20

CHAPTER 2

REVIEW OF LITERATURE

2.1 GENERAL

Activated Sludge process is the most commonly used treatment

process for treating the domestic / industrial wastewater. Although it is a

proven method of treating wastewater, research on this area still continues to

make the process more effective and successful. The major components

involved in this process are aerator and clarifier. The primary action in the

aerator is the conversion of organic substances into gases that escape to the

atmosphere and into the biological cell tissues that can be removed by gravity

settling in the clarifier. Hence more focus is required in these two processes.

An in-depth study was made as to how these two processes could be made

more effective which prompted the researcher to take up the investigation on

enhancing the biological activity in the aerator and improving the settling

characteristics of activated sludge in the clarifier. To meet the objective of the

above studies, the previous works have been reviewed under the following

related heads.

2.2 DAIRY WASTEWATER TREATMENT

Dairy industry is one of the major industries causing water

pollution. In India, dairy industries generate about 6 – 10 liters of wastewater

per liter of milk processed depending upon the process employed and

products manufactured. Considering the increased milk demand by 2020 AD,

21

the milk based industries in India are expected to grow rapidly and the waste

generation and environmental problems will also increase proportionately.

Poorly treated wastewater with high levels of pollutants caused by poor

design, operation or treatment systems creates major environmental problems

when discharged into surface water or land.

Waste from dairy industries may contain proteins, salts, fatty

substances, lactose and various kinds of cleaning chemicals (Kosseva et al

2003). The treatment of effluent generated from dairy industries, involves

several sequential steps to remove one or more classes of contaminants. The

first step involves primary treatment, where a fraction of the suspended solids

is removed from the wastewater. Following primary treatment, the wastewater

undergoes secondary treatment, which is a biological process, often involving

an aerated lagoon or an activated sludge systems.

In practice, aerated lagoons, as a form of biological treatment, have

often been found inadequate for the new water quality criteria and hence a

more sophisticated process is required. Activated sludge process has been

found superior than other systems in treating the domestic and industrial

wastewater and the plants are capable of reducing BOD and suspended solids

up to 90 to 99 percent, if properly designed and operated.

Fang and Herbert (1990) investigated lab scale aerobic treatment of

dairy wastewater by three stages of activated sludge process. Dairy waste with

an average BOD5 of 1060 mg / L and TKN of 109 mg / L was treated in the

lab scale reactor within the over all retention time of 19.8 h and the final

effluent was found to contain 9 mg / L of BOD5 and 10 mg / L of TKN

corresponding to respective reduction of 99 % and 91%. It was ascertained

that the activated sludge process was an effective way of treating the dairy

wastewater.

22

Orhon et al (1993) investigated experimentally the treatability of

dairy wastewaters by introducing the concept of residual COD. An integrated

dairy plant with small whey production was chosen to evaluate the biological

treatability. Experimental data were interpreted to yield correlations between

removal efficiencies attained with some of the key operation parameters such

as organic loading rate, hydraulic detention time, sludge age etc.

2.3 ACTIVATED SLUDGE PROCESS

2.3.1 Historical Development

The antecedents of the activated sludge process date back to the

early 1880s to the work of scientists who investigated the aeration of

wastewater in tanks and the hastening of the oxidation of the organic matter.

The aeration of wastewater was studied subsequently by a number of

investigators and it was reported that a considerable reduction in putrescibility

could be secured by forcing air into wastewater in basins. Following the

experiments by Clark and Adams (1914) with aerated wastewater, Arden and

Locket in 1914 found that the sludge played an important part in the results

obtained by aeration and named the process as activated sludge process as it

involved the production of an activated mass of micro-organisms capable of

stabilizing organic material in wastewater under aerobic condition (Metcalf

and Eddy 2003).

2.3.2 Biological Reactions in the Aerator

The main function of the aerator is to facilitate aerobic environment

for bioconversion of organic substances into cell tissues with the help of

aerobic bacteria partly inherited from the raw wastewater and partly fed in the

form of activated sludge from the clarifier. The aerobic microbes live and

grow enmeshed in extra cellular polymeric substances that build them into

23

discrete colonies forming three dimensional aggregate microbial structures

called floc.

2.3.3 Microorganisms in the Aerator

When the wastewater is exposed to free oxygen, the aerobic

microorganisms develop on own. Further when the microorganisms are

offered a favorable environment with aeration, required temperature,

appropriate pH and nutrient, they multiply in great numbers. The important

microorganisms that are found in biological treatment process are 1) bacteria

2) fungi 3) algae 4) protozoa 5) rotifers 6) crustaceans and 7) viruses. Among

the above micro organisms bacteria play a major role in converting the

colloidal and dissolved carbonaceous organic matter into various gases and

into cell tissues.

Bacteria are single cell protists. They use soluble food and in

general will be found wherever moisture and food sources are available. Their

usual mode of reproduction is by binary fission, although some species

reproduce sexually or by budding. Even though there are thousands of

different species of bacteria, their general form falls into one of the three

categories. Spherical – sphere shaped (coccus), cylindrical – rod shaped

(bacillus) and helical – spiral shaped (spirillum).

Lilia Mehandjiyska et al (1995) investigated the quantity of

microorganisms in samples of activated sludge from municipal wastewater

treatment during different seasons. It was found that the total number of

microorganisms was greatest during summer and the representatives of genus

pseudomonas predominated in quantitative aspect among the bacteria. While

molds were found during all seasons actinomycetes were isolated only during

summer. The performed investigations gave a possibility of pure cultures to

24

be used for recovering of the distributed ratio of microorganisms in the

activated sludge at extreme situations.

Govoreanu et al (2003) compared the settling properties (SVI) of

the activated sludge with on line measurements of floc size and size

distribution and with microbial community dynamics. Three distinct stages in

the SBR evolution were observed. A good correlation between floc size,

settling properties and microbial community evolution was observed in the

first stage, when the activated sludge was found to have predominant presence

of floc forming bacteria. A good balance between floc-forming and

filamentous bacteria with good settling properties and a highly dynamic

community was observed in the second stage. Finally in the third stage with

an increase in the filamentous bacteria, a good correlation between properties

and floc size distribution was observed.

Rajesh Kumar and Jayachandran (2004) analyzed the treatment of

dairy wastewater using a selected bacterial isolate; Alcaligenes sp. gave a

maximum COD reduction of 90% in 240 hrs. This study clearly indicated the

possibility of using Alcaligenes sp. for the effective treatment of dairy

wastewater.

Maghsoodi Vida et al (2007) examined 10 bacterial isolates from

dairy wastewater for their ability to reduce COD and other chemical

compositions during 30 days. The highest reduction of COD was after

10 days by two micro organisms BP3 (Bacillus with curve) and BP4 (Cocci of

bigger size) and these two were considered as most effective microorganisms

for further experimental performance in order to optimize the efficiency of the

test organisms.

Yan (2007) studied the isolation, characterization and identification

of bacteria from activated sludge and soluble microbial products in

wastewater treatment systems. The standard procedure, medium used,

analytical methods and biochemical characterization techniques required for

25

isolation and identification of bacteria responsible for key process of

wastewater treatment systems were discussed in his study. The effect of

seasonal variations and salinity variations on the bacterial species was also

examined.

Mihaela Palela et al (2008) studied the microbiological and

biochemical characterization of dairy and brewery wastewater micro biota.

The brewery and dairy wastewaters presented a diverse micro biota which

consisted of gram-negative bacteria (the predominant micro-organisms),

yeasts and moulds. The microbiological tests showed the following groups of

microorganisms, such as

Bacteria belonging to the genus bacillus, pseudomonas and

Escherichia.

Yeasts belonging to the genus Saccharomyces, Kluyveromyces

and Torulopisis.

Moulds belonging to the genus Aspergillus and Geotrichum.

Lyliam loperena et al (2009) conducted microbial experiments on

dairy wastewater, isolated the milk fat / protein degrading micro organisms

and developed an inoculum containing pseudomonas sp and bacillas sp. The

bio degradation ability of the inoculum of native organisms was compared

with the ability of commercial inoculum. Although the COD removal was

similar in both cases, the removal of fat and proteins was higher in the case of

native inoculum.

2.3.4 Enzymatic Activity in the Aerator

The enzymes as catalysts have the capacity to increase the speed of

chemical reactions greatly without altering themselves. There are two general

types of enzymes, extracellular and intracellular. When the substrate or

26

nutrient required by the cell is unable to enter the cell wall, the extracellular

enzyme converts the substrate or nutrient to a form that can then be

transported into the cell. Intracellular enzymes are involved in the synthesis

and energy reactions within the cell. There are two physical factors which

have a very pronounced effect on enzyme reactions and they are temperature

and hydrogen ion concentration (pH)

Hisham et al (2004) formulated a dynamic model of the activated

sludge process based on the two kinetic relationships, one with an enzyme –

accelerated reaction associated with non-viable biomass and the other with

monod growth associated with viable biomass. The Michaelis kinetic

relationship represented the enzyme kinetics and Monod kinetics represented

the growth, both using double term definition of substrata uptake and uptake

of DO. The equations were implemented on lab-view, instrumentation –

programming package as virtual instruments and the same were linked to real

instruments and actuators of the plant to function on line.

