CHAPTER 11

Other Models

Splitters

The proportional splitters in GPS-X can be set up for constant flow split, variable split based on a timer, or variable split based on flow. In the Parameters > Splitter Set up form you can select the appropriate splitting mode. They are described below:

Constant

When using this splitting mode, the split fractions do not change (as entered under the Constant section), or are read from a file, or are controlled by any other method (controller tool or customization).

Timer based

When using this splitting mode, the split fractions, as entered under the Constant section, will be rotated through the connection points according to the specified time interval.

Flow based

When using this splitting mode, the split fractions, as entered under the Constant section, will be rotated through the connection points according to the specified volume. Once the specified volume has passed through the splitter input, the fractions will be moved to the next output connection.

A typical example of using this functionality is a multiple train SBR plant, where the influent has to be sent to a different SBR tank either by timer control or by discharged volume.

Control Splitter

A control splitter can be used to split flow into a set flow rate and an overflow. This object is useful for setting flow bypass controls.

The pumped flow rate is set in the Parameters > Pumped Flow menu. If the incoming flow is equal to or less than the pumped flow rate, all of the flow will exit through the pump connection point. If the incoming flow is greater than the pumped flow rate, the excess flow will exit through the overflow connection point.

An automatic PID controller can be used in this object to control another variable in GPS-X with the pumped flow rate.

Pumping Station

The noreact model for Pumping Station allows user to use continuous or intermittent pumping based on height or volume-based control.

Model Data Input

The input form is as shown in Figure 11‑1:

Figure 11‑1 - Pumping Station Menu

Continuous pumping - this variable controls whether continuous pumping or intermittent pumping is used.

Average daily pump flow rate - this flow rate is used for continuous pumping rate and also in the calculation of steady state calculations when intermittent pumping is selected.

Control Type - the option gets activated when continuous pumping is OFF. User may select from volume or height based control options.

Low volume threshold for pump - this variable defines the volume in the tank below which the pump operates at low threshold pumping capacity. The option is activated when Volume Based control is selected.

High volume threshold for pump - this variable defines the volume in the tank above which the pump operates at maximum threshold pumping capacity. The option is activated when Volume Based control is selected.

Low height threshold for pump - this variable defines the height in the tank below which the pump operates at low threshold pumping capacity. The option is activated when Height Based control is selected.

High height threshold for pump - this variable defines the height in the tank above which the pump operates at maximum threshold pumping capacity. The option is activated when Height Based control is selected.

Minimum pump capacity - this is the capacity at which the pump operates when the volume in the tank is below the low volume. The option is activated when continuous pumping is OFF.

Maximum pump capacity - this is the capacity at which the pump operates when the volume in the tank is above the high volume. The option is activated when continuous pumping is OFF.

Membrane Filter

One model is available for this object (empiric).

In this zero-volume model, the soluble state variables are not affected, but solids are partitioned into one of two streams based on a user-defined solids separation factor. Flow can be pumped from either of the effluent connection pipes and the difference between the pumped flow and influent flow is diverted to the other effluent connection. Solids concentrations in the two effluent streams are calculated through a mass balance based on flows and the separation factor. An example calculation is given below:

Influent Flow	=	100 m^3/d
Influent xbh	=	1000 g/m^3
Mass Influent xbh	=	100 x 100 = 100,000 g/d
Pumped Flow (user-defined)	=	10 m^3/d – concentrate
Filter Efficiency (user-defined)	=	0.90
Filtrate Flow	=	90 m^3/d
Filtrate xbh Concentration	=	100 g/m^3
Filtrate xbh Mass	=	90 x 100 = 9,000 g/d
Concentrate xbh Mass	=	100,000 – 9,000 = 91,000 g/d
Concentrate xbh Concentration	=	91,000 / 10 = 9,100 g/m^3

Equalization Tank

In the nonreactive noreact model, the effect of dilution on wastewater components is described, but all reaction rates are set to zero (no biological reactions are occurring). The tank has aeration terms (for modelling of pre-aeration) and a built-in pump, and both aeration and pumped flow can be controlled with feedback controllers (P, PI, PID) analogous to the ones found in aeration tanks and settler/clarifier objects.

This model can be used to simulate an equalization tank either off-line or in-line, as the volume is variable. In an off-line or variable volume mode (flow equalization), the pump connection should be used with the proper controller set up, while for concentration equalization in fixed volume tanks the overflow connection is best used.

Reactions can be added by double-selecting the model (noreact) and editing the rate equations of the individual components.

Sludge Pre-treatment

In a sludge pretreatment process, the particulate organic compounds are converted into soluble organic compounds. The degree of solubilization normally depends on the intensity of the treatment. There are many different types of sludge pre-treatment processes. These processes can mainly be divided into thermal, mechanical and chemical treatment. Thermal, mechanical disintegration, ultrasound, microwave, alkaline treatment ozonation, Fenton etc. are some of the treatment which have been normally applied. For thermal and mechanical treatments, the main operational parameter affecting the degree of solubilization is the specific energy input, while for chemical treatment it is the specific chemical dose. The specific energy input and specific chemical dose are normally expressed in terms of per unit of solids. As the mechanisms in sludge pretreatment processes are normally too complex to describe through a mechanistic model, empirical approach of modelling is normally adopted. For general engineering purpose, this approach is sufficient to evaluate different options and help in decision making.

The general sludge pre-treatment process model in GPS-X includes the following transformations.

1. The model describes the relationship between degree of solubilization to the operational parameters like specific energy input or specific chemical dose using a solubilization saturation curve.

2. The model allows conversion of the non-biodegradable organics into biodegradable organics.

3. The model allows for loss of during the treatment

The sludge pretreatment model is available in MANTIS2LIB. Three main transformations are described in the model.

1. Inactivation of bacterial biomass

The sludge pre-treatment normally results in bacterial inactivation. The bacterial inactivation may happen due to disintegration of the cell wall and release of soluble organic products. The biological inactivation will results in production of soluble and particulate organic products.  Depending on the nature of treatment as part of the COD may be lost due to oxidation.

2. Conversion of Inert Organics (Xi and Xu)

The inert particulate COD is reduced during the sludge pre-treatment. A part of the particulate inert COD is assumed to convert to the slowly biodegradable COD. The particulate inert COD also solubilize and results in soluble biodegradable and soluble inert COD. Depending on the treatment a part of the inert organic COD may be lost.

3. Conversion of slowly biodegradable COD (Xs)

The particulate slowly and very slowly biodegradable COD is solubilized and results in the soluble biodegradable substrate.

The implemented model is an input-output model in which all the input states are instantaneously converted to the output states. It is assumed that the retention time in the chemical-mechanical disintegration reactors are small.

Thermal Sludge Pretreatment (THP)

A sludge pre-treatment unit is now available as its own unit process. It models the conversion of particulate organics to soluble compounds resulting in faster degradation rates in anaerobic digestion process.

In-line Chemical Dosage Object

The In-line Chemical Dosage object (Figure 11‑2) can be used to simulate chemical addition for soluble phosphorus removal and coagulation/flocculation of soluble and colloidal components. Two chemical precipitation models chemeq and metaladd are available in GPS-X. The metaladd model is available in all the libraries having soluble phosphorus as a state variable. The chemeq model is not available in Mantis or Process Water libraries. The metaladd model considers the effect of pH and solubility product in the metal precipitation reactions.

Figure 11‑2 - In-line Chemical Dosage Object

Chemeq Precipitation Model

The P-removal model is available only in the CNP library. The model is based on the stoichiometry of the chemical precipitation of soluble phosphorus.  The model is set up to simulate the addition of one of four possible chemicals:

1. alum;

2. ferric compounds;

3. ferrous compounds; and

4. other user defined metal compounds. 

The In-Line Chemical Dosage object has two dosage modes available: Mass Based dosing and Concentration Based dosing. When using the Mass Based dosing option, the user specifies a constant mass flow rate of chemical to be dosed to the stream while Concentration Based dosing requires the user to specify the concentration of metal ion desired per unit flow of liquid entering the object. For example, when dosing alum, using the Mass Based dosing option requires the user to specify the mass flow of alum to be added, while the Concentration Based dosing option requires the user to specify the concentration of Aluminum ion per unit flow of liquid entering the basin.  

As an example, the basic reaction of phosphorus with aluminum is shown in Equation 11‑1.

Equation 11‑1

Therefore, one mole of aluminum ion reacts with one mole of phosphate or, on a mass basis; one gram of aluminum ion (Al³⁺) reacts with 1.148 grams of phosphorus (P). The dosage that is required is the mass of aluminum ion that is added rather than the mass of alum, which may be in different forms (hydrated, etc.). This method of calculating the stoichiometric requirements for alum and the other chemicals avoids the issue of different compounds and their molecular weights. The phosphorus stoichiometric coefficients for alum and the other chemicals are already defined, but could be modified for modelling another chemical that is not predefined.

Although the stoichiometric ratios are used as the basis of the precipitation model, the complexities of wastewater chemistry, including the effects of pH, alkalinity and other elements, means that the stoichiometric ratio is only the maximum achievable phosphorus removal in presence of a large overdose of chemical. In practice, a significantly lower removal is achieved because all of the chemicals added are not available for phosphorus addition. However, the available fraction, and therefore the required dosage, must be estimated or calibrated from site to site and specified in the model.

With a large overdose of metal ion to wastewater, most of the phosphorus would be removed; however, there would still be trace amounts of phosphorus. To model this phenomenon, a saturation function was incorporated in the model. The amount of phosphorus removed (or soluble organic component, si and ss, that would flocculate) becomes a function of the phosphorus concentration (or soluble organic component concentration). The stoichiometric amount of removal is only achieved when the phosphorus concentration is large (with respect to the half-saturation coefficient).  Removal is less than the maximum stoichiometric amount when the phosphorus concentration is small (with respect to the half-saturation coefficient).  This phenomenon is depicted in Figure 11‑3.

