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New Models in STOAT 5.0


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RESTRICTION: This report has the following limited distribution:

External: Freely distributable

WRc plc 2012The contents of this document are subject to copyright and all rights are reserved. No part of this document may bereproduced, stored in a retrieval system or transmitted, in any form or by any means electronic, mechanical,photocopying, recording or otherwise, without the prior written consent of WRc plc.

This document has been produced by WRc plc.

Any enquiries relating to this report should be referred to the Project Manager at the following address:

WRc plc,

Frankland Road, Blagrove,Swindon, Wiltshire, SN5 8YF

Telephone: + 44 (0) 1793 865000

Fax: + 44 (0) 1793 865001Website: www.wrcplc.co.uk

New Models in STOAT 5.0

Report No.: UC8616.04

Date: March 2013

Authors: J. Dudley, L. Poinel

Project Manager: L. Poinel

Project No.: 15504-0

Client: Enviatec

Client Manager: Olaf Sterger


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Contents

1. Overview ................................................................................................................. 12. Pumping .................................................................................................................. 32.1 In-line pumps ........................................................................................................... 32.2 In-tank pumps.......................................................................................................... 73. Aeration ................................................................................................................... 93.1 Surface aeration ...................................................................................................... 93.2 Diffused aeration ................................................................................................... 104. Site Services ......................................................................................................... 134.1 Electricity ............................................................................................................... 134.2 Heat ...................................................................................................................... 155. Fuzzy Logic Control ............................................................................................... 175.1 Inputs .................................................................................................................... 175.2 Rules ..................................................................................................................... 185.3 Configuration ......................................................................................................... 186. Parameter Setter ................................................................................................... 217. Anaerobic Digestion Systems ................................................................................ 237.1 Anaerobic digestionADM1.................................................................................. 247.2 Converters and influents ........................................................................................ 337.3 Gas holder............................................................................................................. 367.4 CHP ...................................................................................................................... 377.5 Gas-fuelled boiler .................................................................................................. 397.6 Heat exchanger model updates ............................................................................. 408. Additional Process Models ..................................................................................... 418.1 Primary sedimentation ........................................................................................... 418.2 Activated sludge with metal adsorption .................................................................. 438.3 Sludge thickener .................................................................................................... 478.4 Instrumentation ...................................................................................................... 479. Additional resources .............................................................................................. 49


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List of Tables

Table 3.1 Typical blower efficiencies .................................................................... 11Table 6.1 Typical U-values ................................................................................... 32Table 6.2

Typical heat exchanger fouling data ...................................................... 32

Table 6.3 Typical heat exchanger fouling data ...................................................... 40Table 7.1 Adsorption rate data ............................................................................. 44Table 7.2 Inhibition effects with nitrifiers ............................................................... 45Table 7.3 Inhibition coefficients for nitrifiers .......................................................... 46Table 7.4 Inhibition effects with heterotrophs ........................................................ 46Table 7.5 Inhibition coefficients for heterotrophs ................................................... 46

List of Figures

Figure 2.1 Default pump curves ............................................................................... 4Figure 3.1 Typical blower curves (Source: Gass, 2009) ......................................... 12


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1. Overview

STOAT has had new models added as part of its use in a project studying energy efficiency at

sewage works and improved process control.

The new models have been based on:

a) including major sources of electrical usagepumps and aeration;

b) minor sources of electrical usage, lumped together as site electrical demand;

c) fuzzy logic control, adding to the existing support for ladder-logic and PID control; and

d) anaerobic digestion, updating the anaerobic digestion process to include the new IWA

ADM1 model, and models for gas storage, heating/electricity generation through CHP,

and heating through a gas boiler.

These models are discussed in more detail in the subsequent sections.

As always when new models are added to STOAT existing databases cannotbe used with

STOAT. They must be upgraded to the new database structure, using the Database Copy

utility that is provided with STOAT, shown in the following figure.

The main user manuals were written for STOAT 4.0 and comprise an installation guide,

getting started guide, user guide to the process models and a technical reference to the

mathematical algorithms behind the process models. Updates to these manuals have been

made in the read me files for STOAT 4.1, 4.2 and 4.3. This read me file, for STOAT 5.0,

provides additional updates.


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2. Pumping

There are two pumping models. One is in-line, where there are stream connections to the

pump inlet and outlet. This is expected to be the more common pump model, representing

where a pump is used to transfer flow between process tanks. The second is in-tank, where

the pump is used to transfer flow within a tank the most common usage is expected to be

with activated sludge aeration tanks, where MLSS recycles can be represented within the

aeration tank symbol, rather than needing explicit representation on the flowsheet.

Name Symbol

Inline tank

In-tank pump

2.1 In-line pumps

The inline pump has one inlet and one outlet. The flowsheet symbol is

At the works level all that needs to be specified is a name. Default names are automatically

generated.

Data can be specified for operation, sewage calibration and process calibration. Optionally the

initial condition (average power usage in the last 24 hours) can also be specified.

2.1.1 Operation


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Specify the pump curve. The head is relativeto the maximum head, which is specified later,

under Process Calibration.The electrical efficiency is that for the pump. The default curve is

specified at 0, 10, 100% of pump throughput. The curves are given below.

Figure 2.1 Default pump curves

2.1.2 Sewage calibration


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If a friction factor is to be calculated (specified under Process Calibration) then the nature of

the liquid needs to be specified. The options for the liquid (rheological model) are:

Water.

Water, with a multiplier applied to the calculated water head.

