Coagulation Chemistry: Effects on the Acid/Base Balance Via chemical equilibrium reactions, consumption of OH in the precipitation step has a domino effect on the concentrations of H +, OH, H 2 CO 3, HCO 3, and CO 3 2. The net changes can be determined by solving the equations for acid/base equilibrium: ( + )( H HCO ) ( + )( 2 ( )( ) 14.0 H CO ) 3 H + OH = 10 6.3 = 10 ( HCO) ( HCO ) 3 2 3 ( ) ( ) ( 2 ) H CO + HCO + CO = TOTCO 2 3 3 3 3 3 10.3 = 10
Coagulation Chemistry: Effects on the Acid/Base Balance The exact results can be obtained numerically, but the approximate change is conversion of one HCO 3 to H 2 CO 3 for each OH consumed, while TOTCO 3 remains constant: ( ) ( s) 3+ Al + 3 OH Al OH 3 + 3 H2O 3 H + 3 OH + 3 + 2 3 3 HCO 3 H 3 H CO ( ) ( s) 3+ Al + 3 HCO 3 + 3 H2O Al OH + 3 H 3 2CO3
Coagulation Chemistry: Effects on the Acid/Base Balance The final ph can be estimated from the initial conditions and the amount of HCO 3 converted to H 2 CO 3. The calculations are often presented in the context of alkalinity (Alk), which is the net capacity to bind H + : ( ) ( ) ( 2 ) ( + ) ( ) 3 3 3 Alk = OH + HCO + 2 CO H HCO where the approximation applies at ph <~ 9.0. Alk is reported in equivalents per liter, where one equiv/l corresponds to the concentration that yields one mol/l for any term on the right (e.g., one mol/l of OH or 0.5 mol/l of CO 3 2 )
Coagulation Chemistry: Effects on the Acid/Base Balance Unlike the concentrations of individual chemical species that contribute to alkalinity, Alk is conservative. This means that if a chemical with a certain Alk is added to a solution, the new Alk is just the original Alk plus the amount added. Very convenient for calculations, as shown by the following example.
Example: Coagulation Chemistry A water supply at ph 7.3 and containing 0.8 meq/l Alk is dosed with 40 mg/l FeCl 3. The reactions are rapid, so no CO 2 exchanges with the atmosphere. The final ph must be 6.0. Will addition of base be required? 1. Approximate (HCO 3 ) init as Alk init. Each mmole of HCO 3 contributes one meq of Alk, so (HCO 3 ) init 0.8 mmol/l. Then, (H 2 CO 3 ) is computed as: ( ) ( )( + ) ( HCO H 8.0x10 4 )( 10 7.2 ) 3 4 2 3 = = = 6.35 K a 1 10 H CO 1.13x10
2. Compute Alk fin from Alk init and FeCl 3 dose (40 mg/l FeCl 3 ). Each mol/l of FeCl 3 added combines with 3 mol/l of OH, and so reduces Alk by 3 equiv/l: equiv Alk destroyed moles FeCl 3 added Alk fin = Alkinit 3 * mol FeCl 3 added L equiv Alk destroyed mg FeCl 1 mol FeCl 8.0x10 3 40 4 3 3 = mol FeCl 3 added L 162,500 mg equiv = = L 5 6.15x10 6.15x10 2 meq L
1.0 3. Approximate (HCO 3 ) fin as Alk fin, compute (H 2 CO 3 ) fin from TOTCO 3 and (HCO 3 ) fin. Fraction as given species 0.8 0.6 0.4 0.2 0.0 H 2 CO 3 HCO 3 3 5 7 9 11 ph CO 3 2 ( ) ( ) TOTCO = TOTCO H CO + HCO 3, fin 3, init 2 3 3 = 1.13x10 + 8.0x10 = 9.13x10 4 4 4 init ( ) ( HCO ) 2 3 TOTCO3, fin HCO3 fin = 9.13x10 6.15x10 = 8.51x10 fin 4 5 4
4. Compute ph fin from (H 2 CO 3 ) fin, (HCO 3 ) fin, and K a1. ( ) ( ) ( HCO ) 3 ( 4)( 6.35) HCO K 8.51x10 10 H 3.36x10 1.13x10 + = 2 3 fin a1 = = 6 fin 4 fin ( + ) ( 6 ) ph = log H = log 3.36x10 = 5.47 fin fin The ph is too low, and lime or some other base would have to be added to increase it to at least 6.0.
Coagulation and NOM Conditions in typical natural waters. Lots of dissolved NOM. Low doses of Fe 3+ or Al 3+ partially neutralize the charge on the NOM. The NOM exerts a coagulant demand. OH -OOC COO Fe 3+ Fe 3+ - O OH - HOOC O O HO O O O OH OH O OH O O COOH COO - High doses of Fe 3+ or Al 3+ generate new surfaces to which the NOM can bind.