Figure 11‑3 - Removal as a Function of P Concentration

The mass of chemical precipitant formed is also calculated and added to the particulate inert inorganic component (xii). The stoichiometric amount of chemical sludge produced is based on the stoichiometry of the basic reaction. For example, in Equation 11‑1, the mass of chemical precipitant (AlPO₄) is 4.52 g per 1g of aluminum ion reacted.

The model provides the user with two dosage methods: mass flow based and concentration based. The former requires the user to specify a continuous flow rate for the chemical, while the latter allows the user to specify a concentration of metal per unit flow of liquid entering the basin. The dosage rate can be set up as a manipulated variable to maintain the phosphorus at a specified set point at any location downstream of the chemical addition.

In addition to precipitating the phosphorus, the chemicals flocculate some of the colloidal organics into particulate matter. In the model this is simulated as a conversion of soluble inert COD and soluble substrate into particulate inerts and particulate substrate. The extent of flocculation will depend on the half-saturation constants for each species. These constants need calibration from plant to plant as the flocculation model is empirical.

Here is a detailed explanation of the constants of the Chemical Dosage menu item:

Number of chemicals:  Currently four chemicals (alum, ferrous and ferric, plus a user defined) are available.

Dosage mode:

Mass flow based:  in mass/time units.

Concentration based:  in mass/volume units.

The following stoichiometry parameters (for all compounds) are found under the More… button, and are described in Table 11‑1.

Table 11‑1 - Chemeq Parameters

Parameter	Description
P precipitation stoichiometry	gP/gMe precipitated stoichiometrically (if metal is in excess)
soluble inert COD flocculation stoichiometry	gCOD/gMe soluble inert COD converted to particulate inert COD (si to xi)
soluble substrate flocculation stoichiometry	gCOD/gMe soluble substrate converted to particulate substrate (ss to xs)
metal hydroxide stoichiometry	g of MeOH formed per g of metal added
P precipitation half-saturation coefficient	gP/m^3 concentration at which half the Metal available for this reaction will be used to precipitate phosphorus
soluble inert flocculation half-saturation coefficient	gCOD/m^3 concentration at which half the metal available for this reaction will be used to flocculate soluble inerts
soluble substrate flocculation half-saturation coefficient	gCOD/m^3 concentration at which half the metal available for this reaction will be used to flocculate soluble substrate

The stoichiometry for inorganic inert solids production from P precipitation that was available as a parameter in previous versions of this model (under the name chemdos) is now calculated from known stoichiometry.

In addition, the chemeq model is written as an equilibrium model, meaning that all precipitation and flocculation is assumed to happen instantaneously, and goes to completion within the flow-through of the In-line Chemical Dosage object.

All nitrogen and phosphorus fractions of COD components are recalculated for mass balance after the precipitation has taken place.

Dosage Controller

The chemeq model contains a P-removal dosage controller, which is available in the Input Parameters > Chemical Dosage menu (see Figure 11‑4).

Figure 11‑4 – Dosage Controller Parameters

The controller allows users to specify a desired effluent P concentration, and back-calculates the required metal ion dosage, based on the stoichiometry settings.

Metaladd Precipitation/Coagulation Model

The metaladd model is a semi-mechanistic model to assess the removal of soluble phosphorus due to metal addition. Following key assumptions are made in the model development.

1. The metal species exists in the form of Me(OH)₃. All the other soluble and insoluble species of metal are not modeled. Thus, the model results outside the pH range of 6-8 may not be applicable.

2. Solubility product of the metal phosphates is used in estimating the amount of expected precipitates.

3. The solubility product is based on the PO₄^3- ion which is estimated based on the solution pH and dissociation coefficient of H₃PO₄.

4. The removal mechanism is assumed to be instantaneous, therefore no volume of the reactor is considered.

Following mass balance equations may be written for different phosphorus components across the In-line Chemical Dosing object.

Mass Balance Equation for Me(OH)₃ Concentration

Equation 11‑2

Mass Balance Equation for Soluble Phosphorus Concentration

Equation 11‑3

Mass Balance Equation for MePO₄ Concentration

Equation 11‑4

The equation describing the relationship among the metal ion, phosphate ion and the solubility product is as below:

Equation 11‑5

where:

X_me(oh)3,o = metal hydroxide concentration at outlet, gMe/m³

X_me(oh)3,in           = metal hydroxide concentration at inlet, gMe /m³

P_me                   = metal dose, gMe /m³

a                      = stoichiometry conversion factor, gMe(OH)₃ /g Me

X_mepo4,formed = concentration of formed precipitate, g MePO₄/m³

b                      = stoichiometry conversion factor, gMe(OH)₃/ g MePO₄

c                       = stoichiometry conversion factor, g P/g MePO₄

a = dissociation factor for estimating PO₄^3- from total orthophosphate concentration

The set of above equations are solved simultaneously to calculate the soluble phosphorus removal in the in-line chemical dosing object. The main calibration parameter in the model is the solubility product of the metal phosphates. The default values are provided based on the evaluation of reported values in literature. The values however may need to be adjusted to account for the differences in wastewater characteristics, mixing regimes and other parameters which affect the P-precipitation process.

In addition to P-precipitation, the metaladd model also models the removal of colloidal COD (scol), soluble inert COD (si) and soluble organic nitrogen (snd) through flocculation. Following assumptions are used in the formulation of this empirical flocculation model.

1. The metal dose to flocculate unit soluble component is a function of the soluble component concentration.

2. The metal dose required per unit soluble component removed is expressed as an exponential function with minimum bound.

The equation for metal dose per unit ss removed can be expressed as below:

Equation 11‑6

where:

F          = metal consumed per unit soluble component at soluble component concentration C, g Me/g component

C          = Concentration of soluble component, g/m³

F_max      = maximum metal consumption per unit soluble component, g Me/g component

F_min      = minimum metal consumption per unit soluble component, g Me/g component

k_a =affinity factor, m³/g

The shape of the curve represented by the above equation is shown in Figure 11‑5:

Figure 11‑5 – Curve Showing Relationship between the Soluble Components Concentration, and Require Metal Dose for Unit Soluble Component Removal

The integration of curve in Figure 11‑5 between the limits of Sin (concentration of soluble component in influent) and So (concentration of soluble component in effluent) results in the following expression. For a given metal dose and inlet concentration, the equation is solved to estimate the So, concentration of soluble component in effluent.

Equation 11‑7

where:

P_me       = metal dose, gMe/m³

S_in        = concentration of soluble component in influent, g/m³

S_o         = concentration of soluble component in effluent, g/m³

There are three parameters F_max, F_min and k_a used in the model. The parameter F_max may be interpreted as the amount of metal required to remove the said component at very low concentrations. It is general observation that the amount of metal required at lower concentration of soluble component is much higher than the metal amount required when the concentration of soluble component is higher. The parameter of F_min, can be interpreted as the amount of metal required to remove the said component at high concentrations. The parameter of k_a signifies the transition between the F_min and F_max. Higher values of k_a will result in a very steep transition between the F_min and F_max, essentially improving the removal efficiencies. Conversely, lower values of k_a will result in a mild transition and thus decreasing the removal efficiencies.

It is important to highlight here regarding the fate of the removed soluble components. As, the flocculation process does not cause any mass loss, the removed soluble compounds are just converted into their particulate state. The si, scol and snd are just transformed to xi, xs and xns states respectively.

It should be noted that the value of these parameters depends on the environmental conditions temperature, pH, mixing, and wastewater characteristics. Therefore, it is recommended that adequate calibration is performed to find the appropriate values for specific cases.

Grit Chamber

The model associated with this object is a simple empirical model where grit production is directly proportional to the flow coming in the grit chamber. The amount of grit produced (in grams per m³ of influent) is user specified.

Struvite Precipitation Model

The struvite precipitation reactor model represents an up-flow granular bed reactor with recycle.  The model uses a bed fluidization model to estimate the granular bed height and the solid concentration in the effluent stream. An internal recycle is provided to achieve bed fluidization velocity required by the user.  A typical layout showing the application of struvite reactor is as shown in Figure 11‑6. Some typical outputs from the struvite precipitation model are as shown in Figure 11‑7and Figure 11‑8.

Figure 11‑6 - Typical Application of Struvite Recovery Reactor

Figure 11‑7 - Typical Process Outputs for Struvite Reactor

Figure 11‑8 - Typical Outputs for Solid Bed Volume and Expansion for Struvite Recovery Reactor

Chemical Disinfection Unit

Empiric Model

The empiric model associated with this object computes the fraction of surviving E. coli based on the detention time in the disinfection unit. The state variables are not affected by this model. The following equation is used as the model for computing the surviving fraction:

Equation 11‑8

where:

survecoli          = fraction of surviving E.coli

kill                   = rate of kill (m³/g/min)

ccl₂                  = chlorine dosage (g/m³)

t_de                     = detention time (computed from volume/flow rate)

The chlorine dosage, rate of kill, and volumeof the unit are specified as operational parameters.

Chlorination Model

Chlorine is widely used for disinfection due to its cost effectiveness and high efficiency. In addition to its disinfection ability, chlorine can also reduce ammonia in the wastewater. However, a major concern of using chlorine as a disinfectant is the formation of toxic by-products such as Tri-Halo-Methane (THM) and Halo-Acetic Acid (HAA). The by-products formation can be extremely difficult to control and monitor due to hypochlorous acid’s high reactivity with various compounds present in wastewater. The optimization of chlorine dosages is often required to meet the disinfection requirement; to minimize the by-product formation and to meet the free chlorine discharge limits in the wastewater effluent to avoid damage to aquatic environment.