Sewage sludge, using the equations from the WRc report TR185, How to design

sewage sludge pumping systems. There are models for activated sludge, primary

sludge and digested sludge. The equations published in TR185 were developed in the

late 1970s to early 1980s, and with the widespread use of polymer conditioning these

equations underpredict the head loss. Therefore, with a polymer-conditioned sludge a

multiplier, > 1, needs to be specified to increase the TR185-calculated headloss.

Using the TR185 models for rheology, specify the rheological parameters for the yield

stress, consistency parameter and flow index.

The TR185 model is based on a Herschel-Bulkley fluid:

= 0+ Kn

Where

Stress, Pa0 Yield stress, Pa. Modelled in TR185 as 0 = a

b

K Consistency parameter. Modelled in TR185 as K = a b

Shear rate, m/s2. Modelled in TR185 as = a

b

n

Flow index. Modelled in TR185 as n = (1 + a b)-1

Volume fraction of suspended solids

The headloss is calculated for Herschel-Bulkley fluids using the method presented in R.A.

Chilton and R. Stainsby, "Pressure Loss Equations for Laminar and Turbulent Non-Newtonian

Pipe Flow", J. Hydr. Engrg., ASCE 124 (5), pp. 522-529 (1998). The procedure is

summarised on the Wikipedia page for Herschel-Bulkley fluids,

http://en.wikipedia.org/wiki/Herschel-Bulkley_fluid. If the volume fraction is less than 0.004

(suspended solids less than 4,000 mg/l) then the Einstein approximation is used where the

fluid is treated as a Newtonian fluid, with the viscosity being that of water multiplied by 1 + 2.5
http://en.wikipedia.org/wiki/Herschel-Bulkley_fluidhttp://en.wikipedia.org/wiki/Herschel-Bulkley_fluidhttp://en.wikipedia.org/wiki/Herschel-Bulkley_fluid


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2.1.3 Process calibration

Process calibration requires specifying the following:

Maximum pumpflow (essential if using the friction factorcalculation method).

Maximum head (essential if using the friction factorcalculation method).


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Pipe diameter (essential if using the friction factorcalculation method).

Pipe length (essential if using the friction factorcalculation method).

Pipe roughness (select a material from the drop down list; if none are suitable then you

can use user specifiedand specify the roughness directly).

Calculation method (the default is fixed headloss; more commonly you would specify

friction factor).

Static head (usually the difference in water levels between the body of fluid feeding the

pump and that of the body of fluid to which the pump discharges).

Headloss (only if using the fixed headlossmethod).

There is a long list of possible pipe fittings, where the number of the relevant pipe fittings

present in the pipe run may be specified. If you are using the fixed headlossmethod then this

is not needed.

The pipe fittings are used to add additional velocity heads to the overall headloss the

headloss is calculated as a multiplier on v2/ (2 g), where v is the velocity in the pipe (m/s),

and g the gravitational acceleration (9.81 m/s2).

2.2 In-tank pumps

In-tanks have the same data requirements as inline tanks. The differences are:

There are no inlet or outlet connections to be made to the pump symbol.

Under Process Calibrationthere are four new parameters.

The first is to specify the process in which the pump is assumed to reside.

The second is to choose if the flow will be specified directly, or will be taken from

the internal MLSS recycle flow with activated sludge units. There are no checks

that the containing process is an activated sludge; the results of using Use

MLSS recycle with a non-activated sludge unit are undefined, as it will pick up

whatever data is found for that process at the same location as would be found

for an activated sludge system.


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If Specify flow is used then the pumped flowrate is given. Otherwise, if Use

MLSS recycle is chosen then the stage from which the MLSS recycle is to be

pumped should be specified. Should you have two MLSS recycles from a single

stage then only the first MLSS recycle can be used. WRc have never

encountered such a system in our experience, or in the published literature foractivated sludge systems.


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3. Aeration

There are two models for activated sludge aeration: surface aerators and diffused air

systems.

Name Symbol

Surface aeration

Diffused aeration

3.1 Surface aeration

Surface aeration requires specifying the correlation used to calculate the power

efficiency, and to list which activated sludge units are using surface aerator in

which stages, along with the alpha factor. Standard practice with surface aeration

systems is to specify an alpha factor of 1.0 unless there is evidence that a different value

should be used.


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There are five correlations to model surface aerators performances:

The standard correlation was taken from data published in the mid-1980s by the

Simplex aerator provider, Ames Crosta: this correlation is the recommended

correlation.

There are four additional correlations, based on work published in India, in Rao, AR, B

Kumar and AK Patel, 2007, Relative performance of different shaped surface aeration

tanks, Water Quality Research Journal of Canada, 42(1). These correlations allow for

the effect of tank shape to be included. Surface aerators are most commonly installed

in tanks with a square shape.

3.2 Diffused aeration

The diffused aeration model estimates the energy required between the blowerand the diffuser, and therefore also requires an estimate of the blower efficiency.

At present the blower efficiency is specified as a single value, rather than varying

with the blower duty.

The data required are:

The activated sludge unit and stage where the diffused aeration system is located. It is

possible to have a system that uses a mixture of surface aerated and diffused air

systems this was a common configuration in the 1990s, where a surface aerator

would be used in the first one or two aerated pockets, and the diffused aeration used

for the rest of the aeration tank.


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The correlation for estimating the power efficiency. The choices are Khudenko1,

Empirical2, Membrane mat

3or Own coefficients.

Diffuser submergence.

Diffuser coverage (percentage of floor area).

Spacing between diffusers (only essential for the Khudenkocorrelation).

Use alpha correlationto use a correlation between MLSS and the alpha factor4.

Use longitudinal variation to use an empirical correlation between location and the

average alpha factor5.

Alpha factora value must be specified.

