Comparative Studies On Coag-flocculation Kinetics …...specific models to evaluate interaction effects of coagulation factors (effluent medium pH, coagulant dosage, settling time)

International Journal of Engineering & Technology IJET-IJENS Vol:13 No:02 134

1311202-7676-IJET-IJENS © April 2013 IJENS I J E N S

Comparative Studies On Coag-flocculation Kinetics

of Pharmaceutical Industry Effluent by Achatina

Maginata Shell Biomass and Aluminum Sulphate.

* Ugonabo, V. I

1, Menkiti, M .C

2, Atuanya, C.U

3. And Onukwuli, D. O

4

1,2,4Department of Chemical Engineering, Nnamdi Azikiwe University, Awka, Nigeria

3Department of Metallurgical and Materials , Nnamdi Azikiwe University, Awka, Nigeria

Corresponding Author: *E-Mail: [email protected], Phone No. +2348033481851

Abstract-- Comparative studies on coag-flocculation kinetics

of pharmaceutical industry effluent by achatina maginata and

aluminum sulphate is reported. To remove total suspended and

dissolved particles (TSDP) from the effluent sample. The experiments were carried using standard nephelometric jar

test method while AMSC production was based on method

reported [15]. Microkinetic data generated were fitted to

specific models to evaluate interaction effects of coagulation

factors (effluent medium pH, coagulant dosage, settling time) on the treatment efficiency. Results obtained indicates that the

best performance for AMSC: 13, 0.2 X 10-3 kg/m3 for pH and

dosage and alum: 10, 0.1 x 10-3 kg/m3 for pH and dosage, were

achieved at 2400Sec. settling time respectively. The optimum

value recorded for both coag-flocculation activities is 93.26% removal efficiency of TSDP at rate constant of 1.34E –

04m3/kg.S for alum. However, AMSC have proved to be good

alternative for alum having achieved good performance even

better in some cases for all pH and dosages studied.

Index Term-- Achatina Maginata, Aluminum Sulphate,

Coagulation/Flocculation, Pharmaceutical Industry Effluent,

Comparism.

I. INTRODUCTION

Pharmaceutical industry operations are association with a

variety of processes including chemical synthesis,

fermentation, extraction and other complex methods [30].

Each of these stages may generate air emissions, solid

wastes and effluents. Effluents resulting from equipment

washing/cleaning after each batch operation produces highly

turbid wastewaters caused by dissolution of solid particles

and presence of toxic organic residues [3],[31] . Since the

pharmaceutical industry produces many products using

different types of raw materials as well as processes, the

composition of the effluent varies , hence it cannot be

generalized [10],[31] . Typically, pharmaceutical effluent is

characterized by high organic chemical content [19].

Pharmaceuticals pose potential risks to the aquatic

environment such as feminization of fish living downstream

of wastewater treatment plant out falls [21]. Furthermore, a

link between a non-steroidal anti-inflammatory drug,

diclofenac and the renal failure of vultures contributing to

the > 95% decline in its population in the Indian

subcontinent since the 1990’s has been reported [26].

Pharmaceuticals entrance to the environment is not limited

to pharmaceutical production plants alone, but it could enter

through municipal wastewater treatment plants, hospital,

landfills and even graveyards [18]. If these compounds are

not removed prior to effluent discharged, into water bodies

could hinder photosynthetic activities thus upsetting

biological processes within a stream and also the toxic

nature of the organic residues could cause adverse effects on

aquatic organisms [4],[5],[7],[11],[13],[20],[27] .

Pharmaceutical effluents are normally treated using

flocculation, floatation, coagulation, filtration, settling, ion

exchange, carbon adsorption, detoxification of active

ingredients by oxidation and biological treatment.

Although pharmaceutical effluent may contain

organic residues that are easily settleable, coagulation and

flocculation treatment method is still a viable and simple

treatment option [9],[31].

Coagulation process is the addition of inorganic

salts (congulant) to water to neutralize the negative charges

on dispersed non-settleable solids such as clay and colour-

producing organic substances resulting in the formation of

microflocs. While flocculation, a gentle mixing stage

involves increase in the particle s ize from submicroscopic

microfloc to visible suspended particles called pinflocs [36].

Aluminum Sulphate is a synthetic coagulant which

has been widely used for wastewater treatment. In view of

its proven performance in treating wastewater and its lower

cost, it has found usage in water purification for domestic

purposes. When added in water, it forms a range of

hydrolysis species (Polymeric Species) and these polymeric

species can be adsorbed to colloidal particles due to

attractive electrostatic charges [16], which invariably are the

active species in coagulation. Its effectiveness is strongly

pH dependent and finished water may have high residual

aluminum concentrations. Equally, significant quantities of

sludge are produced which creates handling and disposal

problems and their long term effects on human health has

been associated with Alzheimer disease [31].

Moreover, for some developing countries like

Nigeria, the cost of importing Aluminum sulphate may be

high and will negate the government policy on backward

integration of the economy. To ameliorate these negative

attributes, natural polyelectrolytes of animal origin can be

workable alternatives to synthetic polyelectrolytes. Natural

polyelectrolytes are easily available, cost effective,

biodegradable, and safe to human health.

mailto:[email protected]



In view of the economic importance, the potential

for effective process and to reduce pollution load to the

environment, necessitated the use of Achatina maginata

shell biomass, a naturally occurring coagulant sequentially

with aluminum sulphate for wastewater treatment. Achatina

maginata shell biomass is a non-toxic, biodegradable

polymer with high molecular weight. The main composition

of the shell is calcium carbonates which appears in form of

crystalline calcite and aragonites while the minor

component is organic matrixes which constitutes a crude

protein extract called conchiolin [15]. Based on the

chemical composition, it can be used in water purification as

a coagulant or adsorbent depending on choice. When

Achatina maginata shell derived carbonate dissociates into

calcium radicals (Ca2+)

and calcium complexes Ca (OH)2+

or

polymeric species which can be attracted to the

predominantly negative charged suspended particles in the

solution to form colloidal particles.