An empirical chlorination model is developed which can model the stoichiometric reactions between chlorine and ammonia to estimated combined and free chlorine residuals. The model also uses empirical relationships to estimate production THM, and HAA formation.  Based on the available chlorine dosage, the disinfection effectiveness is also estimated.

Instantaneous Chlorine Demand

The following expression is used to estimate instantaneous chlorine demand required by the organic compounds in water (Dharmarajah et al., 1991). The parameters of TOC and UV254 are used as representative organic compounds leading the instantaneous chlorine demand.

Equation 11‑9

Where:

C_inst      - instantaneous chlorine demand, mgCl₂/L

  - empirical constant 1, -

  - empirical constant 2, -

  - empirical constant 3,-

  - empirical constant 4,-

sTOC   - soluble total organic carbon, mg/L

   -absorbance at 254nm, cm-1

The UV₂₅₄ is calculated by using a linear relationship between UV₂₅₄ and the the soluble inert organics compounds (si) in wastewater.

Chlorine Decay

A first order rate equation is used to model the decay of chlorine in wastewater.

Equation 11‑10

Where:

C_available             - available chlorine, mgCl₂/L

C_dose                 - chlorine dose, mgCl₂/L

k_inact                 - decay constant, 1/hr

t                       - detention time (t₁₀), hr

In the above expression, the chlorine dose is adjusted for any chlorine lost due to instantaneous demand. The net available chlorine is used in the chlorine-ammonia reaction and in by-product formation.

Chlorine-Ammonia Reactions

A typical breakpoint chlorination curve is as shown in Figure 11‑9. When chlorine is added to water containing ammonia, mono-chloramine is formed (zone 1).  After all of the ammonia has reacted, the free chlorine reacts with the mono-chloramines to form di-chloramine and to nitrogen gas (zone II).  The free chlorine residual starts to appear after breakpoint chlorination (zone 3).

Figure 11‑9 - Breakpoint Chlorination [3]

Zone 1 occurs when the Cl₂/NH₃-N ratio is below 5.07 and involves the formation of chloroorganic and mono-chloramine compounds. The formation of di-chloramine and nitrogen tri-chloride is assumed to be negligible.

Zone 2 occurs when the Cl₂/NH₃-N ratio is above 5.07 and below 7.58. Chloramines and chloroorganic compounds are destroyed in this zone and the chloramines concentration becomes the lowest at breakpoint. In theory, breakpoint occurs when the Cl₂/NH₃-N ratio is 7.58, all the ammonia nitrogen is consumed by chlorine and free chlorine becomes available in zone 3. As the actual stoichiometric ratio may get affected by the water composition, the default stoichiometric ratio may be changed by the user in the model. The chlorination model was developed to reproduce the break point curve shown in Figure 11‑9. Using the above reaction scheme, the model calculates free chlorine (chlorine in HOCl  and OCl^- form), combined chlorine (chlorine in combined form with ammonia) and total chlorine (free + combined chlorine) in the system. Zone 3 is where the by-products formation is assumed to dominate as free chlorine concentration increases.

In zone 1, the ammonia gets converted chloroamine, hence no loss of nitrogen is considered. In zone 2 part of the chloroamine gets fully oxidized leading the nitrogen loss as nitrogen gas.

By-product Formation- Trihalomethane (TTHM)

The formation of Trihalomethane (THM) occurs in the presence of hypochlorous acid (free chlorine).  The expression used to determine the concentration of total trihalomethane (TTHM) formation is based on batch experimental work conducted by Amy et al. (1987) and is presented below.

Equation 11‑11

Where:

C_TTHM  = total trihalomethane concentration (mmole/L)

UV₂₅₄ = water UV absorbance at 254 nm; 1 cm path length (cm-1)

Cl₂      =  chlorine residual (mgCl₂/L)

t          = reaction time in the batch reactor (hr)

T         = temperature (oC)

pH      = pH after chlorine reaction (-)

Br       = bromide concentration in raw water (mg/L)

The reaction time, t, is considered to be represented by the t₅₀ value for a unit process. If chloramines are present, the TTHM formation has been observed to be reduced compared to when only chlorine is present.  As a result, a factor of 0.2 times the general TTHM equation is applied when chloramines are present (USEPA, 1992).

By-product Formation- Haloacetic Acid (HAA)

Haloacetic Acid (HAA) formation is based on the AWWA Technical Advisory Workgroup’s (TAW) work.  The data were obtained from a series of batch experiments, 96 hours in duration, using water from eight facilities.  The work is presented in USEPA (1992), and is used as the default model.  The expressions used to determine HAA formation are presented below and were obtained from the USEPA (1992).

Equation 11‑12

Equation 11‑13

Equation 11‑14

Where:

C_MCAA   = monochloroacetic acid (ug/L)

C_DCAA   = dichloroacetic acid (ug/L)

C_TCAA   = trichloroacetic acid (ug/L)

sTOC   = soluble TOC, mg/L

The Ai,j  are the empirical model constants. It is assumed that the t₅₀ value represents the reactor time t.  If chloramines are present, a factor of 0.2 is applied to each of the individual HAA species concentrations, similar to the approach used for TTHMS when chloramines are present.

Disinfection Model

The following equation is used for estimating the log inactivation for disinfection.

Equation 11‑15

Equation 11‑16

Equation 11‑17

Where:

t                       = time in batch reactor (min)

V                      = volume of batch reactor ()

Q                      = flow rate of wastewater ()

chlorine dose    = chlorine present in the batch reactor (g/m3)

 ratio            = baffling factor (short circuiting factor)

pH                    = pH after chlorine reaction (-)

T                      = temperature (oC)

Nitrite Addition

Like ammonia, nitrite can react with free chlorine and chloramine. The nitrite reactions with the free chlorine species produce nitrate. The ammonia-chlorine reaction is faster than the nitrite-chlorine reaction. When ammonia and nitrite are present at a 1 to 1 ratio with excess chlorine, the ammonia reaction is expected to proceed ~30 times faster (Khawaga et al. 2018). To account for this in the empirical model, two options were developed.

Option 1:Supress nitrite oxidation by specifying an inhibitory ammonia to nitrite ratio (I_snhtosnoi)

Equation 11‑18

Option 2:Supress nitrite oxidation by specifying an inhibitory ammonia concentration (I_snh)

Equation 11‑19

The default value of  is set to 0.03333. This value was selected as it allows for 30 times more chlorine to go to the ammonia pathway vs. the nitrite pathway when they are present at a 1:1 ratio.

The two options above limit the available chlorine for nitrite oxidation only when chlorine is the limiting reactant with ammonia. When all the ammonia has reacted, any remaining chloramine and nitrite will react with excess chlorine added to the system. The nitrite-chlorine reaction is ~4000 times faster than the chloramine-chlorine reaction (Khawaga et al. 2018), so it is assumed that the chlorine-nitrite reaction goes to completion before the chloramine-chlorine reaction occurs. The chloramine-nitrite reaction is ignored as it is ~100 times slower than the chloramine-chlorine reaction (Khawaga et al. 2018).

Figure 11‑10 - Breakdown of the chlorine/ammonia/nitrite reactions

Dechlorination

Sulfur dioxide reacts with water to form sulfite, which subsequently reacts with the available chlorine residual. Ammonia is produced via dechlorination of chloramine.

Stoichiometrically 0.903 grams of SO₂ are needed to dechlorinate a 1 mgCl₂/L chlorine residual, but in practice 1-1.2 grams per 1 mgCl₂/L residual is typically used. (Tchobanoglous, Burton, and Stensel 2003)The user has the option to define this ratio in the model. Other chemicals available for dechlorination are listed in the Table 11‑2 below.

Table 11‑2 - Dechlorination Chemicals

Chemical	Sulfur Dioxide	Sodium Bisulfite	Sodium Metabisulfite	Sodium Sulfite
Chemical Formula	SO2	NaHSO3	Na2S2O5	Na2SO3
Molecular Weight	64.066	104.0461	190.107	126.04
SO2 Equivalents	1	1	2	1

Anion and Cation Reactions

1. Reactions of chlorination chemicals with water

2. Breakpoint chlorination

3. Nitrite oxidation

4. Dechlorination (chlorine residual)

5. Dechlorination (chloramine residual)

6. Leftover HOCl (free residual)

HOCl partially ionizes to OCl^- according to the below equation and the fraction of OCl^- is given by .

The mols of free residual multiplied by  is added as anionic equivalents. Equation derived from data in Metcalf and Eddy (page 1235, 4^th edition).

7. Leftover dechlorination chemical

The leftover dechlorination chemical react with water to produce bisulfite and NaOH (in some cases). The bisulfite can further react with dissolved oxygen to form sulfite, or just stay as bisulfite in the liquid stream.

If sufficient oxygen is present, it reacts with the bisulfite to produce sulfate which adds two anionic equivalents per mol. Otherwise the bisulfite adds one anionic equivalent per mol. The bisulfite reaction with dissolved oxygen is below.

New chemical prices are listed in Table 11‑3 below.

Table 11‑3 - Chlorination chemical prices

Chemical	Price/kg	Purity	Reference
Sulfur Dioxide	$2.30	99.9%	Alibaba
Sodium Sulfite	$0.34	98%	Alibaba
Sodium Bisulfite	$0.25	99%	Alibaba
Sodium Metabisulfite	$0.25	99%	Alibaba
Calcium Hypochlorite	$1.05	70%	Alibaba
Chlorine gas	$0.94	99.99%	Indiamart

UV Disinfection Object

WERF Model (werfuv) with UV Disinfection

The werfuv model is based upon an empirical model presented in a 1995 WERF report comparing UV irradiation to chlorination (Water Environment Research Foundation, 1995). The model estimates the effluent coliform density:

Equation 11‑20

where:

N          = effluent coliform density (MPN/100mL)

D         = UV dose (mW·s/cm³)

n          = empirical coefficient related to UV dose (unitless)

f           = empirical water quality factor (unitless)

The empirical water quality factor, f, was determined from regression to be described by:

Equation 11‑21

where:

SS        = suspended solids concentration (mg/L)

UFT     = unfiltered UV transmittance at 253.7nm (fraction)

A,a,b    = empirical coefficients (unitless)

Dewatering

Four models are associated with this object: asce, empiric, simple and press.  The models all simulate solids separation, but do so via different models that have been calibrated for different dewatering technologies.