Model parameters if Own coefficients was chosen. The model is KLa = (a0 Floor

coveragea1

+ a2)

QGa3

, where Floor coverage is in percent, and the gas flow, QG, is in

m3/d.

Some typical data6for blower efficiencies are presented in Table 3.1 andFigure 3.1

Table 3.1 Typical blower efficiencies

Blower typeNominal blower

efficiency, %

Nominal turndown,

% of rated flow

Positive displacement with 60 to 45 50

1 B.M. Khudenko and E. Shpirt, 1986 ,Hydrodynamic parameters of diffused air systems. Water Research, 20(7),

905-915.2 Envirosim correlation.

http://www.envirosim.com/products/bw32/techref/correlationformasstransfercoefficient.php3 Jolly, M., Green, S., Wallis-Lage, C. and Buchanan, A. (2010), Energy saving in activated sludge plants by the

use of more efficient fine bubble diffusers. Water and Environment Journal, 24: 5864. doi: 10.1111/j.1747-

6593.2009.00164.x.4 Gunder, B, 2001, The Membrane Coupled-Activated Sludge Process in Municipal Wastewater Treatment,

Technomic Publishing, Lancaster, USA.5 Assumption: alpha factor increases linearly with distance, and close to 0.9 by the end. The alpha-factor from

the correlation is an average value. The alpha factor is then calculated from the linear model V = 0.5: alpha =

calculated from correlation; V = 1.0: alpha = 0.9.6 Gass, JV, 2009, Scoping the Energy Savings Opportunities in Municipal Wastewater Treatment, presentation at

Consortium for Energy Efficiency, July 2009. Available online at: http://www.cee1.org/cee/mtg/09-

09mtg/files/WWWGass.pdf.


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Blower typeNominal blower

efficiency, %

Nominal turndown,

% of rated flow

VFD

Multi-stage centrifugal 76 to 50 60

Single-stage integrally geared

centrifugal80 to 72 45

High speed turbo gearless

centrifugal80 to 72 50

Figure 3.1 Typical blower curves (Source: Gass, 2009)


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4. Site Services

Site services are used to represent the sum of minor electricity requirements and space

heating requirements. The site electricity process is also used to calculate the site electricity

costs.

Name Symbol

Site electricity

Site heat

4.1 Electricity

There should be only one electricity demand process on a flowsheet.

Data is provided in two places. Major power users are specified under Process

Calibration, and minor users under Operation.

Process Calibration provides a simple list of pumps and aerators already included in the

works model, and all that is required is to select those required. Usually all would be selected.

Operation then adds the total electricity demand from minor users. This can be specified

varying with time.

Electricity charges are specified from the menu item Utilities.


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This brings up the data entry form.

Here you can specify the currency unit (STOAT should pick up your local currency unit, but

this can be over-ridden if required).

The electricity charges are based on an amalgamation of several UK electricity-providing

companies, to ensure that most options are covered. The charges are typically based on:

An availability charge, based on the contracted power, which is then charged on the

contracted power demand, whether used or not. STOAT currently ignores this element

of the cost.

An excess demand charge, should the site require more than the contracted demand.

The default contracted demand is 0 kW, so the contracted demand mustbe changed to

reflect the values used at the site under investigation.

Demand-based charges:

A price for usage during peak winter daytime hours (for which the months and

time where the charge is in operation should be specified; the defaults are


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December February, 16:00 19:00. The possibility of having a summer peak

demand is also available.

A price for usage during winter, for weekends, day and night. The winter months

need not be exactly the same as the peak winter months. The defaults areNovemberMarch. Night is defined as 00:0007:00.

Fossil fuel and climate change charges.

If electricity is generated on site and sent back to the grid then there is a feed-in-

tariff available to generate income for the site.

4.2 Heat

The heat demand unit is intended to reflect the requirement for low-grade space

heating. The model has one inlet and one outlet, for the hot water generated by

either a gas boiler or CHP unit. As many heat demand processes as are

required can be used on the flowsheet. The space heat demand is specified as

it varies with time. The required temperature is also specified this is only used if the

temperature of the heating fluid is low, as the heating fluid is required to always be at least

5C hotter than the space heat demand.


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5. Fuzzy Logic Control

The fuzzy logic controller allows specification of control systems using either a

triangular or rectangular fuzzy classification. The rectangular classification will

behave in a similar manner to a ladder logic controller. Data are specified under

four categories: inputs, outputs, control rulesand configuration.The fuzzy logic controller was

based on the work of Tong et al.(1979)7, Stahtaki and King (2007)

8, Beck et al.(1978)

9, and

Tomiello et al.(1999)10

5.1 Inputs

Up to 20 inputs may be specified. Each input needs to specify the minimum and maximum

value. The inputs are regarded as belonging in one of five categories: Very Small (VS), Small

(S), Average (A), Large (L) or Very Large (VL). With a triangular distribution you need to

specify the triangular boundaries, upper and lower, denoted by 1 (lower) and 2 (upper). For

the VS and VL categories you need specify only one boundary, as the other boundary is given

by the values specified by the minimum and maximum values.

5.1.1 Outputs

Up to 20 outputs may also be specified. For each output the minimum and maximum

permitted value must be specified. There are five control changes defined, for which values

must be specified for four. The five values are Large Decrease (LD), Small Decrease (SD),

No change (N), Small Increase (SI) and Large Increase (LI). The values for the changes LD,

SD, SI and LI must be specified.