In this study the coagulation – flocculation, test

was carried out with pharmaceutical effluent to determine

the settling time, pH and dosage of aluminum sulphate and

Achatina maginata shell biomass respectively, required to

achieve optimum results. Based on the results obtained, their

kinetic performance will be evaluated for the purposes of

comparative analysis.

II. THEORETICAL BACKGROUND AND MODEL

DEVELOPMENT .

For a homogeneous aggregating particles (i, j) in

equilibrium state with negligible influence of gravitational,

buoyancy, drag, vander waals and repulsive forces:

[1],[14],[23].

µi = Ui

nS, nV, nj

1

Also

µi =Gi =

p, T, nj = a constant

2

Thus µi = Gi = O

3

For a homogeneous phase solutions

µi = µi + RT ln Ci

4

In a case where drag force (fd) predominant there is a shift

from the equilibrium state

Thus fd = -

5

Note that Boltzman Constant (KB) = Molar gas constant per

particle i.e.

KB =

For a single particle component say i, n = 1, KB = R

6

Substituting equation 6 into 4, yields

µi = µi + KBT ln Ci

7

Where:

µi is chemical potential of component i

Ui is internal energy of component i

Gi is Gibb’s free energy of component i.

ni is the number of moles of component i

nj is the number of moles of component j, indicating that all

moles numbers are held

constant except the ith

.

n is the number of particles

T is absolute temperature

Ci is concentration of particle component i

X is diffusion distance

fd is viscous drag force

R is molar gas constant

KB is Boltzman constant (molar gas constant per particle)

Substituting equation 7 into 5, gives

fd = -

(

+ KBT ln Ci) 8

fd = - KBT

9

dx

But from ficks law

D1 =

⁄

10

Where D1 is diffusion coefficient

B is friction factor

Comparing equations 9 and 10 yields Einstein’s equation

D1 = KBT 11

B

The general model for microkinetic coagulation-flocculation

of mono dispersed particle

under the influence of Brownian motion is given [34].

rk =

- 12

I + j = k

i = 1

Where rk =

is the rate of change of concentration

of particle size K (Conc/time)

α is the fraction of collisions that result in particle

attachment.

is a function of coagulation-flocculation transport for

Brownian, Shear and

differential sedimentation mechanisms

The value of for transport mechanism is given as [34].

BR =

p KBT 13

ղ

Where p is collision efficiency

ղ is the viscousity of effluent medium

KB is boltzman’s constant (J/K)

T is absolute temperature (K)

The general equation representing aggregation rate of

particles is obtained

by solving the combination of equation 12 and 13 to yield

= KNt

α 14

Where Nt is total particle concentration at time t, Nt = ∑nt

(Mass/volume)

K is the αth

order coagulation-flocculation constant

α is the order of coagulation-flocculation

And k = ½ BR 15

Also BR = 2 p KR

16

Combining equations 14, 15 and 16 yields

= p KR Nt

α 17



Where KR is the Von Smoluchowski rate constant for rapid

coagulation

[8]

But KR = 8RD1 18

RP = 2a

19

Where a is particle radius.

Recall, from Einstein’s equation: D1= KBT

B

Where B is the friction factor.

From stokes equation:

B = 6ղa 20

Where ղ is viscosity of coagulating and flocculating

medium.

Combing equations 17 to 20 produce:

=

p KBT Nt

α 21

ղ

Comparing equations 14 and 21 show that

K =

p KBT

ղ 22

For microkinetic aggregation, α theoretically equals 2,

[14],[23].

From fick’s law

Jf = D1 4 Rp

2

23

Where Jf is flux – number of particles per unit surface

entering a sphere with radius r

Re-arranging and integrating equation 23 at initial

conditions Nt = 0, Rp = 2a.

Jf ∫

= ∫

24

Therefore, Jf = 8D1a No 25

In general, for particle of same size under the influence of

Brownian motion, the initial

rate of rapid coagulation – flocculation is

= Jf. p. No 26

On substitution of equations 11, 20 and 25 into 26 produce:

= 8a KBT No p 27

6ղa

Thus

=

p KBT No

2

η 28

Similarly at t > 0

=

p KBT Nt

2

η 29

Hence, equation 29 has confirmed α = 2, equation 7

transpose to

= - KNt

2 30

Re-arranging and integrating equation 30 yields

= ∫

= - K∫

31

Thus

= Kt +

32

Plot of (

Vs t produces a slope of K and intercept of

From equation 32, making Nt the subject matter yield a

relation for the evaluation

of coagulation period (½

Nt = No

1 + No Kt 33

Similarly Nt = No

1 +

34

Where = 1

NoK 35

Putting equation 34 into 35, produce

Nt = No

1 +

36

When t = , equation 36, yields

Nt =

37

Therefore as No 0.5; ½ .

Hence τ1/2=

38

For microkinetic aggregation of singlets, doublets and

triplets under the influence

of Brownian transport mechanism as a function of time (t

40 mins) at early stages can

be obtained by solving equation 1 exactly, resulting in

general expression mth

order.

=

m-1

1 + t m +1

2

39

Similarly

= ⁄

m-1

1 + ⁄

m+1

40

Where 1 = 2

Hence for singlets (m = 1)

N1(t) = No 1

1 + ⁄ 2

41

For doublets (m = 2)

N2(t) = No ⁄

1 + ⁄ 3

42

For triplets (m = 3)



N3(t) = No ⁄

2

1 + ⁄ 4

43

Evaluation of coagulation-flocculation efficiency is given

as:

E(%) = No - Nt x 100

44

No

III. MATERIALS AND METHODS

The sample of achatina maginata shell biomass were sourced from Enugwu-Ukwu Town, Anambra State, Nigeria and processed to AMSC based on the work reported [15] . While the Aluminium sulphate (analytical grade) was sourced from Bridge head market at Onitsha, Anambra State, Nigeria.