Yin Li and Chrost (2006) investigated selected enzymatic activities

of microorganisms in three aerobic sludge model reactors of communal, dairy

and petroleum wastewaters. Four extracellular enzymes viz. leucine –

amniopeptidase (L-Amp), - glucocidase ( - GLC), alkaline phosphates

(APA) and lipase (LIP) were studied. Comparison of three wastewater

reactors showed that L-Amp, -GLC and APA had highest activities in dairy

wastewater and the highest activity of LIP was found in petroleum

wastewater. The results of the study showed that the activities of the studied

enzymes were mainly associated with microbial cells in the activated sludge

flocs rather than being cell – free, extra cellular enzymes.. Kinetic parameters

of the studied enzymes varied notably. High Vmax (the maximal hydrolytic

rate) and followed by high Km (michaelis constant) suggested that inhibition

of enzymes took place in the activated sludge system.

27

2.4 REACTIONS IN THE CLARIFIER

The secondary clarifier in activated sludge process has to perform

two functions. 1) Clarification: - Separation of the mixed liquor suspended

solids from the treated waste water, which results in a clarified effluent and 2)

Thickening: Thickening of the return sludge to achieve the desired underflow

concentration of recycling and also to minimize the sludge handling costs.

This is the final step in the products of a well-clarified, stable effluent, low in

BOD and suspended solids. Thus secondary clarifier represents a critical link

in the operation of ASP.

2.5 MICROBIOLOGICAL PROBLEMS IN ASP

Although the conversion of organic wastes into biomass takes place

in the activated sludge process with the help of microorganisms, a few of

them have been found to be creating problems in the system. The types of

microbiological problems that can occur in activated sludge operation include

dispersed (non – settleable) growth, pin floc problems, zoogloeal bulking and

foaming, polysaccharide (Slime) bulking and foaming, nitrification and

denitrification problems, toxicity and filamentous bulking and foaming. The

best approach to trouble shooting the activated sludge process is based on

microscopic examination and oxygen uptake rate (OUR) testing to determine

the basic cause of the problem or upset and whether it is microbiological in

nature.

2.5.1 Filamentous Bulking

Among the various problems that the activated sludge process

encounters, filamentous bulking is one of most common and serious

problems. Bulking is caused by an over abundance of filamentous

microorganisms and termed as filamentous bulking. Eikel boom (1975)

28

provided a rational basis to identify the different filamentous bacteria found in

activated sludge. These filamentous micro organisms grow under different

conditions and there are specific causes for such growth (Table 2.1),(Eikel

Boom 1975).

Paolo Madoni et al (2000) carried out a survey of both biological

bulking and foaming in 167 activated sludge plants and found that 84 had

foaming problem, 81 had bulking problem, and 55 were affected by both the

problems. Bacterial identification reveled that Microthrix parvicella was the

most common filamentous microorganism involved in bulking and foaming.

Other filamentous microorganisms such as Eikel boom types 0041, 021N,

0092, 0675, Thiothrix and Nocardioform Actinomycetes were also reported to

have been detected at lower frequencies. The survey indicated that the use of

contact zone technique or the addition of chemicals such as oxidants and

coagulants were promising approaches for solving bulking and foaming

problems.

Horan et al (2004) assessed the predominant filamentous bacteria

which were found in bulking activated sludge of both domestic origin and

industrial origin in UK. Most samples (84%) of domestic origin were

dominated by two filament types Viz. microthrix parvicella and Eikel boom

type 021N. Remaining 16% of the samples were found to have been

dominated by Nostocoida limicola and sphaerotilus natans. It was reported

that the samples of industrial origin showed much greater filament diversity,

with eight filament types routinely observed in high numbers. Type 021N was

found to be most prevalent and M parvicella was not observed in the

industrial samples. Based upon knowledge of the predominant filament type

from microscopic examination, it was possible to identify the likely causes for

their proliferation and suggest long term solutions to achieve their eradication.

29

Table 2.1 Causes of filament growth in activated sludge

(Eikel Boom D H, 1975).

S.No. Cause Filaments

1Low Dissolved Oxygen

Concentration

Sphaerotilus natans,type 1701

Haliscomenobacter hydrossis

2 Low F/M type 0041,type 0675,type 1851,type 0803

3 Septicity

type 021N,Thiothrix I and II

Nostocoida limicola I,II,III

type 0914,type 0411,type 0961,type 0581

type 0092

4 Grease and OilNocardia spp.,Microthrix parvicella

type 1863

Nutrient Deficiency

Nitrogentype 021N, Thiothrix I and II

5

Phosphorus:

Nostocoida limicola III

Haliscomenobacter hydrossis

Sphaerotilus natans

6 Low pH fungi

Inchio Lou et al (2006) developed new conceptual qualitative frame

work, integrating kinetics and diffusion inside the activated sludge flocs for

explaining filamentous bulking, by operating sequencing batch reactors at

various substrate concentrations and measuring sludge setteleablity at

different floc size distributions. Three different regions (bulking, transitional

and non-bulking region) based on the substrate concentration were suggested.

In the bulking and non-bulking regions, kinetic selection controlled the

growth rate process and favored filaments and floc formers respectively. In

the transitional region, substrate diffusion limitation determined by the floc

size played an important role in causing bulking.

30

2.5.2 Chlorination as a Control Measure for Filamentous Bulking

As discussed in the previous chapter, there are long-term and short-

term methods in controlling the filamentous bulking. The long-term methods

involve the altering the basic parameters of the system which may not be

feasible immediately and involve huge costs. In such cases, the short-term

methods are carried out to control the bulking. Among the short-term

methods, addition of chlorine is commonly practiced, as it is easily available

and cost effective. The most common chlorine compounds used in wastewater

treatment plants are chlorine gas(Cl2), calcium hypochlorite [Ca(OCl)2],

sodium hypochlorite (NaOCl) and chlorine dioxide(ClO2). Chlorine and

hydrogen peroxide have been used successfully to control filamentous

organisms and stop bulking. Chlorine for bulking control is widespread, used

by more than 50% of treatment plants. It should be pointed out that

chlorination is not a cure-all for all activated sludge microbiological

problems. Chlorination will actually make problems worse if the problem is

non-filamentous, e.g., slime bulking or poor floc development (Michael

Richard 1993).

Chlorine dosages for bulking control ranged from 1 to 15 g/kg

MLSS.d according to Jenkins et al (1982) or from 0.7 to 20 mg/L based on

plant sewage throughput rate in seven different activated sludge plants as

reviewed by Neethling (1985a). Neethling et al (1985 a) recommended that

the chlorine dose should not exceed 35 mg/L. Neethling et al (1985b) found

that the filaments associated with low dissolved oxygen concentrations, such

as type 1701 and spherotilus natans, had a lower resistance against

chlorination than the filaments associated with low nutrient levels, such as

Types 0092, 0041, 0675 and M. parvicella. It was found that a chlorine

dosage of 4 g/kg MLSS.d was insufficient to alleviate bulking but a dosage of

double that, was effective, if disruptive to the delicate nutrient removal processes.

31

According to Michael Richard (1993), success was achieved at frequencies as

low as one per day. The recommended dose of chlorine for bulking control

measure is 1 to 10 mg/L. (Metcalf and Eddy 2003).

Neethling et al (1985) developed a model for activated sludge

bulking control by chlorination. The model predicted the frequency of

exposure of chlorine to solids inventory, which related the growth rates of

filamentous and floc forming microorganisms, the proportion and survival

ratios of these two types of organisms. The experimental data of the

laboratory and prototype activated sludge chlorination experiments

substantiated the relation depicted in the model.

Michael Richard (1993) presented the various types of filamentous

organisms that normally cause the bulking and foaming in the activated

sludge and the causative conditions that promote the growth of such

organisms. He also presented the addition of inert material, polymers and

chlorine as methods of choices in controlling the bulking.

Seka et al (2001) investigated the filamentous bacteria causing

bulking in two activated sludge using morphological features, staining

techniques and fluorescent in-situ hybridization. It was identified that the

filaments present in both the sludge were of Eikel boom type 021N. Both the

sludge were tested with the addition of chlorine for control of filamentous

bacteria but it was found that the filamentous bacteria type 021N in one of the

sludge was chlorine susceptible and in other sludge the same type of bacteria

was chlorine resistant. It confirmed the fact that the efficiency of chlorine

depended on penetration rate through the cell wall and suggested that the type

021N bacteria in second sludge developed less permeable cell wall.

Hossain (2004) reviewed the causes of bulking in the activated

sludge process and the control strategies that were normally practiced. The

32

various causes of bulking included low/high DO, low/high organic loading,

reactor configuration, inadequate micro nutrient, low pH, and low relative

influent macro nutrient content. The control measures for activated sludge

bulking could be either system specific and non specific methods. The non

specific methods like addition of polymer, ozone, chlorine etc were employed

as cost effective control strategies, as these methods were cheaper than

modifying plant’s configuration.