The Enhanced Coagulation Rule Requires NOM removal from many surface waters Removal requirement depends on NOM conc n (quantified as Total Organic Carbon, TOC) and Alkalinity Escape clause available if a point of diminishing returns is reached Enhanced coagulation is a BAT. If it doesn t work, you are off the hook TOC (mg/l) ALK (mg/l CaCO 3 ) 0-60 >60-120 >120 <2 N/A N/A N/A 2-4 35* 25 15 4-8 45 35 25 >8 50 40 30 *Required percentage reduction in TOC
Flocculation Theory: Particles Flocculate by Three Mechanisms Fluid shear: Particles collide by traveling on different streamlines at different velocities Brownian motion: Particles collide due to random motion Differential sedimentation: Particles collide due to different settling velocities The rate of reaction by all mechanisms is expected to be first order with respect to each type of particle second order overall: rk i j = β nn =+ ( ) ij i j
The Rate of Collisions by Each Mechanism Can be Predicted from Theory Sh 1 β = G d + 6 ( d ) 3 ij i j Br β 2kT B 1 1 = + + 3μ di d j ( d d ) ij i j DS β π ( ) 2 = v v d + d 4 π g ( )( ) 3 = ρ ρ d + d d d 72μ ij i j i j p w i j i j
Different mechanisms dominate for different size ranges. The only controllable mechanism is shear, by controlling the shear rate, G.
Conventional Surface Water Treatment for Drinking Water
From: Water on Tap, USEPA pamphlet accessed on 01/04/09 at http://www.epa.gov/safewat er/wot/pdfs/book_wateronta p_full.pdf
(From Opflow, November 2005)
Filter backwash water flowing into (above) and out of (right) launders Photos by Dan Gallagher From: Virginia Tech Water Treatment Primer, accessed on 01/04/09 at http://www.cee.vt.edu/ewr/environmental/teach/wtprimer/backwash/backwash.html
Headloss 90 80 10 gpm/ft 2 70 6 gpm/ft 2 Headloss (inches of water) 60 50 40 30 20 8 gpm/ft 2 4 gpm/ft 2 Coagulant = FeCl 3 (30 mg/l) Temperature = 10 ο C 10 0 0 10 20 30 40 50 60 70 80 Time (hour)
Effluent particle counts Filter 3 effluent particle counts (1-150 µm) (#/ml) 100000 10000 1000 100 10 1 10 gpm/ft 2 8 gpm/ft 2 6 gpm/ft 2 4 gpm/ft 2 Coagulant = FeCl 3 (30 mg/l) Temperature = 10 ο C 0 10 20 30 40 50 60 70 80 Time (hour)
Granular Media Filtration 10 μm 0.8 mm
Filtration Complexity Two dependent variables of importance Headloss Effluent particle concentration Never at (long-term) steady state Two different modes of operation (filtration and backwashing) Numerous independent variables (hydraulic loading rate, influent particle concentration and distribution, media size, backwashing frequency and operation, etc.) Particle removal is clearly not primarily by sieving
Modeling Particle Removal
( ) d NV dt L,CV = QN Q N + dn V r Q= Av 0 ( ) ( ) L,CV d( NV ),CV Assume pseudo-steady state, so L 0 = Av0 dn + VL,CVrp Av dn = V r 0 L,CV p dt = p 0
V L,CV r p Rate of Removal of Particles Number of = by a Single Collector Collectors in Layer Rate of Approach of Removal Efficiency of Number of = Particles to a Collector a Single Collector Collectors in Layer A c π d = 4 2 c Rate of Approach of Nv π d c = 0 Particles to a Collector 4 2 Removal Efficiency of η a Single Collector Total Volume of Number of Collector Media AdL 1 = = Volume of a Single Collector ( ε ) 3 Collectors in Layer π dc /6
V L,CV r p Rate of Approach of Removal Efficiency of Number of = Particles to a Collector a Single Collector Collectors in Layer ( 1 ε ) 2 π d AdL c = Nv0 [ η] 3 4 π dc /6 = 3 2 ( 1 ) ε η Nv0 AdL d c Single Collector Removal Efficiency
Av dn = V r 0 L,CV p Av 0 dn 3 ( 1 ε) η = N v0 AdL d 2 c dn N 3 ( 1 ε) η = dl = λdl d 2 c ln N N out in = λl Filter coefficient N = N exp( λl) out in
Summary: Mass Balance Analysis of Particle Removal in a Granular Filter Removal modeled based on interactions of influent particles with isolated collector grains Assuming short-term, pseudo-steady state, removal of each type of particle is predicted to decline exponentially with depth Coefficient for the exponential loss rate depends on the particle characteristics (size, density, etc.) If we could predict η for a given type of particle, we could predict N out /N in for that particle
Approach: Predict η by treating filter as a tightly packed flocculation basin, where incoming particles flocculate with filter grains
η Br kt B = 0.905 μddv c p 0 2/3
B=Backwash required c=concentration; h=headloss; S=Standard length bed; L=longer bed
B=Backwash required; c=concentration; h=headloss; S=Standard size grains; d=larger diameter grains
Summary: Rapid Rate Filtration Based on relative sizes of particles and collectors, sieving is unimportant and removal can be modeled based on interactions (flocculation) with isolated collector grains Particle removal by clean grains predicted to be least efficient for particles ~1 μm Actual particle removal tends to improve over time due to particle capture by previously captured particles (filter ripening ) Run terminated and filter backwashed when either effluent particle concentration or filter hydraulic headloss exceeds specified criterion Acceptable length of run can be modified by design and operational decisions (grain size, filtration rate, coagulant dose, etc.)