The jar test was conducted based on standard Bench Scale Nephelometric Method. Appropriate dose of AMSC in the range of (0.1 – 0.7) x 10-3 kg/m3 was added to 250ml of pharmaceutical effluent. The suspension, tuned to pH range 1 – 13 by addition of 10M HCL/NaOH, subjected to 2 minutes of rapid mixing (120 rpm), 20 minutes of slow mixing (10 rpm) using 688644A Gulenhamp magnetic stirrer followed by 40 minutes of settling. During settling, samples were withdrawn from 2 cm depth and changes in TSDP measured for kinetic analysis using Lab-Tech Model 212R Turbidimeter at various time intervals of 2 – 40 minutes. The same procedure was followed for the aluminum sulphate coagulant. The whole experiment was carried out at room temperature. The data obtained were subsequently fitted in appropriate kinetic models for performance evaluation and comparative purposes.

T ABLE IA

COAG-FLOCCULATION KINETIC PARAMETERS AND LINEAR REGRESSION COEFFICIENT OF SSC IN PIE AT VARYING PH AND 0.1 X 10-3KG/M3 DOSAGE.

Parameters pH = 1 pH = 3 pH = 5 pH = 7 pH = 10 pH = 13

α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.469 0.843 0.664 0.970 0.830 0.517

K(m3/kg.S) 1.12E-04 6.0E-0.6 6.0E-06 7.8E-05 9.1E-05 7.1E-05

KR(m3/S) 1.5289E-19 1.5494E-19 1.5862E-19 1.5392E-19 1.5545E-19 1.5596E-19

βBR(m3/kg.S) 2.24E-04 1.2E-05 1.2E-05 1.56E-04 1.82E-04 1.04E-04

εp(Kg-1

) 1.4651E+15 7.7449E+13 7.5653E+13 1.0135E+15 1.1708E+15 9.1049E+14

τ1/2 (S) 19.41 241.55 181.16 18.58 15.93 15.31

(-r) 1.12E-04Nt2 6.0E-06Nt

2 6.0E-06Nt

2 7.8E-05Nt

2 9.1E-05Nt

2 7.1E-05Nt

2

No(kg/m3) 211.9093 772.2008 765.6968 364.8304 453.7205 396.3535

T ABLE IB

COAG-FLOCCULATION KINETIC PARAMETERS AND LINEAR REGRESSION COEFFICIENT OF ALUM IN PIE AT VARYING PH

AND 0.1 X 10-3KG/M3 DOSAGE.


α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.511 0.835 0.849 0.714 0.646 0.906

K(m3/kg.S) 4.0E-06 7.0E-06 7.0E-06 1.0E-05 1.34E-05 1. 2E-05

KR(m3/S) 1.5366E-19 1.5453E-19 1.5872E-19 1.5571E-19 1.5647E-19 1.5673E-19

βBR(m3/kg.S) 8.0E-06 1.4-05 1.4E-05 2.0E-04 2.68E-04 2.4E-05

εp (Kg-1

) 5.2063 E+13 9. 0597E+13 8.8821E+13 1.2844E+15 1.7128E+14 1.53130E+14

τ1/2(Sec) 543.48 207.04 207.04 96.02 8.11 362.32

(-r) 4.0E-06Nt2 7.0E-06Nt

2 7.0E-06Nt

2 1.0E-05Nt

2 1.34E-04Nt

2 1.2E-05Nt

2

No(kg/m3) 707.2136 794.9126 645.9948 548.5464 271.0762 637.3486

T ABLE IIA



α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.749 0.748 0.676 0.929 0.806 0.693

K(m3/Kg.S) 6.29E-05 5.23E-06 2.76E-05 8.44E-05 7.07E-05 9.90E-05

KR(m3/S) 1.5315E-19 1.5494E-19 1.5862E-19 1.5392E-19 1.5545E-19 1.5596E-19

βBR(m3/kg.S) 1.258E-04 1.046E-05 5.52E-05 1.688E-04 1.1414E-04 1.98E-04

εp (Kg-1

) 8.2142E+15 6.7510E+13 3.4800E+13 1.0967E+15 9.0962E+14 1.2695E+15

τ1/2 (Sec) 34.56 277.11 39.38 17.17 20.50 10.98

(-r) 6.29E-05Nt2 5.23E-06Nt

2 2.76E-06Nt

2 8.44E-05Nt

2 7.07E-05Nt

2 9.90E-05Nt

2

No(kg/m3) 370.0962 747.3842 845.3085 503.7783 596.3029 495.7858



T ABLE IIB COAG-FLOCCULATION KINETIC PARAMETERS AND LINEAR REGRESSION COEFFICIENT OF ALUM IN PIE AT VARYING PH



α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.837 0.807 0.905 0.673 0.649 0.888

K(m3/kg.S) 2.98E-06 7.08E-06 2.76E-06 2.29E-05 2.35E-05 1. 27E-05

KR(m3/S) 1.5366E-19 1.5453E-19 1.5872E-19 1.5571E-19 1.5647E-19 1.5673E-19

βBR(m3/kg.S) 5.96E-06 1.416-05 5.52E-05 4.58E-05 4.7E-05 2.54E-05

εp (kg-1

) 3.8787 E+13 9. 1633E+13 3.4778E+14 2.9414E+14 3.0038E+14 1.6206E+14

τ1/2(Sec) 929.50 204.70 525.10 42.19 46.25 342.35

(-r) 2.98E-06Nt2 7.08E-06Nt

2 2.76E-06Nt

2 2.29E-05Nt

2 2.35E-05Nt

2 1.27E-05Nt

2

No(kg/m3) 671.5917 766.8712 526.5929 607.1645 481.6956 625.3909

T ABLE IIIA



α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.940 0.850 0.865 0.931 0.844 0.628