7 Tong, RM, MB Beck and A Latten, 1979, Fuzzy control of the activated sludge wastewater treatment process ,

International Institute for Applied Systems Analysis report PP-79-7.8 Stathaki, A and RE King, 2007, An intelligent decision support system for wastewater treatment plant

management, International Journal of Engineering Simulation 8(1).9 Beck, MB, A Latten and RM Tong, 1978, Modelling and operational control of the activated sludge process in

wastewater treatment, International Institute for Applied Systems Analysis report PP-78-10.10

Tomiello, M, E Perrin, M Roubens and M Crine, 1999, Fuzzy control of an activated sludge process, Second

European Congress of Chemical Engineering (ECCE 2).


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If control changes are specified as absolute values then the changes are specified as the

change that should be made directly to the output variable. However, if the change to be

made is relativethen the changes are specified aspercentage changes.

5.2 Rules

Each output can be associated with up to four rules, to indicate that the relevant output

change (LD, SD, SI or LI) should be made. The associated values for the inputs (VS, S, A, L,

VL) should be specified, where there is a sixth possible value for each input, Ignore (I). When

you look at the results the change for each output will be given as LD, etc., and also as NF

(rule Not Firedi.e. no change made to the output value).

5.3 Configuration

There are three configuration parameters:

The first is the sampling interval. Too small a sampling interval will slow down the

simulation and may result in many small changes to the output. Too large a sampling

value will result in poor control. Typical sampling periods for process control are 10

minutes1 hour; the default is 15 minutes.


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The second configuration parameter is whether the fuzzy values should be interpreted

as a rectangular system (where fuzzy values cannot overlap), or triangular. The

rectangular approach will behave in a similar manner to a ladder logic controller; but the

ladder logic control assigns the output a fixed value; the fuzzy controller allows relative

changes to the output value, rather than fixed values. The default is the triangularapproach, which allows for an input value to be present in two categories (for example,

low and medium), with different probabilities for belonging in each category.

The final configuration parameter is whether the changes that are made should be

absolute or relative.


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6. Parameter Setter

The parameter setter allows changes with time for parameters that are

normally kept constant, such as the sewage and process calibration

parameters.

Up to ten inputs may be specified using the Connectivity menu. As usual, the entry form

requires specifying the name of the process, the stage (if the parameter does not depend

upon the stage then this must be specified, but will not be used) and finally the parameter that

will be changed.

Then, under the Operation menu, the values that will be used for a given time must be

specified. Up to 4,320 values may be specified (sufficient for changing values every two hours

for 360 days).


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7. Anaerobic Digestion Systems

STOAT has always had anaerobic digestion models, but these have mostly been based on

BOD. There has been one COD model, that of Droste. The IWA has published a large

consensus model for anaerobic digestion, the Anaerobic Digestion Model Number 1 (ADM1),

which is now seen as the preferred model for anaerobic digestion. This model has been

implemented in STOAT v5.0, but as a different process symbol to the standard anaerobic

digestion models.

The standard anaerobic digestion models (the BOD-based models first orderand Mosey, and

the COD model Droste) looked at the sludge stream, and provided no means of using the

digester gas, other than as reported production values in the digester. In addition they had no

link between the digester temperature and the heating requirements for the digester. The newmodel has provided a new digester symbol to allow for a gas outlet from the digester, and for

a heating water inlet and outlet. The new model is referred to in the process toolbox as

Anaerobic Digester, while the older models have their symbol labelled as Mesophilic

Anaerobic Digester.

Name Symbol

Anaerobic digestion ADM1

Digester gas monitoring

Gas holder

Gas holder flow splitter

ASM1 to ADM1 conversion

ADM1 to ASM1 conversion

Food waste to ASM1conversion

CHP engine


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Name Symbol

Gas boiler

Heat exchanger

7.1 Anaerobic digestion ADM1

The anaerobic digester has two inlets and three outlets. The inlets are feed sudge

(the upper inlet on the left) and heating fluid (the lower inlet on the left; the heating

fluid may be water, if internal heating is used, or sludge, if external heating is

used). The outlets are digester gas (on the top), digested sludge (the upper outleft

on the right) and heating fluid (the lower outlet on the left).

The IWA modelling community has released three reference implementaions, described as

ODE, DAE1 and DAE2. All three are available in STOAT, with DAE2 recommended for most

uses. The ODE model treats organic acid dissassociation (for example, acetic acid,

CH3COOH CH3COO- + H

=) and hydrogen exchange between the gas and air as being

time-varying. DAE1 treats the acid dissassociation equations as being sufficiently fast that

they can be taken as being at equilibrium, while DAE2 assumes that both the acid

dissassociation and the hydgrogen mass transfer are at equilibrium. The difference in model

predictions between the three is usually negligible, with ODE being the most accurate and

DAE2 the fastest to run. ODE requires the use of a stiffsolver, and WRc recommend the use

of either RK-Chebychev or ROCK2 as the solvers; with a large flowsheet the use of ODE as

the anaerboic digester model can result in the simulation taking hours to model hours, and a

switch to DAE2 is advised. ODE usually runs at an acceptable speed when it is used as an

anaerobic digester model separate from the associated sewage works.

The total tank volumesludge plus gasshould be specified under Names and dimensions.

The total volume is only used if, under Process calibration, you specify that the digester has a

fixed, rather than floating roof.

There is a large amount of calibration data that can be specified for the ADM1 model,

available under Sewage calibrationand Process calibration.There is little guidance from the


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IWA ADM1 report on how to calibrate the model, and most users appear to leave the model

parameters at the default values. The parameters are given in the following screen captures.