Figure 11‑11 - Dewatering Object Models

ASCE Model

The asce model is based on a paper by the Task Committee on Belt Filter Presses for the American Society of Civil Engineers (ASCE, 1988). Based on this work, which involved information collected as part of a survey through the United States, a relationship was developed between the feed solids and cake concentration. The primary sludge fraction (i.e. percent of sludge for processing originating from primary sludge) is the main parameter in determining the cake solids from the unit. The solids capture is used to determine the centrate solids concentration and the developed relationship is used to determine the cake solids. A material balance is used to determine the two output flows.

Empiric Model

The empiric dewatering model is used when modellers wish to specify an underflow solids concentration and flow rate, or specify a concentration removal efficiency along with either the underflow flow rate or solids concentration.

Figure 11‑12 - Operational Menu for Empiric Model

In the Operational Menu, users set the method for setting solids removal by selecting from the drop-down menu.  The relevant fields (pumped flow, underflow solids concentration, removal efficiency) are highlighted.

Once two of the three above options have been specified, the model performs a mass and flow balance (similar to the simple model below) to determine the flow and concentrations for the cake and filtrate streams.

Simple Model

In the simple model, the user inputs two operational parameters: Solids capture (%), and Polymer dosage (g/m³). From these two parameters, the dewatering parameters (under Parameters > Calibration) and the input solids concentration (X_i), the cake (X_s) and centrate (X_o) solids concentrations are calculated:

Equation 11‑22

The optimal polymer dosage and sludge treatability are used to define the performance of the unit. The minimal sludge concentration for processing is a limit concentration under which the unit will not operate. The cake dry material content is a function of the treatability and the expected content from easy to treat sludge at optimal polymer dosage, hard to treat sludge at optimal, and no polymer dosage.

Using the following mass balance, the output flow rates (Q_o and Q_s) can be computed:

Equation 11‑23

Press Model

The press model is based on the simple model but has, in addition, a parameter for the filter press surface area and a maximum load on the press. These two parameters are used to model the reduced efficiency of dewatering as the loading on the press increases.

The dewatering object should not be confused with the thickener object. The thickener is configured like a nonreactive settling tank. The Sedimentation Models are described in CHAPTER 8.

Differential Model

In certain solid-liquid separation devices (centrifuges, vortex separator etc.), the inorganic particulate matter may be removed at higher efficiency than the organic particulate matter due to difference in the density. Further, depending on the nature of aggregation/particle size of particulate matter, there may be differences in the relative removal efficiencies of different organic particulate matters.

In some of the sludge reduction systems (i.e. Cannibal Process), it is suggested that the inert organic material is preferentially removed by the solid-liquid separation device in the process. To model such processes, an empirical solid-liquid separation model (differential) is implemented in dewatering unit process considering differential removal of particulates depending on the characteristics of each particulate state variable.

In the differential settling model, the particulate matter concentration in the effluent is calculated based on the following equation:

Equation 11‑24

where:

X_j,o       = concentration of particulate state j in effluent, mg/L

X_{j, in} = concentration of particulate state j in influent, mg/L

a_eff       = removal efficiency

The concentration in the underflow (concentrated) stream is estimated by making a mass balance around the object.

Equation 11‑25

where:

X_{j, un}     = concentration of particulate state j in underflow, mg/L

Q_in       = influent flow rate, m³/d

Q_o        = effluent flow rate, m³/d

Q_un      = underflow rate, m³/d

The underflow rate, Q_un, is specified by the user as a constant value or as a fraction of the influent flow rate.  The overflow rate is estimated by using the flow balance equation as below.

Equation 11‑26

The soluble compounds in the influent are mapped to the effluent and underflow streams without any transformation.

Centrifuge, Gravity Belt Thickener, and Belt Filter Press

The centrifuge, gravity belt thickener, and belt filter press objects contain a modified empiric solids separation model and polymer model based on the empiric and press models described above in the dewatering object. The centrifuge, gravity belt thickener and belt filter press objects are found in the “Biosolids Treatment” object group.

Figure 11‑13 - The Centrifuge and Belt Press Thickener Objects

Empiric Model

The empiric model for centrifuge, gravity belt thickener and belt filter press is modified based on the empiric model for the dewatering object, where the feed sludge can be selected from the selection of raw primary sludge, raw WAS, combined raw primary sludge and raw WAS, and anaerobically digested sludge, where each one has different default solids concentration after dewatering. This model also allows the user to define their own solids concentration for dewatered sludge.

Figure 11‑14 - Empiric model menu for centrifuge, gravity belt thickener and belt filter press

Disc, Belt and Drum Microscreens

The disc and belt microscreen objects also contain the empiric solids separation model and differential model, as described above in the dewatering object, the empiric model for the disc and belt microscreen objects have different default removal rates. The disc microscreen object is found in the “Tertiary Treatment” object group, and the belt microscreen object is found in the “Preliminary Treatment” object group. The drum microscreen object has only the differential model, it can be found in the “Biosolids Treatment” object group.

Figure 11‑15 – Belt, Disc and Drum Microscreen Objects

Hydrocyclone (differential)

The hydrocyclone model found in the Biosolids Treatment section of the unit process table is a simple solids separation unit.  It uses the same differential model as discussed above in the dewatering object.  The solids capture rates for the various particulate components have been calibrated for typical performance.  In particular, the solids capture rate for Anammox biomass is higher than that of the other biomass types, reflecting the heavier nature of the biomass which allows it to settle out preferentially.

High-Rate Model (highrate)

The High-Rate unit process object is found in the “Biosolids Treatment” section of the unit process table.  It contains the highrate solids separation model.

Highrate Model Background

The highrate model is based on a vortex separator and retention treatment basin (RTB) model developed by Gall et al. (1997) and Schraa et al. (2004) for the high-rate treatment of wet weather flows such as combined sewer overflows (CSOs). The model has been applied to primary clarifiers, chemically enhanced primary clarifiers, RTBs, vortex separators, continuous backwash filters, and ballasted flocculation (e.g. Actiflo) and sludge recycle (e.g. Densadeg) systems.

The highrate model contains a hydraulic routing component and a settleability component.  The hydraulic component of the model consists of algorithms to achieve dynamic volumetric and mass balances.  The settleability component of the model includes a relationship between settleability and influent TSS concentration, and a relationship between settleability and the surface overflow rate (SOR).  The settleability model for high-rate retention treatment basins is given below:

Equation 11‑27

where:

E          =concentration based removal efficiency (%)

E_u         = ultimate settleable solids fraction (at high influent TSS)

C_TSS      = parameter that controls the exponential shape of the removal efficiency vs. Influent TSS relationship

TSS_inf   = influent total suspended solids concentration (mg/L)

SOR     = surface overflow rate (m/h)

K          = surface overflow rate a 0.5 E_u

In this model E_u, C_TSS, and K are parameters that must be calibrated using measured data.  Once calibrated, the model can be used to predict the removal efficiency achieved in a high-rate solids separation process, in terms of total suspended solids (TSS), given the influent TSS and the surface overflow rate (SOR).

The default settleability model parameters provided in GPS-X are based on data taken from studies conducted by the University of Windsor on a pilot-scale RTB operated at the Lou Romano Water Reclamation Plant (LRWRP) in Windsor, Ontario, Canada (Li et al., 2004) that used high doses of cationic polymer as the sole coagulant.  A 3‑dimensional scatter plot of the pilot plant settleability data is given in Figure 11‑16.  Tests that did not use polymer were removed from the data set.  The circles represent data points and the lines or stems show where the points lie in the plane.

At a constant influent TSS, the removal efficiency was observed to decrease with increasing surface overflow rate.  At a constant surface overflow rate, the removal efficiency increased exponentially with increasing influent TSS and leveled off at TSS values above approximately 500 mg/L.

Figure 11‑16 - Plot of TSS Removal Efficiency vs. Influent TSS and SOR (Pilot Data from U. of Windsor RTB)

The settleability model was calibrated to the entire set of pilot data shown in Figure 11‑16, using the optimizer tool in GPS-X and the sum of squares objective function (i.e. least squares parameter estimation).  The calibrated parameter values are given below:

E_u = 0.86 (ultimate settleable solids fraction at high influent TSS)

C_TSS       = 82 mg/L (parameter that controls the exponential shape of the removal efficiency vs. influent TSS relationship)

K          = 93 m/h (surface overflow rate at 0.5E_u)

A 3-dimensional plot of the calibrated settleability model is shown in Figure 11‑17.  The model shows the same trends as the measured pilot data shown in Figure 11‑16.  To check the performance of the calibrated settleability model, dynamic simulations were performed using the full RTB model and the measured influent TSS concentrations from the pilot tests.  The simulation results showed reasonable agreement with the measured pilot study results given the estimated measurement error.

Figure 11‑17 - Plot of Calibrated Settleability Model

Physical Setup Form

The Physical Setup form is accessed by right-clicking on the dewatering object and selecting Input Parameters > Physical.  Figure 11‑18 shows the GPS-X form.  The parameters are discussed below:

· Number of trains in operation: Specifies the number of high-rate treatment process trains.

· Surface area per train: Specifies the surface area of each train (assuming all trains have the same surface area).  The default SI units are m².

Figure 11‑18 - Operational Menu for High-Rate Treatment Model

Operational Setup Form

The Operational Setup form is accessed by right-clicking on the dewatering object and selecting Input Parameters > Operational.  Figure 11‑19 shows the GPS-X form.  The parameters are discussed in the next section.