2.6 BATCH ANALYSIS OF HINDERED SETTLING

Design of secondary clarifier for biological systems is slightly

different from that of primary clarifier, because of the high solids contents

entering the clarifier from the biological aeration tank. Incoming solids

concentrations are in the range of 2,500 to 5,000 mg/L. Under this situation,

interactions between particles become important and particle settling is

hindered. There is usually a distinct clarified zone showing a liquid-solid

interface. The slope of the hindered settling region is the zone settling

velocity (Vs) that is equivalent to the surface loading rate (Figure 2.1).

Height of

Interface Transition

Compression

t2t1 t3 t4

Zone Settling

Slope Settling Velocity, Vs

Time

Figure 2.1 Interface settling curve

Renko (1998) proposed a model for a batch-settling curve to

determine the solids layers eliminating the disadvantage of the kynch theory

which only offered a graphical procedure for determination. He built the

33

model based on the graphical approach of work and kohler. The applicability

of the model was tested with calcium carbonate solution.

2.7 SETTLING TESTS FOR THE DESIGN OF CLARIFIER

The rate of settling in the hindered settling region is a function of

the concentration of solids and their characteristics. As the settling continues,

a compressed layer of particles forms at the bottom of the cylinder in the

compression settling region. The particles in this region apparently form a

structure in which there is close physical contact between the particles. As the

compression layer forms, regions containing successively lower concentrations of

solids than those in the compression region extend upward in the cylinder.

Thus in actuality, the hindered settling region contains a gradation of solids

concentration from that found at the interface of the settling region to that

found in the compression settling region.

Because of the variability encountered, settling tests are usually

required to determine the settling characteristics of suspensions where

hindered and compression settling are important conditions. On the basis of

data derived from column settling tests, two different design approaches can

be used to obtain the required area of the settling / thickening facilities. In the

first approach, the data derived from a single (batch) settling tests are used. In

the second approach known as the solid flux method, data from a series of

settling tests conducted at different solid concentrations are used.

Jeyanthi and Saseetharan (2006) performed sedimentation experiments

at steady state on dairy activated sludge using columns of different heights

(65,90,120 cms), having a common internal diameter of 2.25 cm for different

MCRT values (5, 7, 9 days) and for various initial MLSS concentrations

(2 g/L to 20 g/L) and determined the zone settling velocities to analyze

the effect of initial height of settling column on biofloc settling. It was

34

determined that there was no appreciable change in zone settling velocity with

respect to height of the settling column and the variation between them was

insignificant.

2.8 SETTLING CHARACTERISTICS

The settling characteristics of the mixed liquor solids must be

considered when designing the secondary clarifier for liquid-solid separation.

Clarifier design must provide adequate area for clarification of the effluent

and thickening of solids for the activated sludge solids. Two commonly used

measures, developed to quantify the settling characteristics of activated sludge

are the sludge volume index (SVI) and the zone settling velocity (ZSV).

2.8.1 Sludge Volume Index

The sludge volume index (SVI) is the % of volume occupied by

1 gm of sludge after 30 minutes of settling. The SVI is determined by placing

a mixed liquor sample in a 1 to 2 L cylinder and measuring the settled volume

after 30 minutes and the MLSS concentration of corresponding sample.

Koopman and Cadee (1983) predicted the thickening capacity,

using diluted sludge volume index. The parameters characterizing functional

relationship between settling velocity and suspended solids concentration

were correlated with an easily measured index of sludge settleability, the

diluted sludge volume index (SVI).

Daigger and Roper (1985) developed graphical methods to estimate

activated sludge settling characteristics which were claimed to be applicable

over a broad range of sludge settleablity. The secondary clarifier design and

performance evaluation could be accomplished for an expected range of SVIs,

given the solids loading rate and the corresponding underflow concentration.

35

Renko (1998) developed a model for describing a relationship

between zone settling velocity and stirred SVI. The model gave a good fit

with large SVI and concentration ranges offering a promising relationship

linking ZSV and stirred sludge volume index. The model had greater

advantages of eliminating tiresome multiple batch settling tests.

Bo Jin et al (2003) presented a comprehensive study of sludge floc

characteristics and their impact on compressibility and settleability of

activated sludge in full scale wastewater treatment processes. Compressibility

and settleability were defined in terms of the sludge volume index and zone

settling velocity. It was observed that the morphological and physical

properties of floc had important influence on the sludge compressibility and

settleability.

2.8.2 Zone Settling Velocity

The secondary clarifier design has also been related to an expected

zone settling velocity (ZSV). The ZSV is the settling velocity of the sludge /

water interface at the beginning of sludge settleability test. Many empirical

relations have been suggested to describe the relation between ZSV and

MLSS concentration. The Vesilind relation (Vesilind 1974) is generally

accepted in describing the Vs – Co relation as it is considered to be very

accurate and simple.

Vs = Vo e(-a Co)

(2.1)

In the above Vesilind equation Vo is the maximum settling velocity

and ‘a’ is the measure on how fast the settling velocity decreases with the

increase in concentration of particles.

36

Daigger (1995) presented an analysis of five sludge settling

characteristics data sets from many suspended growth biological wastewater

treatment processes. The results indicated that the data could be characterized

using a single relationship between the initial sludge settling velocity (Vi), the

initial sludge Concentration (Ci) and the sludge volume index (SVI) by means

of an equation (2.2)

Vi = 1.871 – (0.1646 + 0.0051586 SVI) Ci (2.2)

Christopher and Peter (1998) developed a simple mechanistic

model to evaluate the effects of solid characteristics and test parameters on

SVI type indices. It was inferred that for solids settleability and compatibility,

the settling column height and solids concentration in the test had an

interactive effect on the measured SVI. This modeling approach provided a

method for assessing the relevance of the different techniques as valid

measures of solids settleability and assessing whether it was valid to correlate

ZSV model parameters to SVI type measures.

Christopher and Peter (1999) evaluated various correlations by

comparing predicted parameters of Vo (m / h) and K (m3 / Kg) to true Vo and

K values in the equation (2.3)

Vs = Vo. e–k x

(2.3)

To determine the parameters, several column-settling tests over a

range of solids concentrations were performed. Minimum and maximum SVI

values for each Vo and K pair were identified from the profile. The difference

in maximum allowable over flowrate of settling tank, when flux theory design

was applied based on the true and correlation generated ZSV parameters, was

also assessed.

37

Alexis Vandehasselt and Peter Vanrolleghem (2000) obtained the

parameters for the settling velocity models by two ways. One, the traditional

approach using zone settling velocity data from dilution experiments and

other, the direct estimation method relying on a single batch settling curve.

Both the parameters were compared. Four distinct sludge settling curves were

recorded at different sludge concentrations and the Vesilind parameters were

calculated in the traditional way. The value of the resulting model was

evaluated by cross-validating it, on its ability to describe complete single

batch settling curves. The flux curves associated with the single batch settling

curves were estimated. It was concluded that the reliability of single batch

settling curve based flux curve predictions was insufficient to warrant

replacement of the traditional estimation of settling characteristics.

Richard et al (2001) evaluated the settling characteristics of mixed

liquor from advanced wastewater treatment and conventional wastewater

treatment facilities. An algorithm was developed that related zone settling

velocity (Vs) of the mixed liquor to initial mixed liquor suspended solids

(MLSS) concentration and unstirred sludge volume index (USVI) of the

mixed liquor. A secondary clarifier operating diagram was presented that

could be used in the design and operation of secondary clarifiers.

2.9 DESIGN APPROACHES OF SECONDARY CLARIFIER

There are two methods of design of secondary clarifier.

(i) Talmadge and Fitch method.

(ii) Solid flux method.

38

2.9.1 Talmadge and Fitch Method

A column of height Ho is filled with a suspension of solids of

uniform concentration Co. The position of the interface as time elapsed and

the suspension settled is given as shown in Figure 2.2. The rate at which the

interface subsided is given by the slope of the curve at any point.

The critical concentration controlling the sludge handling capacity

of the tank occurs at a height, Hc where the concentration is Cc (critical

concentration). This point is determined by extending the tangents to the

hindered and compression settling regions of the settling curve to the point of

intersection and bisecting the angle thus formed as shown in Figure 2.2. From

point Cc, a horizontal line is drawn which cuts the ordinate at Hc.

Figure 2.2 Talmadge and Fitch method

From the mass balance Co Ho = Cc Hc,

Cc = Co Ho / Hc,

HO

HC

HU

tU

CC

Time

Inte

rface h

eig

ht

Ho – Initial height

Hu – Height at which underflow concentration is reached

Hc – Height at which critical concentration is reached

Cc – Point of critical sludge concentration

tu – Point of critical sludge concentration

39

where, Ho = height of the column, m

Co = initial concentration of suspension, mg/L

Hc = Critical height, m

Through point Cc , a tangent is drawn. The desired underflow

concentration of sludge from the tank is taken as Cu. Again from mass balance

Co Ho = Hu Cu, the height Hu at which the desired underflow concentration

reached is determined.