7.1.1 Sewage calibration

The standard ADM1 model has kinetic parameters that are independent of temperaturethe

digester is assumed to be controlled at a constant tempearture, and the kinetic parameters

(mainly growth rates) will be specified as appropriate for that temperature. The ADM1 report

provides three sets of values, all based on typical sewage sludge: low rate mesophilic single-

stage digestion (the most common form of anaerobic digestion); high-rate mespophilic single-

stage digestion; and thermophilic single-stage digestion. WRc have chosen the STOAT model

defaults based on the low-rate mesophilic parameters. WRc have made on main change in

the handling of parameters: many of the kinetic parameters have been given a temperature

dependency, as the STOAT implementation allows for variable temperature (unlike the

reference implementations described above). A further difference between STOAT and thereference implementations is that the STOAT version allows for varying volume, as some

digesters have sludge removed before raw sludge is added, to ensure that there is no short-

circuiting and contaminantion of the treated sludge.

The standard temperature variation model is:

(kinetic parameter) = A1exp(-B1(T30))A2exp(-B2(T30)),

Where T is the temperature in Celsius, and A1, B1, A2and B2are the model parameters. The

default parameter values were chosen to meet the following constraints: to be the same as in

the IWA ADM1 report at 35C; to have a maximum at 45C, as this is commonly quoted as

the mesophilic optimal temperature; to be zero at 55C, as mesophilic activity falls off rapidly

with increasing temperature above the optimal value, and thermophilc bacteria take over as

the preferred bacterial population; and to be around 10% of the maximum value at 10C,

reflecting the fall-off in activity with low temperatures.

For calibrating the model WRc would suggest that the main effort is given to the parameters

on the first page of the calibration data, for the composition of the composites (seen as

complex material which wil be broken down into fats (lipids), carbohydrates and proteins). As

experience is acquired with the use of this model WRc will provide updates on model usage,

parameter values, and calibration.


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7.1.2 Process calibration

Process calibration specifies parameters relating to the digester construction and operation.

The volume specified under the Name and dimensions submenu defines the maximum

volume of the digester, including gas head space.Under Process Calibrationyou can specify

the sludge volume; the minimum sludge volume if the operating mode is chosen to be draw &

fill, rather than overflow; and the pump flowrate and operating schedule when using draw &


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fill. Note that draw & fill can be set to have an intermittent fill; if the raw sludge sent to the

digester has a continuous flow then, during the non-fill periods, that sludge will go nowhere,

and will disappear from the mass balance. You will need to ensure that there are appropriate

balancing tanks or flow splitters so that sludge is only sent to the digester during a fill period.

Gas leaving the digester is assumed to pass through a control valve. The reference

implementation of the ADM1 assumed that the valve had flow varying linearly with the

pressure difference between the digester and the downstream process. In practice most

valves have a flow that varies approximately with the square root of the pressure difference,

unless chocked flow conditions are reached. (Choked flow occurs when the upstream

pressure is twice the downstream pressureunlikely with most anaerobic digesters.)

The gas space can be modelled as either a fixed roof (in which case the gas headspace is the

difference between the total digester volume and the sludge volume), or a floating head (in

which case the gas head space is constant, and specified here the volume specified underNames and dimensionsis then ignored).

The mass transfer rate of gases from the sludge to the gas space can be calculated using

either the same value for all gases (the approach used in the IWA reference implementation)

or varying with the inverse of the square root of the diffusivity (which is the case with most

mass transfer models).

The digester temperature can be specified as either fixed or variable; if variable, the heat

exchange can be either internal (in which case the heating circuit inlet and outlet are assumed

to be water, and to be used with an internal heat exchanger, for which the transfer area and

U-value must be specified), or external (in which case the heating circuit inlet and outlet are

assumed to be sludge taken from, and returned to, the digester).

If a varying digester temperature is specified then the heat loss from the digester must be

specified, through the wall and roof areas and U-values. Many older digesters were part-

buried in the earth, and so there is provision to specify the wall surface areas above and

below the ground levelabove, the external temperature is that of the air; below, that of the

ground. Air and ground temperatures are specified under the main menu item Ambient,

described below.

If an internal heat exchanger is used it is possible to specify at what rate it will foul, what the

fully-fouled heat transfer coefficient (U-value) will be, and when it is cleaned.


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Table 7.1 Typical U-values

MaterialU-value, W/m

2 C

Escritt11

EPA12

Fixed steel cover, plate 4.7 5.2

Fixed concrete cover, 9 thick 2.2 3.3

Floating cover, Downes-type, with wood composition roof 1.4 1.9

Concrete wall, 12 thick, exposed to air 3.2 4.9

Concrete wall, 12 thick, plus 1 air space and 4 brick facing 0.8 1.5

Concrete wall or floor, 12 thick, exposed to 10 wet earth 0.62

Concrete wall or floor, 12 thick, exposed to 10 dry earth 0.36 0.35

Table 7.2 Typical heat exchanger fouling data13

Description Fouling rate Biofilm thickness Fouling coefficient

Biofouling @ 300C 0.0125 150m 4 000 W/m

2K

Biofouling @ 350C 0.0317 250m 2 400 W/m

2K

The overall fouled heat transfer coefficient is calculated from the equation

1/Ufouled= 1/Uclean+ 1/Ufouling material

7.1.3 Ambient conditions

Ambient conditions specify the minimum and maximum air and ground temperatures and the

relative humidity. An annual profile can be specified the defaults are suitable for the

Northern UK.

11 Escritt, LB, 1971, Sewers and sewage works, George Allen and Unwin.

12 EPA, 1979, Process design manualsludge treatment and disposal.

13 Bott, TR, 1990, Fouling notebook, Institution of Chemical Engineers, Rugby, UK, ISBN 0852952597. There is

no direct data suitable for sewage sludge or sewage treatment; WRc have used the graphs presented in the

book to assess likely fouling rates and reductions in U-value.