K(m3/Kg.S) 2.033E-04 8.338E-06 3.925E-06 7.978E-05 4.443E-05 5. 073E-05

KR(m3/S) 1.5315E-19 1.5494E-19 1.5867E-19 1.5417E-19 1.5545E-19 1.5596E-19

βBR(m3/kg.S) 4.066E-04 1.6676E-05 7.85E-06 1.5956E-04 8.886E-04 1.0146E-04

εp (Kg-1

) 2.6549E+15 1.0763E+14 4.9474E+13 1.0350E+15 5.7163E+14 6.5055E+14

τ1/2 (Sec) 10.69 173.82 276.93 18.17 32.62 21.43

(-r) 2.033E-04Nt2 8.338E-06Nt

2 3.925E-06Nt

2 7.978E-05Nt

2 4.443E-05Nt

2 5.073E-05Nt

2

No(kg/m3) 260.6474 733.4067 824.1985 510.8818 584.2487 623.9860

T ABLE IIIB

COAG-FLOCCULATION KINETIC PARAMETERS AND LINEAR REGRESSION COEFFICIENT OF ALUM IN PIE AT VARYING PH AND 0.3 X 10-3KG/M3 DOSAGE.


α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.770 0.907 0.644 0.739 0.631 0.891

K(m3/kg.S) 4.333E-06 1.139E-05 4.547E-06 5.296E-06 8.078E-06 1. 391E-05

KR(m3/S) 1.5392E-19 1.5463E-19 1.5877E-19 1.5571E-19 1.5668E-19 1.5699E-19

βBR(m3/kg.S) 8.666E-06 2.278E-05 9.094E-06 1.0592E-05 1.6156E-05 2.782E-05

εp (kg-1

) 5.6302 E+13 1. 47323E+14 5.7278E+13 6.8024E+13 1.0311E+14 1.7721E+14

τ1/2(Sec) 501.71 127.24 318.73 182.64 134.65 312.57

(-r) 4.333E-06Nt

1.139E-05Nt2

4.547E-06Nt2 5.296E-06Nt

2 8.078E-06Nt

2 1.391E-05Nt

2

No(kg/m3) 766.0487 931.6192 530.3633 1070.7785 867.9802 668.3152

T ABLE IVA COAG-FLOCCULATION KINETIC PARAMETERS AND LINEAR REGRESSION COEFFICIENT OF SSC IN PIE AT VARYING PH



α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.633 0.948 0.586 0.971 0.831 0.790

K(m3/kg.S) 1.402E-04 7.7688E-06 1.266E-05 6.172E-05 4.599E-05 3. 339E-05

KR(m3/S) 1.5341E-19 1.5494E-19 1.5867E-19 1.5417E-19 1.5571E-19 1.5622E-19

βBR(m3/kg.S) 2.804E-04 1.5536E-05 2.532E-05 1.2344E-04 9.198E-05 6.678E-05

εp(Kg-1

) 1.8278E+15 1.0027E+14 1.5958E+14 8.0067E+14 5.9071E+14 4.2747E+14

τ1/2 (Sec) 15.51 186.57 85.86 23.48 31.51 32.55

(-r) 1.402E-04Nt2 7.768E-06Nt

2 1.266E-05Nt

2 6.172E-05Nt

2 4.599E-05Nt

2 3.339E-05Nt

2

No(kg/m3) 234.6041 758.5527 865.6510 1133.7868 841.3259 999.1008



T ABLE IVB COAG-FLOCCULATION KINETIC PARAMETERS AND LINEAR REGRESSION COEFFICIENT OF ALUM IN PIE AT VARYING PH



α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.666 0.939 0.421 0.687 0.432 0.714

K(m3/kg.S) 3.409E-06 1.034E-05 8.391E-06 7.862E-06 3.998E-06 7. 255E-05

KR(m3/S) 1.5392E-19 1.5463E-19 1.5877E-19 1.5596E-19 1.5668E-19 1.5699E-19

βBR(m3/kg.S) 6.818E-06 2.068E-05 1.6782E-06 1.5724E-05 7.996E-06 1.451E-05

εp (kg-1

) 4.4092 E+13 1. 3374E+14 1.0570E+14 1.05724E+14 5.1034E+13 9.2426E+13

τ1/2(Sec) 637.70 140.16 172.72 122.89 271.88 599.29

(-r) 3.409E-06Nt2

1.034E-05Nt2 8.391E-06Nt

2 7.862E-06Nt

2 3.998E-06Nt

2 7.255E-06Nt

2

No(kg/m3) 645.7862 843.5259 490.9421 990.8839 1069.1757 505.0760

T ABLE VA



α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.816 0.972 0.962 0.953 0.876 0.745

K(m3/kg.S) 2.84E-05 2.16E-05 1.22E-05 7.95E-05 6.59E-05 3. 83E-05

KR(m3/S) 1.5341E-19 1.5504E-19 1.5867E-19 1.5417E-19 1.5571E-19 1.5622E-19

βBR(m3/kg.S) 5.68E-05 4.326E-05 2.44E-05 1.59E-04 1.318E-04 7.66E-05

εp (Kg-1

) 3.7025E+14 2. 7864E+14 1.5378E+14 1.0313E+14 8.4644E+14 4.9033E+14

τ1/2(Sec) 76.55 67.10 89.09 18.23 21.99 28.38

(-r) 2.84E-05Nt2 2.16E-05Nt

2 1.22E-05Nt

2 7.95E-05Nt

2 6.59E-05Nt

2 3.83E-05Nt

2

No(kg/m3) 364.3120 484.8955 1082.9543 956.5716 807.4283 822.5714

T ABLE VB



α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.680 0.938 0.669 0.657 0.449 0.77

