CHAPTER 3

GPS-X Model Libraries

What is a Library?

A library in GPS-X is a collection of wastewater process models using a set of basic wastewater components, or state variables. The term state variable refers to the basic variables that are continuously integrated over time. The composite variables are those variables that are calculated from (or composed of) the state variables. In discussing the state variables for the different libraries listed below, volume is not explicitly explained as a state variable as it is common to all libraries. The relationships presented in this chapter between the state and composite variables are used in every connection point of the plant layout.

NOTE: In GPS-X, BOD refers to the carbonaceous BOD₅ (CBOD) unless otherwise stated. This is to distinguish the oxygen demand for organic carbon removal from the oxygen demand for ammonia oxidation. The values of these two analyses for the same sample can be considerably different.

Types of Libraries

Nine libraries are available for GPS-X:

· Comprehensive – Carbon, Nitrogen, Phosphorus, pH (MANTIS2LIB)

· Sulfur and Selenium (MANTIS2SLIB)

· Carbon Footprint – Carbon, Nitrogen, Phosphorus, pH (MANTIS3LIB)

· Process Water Treatment Library (PROCWATERLIB)

· Petrochemical Wastewater Library (MANTISIWLIB

· Legacy: Carbon, Nitrogen (CNLIB)

· Legacy: Carbon, Nitrogen, Custom Components (CNIPLIB)

· Legacy: Carbon, Nitrogen, Phosphorus (CNPLIB)

· Legacy: Carbon, Nitrogen, Phosphorus, Custom Components (CNPIPLIB)

Comprehensive – Carbon, Nitrogen, Phosphorus, pH (MANTIS2LIB)

Introduction

A new comprehensive model incorporating the most commonly observed biological, physical, and chemical processes in wastewater treatment plants was developed and implemented in GPS-X. Mantis2 is the outcome of Hydromantis/Hatch’s commitment to provide state of the art models to its clients through research and development. The Mantis2 model incorporates a large amount of information which has become available in technical literature in the last decade. The motivation behind the development of a comprehensive model arises from the fact that after the publications of ASM2d and ADM1, there has been growing interest in extending the capabilities of models to incorporate the side stream treatment processes like struvite precipitation, nitrification-anammox for nitrogen removal, and other precipitation processes. Although state variable interfaces between ASM2d and ADM1 are well defined, the state variable mapping between two models present practical challenges in model implementation and limits the versatility in modelling.

Mantis2 Model Components

The key features of the Mantis2 model are:

· Carbon, Nitrogen, and Phosphorus removal with integrated anaerobic digestion processes

· Mass balance for COD, C, N, P, Ca, Mg, K, and charge

· 48 state variables (21 soluble + 27 particulate)

· 56 processes

· Two-step nitrification using AOB, NOB

· Two-step denitrification

· Methanol degradation process with methylotroph biomass

· ANAMMOX process

· Anaerobic digestion processes with gas phase modelling for N₂, CO₂, H₂, and CH₄

· pH and alkalinity estimation in both liquid and solid train

· Precipitation of , , , , , and

· Unified composite variable calculation

The Mantis2 model is implemented in MANTIS2LIB. The Mantis2 model allows estimation of pH in both the liquid and solid train, therefore it is possible in Mantis2 to use additional influent objects like acid feed, alkali feed to estimate the chemical required for pH adjustment.

The basic structure of the comprehensive Mantis2 model is based on the following published models:

1. ASM2d – basic reactions for biological carbon, nitrogen, and phosphorus removal

2. UCTADM1 – Anaerobic digestion processes

3. Musvoto Model – Inorganic precipitation processes

In addition to the above, reference is made to other accepted models (i.e., newgeneral model, ADM1 and UCTCN and MANTIS) in formulating the model structure and finalizing the default stoichiometry and kinetics of model parameters. The modelling approaches for two-step nitrification, two-step denitrification, denitrification on methanol and ANNAMOX are adopted from recent research studies.

State Variables

The soluble states in the model can be classified into four categories: Soluble inert organic; Soluble biodegradable organics; Soluble inorganic ions; and Dissolved gases. The states in each category are described in following sections.

Soluble Inert Organics

1. Soluble inert organics (si)

The state represents the inert organics in the wastewater. The soluble inert organics are not degraded in the wastewater treatment processes. Mantis2 model considers the generation of soluble inert organic during the hydrolysis of slowly biodegradable substrate. By default, the fraction of soluble inert organic production is set to zero. In the anaerobic digestion process, the fraction may be set to a non-zero value to calibrate the soluble inert COD from the digesters.

Soluble Biodegradable Organics

1. acetate (sac)

2. propionate (spro)

3. methanol (smet)

4. fermentable substrate (ss)

5. colloidal substrate (scol)

The state of fermented substrate (slf) in ASM2D is split into two states of acetate (sac) and propionate (spro). The inclusion of an additional state of spro is required as it is one of the important intermediate products in anaerobic digestion.

The additional state of methanol (smet) is added to the model. It is now well accepted that the single carbon methanol is degraded by special microorganism which have different kinetics than the ordinary heterotrophic organism. This additional state is helpful to track the degradation of methanol based on the degradation kinetics of methylotrophic biomass.