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7.1.4 Monitoring gas quality

A moving average can be calculated for the gas quality. There is only one

calibration parameterthe averaging period.

7.2 Converters and influents

The ADM1 model uses a set of determinands that are different to those commonly used in

modelling sewage treatment. Because of this there is a need to convert between the ADM1

determinands and those used by activated sludge models. At present the published literature

has focused on converting between the IWA ASM1 and the ADM1, and therefore this is

supported in STOAT. As the research literature offers additional conversions, between ADM1

and ASM2d and ASM3, STOAT will be extended to include these alternates. As there is much

similarity between ASM1, ASM2 and ASM3 the use of ASM1 is seen as an acceptable

starting point the main loss of information is phosphorus, as this is ignored by the ADM1,

and therefore return liquors from the ADM1 will always be treated as having zero phosphate.

7.2.1 Influents

Influents are reached in the usual way, by right-clicking over the influent icon and selecting

Generate profile.


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7.2.2 ADM1 influent

This is available from the influent models, and allows specifying the influent directly using the

ADM1 determinands.

7.2.3 Food waste influent

This is also available from the influent models, and allows specifying the influent using

determinands that are convenient when given food waste compositions (particulate COD,

soluble COD, VFAs as COD, ammonia, total inorganic carbon, and cations). This influent

must be used with the food waste converter to subsequently convert the determinands into

the ADM1 equivalents for use by the digester model.

7.2.4 Food waste converter

The food waste converter is based on work published by Zaher14. The procedure

makes use of two steps. The first maps the food waste parameters (mainly

specified as COD) into an intermediate set of parameters suitable for defining

sewage sludge.

These parameters are, in their turn, mapped onto the ADM1 determinands. As can be seen

many of the parameters have a one-to-one relationship between the food waste and the

ADM1; those that do not generally can be calculated from the assumed stoichiometry ofproteins, lipids and carbohydrates. The default stocihiometry is that commonly quoted for

these mateials, so that the model should be useable for most food wastes the main

characterisiation will be that of the particulate COD into proteins, carbohydrates, lipids, and

organic inerts as part of the first stage of the conversion process.

14 U Zaher, P. Buffiere, J-P Steyer and S. Chen, 2009, A Procedure to Estimate Proximate Analysis of

Mixed Organic Wastes, Water Environment Research 81(4) 407415.


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7.2.5 ASM1 to ADM1

The ASM1 to ADM1 converter is used to convert between STOAT COD-based

sewage streams and the ADM1 determinands. The defaults are those

recommended by the IWA benchmarking taskforce looking at modelling sewage

treatment plants. As an extension to the reference version provided by the IWA benchmarking

group WRc has added phosphorusthe standard (IWA) model ignores phosphorus.

7.2.6 ADM1 to ASM1

The ADM1 to ASM1 converter is used to convert between ADM1 determinands

and STOAT COD-based sewage streams. The defaults are those recommended

by the IWA benchmarking taskforce looking at modelling sewage treatment plants.


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As an extension to the reference version provided by the IWA benchmarking group WRc has

added phosphorusthe standard (IWA) model ignores phosphorus.

7.3 Gas holder

The gas holder has one inletfrom the digester gasand three outlets. These are,

starting from the top, excess gas lost to the atmosphere (usually this will be sent to

a flare stack there is no explicit model for a flare stack in STOAT, but the flows

through this outlet will represent what has been sent to the flare); gas taken from

the gas holder for benefiial use either to power a boiler or a CHP system; and condensate

water, which should normally be a value close to zero. Unlike most models in STOAT the gas

outflow is set by the downstream process the gas flow is set by the demands from the gas

boiler or CHP engine, rather than by any operating conditions set at the gas holder.

The flowrate of gas diverted to the flare can be specified; it is also possible to flare gas before

the gas holder reaches its maximum value. The default values of zero will result in gas being

flared only when the digester is at maximum volume and the flow of gas in exceeds that of

gas out. A non-zero value will result in gas being always sent to the flare stack.


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7.3.1 Using CHP and a gas boiler

The standard flow splitters in STOAT set the outlet flow based on the inlet flow.

However, with the gas holder, the outlet flow is set by the downstream process

the gas demand from a boiler or CHP engine. If both a CHP engine and a boiler are

required then a special flow splitter is required, where the outlet is specified by the

downstream process, and the inlet flow is calculated as the sum of the two outlets.

7.4 CHP

The CHP engine has two inlets: for digester gas (upper left) and heating water

(lower left); and two outlets: for the combustion gas exhaust (lower right) and the

heated water (upper right).

The operational settings specify the required electricity demand and the cooling water

flowrate. The CHP engine is always operated to meet the required electrciity demand, if

possible.

The process calibration data specifies the maximum electrical output, and the maximum

electrical and thermal efficiency. The relative efficiency, as a function of the load, is specified

as the last set of data in the process calibration menu. The default electrical relative efficiency

profile was taken from US EPA reports on CHP systems; WRc were unable to locate any

equivalent data for thermal efficiency, so assumed that the thermal efficiency would be

constant with load.

As well as specifying the electrical demand it is possible to specify that there is an additional

demand that will be taken from the site electricity demand model.


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The air:fuel ratio is used to calculate the air consumption, which is added to the mass flow of

the exhaust combustion gas.


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7.5 Gas-fuelled boiler

The gas boiler has two inlets: digester gas (top left) and heating water (bottom

left); and two outlets: combustion gas (top right) and heated water (bottom right).

There is one operational parameter: the flowrate of the heating water.

Process calibration data requires specifying the maximum boiler ouput and the boiler

efficiency. The digester gas is calculated by specifying that the boiler operating mode is one

of the following: constant output (constant at the maximum value); constant outlet

temperature; a constant water temperature rise; or a constant rise relative to another stream

typically the outlet from a heat exchanger.