Hu = Co Ho/Cu

A horizontal line at depth Hu is constructed. A vertical line from the

point of intersection of the two lines drawn through Cc and Hu to the time axis

is constructed to get the value tu, (i.e.) the time required to reach the desired

under flow concentration Cu. Then the required thickening area is calculated

using the relationship,

Q/A = Ho/tu,

where, Q = Flow m3 / day

A = Surface area of the tank, m2

Ho = Initial column Height, m

tu = Time required to reach the desired under flow concentration

Cu, min

The thickener area, At = Q tu/Ho

The thickener area of settling tank designed based on zone settling,

as above, is checked for adequacy for clarification.

Area required for clarification (Ac) = Q1/ZSV

40

where, Q1 = Q (Ho – Hu)/Ho.

ZSV = Zone Settling Velocity, m/min

Normally the thickener area controls the design.

2.9.2 Solid Flux Analysis

An alternative method of arriving at the area required for hindered

settling has been delineated by Coe and Clevenger (1916), Yoshioka et al

(1957), Keinath et al (1977).

The method is based on the analysis of mass (solids) flux. In a

settler that is operating at steady state, a constant flux of solids is moving

downward. Two mechanisms account for the transport of sludge to the bottom

of the final settling tank, viz. gravity settling and bulk downward movement

due to hydraulic removal of recycle and waste sludge flows. (Figure.2.3)

QR, u = QR /A

Figure 2.3 Definition sketch of a settler operating at steady state

At any point in the tank, the mass flux of solids due to gravity

(hindered) settling is,

Gs= Co Vs (2.4)

Q +QRSludge –Supernatant interface

Solids flux across boundary

41

The settling velocity of the particles should control the design for

this zone. In this zone the solids flux is called the gravity flux.

The mass flux of solids due to the bulk movement of the suspension

(i.e.) due to sludge withdrawal is

Gu = Co u = Co (QR/A) (2.5)

It represents the concentrated sludge zone, where thickening of

sludge occurs. The rate of sludge withdrawal is the controlling factor. In this

zone the solids flux is defined as the underflow flux. The total mass flux is the

sum of previous components and is given by

GT = Gs+ Gu = Co Vs + Co u (2.6)

In a batch test, the downward movement of sludge solids is due to

the settlement velocity. Thus the batch solids flux can be denoted as given in

Eqn 2.4. Also, gravity flux depends on the concentration of solids and the

settling characteristics of the solids at that concentration. The procedure used

to develop a solids flux curve from column settling data is illustrated below.

Hindered settling velocities are derived from column settling test for

suspension at different concentrations. The resulting Velocity-concentration

data pairs can be used to calculate the gravity solids flux. Each value of Gs

represents the gravity solids flux per unit area of the clarifier that would be

expected to occur at the corresponding activated sludge concentration.

At low concentration, (below 1g/L) the movement of solids due to

gravity is small, because the settling velocity of the solids is more or less

independent of concentration. If the velocity remains essentially the same, as

the concentration increases, the total flux due to gravity starts to increase. At

very high solids concentrations, the hindered settling velocity approaches zero

42

and the total solids flux due to gravity again becomes extremely low. Thus it

can be concluded that the solids flux due to gravity must pass through a

maximum value, as concentration is increased. (Figure 2.4(a)) The solids flux

due to bulk transport is a linear function of the concentration, with the slope

equal to u, the underflow velocity. Because the underflow velocity can be

controlled, it is used for process control.

The required cross sectional area for thickening is determined by

drawing a horizontal line tangent to the total flux curve (Figure 2.4(a)). Its

intersection with the vertical axis represents the limiting solids flux GL, which

can be processed in the settling tank. The corresponding underflow

concentration is obtained by dropping a vertical line to X-axis from the

intersection of the horizontal line and the underflow flux line (Metcalf and

Eddy 2003). GL is the maximum allowable flux loading if the settling is to be

successful. If the influent flux to the settler is larger than GL, the sludge

blanket will increase, resulting in solids in the effluent. The limiting flux can

be used for the design of the area of the settler.

Saseetheeran et al (1997) performed zone settling studies on

domestic activated sludge and developed mathematical models for calculating

surface area for secondary settling tank keeping initial suspended solid

concentration of the sludge (Co), recycling ratio(R), desired underflow sludge

concentration (Cu) and mean cell residence time ( c) as process parameters.

The developed models were used for preparing nomograms from which the

area of secondary settling tank could be found graphically for any known

process parameters. The range of a mass concentration to be maintained in the

reactor in-order to obtain the required underflow concentration was also

suggested.

43

Jiri Wanner (2003) analyzed the three functions of secondary

clarifiers Viz.

Solid – liquid separation having direct effect on effluent

quality.

Sludge thickening importance to control the sludge age.

Storage capacity determining the sludge distribution between

biological reactor and settler.

The various flow configurations for both circular and rectangular

settlers and the specific case of a circular secondary settler with radial flow

was analyzed in detail. Range of values for the classical design parameters

like hydraulic loading, hydraulic retention time, solid flux, hydraulic loading

of the effluent weirs, recycle ratio were identified.

Sekaran and Rajagopal (2007) conducted batch settling tests for the

activated sludge of sugar and tannery industries using a cylindrical thickener.

Zone settling velocities and suspended solids concentration were used to

develop logarithmic models. Limiting solids flux was obtained from solid

flux curves that were prepared for both the wastewaters. The solids flux

loading was found to be high for sugar industry wastewater compared to

tannery industry wastewater.

2.9.3 Modified Solids Flux Analysis

An alternative graphical technique of analysis for determining GL,

developed by Yoshioka et al (1957) allows the determination of the limiting

flux simply by drawing a line tangent to the flux curve which intersects the

X- axis at the desired underflow concentration, Cu. (Figure 2.4b). This line

has a negative slope of ‘u’. Similarly a line from the origin to any point on the

44

settling flux curve has a slope of Q/A. The analogy between the total flux

curve and this technique can be observed by inspection. The latter has been

employed because it offers the advantages of simplicity and versatility in

application. This method is especially useful where the effect of the use of

various underflow concentrations on the size of the treatment facilities is to be

evaluated.

C0 CL

Total flux

GL

Soli

d F

lux

K

g/m

².h

r

MLSS Concentration g/LCu

GL

-u

Go

Soli

d F

lux

K

g/m

².h

r

MLSS Concentration g/L

Q/A

A

CuCo

Underflow Flux

Gravity Flux

Limiting Flux

Figure 2.4(a) Solid flux analysis Figure 2.4(b) Modified solid flux analysis

The gravity solid flux for any value of Co is obtained by

multiplying zone settling velocity Vs with its respective initial MLSS

concentration Co. A plot was drawn between GG and Co to obtain the limiting

solids flux (GL) graphically by modified solids flux method of analysis as

shown in Figure 2.5. A tangent drawn to the gravity solids flux curve

originating from the desired value of Cu will yield the value of GL on the

Y- axis. By dropping a vertical line from the point where the tangent touches

the gravity flux curve, the value of maximum MLSS concentration (Cmax) to

be maintained in the reactor is obtained on the X-axis for Cu under

consideration. The equations derived from materials balance for calculating

Co and A/Q for any value of Cu, GL and R are given below.

45

Figure 2.5 Modified solids flux method of analysis

Co = [R / (1+R)] Cu (2.7)

A /Q = [(1+R) Co] / GL = R Cu/GL (2.8)

where, A/Q = Surface area per unit flow rate, m2/m

3/h

GL = Limiting solids flux, Kg/m2.h

R = Recycling ratio, QR /Q

Co = Influent MLSS concentration, g/L

Cu = Desired underflow concentration, g/L

Yunsheng Zheng and David (1998) proposed a model for predicting

the solids concentration profile in the zone settling and compression regimes

of the thickener. The model was based on a function relating effective solids

pressure to solids concentration and the rate of change of solids concentration.

The model was applied to steady state secondary clarifiers and provided good

agreement with literature data for both laboratory and field scale clarifiers.

Total flux

Underflow flux

Limiting flux

Gravity flux

Cu

CLVL

GL =

CLV

L+

CLU

b

Ci CL

CiVi

CiUb

Ub

So

lid

flu

x,

kg

/m2.h

Solids concentration, Kg/m3 (g/L)

CiVi + CiUb

GL

46

Jeyanthi and Saseetharan (2007) performed multiple batch settling

tests on dairy activated sludge at steady state, stationary phase and in

endogenous phase. Mathematical models were developed for the thickener

area of clarifier by correlating process control parameters such as MCRT,

MLSS concentration, underflow concentration and recycling ratio. The flux

curves were utilized in the generation of clarifier operation and control data.

2.10 PROCESS DESIGN PARAMETERS

The clarifier cannot be designed in isolation without considering

the process output of an aerator, as clarifier is an integral part of the ASP.

Hence it is very essential to consider all the parameters that influence the

overall process. Following process loading parameters determine the

efficiency of the activated sludge settling (Martin and James 1999).

2.10.1 F/M Ratio

The food / microorganism ratio, commonly referred to as F/M is

equivalent to the BOD loading rate divided by mass of MLSS in the reactor.

