Deterioration of COD removal efficiency in UASB from day 160 and (b) observed short-circuiting in the reactor

Height (cm) ph Alkalinity (mg CaCO 3 /L) COD concentration (mg/l) 160 Layer 0 cm Layer 10 cm Layer 20 cm Layer 30 cm Layer 40 cm 120 80 40 0 Layers Figure S2. Soluble COD concentration profiles at different sludge layers (a) 8.0 7.5 7.0 6.5 6.0 5.5 5.0 (b) 700 600 500 400 300 200 4.5 100 INF 0 10 20 30 40 EFF INF 0 10 20 30 40 EFF Height (cm) Height (cm) Figure S3. The ph (a) and alkalinity (b) variation profile along the height of the SRUSB 40 30 20 10 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 Hydrophobicity (%) Figure S4. Hydrophobicity profile of sludge surface at different sludge layers

Height(cm) OTUs g/l 30 simulated granular sludge concentration measured granular sludge concentration 25 Baffle 1location Baffle 2 location 20 15 10 5 0.0 0.1 0.2 0.3 0.4 0.5 Height (m) Figure S5. The simulated and measured concentration profile of the granular sludge along the SRUSB height (g TSS/L). 1200 1000 800 600 sublayer 0.03 sublayer 0.05 sublayer 0.1 toplayer 0.03 toplayer 0.05 toplayer 0.1 400 200 0 0 2000 4000 6000 8000 10000 12000 Number of Tags Sampled Figure S6. Rarefaction curves of 454 pyrosequencing EFF 40 30 20 Baffle 10 0 INF 0 50 100 150 200 250 300 350 400 COD concentration (mg/l) Figure S7. COD degradation profile along the SRUSB axis after baffle optimization

Figure S8. Schematic of SRUSB after two baffles installation at day 193 Table S1. Main physical characteristics of the SRB granules in the steady-state SRUSB. VSS/TSS Diameter (µm) SVI 5 (ml/g) Specific gravity Settling velocity (m/h) 72% 420 450 ~30 1.067 1.074 18 65 VSS: volatile suspended solid; TSS: total suspended solid

Table S2. Relevant model equations in Fluent 14.0 CFD code Continuity equation (α q ρ q ) + (α q ρ q v q ) = 0 t Momentum equations (α l ρ l v l ) + (α q ρ q v 2 q ) = α l p + τ l + α l ρ l g +K sl (v s v l ) t (α s ρ s v s ) + (α s ρ s v 2 s ) = α s p + p s + α s ρ s g +K ls (v l v s ) t τ q = α q μ q ( v q + v T q ) + α q (λ q 2 3 μ q) v q I Liquid solid drag K sl = 3 4 C α s α l ρ l v s v l D α 2.65 d l α l s 3 4 C α s α l ρ l v s v l D α 2.65 d l α l > 0.8 K sl = s 150 α s(1 α l )μ l 2 + 1.75 α sρ l v s v l α { α l d s d l 0.8 s C D = 24 α l Re s [1 + 0.15(α l Re s ) 0.687 ] Re s = d sρ l v s v l μ l Wen and Yu (1966) Gidaspow et al. (1992) Solids pressure p s = α s ρ s θ s + 2ρ s (1 + e ss )α s g 0,ss θ s Lun et al. (1984) Radial distribution function Solids shear stress g 0 = [1 ( 1 α s ) 3 ] 1 α s,max μ s = μ s,col + μ s,kin + μ s,fr Ding and Gidaspow (1990) μ s,col = 4 5 α sρ s d s g 0,ss (1 + e ss ) Ѳ s π Gidaspow et al. (1992) Bulk viscosity μ s,kin = α sd s ρ s πѳ s 6(3 + e ss ) [1 + 2 5 (1 + e ss )(3e ss 1)α s g 0,ss ] μ s,fr = ρ s sin Φ 2 I 2D λ s = 4 5 α sd s ρ s g 0,ss (1 + e ss ) Ѳ s π Syamlal et al. (1993) Schaeffer (1987) Lun et al. (1984) Granular temperature 3 2 [ t (ρ sα s Ѳ s ) +. (ρ s α s v sѳ s )] = ( ρ s I + τ s ):. v s +. (k θs Ѳ s ) γ θs + Φ ls k Θs = 15d sρ s α s Θ s π 12 [1 + 4(41 33η) 5 η2 (4η 3)α s g 0,ss + 16 15π (41 Syamlal et al. (1993) 3η)ηα s g 0,ss ] η = 1 2 (1 + e ss) γ Θs = 12(1 e ss 2 )g 0,ss ρ s α 2 3/2 s Θ s d s π Lun et al. (1984) Φ ls = 3k ls Θ s Packing limit α s,max = ρ b ρ s