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7.6 Heat exchanger model updates

The heat exchanger models (co- and counter-currrent)have been updated to

include time-varying fouling of the heat exchanger.

Table 7.3 Typical heat exchanger fouling data15

Description Fouling rate Biofilm thickness Fouling coefficient

Biofouling @ 300C 0.0125 150m 4 000 W/m

2K

Biofouling @ 350C 0.0317 250m 2 400 W/m

2K

The overall fouled heat transfer coefficient is calculated from the equation:

1/Ufouled= 1/Uclean+ 1/Ufouling material

15 Bott, TR, 1990, Fouling notebook, Institution of Chemical Engineers, Rugby, UK, ISBN 0852952597. There is

no direct data suitable for sewage sludge or sewage treatment; WRc have used the graphs presented in the

book to assess likely fouling rates and reductions in U-value.


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8. Additional Process Models

8.1 Primary sedimentation

There is an IWA benchmarking group focused on modelling complete sewage treatment

plants. This working group has currently developed two benchmark systems, called BSM1

(Benchmark System 1) and BSM2. BSM2 added a simple primary tank model based on the

work of Otterpohl and Freund16

. STOAT provides two implementations of this model. The

standard Otterpohl model calculates the hydraulic retention time as the differential equation d

/dt = (Q ) / 3 and the relationship HRT = V / (0.001 + ). The alternate model uses the

preferred WRc calculation, d HRT/dt = 1 Q HRT / V. Performance of the primary tank is

based upon an empirical correlation developed by Otterpohl and Freund from data collected

at German sewage treatment plants.

There are two extensions to standard models. When industrial determinands are being

modelled there is a model which will allow metals in the influent to be adsorbed to the solids

and settle. There is also an extension to the standard primary tank model (PSED3) to allow

for chemical phosphorus removal to take place within the tank.

16R. Otterpohl and M. Freund, 1992, Dynamic models for clarifier of activated sludge plants with dry and wetweather flows. Water Science and Technology. 26, pp. 1391-1400.


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8.1.1 Otterpohl model

Sewage calibration is just the settleable fraction of the particulate BOD.

Under operational data, instead of specifying the primary sludge solids content, the

percentage of flow removed as sludge is specified.

8.1.2 Industrial with metal adsorption

Adsorption and desorption rates are calculated from an empirical correlation based on the

octanol-water coefficient. This coefficient is specified under the main menu bar, under

Edit/Component properties.The default components are all organic compounds.

8.1.3 PSED3 with chemical phosphorus removal

The sewage calibration parameters have been enhanced with the ASM2 model parametersfor chemical phosphorus reactions.


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8.2 Activated sludge with metal adsorption

8.2.1 Adsorption

Adsorption was modelled using the following model (Huang et al., 2000)17

:

TKpHK

TKpHK

CKCCKdt

dC

D

A

DA

76.343log0632.00892.00635.1log

2.1848

log1285.021.02775.7log

11010

11010

***

where

KA Adsorption rate, mol-1min

-1

KD Desorption rate, min-1

Maximum metal uptake, mg metal/g biomass

C* Adsorbed metal, mg metal/g biomass

C Bulk liquid metal concentration, mol/

NOTE: if C is measured in mg/ then the value for KAshould be divided by 103

MW, where MW is the molar weight

pH pH; log10[H+]

K1 First hydrolysis constant, [M2+

] + [H2O] [MOH+] + [H

+]

17 Huang, CP, HE Allen, J Wang, LR Takiyama, H Poesponegro, I Poesponegro, D Pirestani, SP Myoda and D

Crumety, 2000, Chemical Characteristics and Solids Uptake of Heavy Metals in Wastewater Treatment, Water

Environment Research Foundation report D93013/93-CTS-1ISBN 1-893664-07-04, WERF, Alexandria,

Virginia, USA


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The fundamental equation, the dC*/dt equation, underlies the Langmuir adsorption model,

which has been found to be the best predictor of the adsorption properties of activated sludge

systems (Hussein et al., 200418

; Ahalya et al,.200319

; Tilaki and Alli, 200320

; Nelson et al.,

198121

).

A spreadsheet provided with the Huang et al.report includes the following data.

Table 8.1 Adsorption rate data

Metal

, mg/g

log10K1Primary sludge

22

Secondary

sludge23

Cadmium 269.76 191.08 3.9

Cobalt 141.432 100.181 4.3

Nickel 140.904 99.807 4.1

Zinc 156.912 111.146 5

This correlation is used to partition the four metals between the soluble phase (where the

metal cannot be removed) and the adsorbed phase (where the metal is removed by

sedimentation, and subsequent removal of the settled sludge).

8.2.2 Inhibition effects

Nitrification inhibition data was taken principally from Richardson (1985)24

, and is summarised

in the following table.