F/M = Q S / V X (2.9)

It is clear from the above equation that the F/M ratio is otherwise a

feeding rate. The lower the F/M ratio, the lower the feeding rate is, the

hungrier the microorganisms and the more efficient the removal will be. At

high F/M ratios, the microorganisms are maintained in the accelerating or

exponential growth phase. These organisms are more food saturated (i.e.)

there is an excess of substrate and thus BOD removal is less efficient.

Common ranges for F/M for a conventional activated sludge plant are from

0.15 to 0.5.

47

2.10.2 Sludge Age

Sludge age is defined as the average time in days that the

suspended solids remain in the entire system.

MLSS in aerator (kg)

Sludge age in days = (2.10)

Primary effluent SS (kg/day)

The common range for sludge age for a conventional activated

sludge plant is between 3 and 15 days.

2.10.3 Growth Rate

In order to evaluate the effect of substrate concentration and other

factors of the microbial activity, it is necessary to study the growth rate of

microorganisms (Benefield and Clifford 1980). The specific growth rate of

microorganisms is given by

x

dtdx

(2.11)

= kg. MLSS produced /kg MLSS present in the reactor per day

increases as substrate concentration increases until it levels off at

the maximum possible valve of given by Monod`s empirical function.

Sk

S

s

max (2.12)

Abdul Kareem (2004) developed a model to predict the values of

the specific growth rate of micro-organisms in the plant and the biomass

concentration, given the substrate utilized and the dilution rate with the

48

wastewater plant of textile industry. In evaluating the model, some kinetic

parameters such as Kd, Y, Ks, M, Ka1 and Ka2 were determined by statistical

analysis of the experimental results. There was a remarkable agreement

between the simulation results and the experimental results.

Stroot et al (2005) used mathematical modelling and quantitative

whole cell 16s rRNA – targeted fluorescence in-situ hybridizations (FISH) to

predict and determined experimentally the in- silico and in-situ net growth

rates of individual microbial populations in activated sludge systems during

start-up to steady state conditions. The results challenged the perception that

the net growth rate of microorganisms in an activated sludge system reached a

“steady state” value after three times the MCRT.

2.10.4 Mean Cell Residence Time / Solids Retention Time

In order to keep the F/M stable, some MLSS must be continuously

wasted to balance this microorganism’s biomass produced through growth.

The design and operation parameter for determining rates of MLSS wastage is

the solids retention time ( c days) defined as the mass of solids present in the

reactor over the mass of solids wasted per unit time.

V X

c days = (2.13)

Qw Xw

= kg MLSS present in reactor / kg MLSS wasted per day

where, Qw is the waste sludge flow from the reactor. Values of c, ranging

from 3 to 15 days result in the production of a stable, high quality effluent

with excellent characteristics. Thus recommended values for c can be used to

calculate the required waste sludge flow. Also

49

1 / c, day-1

= Qw Xw / VX (2.14)

= Kg MLSS wasted / kg MLSS present in the reactor per day.

From the definition of the specific growth rate of microorganisms,

dx / dt

= (2.15)

X

it is seen that MCRT is equal to 1/ . Hence by controlling c, the specific

growth rate and thus the physiological state of the organisms in the system

can be controlled. That is, the control of the process can be exercised simply

by regulating Qw.

Adrien (1998) derived a formula for the mean cell residence time,

considering the total time of microorganisms remained in suspended solids

with the treatment system, from the entry of the cells into the reactor to the

solid separation unit including any channel, where the cell residence time was

significant. It was finally shown that under appropriate assumptions, it was

equivalent to the formula commonly used.

Jenson (2001) explored the validity of the general assumption that

the steady state in a mixed flow reactor approaches at three hydraulic

residence times. For well mixed systems, the time to approach steady state

depends on the kinetic order of removal mechanisms, the initial pollutant

concentration in the control volume and the kinetic rate constant. The

assumption of three hydraulic residence times to steady state was found to be

conservative.

50

2.10.5 Clarifier overflow Rate

As the floc settles in a clarifier, the displaced water rises upward.

The upward velocity of water is termed the over flowrate (OFR) with unit m3

/ day / m2 and is determined by dividing flow (m

3 / d) by the clarifier surface

area m2. When a clarifier is operated at a specified OFR, all particles having

settling velocities higher than the operating OFR will be removed, while

particles with lower settling velocities are carried over the effluent weir. By

selecting a proper OFR, clarification is ensured. The clarifier overflow rate is

independent of recycle flow rate and is expressed as

OFR= QE / A (2.16)

QE = clarifier effluent flow rate [m³/d]

A = clarifier surface area [m²]

Bryan and Keinath (1983) constructed a small pilot scale

completely mixed activated sludge treatment plant and investigated the

functional effects of three parameters Viz. Solids retention time, hydraulic

retention time and clarifier over flowrate on clarification efficiency. It was

concluded that the changes in HRT and SRT together in the same phase

resulted in higher effluent solids and increasing either variable, while

decreasing other resulted in improved quality of the effluent with respect to

suspended solids. Maintaining one variable constant, while varying the other

showed that initial suspended solids concentration was slightly more sensitive

to SRT than HRT.

2.10.6 Solids Loading Rate

Solids loading rate is the mass rate of suspended solids into the

clarifier divided by the tank cross sectional area. The total mass rate is the

51

sum of tank effluent flow rate and RAS flow rate. The solids loading rate to

an activated sludge clarifier should not be the greater than the limiting solids

flux in the clarifier. This parameter is as important as over flowrate, in

determining the capacity of an activated sludge clarifier.

[Q influent + Q RAS] C influent

SLR= ------------------------------------------------------ (2.17)

A clarifier

Youngchul Kim and Wesely (1999) studied the estimation of the

sludge blanket and commented that the method of obtaining the suspended

solids concentration, by averaging the mixed liquor suspended solids and the

return sludge suspended solids concentration, frequently resulted in over

estimated by large amount. Hence an exponential equation was developed by

analysis of data from plant operation and from a special study of sludge

blanket, for various over flow rates and sludge characteristics.

2.10.7 MLSS Concentration

MLSS concentrations in the range of 1500 to 3500 mg / L are often

used. Because the MLSS concentration affects the solids loading on the

secondary clarifier, selection of the MLSS concentration must be coordinated

with the secondary clarifier design.

Kazmi and Furumai (2000) proposed a simple settling model for

the batch activated sludge process, that could predict sludge concentration

profile as a function of time. The model was applied by giving easily measurable

parameters namely, the initial mixed liquor suspended solids concentration,

sludge interface variation and sludge volume index. The model described the

sludge sedimentation process by linking three concentrations.1) MLSS on

sludge interface. 2) Constant MLSS on sediment surface 3) Variable MLSS at the

bottom. The model was tested for wide ranges of activated sludge

52

concentrations (1750 – 4630 mg/L) and SVI (104 – 265). A simulated MLSS

profile linking three critical concentrations agreed well with observation data.

2.10.8 Oxygen Requirement

The oxygen requirement must be adequate to satisfy carbonaceous

BOD of the waste, the endogenous respiration of the biomass, the oxygen

demand for nitrification, adequate mixing and maintain a minimum DO

concentration throughout the aeration tank.

2.10.9 Sludge Recycling Rate or Return Sludge Flow Rate

The recycle rate depends on the settling and thickening

characteristics of the sludge with clarifier and hence it varies. It influences the

design of the clarifier in terms of the size without influencing the size of the

aeration tank. The range is typically from 25 to 100% of the average design

flow, though peak hourly flow needs must be accommodated.

Antonio and Carbone (1987) compared two models of the kinetics

of the biological removal of organics and solid flux in ASP. The findings

clearly showed that the sludge recycling ratio (R) was a parameter of the

control of the process. The results were presented in an operational chart from

which, for given aeration and settling tank volumes, it was possible to

1 determine the parameters of the process (Co expressed as

MLSS and R) for a given sludge age ( c) and for different

characteristics of the influent flow rate Q and concentration of

the soluble organic substances (So)

2 evaluate the flexibility of the plant, i.e., its capacity to absorb

increase in organic load without altering the design c.

53

3 determine the operating conditions that would lead to process

failure and to estimate the variation to be made in c.

2.11 MODELLING OF A SECONDARY CLARIFIER

A model is a small scale representation of reality. This means that

a model could be a scaled down physical model (e.g. Pilot plant), an

analogous representation (e.g. using electrical devices for simulation) or a

mathematical model (e.g. a set of equations describing the real system).

2.11.1 Mathematical Modelling

Mathematical models are excellent tools to conceptualize

knowledge about a process and to communicate it to other people. It can be

stated that mathematical models are the ultimate and crisp summary of

knowledge.

The activated sludge model No.1 (ASM1) can be considered as the

reference model, since this model triggered the general acceptance of WWTP

modelling, first in research community and later on also in industry. Even to

day the ASM 1 model is in many cases, still the state of the art for modelling

activated sludge systems.