Table S2-1. Definition of symbol in table A1. Symbol Description Units Alphabetic C D drag coefficient. dimensionless d diameter, m e coefficient of restitution, dimensionless g gravitational acceleration, m/s 2 g0 radial distribution coefficient, dimensionless k θs diffusion coefficient for granular energy, dimensionless K interphase exchange coefficient, dimensionless P pressure, Pa Re Reynolds number, dimensionless t time, s Greek letters α volume fraction, dimensionless γ θs collision dissipation of energy, kg/s 3 m η dynamic viscosity, Pas θ granular temperature, m 2 /s 2 I stress tensor, dimensionless I 2D second invariant of the deviatoric stress tensor, dimensionless λ bulk viscosity, Pas μ shear viscosity, Pas ν velocity, m/s ρ density, kg/m 3 τ stress tensor, Pa φ angle of internal friction, deg Φ Transfer rate of kinetic energy, kg/s 3 m Subscripts col collision, dimensionless fr friction, dimensionless kin kinetic, dimensionless l liquid phase, dimensionless max maximum value, dimensionless q either liquid or solid phase, dimensionless s solid phase, dimensionless

Table S3. Base case simulation settings Description Base case setting/value Mesh size, time step, convergence criteria and discretization method Mesh resolution 602464 Convergence criteria 10 3 Maximum iterations 30 Discretization method First order upwind Time step 0.01 Geometry, boundary, initial and operating conditions Bed width 88 mm Bed length 450 mm Initial bed height 310 mm Initial solids packing 0.60 Outlet boundary condition Pressure outlet Wall boundary condition No slip (liquid) condition Gravitational acceleration 9.81 m/s 2 Operating pressure 1.013x10 5 pa Liquid superficial velocity 7.4774e-4 m/s Inlet boundary condition Uniform velocity inlet Liquid properties At room temperature 298 K viscosity 0.001003 Pa s density 998.2 kg/m 3 Granular sludge properties Mean diameter 0.42 mm density 1130 kg/m 3 Packed bed solids volume fraction 0.60 Initial inventory 1.2279 kg

Reference Cole, J. R., Wang, Q., Cardenas, E., Fish, J., Chai, B., Farris, R. J., Kulam-Syed-Mohideen, A. S., McGarrell, D. M., Marsh, T., Garrity, G. M. & Tiedje, J. M. The Ribosomal Database Project: improved alignments and new tools for rrna analysis. Nucleic Acids Research. 37, 141 145 (2009). Ding, J. & Gidaspow, D. A. A bubbling fluidization model using kinetic theory of granular flow. A.I.Ch.E. Journal. 36, 523 538 (1990). Gidaspow, D., Bezburuah, R. & Ding, J. Hydrodynamics of circulating fluidized beds, kinetic theory approach. In: Potter, O.E., Nicklin, D.J. (Eds.), Fluidization VII. Engineering Foundation, New York. 75 82 (1992). Lun, C. K. K., Savage, S. B., Jeffrey, D. J. & Chepurniy, N. Kinetic theories for granular flow: inelastic particle in Couette flow and slightly inelastic particles in a general flow field. Journal of Fluid Mechanics. 140, 223 256 (1984). Quince, C., Lanzen, A., Davenport, R. & Turnbaugh, P. J. Removing noise from pyrosequenced amplicons. BMC Bioinformatics. 12, 38 56 (2011). Reeder, J. & Knight, R. Rapidly denoising pyrosequencing amplicon reads by exploiting rank-abundance distributions. Nature Methods. 7, 668 669 (2010). Schloss, P. D., Westcott, S, L,, Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., Sahl, J. W., Stres, B., Thallinger, G. G., Van Horn, D. J. & Weber, C. F. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology. 75, 7537 41 (2009). Schaeffer, D. G. Instability in the evolution equations describing incompressible granular flow. Journal of Differential Equations. 66, 19 50 (1987). Syamlal, M., Rogers, W. & O Brien, T. J. MFIX Documentation: Theory Guide. National Technical Information Service, vol. 1. Springfield, VA, DOE/METC-9411004, NTIS/DE9400087. (1993). Wen, C. Y. & Yu, Y. H. Mechanics of fluidization. Chemical Engineering Progress Symposium Series. 62, 100 111(1966).