18 Hussein, H, SF Ibrahamin, K Kandeel and H Moawad, 2004, Biosorption of heavy metals from wastewater

using Pseudomonas sp., Electronic Journal of Biotechnology. Available at:

www.ejbiotechnology.info/content/vol7/issue1/full/2,accessed 10 July 2006.19

Ahalya, N, TV Ramachandra and RD Kanamadi, 2003, Biosorption of heavy metals, Research Journal of

Chemistry and Environment 7(4) 7179.20

Tilaki, D and R Ali, 2003, Study on removal of cadmium from water environment by adsorption on GAC, BAC

and biofilter, Diffuse Pollution Conference, Dublin.21

Nelson, PO, AK Chung and MC Hudson, 1981, Factors affecting the fate of heavy metals in the activated

sludge process, Journal of the Water Pollution Control Federation, 53(8) 13231333.22

Correlated as 0.0024 MW g/g.23

Correlated as 0.0017 MW g/g/.24

Richardson, M, 1985, Nitrification inhibition in the treatment of sewage, The Royal Society of Chemistry,

London, ISBN 0-85186-596-8.
http://www.ejbiotechnology.info/content/vol7/issue1/full/2http://www.ejbiotechnology.info/content/vol7/issue1/full/2


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Table 8.2 Inhibition effects with nitrifiers

Metal Degree Concentration

Cadmium

Threshold 10 mg/l

42% reduction 14.3 mg/g VSS

Cobalt

Threshold No data

50%25

1 mg/l

78% 10 mg/l

Nickel

Threshold 1 mg/l

100% 5 mg/l

88% 12 mg/g VSS

Zinc Threshold 10 mg/l

25% 11 mg/ g VSS

Assuming a simple inhibition model26

:

C

I

CKC/1

10

where KI Inhibition coefficient

C Inhibitor concentration, mg/

X Biomass concentration, mg/

growth rate

C=0 growth rate with no inhibitor

25 MLSS concentrations were 360 mg/ for the 1 ppm test and 148 mg/l for the 10 ppm test. Data not for

nitrification, but used here as a bestestimate.

Constable, SWC, AF Rozich, R DeHaas and RJ Colvin, 1992, Respirometric investigation of activated sludge

bioinhibition by cobalt/manganese catalyst, 46th Purdue Industrial Waste Conference Proceedings, Lewis

Publishers, Chelsea, Michigan, USA.26

This form of inhibition model has been used previously in the IWA Activated Sludge Models 1, 2 and 3. Henze,

M, W Gujer, T Mino and M van Loosdrecht, 2000, Activated sludge models ASM1, ASM2, ASM2d and ASM3,

IWA Publishing, London. ISBN 1-900222-24-8.


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The estimates of KIfrom the above data are presented in the table below:

Table 8.3 Inhibition coefficients for nitrifiers

Metal KI

Cadmium 0.02

Cobalt 0.019

Nickel 0.0016

Zinc 0.033

For heterotrophs the effect of heavy metals is commonly regarded as much less than for

nitrifiers, and typically is regarded as no significant inhibition (Sujarittnonta and Sherrard,

1981)27

However, other studies have found different results.

Knoetze et al

28

.has the following data for a biological phosphorus removal plant (similar to theReading sewage treatment works).

Table 8.4 Inhibition effects with heterotrophs

Metal Threshold for general activated sludge,

mg/l29

Cadmium 15

Cobalt No data

Nickel 15

Zinc 10

Knoetze et al.provide data for inhibition. The inhibition coefficients are given in the following

table:

Table 8.5 Inhibition coefficients for heterotrophs

Metal KI

Cadmium 0.0675

Cobalt 0.019

Nickel 0.0675

Zinc 0.045

27 Sujarittnonta, S and JH Sherrard, 1981, Activated sludge nickel toxicity studies, Journal of the Water Pollution

Control Federation 53(8) 13141322.28

Knoetze, C, TR Davies and SG Wiechers, 1980, Chemical inhibition of biological nutrient removal processes,

Water SA 6(4) 171180.29

MLSS c. 2000 mg/. Threshold taken as being a 5% reduction in activity.


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8.3 Sludge thickener

The thickener has one inlet, the feed, and two outlets supernatant and

thickened sludge.

The model is based on a simple steady-state mass balance, and had no dynamic behaviour.

This model was defined as a required model by the IWA benchmarking group. Calibration is

to specify the thickened sludge concentration and the capture of solids.

8.4 Instrumentation

As part of the IWA benchmarking project new controllers have been specified. These

are a standard sensor, but with the addition of noise, and a sensor in which there is a

delay between taking the sample and the result being available. The IWA models

also require a version in which the noise is taken from a data file, to ensure

repeatable results between simulations, but this has not been implemented in STOAT.

8.4.1 Noisy probe

The noisy probe requires specifying the noise parameters, which can be white noise (absolute

values the noise is independent of the magnitude of the signal), and also noise where the

noise is proportional to the magnitude of the signal.


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8.4.2 Delay sampler

The delay sampler requires specifying the time delay between the signal being taken and

being made available, and the noise parameters, which can be white noise (absolute values

the noise is independent of the magnitude of the signal), and also noise where the noise is

proportional to the magnitude of the signal.


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9. Additional resources

Our German partner, EnviaTec, has a variety of STOAT flowsheets that may be downloaded

at http://www.enviatec.de/de/de_downloads.htm . The webpage is in German, but may be

accessed through

www.microsofttranslator.com/bv.aspx?from=de&to=en&a=www.enviatec.de/de/de_downloads

to provide an English translation.

The available flowsheets are

Pre-denitrification

Cascade denitrif ication

Simultaneous denitrification

Intermittent denitrification

Alternating denitrification

Post-denitrification

SBR system (sequencing batch reactor)

There are also additional resources on using STOAT, including some in English as well as

German.
http://www.enviatec.de/de/de_downloads.htmhttp://www.enviatec.de/de/de_downloads.htmhttp://www.microsofttranslator.com/bv.aspx?from=de&to=en&a=/www.enviatec.de/de/de_downloads.htmhttp://www.microsofttranslator.com/bv.aspx?from=de&to=en&a=/www.enviatec.de/de/de_downloads.htmhttp://www.microsofttranslator.com/bv.aspx?from=de&to=en&a=/www.enviatec.de/de/de_downloads.htmhttp://www.enviatec.de/de/de_downloads.htm

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