Figure 11‑19 – Operational Menu for High-Rate Treatment Model

High-Rate Treatment Model Sub-Section

Equation Shape – specifies the settleability model to be used.  The user can select the following options:  Constant TSS Removal Efficiency, Exponential Function of Influent TSS, Switching Function-Based SOR Relationship, Combined Equation

· Constant TSS Removal Efficiency:  Uses the maximum settleable solids fraction to calculate the effluent TSS based on the influent TSS. For example, using E = 100E_uas the settleability model.

· Exponential Function of Influent TSS:  Uses an exponential-based expression to calculate the effluent TSS based on the influent TSS. For example, using the following equation as the settleability model.  This model increases TSS removal efficiency as the influent TSS increases

Equation 11‑28

· Switching Function-Based SOR Relationship:  Uses a switching function, based on the surface overflow rate (SOR) to calculate effluent TSS. For example, using the following equation as the settleability model.  This model reduces the removal efficiency at higher SORs.

Equation 11‑29

· Combined Equation:  Uses the entire settleability equation to calculate the effluent TSS, based on the influent TSS and the SOR. For example, using Equation 11‑27 (shown below) as the settleability model.

Equation 11‑30

Maximum Settleable Solids Fraction:  Specifies a maximum concentration-based TSS removal efficiency fraction.  This is the highest TSS removal efficiency that can be achieved. The quantity is dimensionless (-) but can also be entered as a percentage

(%).Exponential constant for TSS dependence: This parameter controls the exponential shape of the removal efficiency vs. influent TSS relationship. Increasing this parameter will decrease the TSS-based removal efficiency.  The default SI units are mg/L.

Surface overflow rate at half the maximum TSS removal efficiency: This parameter is the half-saturation coefficient in the removal efficiency vs. SOR switching function-based relationship.  Increasing this parameter will increase the TSS removal efficiency. The default SI units are in m/d.

Operational Parameters Sub-Section

Underflow Setup:  This drop-down menu allows the user to select how the underflow rate and concentration are calculated. (Underflow Rate, Underflow Concentration)

· Underflow Rate:  The underflow rate is specified and GPS-X calculates the underflow concentration using a mass balance.

· Underflow Concentration:  The underflow TSS concentration is specified and GPS-X calculates the underflow using a mass balance.

Underflow Rate:  This is the underflow rate used when the Underflow Rate option is selected.  The default SI units are m³/d.

Underflow Concentration:  This is the underflow TSS concentration used when the Underflow Concentration option is selected.  The default SI units are mg/L.

Summary of the High-Rate Treatment Model Output Forms

The Outputs menu is accessed by right-clicking on the dewatering object and selecting Output Variables. Figure 11‑20 shows the Output Variables menu options.

Figure 11‑20 - Output Variables Menu in High-Rate Treatment Model

The Output Variables menu options that are unique to the high-rate treatment model are discussed below:

Solids Removal Efficiency:  The solids removal efficiency form is shown in Figure 11‑21.  The user can plot or track the solids removal efficiency.

High-Rate Treatment Performance Parameters:  The high-rate treatment process loading rates form is shown in Figure 11‑22.  The user can display the surface overflow rate and/or the solids loading rate.

Figure 11‑21 - Solids Removal Efficiency Output Variable Form

Figure 11‑22 - High-Rate Treatment Performance Parameters Output Variables Form

Advanced Oxidation Process (AOP)

The advanced oxidation unit process is found in the Tertiary Treatment panel in the unit process table.  The model is an empirical model for modeling soluble COD removal from wastewater using oxidising agents like ozone, hydrogen peroxide etc.. The user specified oxidant dose and oxidant effectiveness factor are used to oxidize soluble organic compounds like soluble inert COD, colloidal COD, readily biodegradable COD, acetic acid COD, propionic acid COD and methanol COD. The nitrogen and phosphorus fractions associated with the soluble inert COD are hydrolysed, thus increasing the concentration of ortho-phosphorus and ammonia-N concentration.

Figure 11‑23 - Advanced Oxidation Process Object

The input for maximum COD removal efficiency is used to control the residual COD in effluent at chemical overdose. The influent flow and chemical dose are used to estimate the mass of chemical required for treatment.

Incinerator

The model uses a mass and energy balance model to estimate the incinerator temperature, the flue gas composition and fly ash composition. The sensitivity of air supply, fuel supply and water content in the feed solids to incinerator temperature and flue gas composition is calculated.

Sludge Drying

The sludge drying model simulates the sludge drying process via the empiric model. This object is found under the Biosolids Treatment section of the Unit Process Table.

Figure 11‑24 - Sludge Drying Object

Empiric Model

The operational menu for the sludge drying object is shown below. The operational parameters are under the Input Parameters sub-menu item Operational. It contains operational settings for the sludge drying object like the inlet stream temperature, dryer operational temperature, dry solids content in dried sludge and thermal efficiency of the dryer.

Figure 11‑25 - Sludge Drying Object Operational Menu

Evaporator (procwaterlib only)

The evaporator model simulates solids-liquid separation via the default model. This object is only found in PROCESS WATER Library under Solids Handling section of the Unit Process Table.

Figure 11‑26 - Evaporator Object

In evaporator model the fed water is evaporated and collected as condensate, while the concentrated residual material is collected as brine. The model uses mass balance equations for each compound in water to estimate the concentration of compound in condensate and brine. The model requires a user defined water recovery factor to estimate the amounts of condensate and brine. The model assumes, that all the soluble compounds are concentrated in brine. A pure condensate is produced in the model.

The model also uses the operating temperature and pressure information to estimate the power requirement to operate the evaporator.

Note for Improvement: The algorithm to volatilize some of the gases and volatile compounds to steam will improve the prediction of steam and brine quality.

Default Model

An example of the physical menu for the Cation Exchange – H object is shown below. The physical parameters are found under the Input Parameters sub-menu item Physical. It contains physical settings for the Ion Exchange object like the resin properties including the volume of cation exchange resin and ion exchange capacity, which allow the user to specify how the physical system will be modelled.

Physical

The physical menu for the evaporator is shown below. The physical parameters are under the Input Parameters sub-menu item Physical. It contains physical settings for the evaporator object like the tank dimensions, evaporator parameters and water recovery.

Figure 11‑27 - Evaporator Object Physical Menu

Settling Pond (procwaterlib only)

The settling pond model simulates solids-liquid separation via the empiric model. This object is only found in PROCESS WATER Library under Solids Handling section of the Unit Process Table.

Figure 11‑28 - Settling Pond Object

The empiric model allows three methods of setup.

1) Specify the flow rate of concentrated stream along-with the stream concentration

2) Specify the underflow rate and the removal efficiency of solids

3) Specify the solid concentration and removal efficiency

Generally, the underflow rates and solid concentrations are available from operational data, therefore using the first option shall allow estimation of the overflow water flow rate and the concentration of water constituents based on the flow and mass balance of each constituent.

The attribute table for the settling pond is as shown in table below.

Table 11‑4 - Model attributes for settling pond

Model Attribute	Value	Comment
Volume	N/A	The unit volume is not required for this process
Biological reactions	No	No biological reactions are modeled in tank
Inorganic precipitation reactions	No	No precipitation reactions are included
Aeration	No	No aeration model
Gas-Liquid Transfer	No	No gas transfer model
Solid-Liquid Separation	Yes	Empirical model

Empiric Model

The empiric dewatering model is used when modellers wish to specify an underflow solids concentration and flow rate or specify a concentration removal efficiency along with either the underflow flow rate or solids concentration.

Figure 11‑29 - Operational Menu for Empiric Model

In the Operational Menu, users set the method for setting solids removal by selecting from the drop-down menu.  The relevant fields (pumped flow, underflow solids concentration, removal efficiency) are highlighted.

Once two of the three above options have been specified, the model performs a mass and flow balance (similar to the simple model below) to determine the flow and concentrations for the cake and filtrate streams.

Neutralization (procwaterlib only)

The neutralization is primarily used for pH adjustment. The tank is provided with two connection points. The top inlet is used to add chemicals (acid/base) using the chemical dosing unit process. The bottom inlet is for feeding water to the tank. Contrary to Equalization tank the tank is modeled as fixed volume reactor. The key attributes of the unit process are as shown in table below.

Figure 11‑30 - Neutralization Object

Table 11‑5 - Model attributes for neutralization tank

Model Attribute	Value	Comment
Volume	Fixed	The overflow is calculated as the total of inlet flows.
Biological reactions	No	No biological reactions are modeled in tank
Inorganic precipitation reactions	Yes	Precipitation reactions are included. The kinetic parameters to control the precipitation rate available from Input Parameters à Kinetics menu of the unit process.
Mixing	Yes	Subsurface mixing leading to O2 transfer
Gas-Liquid Transfer	Yes	Gas transfer of CO2, N2 and NH3 are modelled. The extent of gas-liquid mass transfer is calculated by the mass transfer parameter of KLa for each gas.

Lime Softening (procwaterlib only)

The lime softening object is only found in PROCESS WATER Library under Chemical Treatment section of the Unit Process Table.

Figure 11‑31 - Lime Softening Object

The lime softening process is used to remove the Ca and Mg hardness in water. The model precipitates Ca ion as CaCO₃ in the model. Another precipitate that is considered is MgCO₃. The model allows for additions of chemicals like Ca(OH)₂, Na₂CO₃ and NaOH for removing carbonate or non-carbonate hardness. The model estimates a pH depended on speciation of the CO₂, HCO₃^- and CO₃^2- and uses the solubility product of the precipitates to estimate the removal of Ca and Mg ions.

The kinetic expression used for the precipitation reaction is as below.