K(m3/kg.S) 4.43E-06 9.57E-06 4.06E-05 3.30E-06 5.95E-06 1. 06E-05

KR(m3/S) 1.5417E-19 1.5463E-19 1.5877E-19 1.5596E-19 1.5668E-19 1.5699E-19

βBR(m3/kg.S) 8.86E-06 1.914E-05 8.12E-05 6.60E-06 1.19E-05 2.12E-05

εp (kg-1

) 5.7469 E+13 1. 2378E+14 5.1143E+14 4.2319E+13 7.5951E+13 1.3504E+14

τ1/2(Sec) 490.73 151.44 35.70 292.78 182.68 410.17

(-r) 4.43E-06Nt2 9.57E-06Nt

2 4.061E-05Nt

2 3.30E-06Nt

2 5.95E-06Nt

2 1.06E-05Nt

2

No(kg/m3) 733.2991 815.0624 791.7030 1251.8778 1061.3458 570.1904

T ABLE VIA



α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.953 0.722 0.945 0.919 0.871 0.793

K(m3/kg.S) 1.733E-04 4.455E-06 4.763E-06 6.604E-05 4.840E-05 1.736E-05

KR(m3/S) 1.5366E-19 1.5504E-19 1.5872E-19 1.5417E-19 1.5571E-19 1.5622E-19

βBR(m3/kg.S) 3.466E-04 8.91E-06 9.526E-06 1.3208E-04 9.68E-05 3.472E-05

εp (Kg-1

) 2.2556E+15 5. 7469E+13 6.0018E+13 8.5672E+14 6.2167E+14 2.2225E+14

τ1/2(Sec) 12.54 325.31 228.21 21.95 29.94 62.61

(-r) 1.733E-04Nt2 4.455E-06Nt

2 4.763E-06Nt

2 6.604E-05Nt

2 4.840E-05Nt

2 1.736E-05Nt

2

No(kg/m3) 199.5849 928.0202 1084.2459 914.0768 984.0583 1161.1705



T ABLE VIB COAG-FLOCCULATION KINETIC PARAMETERS AND LINEAR REGRESSION COEFFICIENT OF ALUM IN PIE AT VARYING PH



α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.937 0.870 0.936 0.499 0.607 0.654

K(m3/kg.S) 3.119E-06 9.864E-06 5.189E-05 1.495E-06 6.243E-06 1. 108E-05

KR(m3/S) 1.5443E-19 1.5463E-19 1.5877E-19 1.5596E-19 1.5699E-19 1.5724E-19

βBR(m3/kg.S) 6.238E-06 1.9728E-05 1.0378E-04 2.99E-06 1.2486E-05 2.216E-05

εp (kg-1

) 4.039E+13 1. 2622E+14 6.536E+14 1.9172E+13 7.9533E+13 1.4093E+14

τ1/2(Sec) 696.99 146.93 27.93 646.28 174.11 392.40

(-r) 3.119E-06Nt2 9.864E-06Nt

2 5.189E-05Nt

2 1.495E-06Nt

2 6.243E-06Nt

2 1.108E-05Nt

2

No(kg/m3) 764.43675 884.4861 969.0862 1627.0745 750.2438 683.0601

T ABLE VIIA



α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.956 0.806 0.952 0.963 0.909 0.730

K(m3/kg.S) 2.44E-05 9.54E-06 7.57E-06 9.69E-05 9.43E-05 9. 38E-6

KR(m3/S) 1.5366E-19 1.5504E-19 1.5872E-19 1.5417E-19 1.5571E-19 1.5647E-19

βBR(m3/kg.S) 4.88E-05 1.908-05 1.514E-05 1.938E-04 1.886E-04 1.876E-05

εp (Kg-1

) 3.1758 E+14 1. 2307E+14 9.5388E+13 1.2571E+15 1.2112E+15 1.1990E+14

τ1/2(Sec) 89.09 151.92 143.59 14.96 15.37 115.88

(-r) 2.44E-05Nt2 9.54E-06Nt

2 7.57E-06Nt

2 9.69E-05Nt

2 9.43E-05Nt

2 9.38E-06Nt

2

No(kg/m3) 353.9823 858.3691 1366.1202 939.8496 8714.0801 1440.9222

T ABLE VIIB



α 2.000 2.000 2.000 2.000 2.000 2.000

R2 0.916 0.849 0.985 0.765 0.675 0.842

K(m3/kg.S) 9.95E-06 1.14E-05 3.87E-05 4.423E-06 6.20E-06 6.29E-06

KR(m3/S) 1.5468E-19 1.5468E-19 1.5877E-19 1 .5622E-19 1.5699E-19 1.5724E-19

βBR(m3/kg.S) 1.99E-05 2.28E-05 7.74E-05 8.46E-06 1.24E-05 1.56E-05

εp (kg-1

) 1.0447E+14 1. 4740E+14 4.8750E+14 5.4154E+13 7.8986E+13 8.0005E+13

τ1/2(Sec) 218.48 127.13 37.45 228.41 175.32 691.23

(-r) 9.95E-06Nt2 1.14E-05Nt

2 3.87E-05Nt

2 4.23E-06Nt

2 6.20E-06Nt

2 6.29E-06Nt

2

No(kg/m3) 689.1799 689.1799 897.6661 1680.6723 1416.4306 676.1325

Fig. 1a. Representative Kinetic plot of 1/TSDP as a function of time for (AMSC)

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0 10 20 30 40 50

1/T

SDP

(

m3

/kg)

Time (x60sec)

0.1x10-3 kg/m3

0.3x10-3 kg/m3

0.4x10-3 kg/m3

0.5x10-3 kg/m3

0.6x10-3 kg/m3

0.7x10-3 kg/m3



Fig. 1b. Representative Kinetic plot of 1/TSDP as a function of time for (ALUM)

Fig. 2a. Representative plot of Aggregation efficiency profile for pH varying PIE at 0.1x10-3kg/m3 AMSC

Fig. 2b. Representative plot of Aggregation efficiency profile for pH varying PIE at 0.1x10-3kg/m3 ALUM

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

2 4 6 10 20 30 40

E (%

)