Aldermans et al (1994) developed a mathematical model based on

the previous work by Dick (1980), who had shown that the solids flux (G) is

related to the product of the solids concentration (Xi) and the total downward

transport velocity, which in turn was broken into bulk transport velocity (U)

and gravity settling velocity (Vi).

Gi = Xi Vi + Xi U (2.18)

54

The final model derived by him could not be solved explicitly but

the available values could be plotted on a graph to search for the expected

values.

Saseetharan et al (1997) conducted zone settling studies on

domestic waste water for various MLSS concentrations and mean cell

residence times. The expression connecting the settling velocity Vs, Co and c

was formed as

Vs = 5.670 – 1.321 Co + 0.442 c (2.19)

The parameters Cc Co and c were correlated and found to have a

relation as

Cc = 5.326 + 0.996 Co + 0.515 c (2.20)

An expression connecting tc, Co and c was also developed as

1/ tc = 0.054 – 0.0009 Co + 0.004 c (2.21)

A model for the thickener was made correlating the parameters tu,

Co, Cu and c by regression analysis as

log(tu/Ho) = -0.8287 + 0.453 log Co + 0.569 log Cu – 0.756 log c (2.22)

The correlation coefficient for all the above relations was found to

be more than 0.97.

Hasselblad et al (1998) applied a simple discrete linear dynamic

model to secondary clarifier performance. This dynamic modeling could

accurately predict movement of the sludge blanket height in secondary

clarifiers and also indicate the level of the corresponding limiting solids flux.

The dynamic model was proposed based on online measured input (solid flux,

55

G) and output (sludge blanket height, SBH). Given a period, when the

coefficients remained fairly constant and that the model was well fitted, new

sets of solids flux data could be used to predict the sludge blanket dynamic

variations in the secondary clarifier.

Dimosthenis et al (2003) addressed the advantages and drawbacks

of various models evolved hitherto in the settling of activated sludge. An

integrated and unified settling characteristics database was used to bring

theoretical and laboratory practical results on secondary settling design and

simulation closer together. The real time data of settling velocity was used for

simulation analysis and an integrated database was proposed as a means for a

more robust and universally accepted procedure.

Jeyanthi and Saseetharan (2007) developed mathematical models

based on the results of sedimentation tests, conducted on dairy activated

sludge for a mean cell residence time of 11 days and determined the thickener

area relating the parameters, that control efficiency of growth and substrate

removal. Based on the thickener area arrived from the model and the flow

rate of wastewater generated from the dairy industry, an operational diagram

was developed for a MCRT of 11 days. It was claimed that the diagram could

be used to design secondary clarifier and evaluate operational control options

in response to changes in flow rate on solids settleability.

2.12 ARTIFICIAL NEURAL NETWORK

An artificial neural network (ANN) usually called neural network

(NN) is a mathematical model or computational model, that is inspired by the

structure and functional aspects of biological neural networks. It consists of

an interconnected group of artificial neurons and processes information

using a connectionist approach to computation. In most cases, an ANN is an

adaptive system that changes its structure based on external or internal

56

information that flows through the network during the learning phase.

Modern neutral networks are non-linear statistical data modeling tools. They

are normally used to model complex relationship between inputs and outputs

or to find patterns in data. (Wikipedia)

2.12.1 Neuron Model

There is a close analogy between the structure of a biological

neuron (i.e. a brain of nerve cell) and the artificial neuron (the processing

element of the network). A biological neuron (Figure 2.6) has three types of

components that are of particular interest in similarity with artificial neuron:

its dendrites, soma and axon. The many dendrites receive signals from other

neurons. The signals are electric impulses that are transmitted across a

synaptic gap by means of chemical process. The action of the chemical

transmitter modifies the incoming signal typically by scaling the frequency of

the signals, that are received in a manner similar to the action of weights in an

artificial neural network (Laurence Fausett 2007). .

Figure 2.6 Model of a biological neuron

A simple neuron in ANN consists of input layer, activation function

and output layer. Input layer receives input signal from external environment

(or other neuron). Activation function is the internal state of the neuron, that

calculates and sums the input signals. The signals are then transmitted to the

57

output layer. The input layer, activation function and output layer in artificial

neuron are similar to the function of dendrites, soma and axon in biological

neuron

2.12.2 Network Architecture

The arrangement of neurons into layers and the connection patterns

within and between layers is called the net architecture. Key factors in

determining the behavior of neuron are its activation function and the pattern

of weighted connections, over which it sends and receives signals. Within

each layer, neurons usually have the same activation function and the same

pattern of connections to other neurons. The number of layers in the net can

be defined to be the number of layers of weighted interconnected links

between the slabs of neurons. More complex systems will have more layers of

neurons with some having increased layers of input neurons and output

neurons. Figure 2.7 shows a typical a multi layer neural net.

Figure 2.7 Typical multi layer net

58

2.12.3 Activation Functions

Identity function, Binary step function, Binary sigmoid and bipolar

sigmoid are available activation functions. The most frequently used

activation function is the sigmoid function. The option of selecting Binary or

Bipolar depends on the choice of range from 0 to1 or from -1 to 1.

2.12.4 Learning Paradigms

There are three major paradigms, each corresponding to a particular

abstract learning task. These are supervised learning, unsupervised learning

and reinforcement learning. The neural networks learn by examples. Thus

neural networks can be trained with known examples of a problem before

they are tested for their inferences capability of unknown instances of the

problem (Rajasekaran and Vijayalakshmi 2003). Perhaps, in the most typical

neural net setting, training is accomplished by presenting a sequence of

training vectors or patterns each with an associated target output vector. The

weights are then adjusted according to a learning algorithm. This process is

known as supervised training. Self organizing neural nets group similar input

vectors together without the use of training data to specify what a typical

member of each group looks like or to which group each vector belongs.

Tasks that fall within the paradigms of unsupervised learning are in general

estimation problems.

2.12.5 Back Propagation

Back propagation is simply a gradient descent method to minimize

the total squared error of the output computed by the net. Applications of a

multilayer, feed forward net trained by back propagation can be found

virtually in every field that uses neural nets for problems, that involve

59

mapping a given set of inputs to a specified set of target outputs. The training

of a network by back propagation involves three stages.

1. The feed forward of the input training patterns.

2. The calculation and back propagations of the associated errors.

3. The adjustment of weights.

More than one hidden layer may be beneficial for some

applications but one hidden layer is sufficient.

Jeyanthi and Saseetharan (2006) developed mathematical models

for the surface area of secondary clarifier by conducting settling experiments

in combined domestic and dairy waste water and by correlating the

parameters namely, surface area per unit flow rate (A/Q), influent

Concentration (Co), under flow Concentration (Cu), recycling ratio (r) and

mean cell residence time ( c). Back propagation training algorithm was used

to find out the results through artificial neural network and the results of

surface area per unit flow rate (A/Q) were compared with the results obtained

through regression analysis and concluded that the results from ANN

approach were consistent and closer to the experimental results.

2.13 PROCESS CONTROL STRATEGIES

The effluent quality coming out of secondary clarifier depends not

only on the design of the clarifier but also the design and process parameters

of the over all the process. Having designed the aerator and the clarifier for

the maximum efficiency, it is not certain that the expected efficiency will be

maintained all the time. The ASP will encounter a lot of fluctuations in the

parameters like wastewater sources, chemical composition, flowrate,

biological process condition and recycle rate. In such cases the plant operators

should have a handy tool to bring the operating conditions to normalcy with

60

easy procedure. The process control strategies are such tools for the plant

operators to manipulate and control the real time fluctuations and maintain the

efficient functioning of the ASP.

Chiang (1977) developed process stability indicators and

considered the importance of these indicators to completely mixed activated

sludge processes. He presented the theory of estimating the parameters that

characterized the process stability and showed that these parameters were a

function of process variables ( c, U, and X) prior to the shock over which

the design engineers and plant operators could exercise their control. The

process stability was characterized by the process response and the treatment

concentration of soluble effluent substrate. The relative influence of solids

retention time and hydraulic detention time was identified in determining the

process response.

2.13.1 Steady-State Control or Long-Term Control Strategies

These strategies are adopted when the fluctuations in flowrate and

substrate conditions extend over a period of a week or longer. When there are

excessive flowrate and high substrate in the influent, the biological and

physical system will not meet out the required performance. With the result,

the process will deliver an effluent of poor quality and cause loss of biomass.

This affects the behavior of ASP by uncontrolled decrease of MLSS. Process

Control parameters such as over flow rate, substrate concentration, MCRT

and recycling ratio are related by the mathematical Models of activated sludge

kinetics and solids flux.

Cho et al (1996) carried out the steady state analysis of a combined

aerator and settler in activated sludge process by applying limit flux theory.

The aerator was assumed to be a continuous flow stirred tank reactor. The

responses of the output variables viz. biomass concentration in the aerator,

61

dissolved pollutant and solid concentration in effluent were represented as

response surfaces and iso-response curves, while using sludge recycle ratio

and sludge waste as operating parameters. The acceptable operating zone and

optimum operating parameters were decided by combining the iso-response

curves of the three output variables.