Equation 11‑31

Where:

: precipitation rate of CaCO₃, unit/d

 : concentration of calcium, unit/L

: concentration of carbonate ion, unit/L

 : solubility product of CaCO₃, unit²/L²

 : re-solubilization switching factor, unit/L

 : concentration of CaCO₃ precipitate, unit/L

The default kinetic parameters for precipitation reaction can be accessed through the kinetic menu of the unit process and adjusted by the user during calibration process.

The user can use chemical feeding objects to either feed Ca(OH)₂, soda ash or sodium

Ion Exchange (procwaterlib only)

The Ion Exchange models contains four objects, where the regeneration chemicals used are different. These objects are only found in PROCESS WATER Library under Chemical Treatment section of the Unit Process Table.

Figure 11‑32 - Ion Exchange Objects

· Cation Exchange – H – This model uses H⁺ based resin. The H⁺ ion is exchanged with other cations. The pH of effluent water is reduced as the water passes through this process.

· Anion Exchange – H – The model uses OH^- based resin. The OH- ion is exchanged with other anionic species in water. The pH of the water is increased as the water passes through this process.

· Cation Exchange-Na – The model uses a Na⁺ based resin. The Na⁺ resin is exchanged with other cation in water having higher affinity than Na⁺. The pH of the water is unaffected during this ionic exchange process.

· Anion Exchange-Cl - The model uses a Cl^- based resin. The Cl^- resin is exchanged with other cation in water having higher affinity than Cl^-. The pH of the water is unaffected during this ionic exchange process.

The ion-exchange process estimates the required chemical amount for resin and the resin regeneration frequency based on the feed water composition and flow rate.

The ion exchange unit process performance is typically transient (time dependent) as it depends on the remaining capacity of resin and exchange front that develops in the reactor. The current model is developed using the steady performance of the exchanger. The effluent water quality is estimated based on the individual removal efficiency of ionic species in water.

The attribute table for the ion exchange model is as shown in table below.

Table 11‑6 - Model attributes for Ion-Exchange

Model Attribute	Value	Comment
Volume	Yes	The fixed unit volume is used to estimate the ion-exchange capacity, regeneration frequency and volume of the chemical.
Biological reactions	No	No biological reactions are modeled in tank
Inorganic precipitation reactions	No	No precipitation reactions are included
Aeration	No	No aeration model
Gas-Liquid Transfer	No	No Gas-liquid transfer is modeled
Solid-Liquid Separation	No	No Solid Liquid Separation

Physical

An example of the physical menu for the Cation Exchange – H object is shown below. The physical parameters are found under the Input Parameters sub-menu item Physical. It contains physical settings for the Ion Exchange object like the resin properties including the volume of cation exchange resin and ion exchange capacity, which allow the user to specify how the physical system will be modelled.

Figure 11‑33 - Physical menu for the Cation Exchange - H object.

Operational

An example of the operational menu for the Cation Exchange – H object is shown below. The operational parameters are found under the Input Parameters sub-menu item Operational.  The removal efficiencies for cations can be defined through setting the removal efficiency of each ionic species or setting the removal efficiency for each individual ion.

Figure 11‑34 - Operational menu for Cation Exchange - H object.

Decarbonation Tower (procwaterlib only)

The decarbonation tower model simulates decarbonation process via the empiric model. This object is only found in PROCESS WATER Library under Physical Treatment section of the Unit Process Table.

Figure 11‑35 - Decarbonation Tower Object

The decarbonation model is an empirical model to remove dissolved CO₂ from the feed water. The decarbonation model is a simple model in which the effluent dissolved CO₂ is set by the user. The model estimates the expected change in the effluent pH/alkalinity due to removal of dissolved CO₂ from solution. The attribute of the model is as shown in table below.

Table 11‑7 - Model attributes for decarbonation

Model Attribute	Value	Comment
Volume	N/A	The unit volume is not required for this process
Biological reactions	No	No biological reactions are modeled in tank
Inorganic precipitation reactions	No	No precipitation reactions are included
Aeration	No	No aeration model
Gas-Liquid Transfer	No	No gas transfer model

Empiric Model

The empiric decarbonation model is used when modellers wish to specify the dissolved total carbon concentration in effluent.

Figure 11‑36 - Operational Menu for Empiric Model

In the Operational Menu, users simply set the specify the dissolved total carbon concentration in effluent, then the model calculates the mass of carbon removed.

Deaerator (procwaterlib only)

The deaerator model simulates de-aeration process via the empiric model. This object is only found in PROCESS WATER Library under Physical Treatment section of the Unit Process Table.

Figure 11‑37 - Deaerator Object

The deaerator model allows user to change the dissolved oxygen concentration in the feed water. A deaerator model is a simple model in which the effluent dissolved oxygen concentration is changed to a value specified by the user. The attribute of the model is as shown in table below.

Table 11‑8 - Model attributes for deaerator

Model Attribute	Value	Comment
Volume	N/A	The unit volume is not required for this process
Biological reactions	No	No biological reactions are modeled in tank
Inorganic precipitation reactions	No	No precipitation reactions are included
Aeration	No	No aeration model
Gas-Liquid Transfer	No	No gas transfer model

Empiric Model

The empiric de-aeration model is used when modellers wish to specify the effluent dissolved oxygen concentration.

Figure 11‑38 - Operational Menu for Empiric Model

In the Operational Menu, users simply set the specify the dissolved oxygen concentration in effluent, then the model calculates the mass of oxygen removed.

Flocculator (procwaterlib only)

The flocculator model simulates flocculation process via the flocculation model. This object is only found in PROCESS WATER Library under Physical Treatment section of the Unit Process Table.

Figure 11‑39 - Flocculator Object

Physical

The physical menu for the flocculator is shown below. The physical parameters are under the Input Parameters sub-menu item Physical. It contains physical settings for the flocculator object like the tank dimensions and volumes.

Figure 11‑40 - Flocculator Physical Menu

Operational

The operational menu for the flocculator is shown below. The operational parameters are under the Input Parameters sub-menu item Operational. It contains operational settings for the flocculator object like the flocculator setup, floc formation parameters and floc breaking parameters.

Figure 11‑41 - Flocculator Operational Menu

Retention Pond (procwaterlib only)

The retention pond is used for storing raw water. The underlying model for this unit process is same as the Equalization tank model.

Reverse Osmosis (procwaterlib only)

Two models are associated with this object: advanced and default, the model The advanced model estimates the solids capture in membrane filter, salt rejection and water recovery in the RO unit. The RO object is found in PROCESS WATER Library under the Physical Treatment section of the Unit Process Table.

Figure 11‑42 - RO object models.

The reverse osmosis process models the rejection of ionic species in water across the semipermeable membranes. The feed water to the RO system is passed through the system which produces two outlet streams of permeate and brine. The permeate stream represent the product water while brine is the concentrated waste stream.

There are two models implemented in GPS-X RO unit process, 1) Default model 2) Advanced model.

Default Model

The model uses the ion rejection values and recovery values specified by the user in estimating the quality of permeate and brine based on mass balance equations.

In addition to estimating the water quality of the permeate and brine stream, the model also estimates some of the important RO operation parameters like flux, pressure normalized flux, driving pressure and standardized flux.

The attribute table for the ion exchange model is as below:

Table 11‑9 - Model attributes for reverse osmosis

Model Attribute	Value	Comment
Volume	No	No volume information is required in the module
Biological reactions	No	No biological reactions are modeled in tank
Inorganic precipitation reactions	No	No precipitation reactions are included
Aeration	No	No aeration model
Gas-Liquid Transfer	No	No Gas-liquid transfer is modeled
Solid-Liquid Separation	Yes	No Solid Liquid Separation

Advanced Model

In advanced RO model, the membrane properties are used to estimate the anticipated rejection of the ionic species. For the non-ionizable or undissociated compounds, the user can set the rejection. The recovery is determined using the water mass transport coefficient. The operating pressure also affect the water recovery.

The model is an adaptation of the methodology from the MWH water treatment handbook.

Utilities (procwaterlib only)

Cooling Tower

The cooling tower process model is used to estimate the water loss and the effect of lost water on the concentration of constituents. The unit process is provided with 2 inlet streams and one effluent stream. One influent stream is to feed the cooling tower chemicals, the second inlet is used for the make-up water feed. The recirculation water is not shown explicitly as a connection point but is included in the model calculations.

The water losses in a cooling tower system are characterized in three categories.

1) Evaporation losses

2) Drift losses

3) Leak losses in recycle system

4) Blowdown

Figure 11‑43 - Conceptual diagram for mass balance of chlorine in a cooling tower

The evaporation losses are estimated using the following expression.

Equation 11‑32

Where:

 : water loss rate due to evaporation, m³/d

 : net evaporation coefficient, 1/^oC

 : recycle flow rate, m³/d

 : Temperature of recycle water at Inlet, ^oC

 : Temperature of recycle water at outlet, ^oC

The net evaporation coefficient can be estimated depending in the air temperature in the model or can be specified directly by the user. To estimate the net evaporation coefficient using the air temperature following equation is used.

Equation 11‑33

Where:

 : Temperature of ambient air, ^oC

The net evaporation coefficient can also be calculated by the evaporation coefficient and evaporation efficiency specified by the user as below.

Equation 11‑34

Where:

 : evaporation efficiency, - [DV = 0.75]

 : standard evaporation coefficient, 1/^oC [DV = 0.0018]

The water lost due to drift is calculated using the following expression.

Equation 11‑35

Where:

 : water loss rate due to drift, m³/d

 : drift loss factor, -

The leakage loss in the recirculation system is either directly specified by the user or are estimated based on a leakage loss factor specified by the user. The expression used to estimate leakage loss in the later case is as below.

Equation 11‑36

Where:

 : water loss rate due to leakage, m³/d

 : drift loss factor, -

The blowdown flow rate is estimated using three methods 1) direct specification of blowdown rate 2) fraction of recycle flow rate and 3) using the desired concentration cycle in cooling tower. The equations sued for methods 2) and 3) are as below.