Time (x60S)

pH=1

pH=3

pH=5

pH=7

pH=10

pH=13

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.01

0 10 20 30 40 50

1/T

SDP

(m

3/k

g)

TIME (x60sec)

0.1x10-3 kg/m3

0.2x10-3 kg/m3

0.3x10-3 kg/m3

0.4x10-3 kg/m3

0.5x10-3 kg/m3

0.6x10-3 kg/m3

0.7x10-3 kg/m3

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

2 4 6 10 20 30 40

E (%

)

Time (x60S)

pH=1

pH=3

pH=5

pH=7

pH=10

pH=13



Fig. 3a. Plot of E (%) VS pH at 2400Secs. for varying AMSC dosage Fig. 3b. Plot of E (%) VS pH at 2400Secs. for varying ALUM dosage

Fig. 4a. Plot of E (%) VS Dosage(AMSC) at2400Secs.for varying pH

0

10

20

30

40

50

60

70

80

90

100

pH=1 pH=3 pH=5 pH=7 pH=10 pH=13

0.1x10-3 kg/m3

0.2x10-3 kg/m3

0.3x10-3 kg/m3

0.4x10-3 kg/m3

0.5x10-3 kg/m3

0.6x10-3 kg/m3

0.7x10-3 kg/m3

0

10

20

30

40

50

60

70

80

90

100

0.1 0.2 0.3 0.4 0.5 0.6 0.7

E (%

)

Dosage (x10-3kg/m3)

pH=1

pH=3

pH=5

pH=7

pH=10

pH=13

0

10

20

30

40

50

60

70

80

90

100

0.1 0.2 0.3 0.4 0.5 0.6 0.7

E (

% )

Dosage x10-3kg/m3

pH=1

pH=3

pH=5

pH=7

pH=10

pH=13



Fig. 4b. Plot of (E %) VS Dosage(ALUM) at 2400Secs. For varying pH

Fig. 5a. Particle distribution plot(AMSC) for minimum half life of 10.69Sec

Fig. 5b. Particle distribution plot (ALUM) for minimum half life of 8.11Sec

-200

0

200

400

600

800

1000

0 500 1000 1500 2000 2500 3000

Co

nc.

of

TSD

P (

kg/m

3)

Time (Sec)

Singlet

Doublet

Triplet

Sum

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 500 1000 1500 2000 2500 3000

Co

nc.

of

TSD

P (

kg/m

3)

Time (Sec)

Singlet

Doublet

Triplet

Sum

0

200

400

600

800

1000

1200

1400

1600

0 500 1000 1500 2000 2500 3000

Co

nc.

of

TSD

P (

kg/m

3)

Time (Sec)

Singlet

Doublet

Triplet

Sum



Fig. 6a. Particle distribution plot (AMSC) for maximum half life of 325.31Sec

Fig. 6b. Particle distribution plot (ALUM) for maximum half life of 929.50Sec

Fig. 7. Aggregation performance at 2400Secs for 0.1x10-3kg/m3 AMC and ALUM dosages in pH varying PIE

IV. RESULTS AND DISCUSSION

1. Comparative Coag-Flocculation Kinetic

Parameters.

The values of comparative coag-flocculation kinetic

parameters are presented in tables 1a-7a and 1b-7b . The

kinetic parameters were evaluated based on the assumption

that microkinetic aggregation of particles is diffusion

controlled second order reaction (i.e α = 2) for all pH. This,

is supported by smoluchowski’s previous work.

The optimum K are recorded more for AMSC at pH of 5 at

different dosages respectively, though maximum value of K

is recorded at pH of 10. These facts are supported by the

0

100

200

300

400

500

600

700

800

900

1000

0 500 1000 1500 2000 2500 3000

C0

on

c.0

f T

SDP

(kg/

m3

)

Time (Sec)

Singlet

Doublet

Triplet

Sum

0

10

20

30

40

50

60

70

80

90

100

pH=1 pH=3 pH=5 pH=7 pH=10 pH=13

AMSC 85.22 49.86 66.96 87.25 86.38 89.57

ALUM 29.13 51.88 59.71 77.49 93.26 36.75

Effi

cie

ncy

( E

%)

AMSC

ALUM

0

200

400

600

800

1000

0 500 1000 1500 2000 2500 3000

C0

on

c.0

f T

SDP

(kg/

m3

)

Time (Sec)

Singlet

Doublet

Triplet

Sum



lower values of ½ recorded at pH of 1, for AMSC at

constant dosage of (0.1, 0.3, 0.4, 0.6) x 10-3

kg/m3

and at pH

of 5, for alum (0.5 – 0.7) x 10-3

kg/m3. This is an indication

that AMSC is more effective in strong acidic medium than

alum for micro kinetic particle aggregation process. This

phenomenon is expected because AMSC having calcium as

major constituent readily hydrolyses in strong acidic

medium and in the process combines with calcium

bicarbonate present in PIE already to produce more cationic

charges (CaOH2+

) thereby making available more

attachment sites for TSDP hence increasing the adherence

ratio of TSDP on AMSC (i.e. rTSDP/AMSC). Similarly the

hydrolysed specie of alum (Al(OH)3) particles in strong acid

medium reduces the total cationic charge making it less

effective for adsorption of TSDP. But the sorption ratio

between TSDP and alum (i.e. rTSDP/ALum) increases as the pH

decreases to 5 and even beyond (pH of 10 as is observed in

this work). Generally K values were evaluated from

graphical illustration of equation 32, known as

representative kinetic linear plots presented in figures 1a and

1b . Since K is basically the rate per particle concentration

i.e. a measure of the rate of disappearance of primary

particles in a coag-flocculation process [34]. This is an

indication that K is associated with energy barrier (KT)