Saseetharan et al (1997) conducted experiments on domestic

activated sludge for various mean cell residence times ( c) both in stationary

phase and endogenous phase. The results of such batch settling studies were

used for steady-state control analysis considering long-term variations in

wastewater flow rate and organic substrate concentration. Mathematical

models of activated sludge kinetics and solid flux were developed using the

data generated with computer programs, relating the various design

parameters. The steady-state control parameter chosen was c and control was

exercised by varying the recycle rate according to changes in influent

characteristics. The model facilitates the plant operator to adjust the recycle

rate on his own using the charts prepared with the help of models.

Jeyanthi and Saseetharan (2007) performed hindered batch settling

experiments on activated sludge generated from dairy wastewater and

combined (dairy – domestic mixture 1:3) wastewater for different initial

MLSS and for varied MCRT. Bio kinetic coefficients of combined

wastewater were determined using a bench scale CFSTR. Vesilind parameters

were also determined experimentally and the operational diagrams for long-

term fluctuations in plant inflow and substrate concentrations were prepared.

The allowable overflow rate to prevent process failure could be predicted

from the chart. The adjustments to be made in recycling ratio and MCRT

incase of fluctuations, could also be predicted from the operational diagrams.

62

2.13.1.1 Mathematical Models of ASP Kinetics and Solid Flux

The mathematical model of microbial growth relates the total

microbial mass in the reactor per unit area of secondary clarifier to the

overflow rate.

d

C

e0

K1

A

SSYQ

A

VX (2.23)

where, Q and So are the flow rate and initial substrate concentration of

wastewater respectively and Se is the desired effluent substrate concentration.

Solid flux is the mass of the solids passing a unit area of a particular plane in

a unit time. Activated sludge entering secondary clarifier from the activated

sludge basin settles towards the bottom of the clarifier by two velocity

components

A compression that results from gravitational forces Vs and

A compression resulting from the removal of sludge from the

bottom of the clarifier (u = QR / A)

Equation for total flux can be written as, (Referring equations (2.3)

and (2.6))

GT = Vo e-a Co

Co + u Co (2.24)

The limiting solid flux (GL) may be found by differentiating

equation (2.15) with respect to Co and equating the same to zero. This limiting

flux will correspond to the minimum point on the total flux (Figure 2.4(a))

only when the second derivative of the equation (2.15) is positive. The value

63

of GL represents the maximum allowable flux loading, if the settling is to be

successful. If the influent flux to the settler is larger than GL, the sludge

blanket will rise resulting in solids in the effluent.

The limiting solids concentration corresponding to GL is,

5..0

o

2

2

o

2

o

LaR

CR1

R4

CR1

R2

CR1C (2.26)

The critical recycling ratio is given by,

o

o

C

Ca

4

CR 2.27)

For the Secondary Settling Tank to satisfy the thickening criteria,

flux applied to it must not exceed the limiting flux. Equating applied flux and

limiting flux, the design equations for calculating overflow rate to prevent

secondary clarifier deviating from its thickening performance are obtained as

below (Antonio et al 1987).

2

o

L

aC

o

C)R1(

aC.eV

A

Q L

for R < RC (2.28)

Re

V

A

Q2

o for R = RC (2.29)

0aC

0eVA

Q for R > RC (2.30)

2.13.2 Dynamic-State Control or Short-Term Control Strategies

These strategies are adopted when the plant faces short-term

variations in wastewater characteristics, resulting mainly from hydraulic

surge. The dynamic-state control is affected through the distribution of solids

64

between the aeration tank and the secondary settling tank by adjusting the

recycling ratio. The adjustment of recycling ratio is normally based on the

operational experience, gained over a variety of operating conditions and the

settling characteristics of the MLSS.

The operating conditions do not remain the same at all times, and

hence the behavior of the secondary clarifier often changes under different

operating scenarios. The state point represents the operating point of a

clarifier and it is very essential that the plant operator must continuously

monitor the state points of a clarifier and immediately take corrective

measures, when the state point deviates from the normal conditions. The state

point is analyzed with a help of a graph drawn between solid flux and solid

concentration. The state point is defined by the intersection of two operating

lines on the batch flux curve (point A in 2.4b). One of the lines has a slope of

–u and describes the recycle flow (under flow operating line) from the

clarifier to the aerator. The other begins at the origin and has a slope

equivalent to the clarifier over flow rate Q/A. Accordingly the state point has

the co- ordinates (Go Co) ( Figure 2.4 (b)). The batch settling flux approach

can be used to monitor the operational state of an activated sludge system

Keinath (1985) proposed two solids inventory control strategies for

prevention of clarification failure in the ASP. It was also suggested that

clarification failure could be prevented by adopting two types of control

strategies; recycle rate control and step feed control. Recycle rate control

strategy was shown to be effective for thickening over load and certain class

of thickening and clarification over loads. The step feed control strategy in

which location of feed point to the aeration tank by multiple basin at down

stream was shown to be effective, when the ASP experiencing severe

hydraulic surges.

65

Don Hee Park et al (1995) analyzed the data describing aerated

synthetic wastewater treated in a CFSTR, to understand the dynamic response

to step changes in the dilution rate D. The change in D between steady state

leading to hysteresis trajectories on both graph of specific growth rate (µ) Vs

limiting substrate level (S) and the graph of S Vs the cell level (X) were

compared for three different monitored cases and obtained a simple model at

various dilution level changes, in order to gain understanding of the dynamic

in the ASP.

Chancelier et al (1997) proposed an ordinary differential equation

that reproduced the dynamic behavior of the position of a shock between pure

water and concentrated sludge and formulated certain control laws to stabilize

the sludge blanket at a prescribed depth. The evolution of the sizes with small

particles flocculating into big ones and the residence time of particles in the

settler were considered in the model.

Saseetharan et al (1998) investigated the behavior of SST that

controlled the ASP operating in a dynamically changing environment by

adjusting the recycle rate to provide the required biomass concentration in the

aeration tank. The state point concept was used to adjust the recycling ratio.

The recycling ratio and the desired underflow concentration were chosen as

the influencing parameters. Mathematical models were developed to

determine the new recycle ratio in case of fluctuations in flow rate, substrate

concentration or both.

William et al (1999) carried out tests at a dry weather design flow

activated sludge plant of capacity 190 m3

/ h. The height of the sludge blanket

was measured and the return sludge rate was fine tuned to the desired rate.

The storm surge was simulated by controlling the drain valve in the storage

aeration basin. Results obtained during this study reflected the behavior of

clarifiers over a period of time during high hydraulic loading. The behavior of

66

the sludge blanket in the clarifier and the effluent suspended solids

concentration were studied extensively.

Takacs et al (2003) presented a dynamic model of the clarification

thickening process. Based on the solids flux concept and on a mass balance

around each layer of a one dimensional settler, the model could simulate the

solids profile throughout the settling column, including the underflow and

effluent suspended solids concentrations under steady state and dynamic

conditions. The model made use of a special settling velocity equation

designed to simulate the settling velocity of dilute and more concentrated

suspension. Results of the analysis confirmed that the model could serve to

predict the effluent and underflow suspended solids concentrations under

variety of conditions. .

2.13.2.1 Variable Influent Flowrate

All wastewater treatment plants experience short, intermediate and

long term fluctuations in influent flowrate. The operational state of the plant

correspondingly change due to fluctuations in the influent flowrate and plant

operator must immediately respond and take remedial measures to restore the

normal functioning of the plant. Whenever fluctuations cause an increase in

flowrate, state point gets shifted upwards from A to B in figure 2.8for certain

period of time, reactor MLSS remain the same.

Unaltered recycle flowrate may lead to an over loaded clarified

(dash double dotted line in Figure 2.8). The control can be exercised by

adjusting the recycle rate (i.e.) by making the recycle line (dotted line in

Figure 2.8) pass through the new state point B tangential to this flux curve

(Keinath et al 1977).

67

C0 CU

A

Q1/A

MLSS Concentration g/L

Soli

d F

lux K

g/m

².hr

GL'

GL

C0'

D

B

C

CU'

Q3/A

Q2/A

Figure 2.8 Loci of state points resulting from changes in hydraulic flow rate

2.13.2.2 Decreased Hydraulic Flowrate

In the case of decreased influent flowrate, the state point will shift

downwards (point D in Figure 2.8) at the same MLSS. If the plant operator

does not respond to the change of influent flowrate, a recycle operating line

will be parallel to the original line. However, for the operational state, the

clarifier is under loaded (dash - dot line in figure 2.8). The recycle rate can

accordingly be reduced to the point of critical loading. The control action is

beneficial, as it results in lowering of pumping cost and reduces turbulence in

clarifier, which should in turn result in upgraded clarification efficiencies.

2.13.2.3 Variable Substrate Concentration

From Figure 2.9, if at a given flowrate, conditions are such that the

state point of an activated sludge process is at ‘A’, the clarifier-thickener is

critically loaded (the operating line falling tangent to the flux curve). Then an

increase in substrate concentration in the influent provides the potential for an

increase in the suspended solids concentration in the aeration basin.