Equation 11‑37

Where:

 : water loss rate due to blowdown, m³/d

 : blowdown factor, -

Equation 11‑38

Where:

 : water loss rate due to blowdown, m³/d

 : concentration cycle, -

The total water loss from the cooling tower is estimated as below.

Equation 11‑39

Where:

 : total water loss rate from cooling tower, m³/d

The makeup water requirement is estimated as below.

Equation 11‑40

Where:

 : makeup water requirement in cooling tower, m³/d

 : total water loss rate from cooling tower, m³/d

 : chemical feed rate, m³/d

In situations where the user has not specified the concentration cycle to calculate the blowdown flow rate, the model estimates the concentration cycle for the system using the flowing equation.

Equation 11‑41

The model also estimates the gas-liquid transfer of gases from the cooling tower and models any precipitates that may form in the system. For simplicity, the cooling tower reactions are modeled as single completely mixed tank reactor. The model attributes for cooling tower are as shown in table below.

Table 11‑10 - Model attributes for cooling tower (Default Model)

Model Attribute	Value	Comment
Volume	Fixed	The makeup can be connected to any of the water influents. The pump connections from other unit processes can also connect to the makeup water. The makeup water flow rate is set by the cooling tower and any flow rate specified by the user in the influent object/pump menu will be overwritten by the cooling tower object. The blow down water concentrations is same as the concentration of blowdown, drift water and leakage water.
Biological reactions	No	No biological reactions are modeled in tank
Inorganic precipitation reactions	Yes	Precipitation reactions are included. The kinetic parameters to control the precipitation rate available from Input Parameters à Kinetics menu of the unit process.
Aeration	Yes	Air is fed in the unit process. Depending on the air flow rate and estimated mass transfer of oxygen, the gas-liquid transfer of other gases is estimated.
Gas-Liquid Transfer	Yes	Gas transfer of CO2, N2 and NH3 are modelled. The extent of gas-liquid mass transfer is calculated by the mass transfer parameter of KLa for each gas.

In addition to the “default”, a simple model is implemented to allow user to add the chemicals within the cooling tower unit process. In the simple model, the Gas-liquid Transfer, inorganic precipitation, aeration and precipitation reactions are not modelled. A pH-alkalinity relationship is used to strip the CO₂ from the water.

In the “simple” model, the steady state mass balances are prepared for all the state variables. Below is an example of mass balance calculations for the

The mass balance of different compounds around the cooling tower is estimated using the mass balance equation below described for chlorine.

Equation 11‑42

Where:

 : Mass of chlorine in chemical stream, g/d

 : Mass of chlorine in makeup stream, g/d

: Mass of chlorine in return stream, g/d

: Mass of chlorine lost in due to volatilization, g/d

: Mass of chlorine lost from leakage in return system, g/d

: Mass of chlorine lost due to drift, g/d

: Mass of chlorine in supply, g/d

The main objective of the mass balance equation is to estimate the concentration of chlorine in the blow down considering the chlorine losses due to volatilization and losses in the return stream.

The concentration of chlorine in cooling tower, blowdown stream, supply stream, drift and leak are considered the same.

Equation 11‑43

Where:

 : chlorine concentration at the outlet of cooling tower, g/m³

 : chlorine concentration in drift, g/m³

 : chlorine concentration in controlled blowdown, g/m³

 : chlorine concentration in return supply, g/m³

 : chlorine concentration in leak, g/m³

As the chlorine passes through the return system, it reacts with organic and inorganic impurities in the system leading to loss of chlorine. The loss of chlorine also leads to increase in the chloride ion concentration in the return stream. An empirical return stream chlorine loss factor is used to estimate the chlorine concentration in return stream in following equation.

Equation 11‑44

Where:

 : chlorine concentration in return stream, g/m³

 : chlorine loss factor in return stream, g/m³

The free chlorine in water is present as HOCl and OCl^- ion. The speciation of HOCl and OCl is pH dependent as shown below.

The dissociated fraction is estimated using the following equation.

Equation 11‑45

Equation 11‑46

Where:

 : fraction of dissociated species, -

 : fraction of non-dissociated species, -

 : pH, -

 : acid dissociation constant for HOCl,-

A value of 3.5e-8 is used for the acid dissociation coefficient of HOCl. The loss of chlorine due to volatilization are estimated based on the non-dissociated fraction (HOCl) of free chlorine.

The chlorine loss due to volatilization is estimated as below.

Equation 11‑47

Where:

 : empirical factor for chlorine volatilization, -

 : return flow rate, m³/d

The mass of chlorine in other streams are estimated using following equations.

Equation 11‑48

Equation 11‑49

Equation 11‑50

Equation 11‑51

Equation 11‑52

Equation 11‑53

Equation 11‑54

The mass balance equation 1 for chlorine is rewritten using the above expression. After simplification, the chlorine concentration at the outlet of cooling tower is calculated using the following expression.

Equation 11‑55

Boiler

The boiler unit process includes feed water inlet and has two outlets for steam and blowdown. The model is used to estimate the blowdown flow rate and water constituent concentration. Steam flow rate and steam property is also determined based on the user specified loss of constituent from the feed water. The boiler model also estimates the heating/energy required for the boiler operation. The blowdown rate may be directly set by the user. The other two methods to estimate the blowdown rates are as below:

When fraction of steam as blowdown is specified,

Equation 11‑56

Where:

: Blowdown flow rate, m³/d

 : fraction of steam flow that goes as blowdown, -

 : Feed flow rate, m³/d

When concentration cycle is specified,

Equation 11‑57

Where:

 : blowdown flow rate, m³/d

 : concentration cycles, -

The steam flow rate is estimated using the flow balance using the feed flow rate and estimated blowdown rates.

The attribute table for the boiler model is shown in table below.

Table 11‑11 - Model attributes for boiler

Model Attribute	Value	Comment
Volume	Yes	The fixed unit volume is not used in calculations and reserved for future use.
Biological reactions	No	No biological reactions are modeled in tank
Inorganic precipitation reactions	No	No precipitation reactions are included
Aeration	No	No aeration model
Gas-Liquid Transfer	Yes	Fraction transfer of dissolved gases and HF, NH3 and HNO2 compounds to steam are considered.
Solid-Liquid Separation	No	No Solid Liquid Separation

API (mantisiwlib only)

The API object uses the noreact model. The general equations below used in the API water oil separator unit process.

Oil Removal Efficiency

Following equations are used in the API unit process. Two methods are provided.

1. Oil removal efficiency is directly specified

2. Oil removal efficiency is estimated based on the following equations.

The following procedure is used to estimate the oil removal efficiency in API unit process.

1. Calculate the average horizontal velocity in separator (m/d):

Equation 11‑58

where:

v_H: theoretical horizontal velocity, m/d

Q_in: feed flow rate, m³/d

b: width of the API separator, m

d: depth of the tan, m

2. Calculate the surface overflow rate (m/d):

Equation 11‑59

Where:

Lv: surface overflow rate

l: length of the tank

3. Calculate the upflow velocity for average size particle (m/s):

Equation 11‑60

Where:

v_oil,avg: average upflow velocity of oil droplet, m/d

g: acceleration due to gravity (m²/s)

: absolute viscosity of wastewater at design temperature (Pa.s)

: density of wastewater at design temperature (kg/m3)

 : density of oil at design temperature (kg/m3)

: average diameter of the oil globule, m

4. Calculate turbulence factor:

Equation 11‑61

5. Calculate the removal efficiency:

Equation 11‑62

Where:

 : Oil removal efficiency, -

Flow Balance Equation

Equation 11‑63

Where:

: Effluent flow rate, m³/d

: sludge flow rate, m³/d

: oil stream flow rate, m³/d

Oil Concentration in the Streams

Equation 11‑64

Equation 11‑65

Where:

: Oil concentration in effluent stream, mg/L

: Oil concentration in oil stream, m³/d

Model Inputs

The API model requires following physical input for the API unit (Figure 11‑44).

Figure 11‑44 - API Physical Input Menu

The volume, water height and length of the API unit is used to estimate the tank width, vertical and horizontal cross-sectional area.

The operational input to the model is as listed below. (Figure 11‑45)

Figure 11‑45 - API Operational Input Menu

The API unit allows both the removal of suspended solids and oil in water. The oil float is removed from the top of the tank, while the suspended solids are removed from the tank bottom. The pump rate for both the sludge and oil are specified by the user.

Solid removal efficiency is directly specified by the user, while the oil removal efficiency is specified or estimated based on the density and average size of the droplet.

Black Box

The Black Box object contains one model:  Interchange

Interchange Model

There are two options available to specify the state variable mapping:  From menu, and From custom macro:

From custom macro:  State variable mapping is specified in the custom macro file in the interchange model file (accessible by clicking on the interchange option from  the model menu).  More advanced mapping calculations can be performed in the custom macro (see Figure 11‑46). For details on how to write custom calculations, see the Customizing GPS-X chapter in the User’s Guide.

Figure 11‑46 – Custom Interchange Macro

Pipe Model

The pipe model can be used to simulate the lag effect caused by significant residence times in pipes or channels between unit process objects.  By default, GPS-X does not simulate any travel time between objects.

The pipe model is a plug-flow tank model without any biological reactions.

Pump Model

Advanced Pump Energy Model

The energy consumed in a pumping system depends on the pumped flow rate, pump head and pump efficiency. For a give pumped flow rate, the energy consumption will depend on pump head and pump efficiency. The required pump head for a pumping system has comprises of static head and dynamic head loss in the pipes, valves and fittings. While static head is independent of the pumped flow rate, the dynamic pump head is a function of pumped flow rate and piping system. The pump efficiency is also a function of the pump flow rate and normally available from the pump characteristic curve. The simple pump energy models applied to variable flow conditions typically use a constant head and constant efficiency, leading to inaccurate estimation of pumping requirement. These models are also not suitable to compare energy performance of pumps having different pump characteristics curves.  Therefore, in situations where large variation in the pumped flow rates are expected, better energy consumption estimates can be made giving adequate attention to pump characteristics curve. 