between two potential coag-flocculating particles. The

values of K(= 0.5 Br) presented in tables 1a –7a and 1b-7b

are very sensitive to all the pH and dosages studied with the

exception of pH 3 and 13 for alum at the dosages (0.1 and

0.2) x 10-3

kg/m3. Generally, there is minimal variations in

KR values presented in tables 1a –7a and 1b-7b. This is

because KR being a linking factor between temperature (T)

and viscosity (ղ) of the fluid (PIE) medium, the fluid

viscosity is constant, only T, which represent room

temperature is varying minimally. This phenomenon

affected the values of KR obtained in the experiment. At

near constant value of KR, p relates directly to 2K = BR as

expressed in equation 15 and p is associated with kinetic

energy required by the potential, colliding particles. The

consequence is that high p results in high kinetic energy to

overcome electrostatic repulsive forces prevalent between

potential coag-flocculating particles. Because low repulsive

force is a condition for low Zeta potential which is desirable

in coag-flocculating process. This could be achieved by

either compression of the double layer or colloidal particles

destabilization to ensure low ½ which favors rapid coag-

flocculation [34]. Generally, this low Zeta potential entails

low use of coag-flocculant and invariably low consumption

of energy during water and wastewater treatment. The

implication in this work is that relatively low repulsion

exists in alkaline medium, for alum and in acidic medium

for AMSC, indicated by highest K values obtained at pH of

10 and 1 respectively. The results presented in tables 1a –7a

and 1b- 7b show that low values of ½ corresponds to high

value of p and K. The best ½ value in this work is from

alum which is in fraction of seconds lies within the range of

previous work where milliseconds had been obtained [34].

In addition, the rate equation (-r) or (-

) which accounts

for the rate of depletion of TSDP in effluent sample during

coag-flocculation process are evaluated from equation 14

and presented in tables 1a –7a and 1b-7b . The result

obtained show that (-r) is a function of K and ½. The

implication is that high rate of TSDP depletion is as result of

high K (which accounts for high particle aggregation and a

complimentary, low ½. This phenomenon is in indication

that at low ½, initial particle concentration in form of TSDP

in the effluent sample is deemed to be halved with probably

low energy consumption.

Furthermore, from equation 38, it can be deduced that ½ is

a function of rate constant and initial TSDP concentration

(i.e Theoretical No or (TSDP) which is mathematically

represented as ½ = f(0.5 No K)-1

. The consequence of

equation 38 is that, the higher the No, the lesser the ½. This

phenomenon accounts for obtainable high rate of

aggregation/agglomeration and settling in high turbidity

water at low ½. However, in these studies, the Theoretical

No values presented in tables : 1a – 7a and 1b – 7b are in

disagreement with equation 38 interpreted result except

table 3b of alum. The discrepancies observed in the tables

could be as a result of unattainable assumptions.

Homogeneity of PIE particles and AMSC., PIE particles and

alum throughout the dispersions prior to particles

aggregation [24],[31]. The second limitation is the reactions

between vander waal’s and repulsive forces and finally, low

or high coagulant dosages could alter the theoretically

predicted values, because low coagulant dosage coagulation

may not be effective, particularly if a certain amount of the

paraticles exists in soluble forms (ionic or complex), also

the fraction of the particle that exists in colloidal form is not

known. Equally high coagulant dosage could lead to

returbidization of the PIE sample [6].

2. Coag-flocculation settling time influence

These are illustrated in the representative plots,

figures 2a and 2b for AMSC and Alum respectively. The

general feature of the figures show that efficiency of both

coagulants increases with increase in time, though the

magnitude differ for a particular pH. The important feature

of these figures confirm that the best performance are

recorded for AMSC at the pH = 13 and 1, though the

performance at the pH = 7 and 10 are quite satisfactory. The

poor/under performance of pH of 3 and 5 illustrated in the

figures 2a, could be attributed to low degree of AMSC

solubility in relatively weak acidic medium which gave rise

to the provision of less adsorption site for particles

attachments on the coagulants ions or complexes. It is

worthy to note that starting from 10mins (600 Secs) the

performance/efficiency values recorded for pH of 1, 7, 10

and 13 are impressive. With the least E > 70%, it confirms

the effectiveness of AMSC to remove turbidity from the

effluent. Similarly in the case of alum it is recorded at pH =

10 as can be seen in figure 2b, though the performance

recorded at pH 7 and 5 are relatively satisfactory. The

appreciable values observed in the alkaline medium could

be attributed to large amount of TSDP that are adsorbed

onto the pre-formed hydroxide flocs. This result is in

agreement with previous work [22]. Also it was observed

that the good performance of alum did not follow a

particular trends, it generally fluctuates from 10 – 40 mins

for pH of 5, 7 and 10. With the least efficiency E < 70%

show that the performance is not consistent for all the period

and pH under consideration. Comparatively, in general

terms, it can be deduced that the performance of AMSC



within the period and pH under consideration proved to be

more effective in removing turbidity from PIE than alum.

3. Coag-flocculation pH variation influence.

These are illustrated in the representative plots ,

figures 3a and 3b for AMSC and Alum respectively. They

indicates the performance of various doses of AMSC and

Alum at varying pH. The significant feature indicates that

between pH of 1 and 13 (dosage of 0.3, 0.6 and 0.2) x 10-

3kg/m

3 in figure 3a near constant performance value is

recorded followed by drastic decrease in performance

especially at the pH of 3 and 5. At pH of 13 optimum

performance is recorded. Thus, it can be deduced that

between pH of 1 and 13, the dosage has minimal effect on

the performance (E%). This phenomenon indicates that the

interaction between the anionic TSDP in PIE and cationic

AMSC or protonated AMSC occurs more in high acidic and

alkaline medium respectively and this is in line with

previous works [35]. Similarly in figure 3b, the optimum

performance is recorded at the pH of 10 (0.1 x 10-3

kg/m

dosage), followed by downward movement of performance

(E%) to the converging point of pH = 13. It could be

observed that the alum performance (E%) is affected by

dosage. This is an indication that at low dosage of 0.1 x 10-

3kg/m

3 alum reacts more with the available alkalinity in PIE

such as carbonate, bicarbonate and hydroxides to form

insoluble alum salt (metal hydroxide floc) which

incorporates TSDP more at the optimum pH of 10. Hence it

can be concluded that alum operates better in PIE tuned

alkaline medium.