68

C 01 C U

A

Soli

d F

lux

Kg/m

².h

r

G L '

G L

C 0

B

C U '

1

C

C 02

MLSS Concentration, kg/m3

Figure 2.9 Loci of state points resulting from changes in substrate

concentration

However, because the clarifier-thickener is critically loaded, solids

begin to accumulate in the unit and the expected increase in solids

concentration in the aeration basin fails to materialize. If unchecked, the

accumulation will continue until the storage capacity of the clarifier-thickener

is depleted and solids are discharged in the overflow. The solids concentration

in the aeration basin can be increased only by increasing the recycle rate.

Such a situation is represented by state point ‘B’ and the steepest operating

line in Figure 2.9. As indicated by point ‘C’ in the figure, the reverse response

takes place when the substrate concentration decreases.

2.13.2.4 Variable Influent Flow and Substrate Concentration

In practice, changes in hydraulic flowrate and in substrate

concentration occur simultaneously and their effects are additive. The state

points corresponding to the critically loaded condition and over loaded

condition are shown in Figure 2.10 and the adjustment to be done in the

recycling ratio to avoid process failure is also shown. If the flow rate

69

increases by ‘n’ times, n (Q/A) = Q1/A and substrate increases by ‘n’ times to

n S, the combined effect of substrate and flow increase locate the new state

point at ‘C’ indicating a overloaded clarifier. The system response would be a

shift from ‘C’ to ‘D’ with time.

C 0 1 C U

A

M L S S C o nc entration g /L

So

lid

Flu

x K

g/m

².hr

G L '

G L

C 0

C

C U '

Q 1 /A

D

C 02

Q /A

B

Figure 2.10 Loci of state points resulting from flow rate and substrate

variation

The operator response at this situation would be to increase the

RAS rate, so that the recycle operating line (underflow operating line) passes

through ‘C’ and is tangential to the solid flux curve (dotted line in Figure 2.10).

2.14 OPERATIONAL DIAGRAMS FOR SECONDARY

CLARIFIERS

Daigger and Roper (1985) correlated batch settling test data with

SVI for estimating activated sludge settling characteristics. The following

relationship was determined

70

iC)SVI(00210.0148.0

e80.7Vi

(2.31)

where Vi = Initial settling velocity corresponding to the concentration Ci .

Using this equation, clarifier design and operating criteria were

developed for SVIs ranging from 50 mL/g to 350 mL/g in increments of 50

mL/g. Clarifier design and operating diagrams were presented, which allowed

the design engineers and plant operators to apply settling flux theory easily

and directly. It is interpreted from the chart that, for a constant return sludge

flow rate, state point will move upward when plant flow / MLSS increases.

Increasing RAS flow rate will shift the operating line upward.

Keinath (1990) developed a broadly applicable design and

operations chart for use by design engineers and wastewater treatment plant

operators. A regression characterizing the settleability of biological solids

developed, using experimental batch settling data collected at 21 full scale

wastewater treatment plants of different types was used to develop the chart.

The design examples showed that, the chart could be used with ease for

designing a secondary clarifier according to the thickening criterion and

evaluating various economic trades-off to determine a cost effective design.

The operation examples showed how return sludge flowrate or step feed

control strategies could be implemented to ameliorate thickening over loaded

conditions in an operating secondary clarifier.

Daigger (1995) developed a refined clarifier operating diagrams

using an updated settling characteristics data base. The results from the

analysis of five sets of settling characteristics representing 51 separate

suspended growth biological wastewater treatment processes indicated that

the data could be characterized using a single relationship between the initial

settling velocity, the initial sludge concentration and the sludge volume index.

71

These results suggested that this single relationship could be used to

accurately characterize the settling characteristics of an activated sludge based

only on the measured SVI. Based on the developed relationship the

secondary clarifier operating diagrams were presented.

Hermanowicz (1998) presented operational diagrams that were

developed in a unified way, relating the operating parameters of activated

sludge operation. The diagrams explicitly and directly related the MLSS and

RAS concentrations with the recycle ratio and clarifier hydraulic loading. The

diagrams could be used to evaluate operational control options and to design

secondary clarifiers. A procedure was also presented to develop a modified

diagram, based on a specific relationship between solids concentration and

settling velocity. This procedure could be used to customize the diagrams in

response to changes in solids settling characteristics.

Richard et al (2001) compared the settling characteristics of mixed

liquor produced by different types of biological treatment processes.

Experimental SVI values fitted well with the equation proposed by Daigger

(1985). A secondary clarifier operating diagram was also developed at uSVI’s

of 100, 150 and 200 mL/g, relating MLSS, RAS, R and hydraulic loading.

The charts could be used by the treatment plant operators for determining

appropriate recycle and wastage rates or by consultants for designing

secondary clarifiers.

Giokas et al (2003) made an investigation on the real-time

database of zone settling data sets. In order to correlate the design and

simulation of secondary clarifiers, zone settling velocity parameters, unstirred

SVI, stirred SVI and sludge blanket height were considered. For Design

purpose, an update of the existing generalized values of settling velocity

parameters was proposed. Using the database solid flux and overflow rate

diagrams were prepared.

72

Jeyanthi and Saseetharan (2007) developed an operational diagram

for the process control of the secondary clarifier for treating dairy wastewater

with a MCRT of 11 days, subsequent to the determination of mathematical

models for the surface area of the settler. It was claimed that the diagram

could be used to design secondary clarifier and evaluate operational control

options in response to changes in flow rate on solids settleability

2.15 CRITICAL REVIEW

Many research works in activated sludge process have been carried

out that have improved the system efficiency. Most of the works are related to

the design of ASP as a whole unit. Mathematical models that were developed

earlier, did not offer greater importance to the design of clarifier. Especially,

the variations in the operating conditions were not considered in the system

design, with an exception that an excess factor of safety was assumed to take

care of such occasional variations. But subsequently few scholars like

Keinath (1990), Saseetharan et al (1997), Jeyanthi and Saseetharan. (2006)

besides developing mathematical models, provided operational diagrams to

facilitate the design and operation of secondary clarifier. Richard. O Mines

(2001) also presented secondary clarifier operational diagrams. Wahl Berg

et al (1994) provided the state point analysis for making timely process

control decision for activated sludge plant operation. Keinath (1997)

presented a detailed analysis on the state point concept and formulated control

strategies to tackle the over loading of clarifiers. Hermanowicz (1998)

developed easily readable operating diagrams for the secondary clarifier

solids flux theory and zone settling velocity. Jeyanthi and Saseetharan (2006)

developed models and operational diagrams for the secondary clarifier

treating combined dairy and domestic waste waters.

All the above works in the secondary clarifiers have been carried

out envisaging the variations in operating conditions viz., influent flowrate,

73

substrate concentration and combination of both. But so far, not much of

works seem to have been carried out in the design of secondary clarifier and

the operational diagrams, considering the most important and common

problems of filamentous bulking in the clarifier. Although, Jenkins et al

(1993), Michael Richard (1993) and Eikel Boom (1995) have identified the

causes of filamentous bulking and the control measures to combat the

bulking; the same has not been incorporated in the design of secondary

clarifier.

2.16 SCOPE OF THE RESEARCH

The secondary clarifier is an integral unit of activated sludge

process and often determines the capacity and the efficiency of the treatment

plant. The functioning of secondary clarifier is interrelated with the

functioning of the aerator. The failure to consider this interdependency

between the aerator and the clarifier will lead to poor clarifier design and

operation. The design of secondary clarifier will not be complete and perfect

without considering the fluctuations in operating parameters like influent

flowrate, variations in substrate concentrations and the combination of both.

Necessary remedial measures are required to take care of occasional, seasonal

and sudden variations in such operating parameters. It is equally essential

that the filamentous bulking, the most common problem in the secondary

clarifier must be duly addressed while designing and operating the secondary

clarifier.

Hence in the research, the coupled system of aerator and secondary

clarifier were studied using Monod’s kinetics of activated sludge, solids flux

models along with addition of optimum dosages of chlorine in combating the

filamentous bulking. Before taking up the study, a clear understanding was

made on the bacteriological activities by identifying the predominant

biodegrading bacteria present in the dairy wastewater.

74

The effect of the native bacterial isolates in the treatment of dairy

wastewater was studied by inoculating the bacterial isolates in different

strength. The optimum parameters like pH, temperature and incubation time

were standardized for maximum enzyme production, which facilitated the

bacterial growth, in the aerator. While facilitating the biodegrading

functioning in the aerator, the filamentous organisms responsible for bulking

in the clarifier was also studied. The optimum dosages of chlorine that would

control the filamentous bulking without affecting the biodegrading bacteria,

for different MCRT were studied.

The models and the operational diagrams were extended with those

of Daigger and Roper (1985), Keinath (1990), Saseetharan et al (1997),

Hermanowicz (1998), Richard et al (2001), Jeyanthi and Saseetharan (2006)

incorporating the bulking control with the addition of chlorine in the clarifier

and relating all of the process control parameters in simple graphical forms.

The scope of this research was taken in different phases and the

results are presented separately one after another. However, the methodologies in

carrying out the different experiments are grouped together and presented in

chapter 3.