The advanced pump energy model in GPS-X allows user to dynamically estimate the pump head and pump efficiency under variable pump flow conditions using the pump characteristics curves. Two pump models are implemented 1) Fixed speed pump model and 2) Variable speed pump model.

Fixed Speed Pump Model

System Curve Characteristics

The pump model requires that a system curve is defined for the pumping system. In the GPS-X model following equation is used to define the system curve.

Equation 11‑66

Equation 11‑67

Where:

 = static system head, m

= static dynamic head, m

         = dynamic head-loss coefficient, -

          = flow rate in the system, -

The model requires an input value for hstatic, the static head of the system. In addition to this, a point (Q, hdynamic) on the dynamic head curve needs to be specified by the user. This point on the curve is estimate the dynamic head-loss coefficient (K). The GPS-X input screen for system curve characteristics is as shown below in Figure 11‑47 and Figure 11‑48:

Figure 11‑47 - Inputs for System Curve Definition – Static Head

Figure 11‑48 - Inputs for System Curve Definition - Dynamic Head

Pump Curve Characteristics

The pump model requires an input of pump characteristics curve. The user can provide the pump characteristics curve for a specific speed of the pump by setting pump flow rate, pump head and pump efficiency for a selected number of points on the pump curve. An example is shown in the table below:

Table 11‑12 - Sample Pump Characteristics

Points on Pump Curve	Pump Flowrate	Pump Head	Pump Efficiency
( - )	(m^3/hr)	(m)	(%)
Point 1	0	22.7	0.0
Point 2	250	21.8	19.1
Point 3	500	20.7	20.0
Point 4	750	19.8	37.3
Point 5	1000	18.7	63.6
Point 6	1250	17.6	72.7
Point 7	1500	16.5	78.2
Point 8	1750	15.6	82.7
Point 9	2000	14.5	84.5
Point 10	2250	13.6	86.4
Point 11	2500	12.4	86.4
Point 12	2750	11.3	85.5
Point 13	3000	10.0	82.7
Point 14	3250	8.5	77.3
Point 15	3500	6.9	69.1

For any given flow rate, the model uses pump characteristics curve and system head loss properties to calculate the system operating point (Figure 11‑49). Based on the pump operating point, the model calculate pump efficiency, number of pump and energy consumption in pumping.

Figure 11‑49 - Pump operating point based on pump and system curves

Variable Speed Pump

In this model, user can run the pump in a variable speed mode and estimate the energy saving that may be achieved by converting a fixed speed pump to a variable speed pump for a given system conditions. An ON and OFF switch is provided in model for the variable speed pump. In addition to above user inputs required for the fixed speed pump, additional inputs for the minimum and maximum speed of the pump are required. The variable speed model is built with an energy optimization algorithm in which the model finds a pump speed at which consumption of pumping energy is minimized. The typical outputs for the variable speed pumps are:

1) Number of pumps required to be operated

2) speed of the pumps

3) efficiency of the pump

4) system head, pump head

The following affinity equations are used to calculate the flow rate, pump head and power requirement at different speed of the pump.

Equation 11‑68

Affinity Law #1:

Equation 11‑69

Affinity Law #2:

Equation 11‑70

Affinity Law #3: 

where,

Q₁ = flow rate at N₁ speed, m³/d

Q₂ = flow rate at N₂ speed, m³/d

H₁ = pump head at N₁ speed, m

H₂ = pump head at N₂ speed, m

P₁ = power absorbed at N₁ speed, kW

P₂ = power absorbed at N₁ speed, kW

The change in the pump curve at different pump speeds are as shown in Figure 11‑50.

Figure 11‑50 - Pump curves at different pump speeds

In the variable speed pumping, a VFD power efficiency factor is introduced to take into account the power. The equations for calculating different power consumptions are as shown below.

Equation 11‑71

P_hydraulic = ρ·g·Q·H

Equation 11‑72

P_shaft     = P_hydraulic / η_pump

Equation 11‑73

P_motor    = P_shaft / η_motor

Equation 11‑74

P_input     = P_motor / η_VFD

where,

P = power, kW

ρ = water density, kg/m³

g = gravity acceleration, m/s²

Q = flow rate, m³/d

H = total head, m

η_pump = pump hydraulic efficiency, -

η_motor = motor efficiency, -

η_VFD = VFD (variable frequency drive) efficiency, -

Figure 11‑51 - Pump Characteristics Curve - Pump Speed for Pump Curve

Figure 11‑52 - Pump Characteristic Curve Inputs

Typical Model Outputs

Based on the input data of system head loss, pump characteristics and a given flow rate, the model find the operating point for the pump. It estimates the number of pumps required to pump the flow and efficiency of the pump at the operating point.

A typical output for a pump system pumping a diurnal flow is shown in Figure 11‑53.

Figure 11‑53 - Typical Output from a Fixed Pump Speed

It can be seen that when the pump flow rate increases to more than the pumping capacity of a single pump, two pumps become operational, each pumping half of the total flow rate [Figure 11‑53 A]. At this point since the flow rate are decreased for each pump, the change in the pump operating point leads to reduced pump efficiency [Figure 11‑53 C]. This reduced pump efficiency increases the energy consumption significantly for the pumping system. During the model calculation, head used in the energy calculation is the greater of the system head and pump head Figure 11‑53 C]. In general, the pump head should always be higher than the system head for proper pumping. This condition should always be checked by the user. If the pump head is lower than the system head, an alternative pump having more appropriate pump characteristics curve should be used. In this particular example, constant speed is used throughout the simulation.

Typical Model Outputs

The typical outputs from the variable speed pump model are as shown in Figure 11‑54. Following observations may be made by comparing the outputs of variable speed pump and fixed speed pump.

1) The pump head follows the required system head more closely [Figure 11‑54 B] than that in the fixed speed pump system. Synchronization of the pump head with the systemvariableTypical Model Outputs head results in reducing energy consumption.

2) The increase in the speed of the pump follows the increase in the flow rate. The optimum speed of the pump is estimated to achieve the lowest power consumption.

3) The pump efficiency for the variable speed system 0.84 – 0.88 as against the efficiency variation in the range of 0.77 – 0.88 for the fixed speed system.

4) The power consumption for the variable speed pump varies from 62-132 kW while for fixed speed system the observed range was 100-180 kW.

It is clear from the above analysis that the variable speed pumps can offer significant energy saving in a pumping system where flow variations are large.

Figure 11‑54 - Typical Outputs for Variable Speed Pump

Building

Figure 11‑55 - Building Object

The building object does not have any effect on the treatment of liquid or solids streams.  It is used for the estimation of energy requirements of buildings on the treatment plant site.  Users can place as many building objects as needed on the GPS-X layout. The building object calculates the energy usage for HVAC, lighting, refrigeration, and miscellaneous in the building. Based on the energy usage, the operational cost for the building is calculated.

HVAC

Heating, cooling, and HVAC energy requirement for the building is estimated using conductive heat transfer equation as shown below:

Equation 11‑75

where:

        = heat transferred per second ()

          = area ()

        = temperature difference)

          = thermal resistance parameter ()

          = performance factor (-)

HVAC energy can be estimated in GPS-X by specifying temperatures, physical and heat resistance parameters of the wall, HVAC efficiency as stated by the manufacturer, 

Lighting

Light energy usage for the building is calculated by specifying the quantity and wattage of the lighting device.

Refrigeration

Refrigeration energy usage is estimated based on conductive heat transfer through the walls, frequency of usage (air changed in the cooler), and refrigerated material load. In GPS-X, the user specifies the temperature, physical and heat resistance parameters of the cooler, cooler performance as stated by the manufacturer, refrigerated load characteristics, and usage frequency.

Other Miscellaneous

Other miscellaneous energy required for the building can be specified under this section. The input can be specified by entering a power value or entering a fraction of the total building power usage.

Energy requirements and cost estimations for the building are shown in the Operating Cost output menu, as shown in Figure 11‑56 below.  Note that the energy requirement for the building is included in the miscellaneous category for the plant-wide energy demand and operating cost calculations.

Figure 11‑56 – Operating Cost Output Menu

Receiving water Object

Water Quality Index (WQI) model

Effluent Quality Index model in effluent unit process uses the user specified concentration limits and compound specific weight factors to estimate 1) time duration for which a plant is under violation with respect to specified concentration limits 2) estimates the instantaneous and moving average Effluent Quality Index and 3) estimate the net and moving average Effluent Quality index.

The WQI is representative of the effluent pollution load to a receiving water body and is estimated using the expression provided in COST simulation benchmark (EC, 2002).

Equation 11‑76

Equation 11‑77

Where:

 – Instantaneous effluent flow rate, m3/d

n       – number of compounds in EQI estimation (-)

     – weight factor of compound in EQI (-)

 –instantaneous concentration of compound in EQI estimation

T        – time period for moving average calculation

The net WQI, which is defined as the weighted pollution load over and above the violation concentrations, is also calculated in the model as below.

Equation 11‑78

Equation 11‑79

Where,

 – violation concentration of compound in EQI estimation

By default, a total of 9 compounds including TSS, COD, BOD, NH₃-N, TKN, TN, NOx-N, Ortho-Phosphorus, and total phosphorus are used in the EQI calculation. User can exclude contribution from any of these compounds by setting the weight factor of that compound to zero.

The cumulative time of violation for a specific parameter is estimated by the integrating the time during which the plant is out of compliance with respect to that parameter.

The cumulative time of violation is divided by the total time to estimate the time fraction in violation.

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