4. Coag-flocculation Dosage Variation Influence.

These are presented in figures 4a and 4b for AMSC

and Alum respectively. The significant feature shows that in

general, there is very negligible variation in the values of

E% at the pH of 1, 7, 10 and 13 for all the dosages in figure

4a. But in figure 4b the variation in dosage affected values

of E% for all dosages and pH except pH of 10 (dosage of

0.1 – 0.2) x 10-3

kg/m3

. Thus it can be observed from

figure 8a that the optimum performance are recorded at pH

= 13 (dosages of 0.1 – 0.2) x 10-3

kg/m3, pH = 1 (dosages of

0.3, 0.4 and 0.6) x 10-3

kg/m3

and pH = 7 (dosages of 0.5,

0.7) x 10-3

kg/m3. Similarly in fig 4b they are record at pH =

10 (dosages of 0.1 – 0.2) 10-3

kg/m3

and pH 5 (dosage of 0.3

– 0.7) x 10-3

kg/m3. It can be deduced from the figures that

the best performance are obtained at dosage of (0.1 – 0.2)

10-3

kg/m3 for both coagulants . A possible explanation for

this phenomenon is that increased amounts of coagulants

beyond a certain dosage may favour competitive AMSC –

AMSC and Alum – Alum associations at the expense of

AMSC – TSDP and Alum – TSDP particle interactions.

These results imply that the mechanism of coag-flocculation

by AMSC and Alum may be similar to those by-polymers.

This is because low dosages of polymers have been found to

achieve a fast and efficient removal of TSDP (colloids) as

observed [2],[28] .

5. Time Evolution of Particle Cluster Size

Distribution.

Applying K, obtained from the linear plots of

equation 32, equations 41 – 43 are able to predict the time

evolution of particles aggregates (Singlets, doublets, triplets

for m = 1, 2, 3 respectively). The typical nature of the

particles behavior in response to various periods of

10.69Secs, 8.11Secs, 325.31Secs, 929.50Secs respectively

are represented by the curves in figures 5a – 6b. From

figures 5a and 5b, the primary particle (singlets) and total

number of particle are seen to decrease linearly more

rapidly. This phenomenon is an indication of high rate of

coag-flocculation illustrated at low ½ of 10.69Secs and

8.11Secs, and also a process being controlled by similar

mechanisms. The mechanism that supports this particles

distribution behavior includes, charge neutralization,

bridging and sweep floc, [24],[29], though not at the same

degree. The cationic complexes of achatina maginata (such

as calcium hydroxide ion) and alum (such as aluminum

hydroxide ion) and their various species which are formed

usually neutralize particle charges hence lowering the

repulsive forces or removal of the kinetic energy barrier.

With negligible repulsive forces, the classes of particles in

figure 5a and 5b are seen to fuse into one particle kernel

starting from 600Secs to infinity (∞) with the exception of

total number of particles (sum) in figure 5b. This is evidence

that either particle colloidal entrapment predominates or the

cationic charges of the coagulants overwhelms the anionic

charges of the colloidal particles in PIE.

Similarly, figures 6a and 6b followed the same trend with

figures 5a and 5b where the singlets and total number of

particles (sum) are seen to decrease more. This depicts

slopes with moderately high degree with time. The physical,

actual response of the process is relatively high period that

runs into hundreds of seconds instead of milliseconds [34].

The behavior of the particles in figure 6a is indicative of

middle level of repulsive forces that leads to moderately

sweeping away of TSDP under gravity from the PIE at the

maximum coag-flocculation period (2400 Secs) for the

particle classes of singlets, doublets and triplets. Conversely,

in figure 6b, the particle distribution demonstrates relatively

high shear resistance among the particles though not too

strong to operate outside microkinetic controlled process.

The curves, clearly depicts a process devoid of sweeping

phenomenon being in action and this is supported by

relatively high period of 929.50Secs which may not favor

most treatment operations.

6. Comparative Performance of AMSC and Alum.

The coag-flocculation activity of AMSC and Alum

was compared at same experimental conditions as presented

in figure 7. The result, indicate that AMSC performed better

than alum at all pH except pH of 3 and 10. However, the

optimum performance is achieved by alum at pH of 10.

Generally, it can be seen that the performance compares

favorably to that of alum at all pH studied. The main

advantages of AMSC over alum is that it is environment

friendly (because of production of low biodegradable sludge

and medically safe). Furthermore, achievement of good

performance over a wide range of pH and dosages.

V. CONCLUSION.

Results obtained indicate that the optimal

conditions for process operations are: pH of 13, 0.2 x

10-3

kg/m3 coagulant dosage, 91.30% removal efficiency at

rate constant of 2.033E – 0.4m3/kg.S for AMSC and pH of

10, 0.1 x 10-3

kg/m3 coagulant dosage, 93.26% removal



efficiency at rate constant of 1.34E – 04m3/kg.S for alum, at

2400 secs settling time. Overall, the results obtained are in

line with previous works [12],[17],[25],[32],[33].

NOMENCLATURE

AMSC: Achatina Shell Maginata Coagulant

PIE: Pharmaceutical Industry Effluent

Alum: Aluminum Sulphate

-r: Coag-floccculation reaction rate

K: αth

order coag-flocculation constant

α: Coag-flocculation reaction order

R2: Regression Coefficient

TSDP: Total suspend and dissolved particles

p: Collision Efficiency

½: Coagulation period / Half life

BR: Collision factor for Brownian Transport.

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Comparative Studies On Coag-flocculation Kinetics …...specific models to evaluate interaction effects of coagulation factors (effluent medium pH, coagulant dosage, settling time)