Although there is no well accepted definition of the state of colloidal substrate in wastewater, for the purpose of modelling, it is assumed that this fraction represents the portion of the substrate COD which lies in the size range of 0.45 micrometers to 1.2 micrometers. These are typically macromolecules which require hydrolysis before oxidation. This fraction of COD is assumed to behave differently in different unit processes for example the colloidal COD will behave:

· as soluble in solid liquid separation in settlers

· as particulate in biological degradation

· as particulate or soluble in membrane separation

Soluble Inorganic

In addition to the soluble organic states, the Mantis2 model considers inorganic states of soluble inorganic carbon (stic), soluble nitrite-N (snoi), soluble nitrate-N (snoa), soluble Ammonia-N (snh), soluble Organic nitrogen (snd), soluble ortho-P (sp), dissolved calcium (sca), dissolved (smg), dissolved potassium (spot), dissolved anion (sana) and dissolved cation (scat). These inorganic states are chosen so as to appropriately describe the N and P transformation, inorganic precipitation and pH changes across the various unit processes in wastewater treatment plant.

The soluble inorganic carbon, stic, represents the sum of carbon in all the ionic species in the carbonic acid system i.e., , , . In previous models like ASM2D and New General, the alkalinity (salk) is used to express the buffer capacity in the wastewater. In Mantis2, soluble inorganic carbon is used instead of alkalinity as the state variable as it is a conserved quantity. The alkalinity in the model is estimated by considering the soluble inorganic carbon and the estimated pH of the system.

Two oxidized forms of soluble nitrogen, (i.e., soluble nitrite and nitrate) are considered in the model. This choice is necessary to model the two-step nitrification process.

The additional inorganic states like soluble calcium (sca), soluble magnesium (smg) are introduced to model the key precipitation reaction involving these ionic species. The state variable of soluble potassium (spot) is included to model the uptake and release of potassium during polyphosphate formation and degradation. The dissolved anion (sana) and cation (scat) represent all other strong anion and cation in the wastewater. These states are used in formulating the charge balance equation for estimating the pH in the wastewater.

Soluble Gases

In addition to soluble oxygen (so), Mantis2 includes soluble gases states for nitrogen (sn2), methane (sch4) and hydrogen (sh2). The soluble CO₂ is estimated by the pH and the stic concentration in the wastewater. The soluble oxygen plays an important role in the aerobic biological systems. The other gases are more relevant in the anaerobic digestion and fermentation processes. For each soluble gas, gas-liquid transfer equation is used to model the dissolution/stripping of the gas in the unit processes.

The particulate states in the model are classified into four categories of Particulate Inert Organics, Particulate Organic Substrate/Storage, Active Biomass and Particulate Inorganic.

Particulate Inert Organics

The model includes two states of Inert Organic Particulate (xi) and Unbiodegradable Organic Matter from cell decay (xu) in the model. The inert organic particulate is the inert organics fraction that is contributed by the influent wastewater. Having two states for inert organic compounds makes it easier to differentiate between the amount of inert organics accumulated from the wastewater and the amount that is produced by cell decay in the system.

Particulate Organic Substrate/Storage

Mantis2 provides one particulate Organic Substrate (xs) and one organic storage compound for intracellular PHA (xbt). These states are equivalent to the states in ASM2D and New General Models.

Active Biomass

Several biomass states are added in Mantis2. To model the two-step nitrification process, two autotrophic biomass Ammonia Oxidizer (xbai) and Nitrite Oxidizer (xbaa) are considered. Methylotrophic biomass (xbmet) is included to model the biodegradation of methanol. The fermentive biomass (xbf), Acetogen (xbpro), acetate methanogens (xbacm) and hydrogen methanogens (xbh2m) are included to model the anaerobic transformation in anaerobic digestion.

Particulate Inorganic

The particulate inorganic states include the model precipitates like aluminum hydroxide, aluminum phosphate, iron hydroxide, iron phosphate, calcium carbonate, calcium phosphate, magnesium hydrogen phosphate, magnesium carbonate, and ammonium magnesium phosphate (struvite). In addition to these precipitates, particulate inert inorganic is used as a composite state for the unidentified inorganic in the wastewater. The N and P components associated with the slowly biodegradable organics are included as nitrogen in slowly deg. organics and phosphorous in slowly deg. organics. The inorganic poly-phosphate accumulated in PAO is also included as a state.

Composite Variables

Mantis2 uses a well-defined stoichiometry for each state variable to estimate the composite variables. The state variables and their relationships to the composite variables are shown in the Composite Variables in MANTIS2LIB section of CHAPTER 4.

Processes

The processes included in the Mantis2 model are described below:

Adsorption/Enmeshment

1. Adsorption of colloidal COD: The colloidal COD (scol) is considered to first adsorb on the heterotrophic biomass. The adsorbed colloidal COD then becomes a part of slowly biodegradable COD (xs) and requires hydrolyses before it become available for bacterial metabolism. As both the ordinary heterotrophic organism and fermentative organism are considered to participate in the hydrolysis process, the rate of adsorption is defined with respect to the sum of the concentration of two organisms. The adsorption rate is first order to the colloidal COD. A rate inhibition term is added to reduce the rate of adsorption as the ratio of slowly biodegradable COD to adsorbing biomass increases.

Processes mediated by heterotrophic organisms

2. Aerobic hydrolysis: The heterotrophic microorganisms are considered to participate in the hydrolysis of slowly biodegradable substrate X_S resulting in production of soluble fermentable substrate (ss). Surface limited hydrolysis kinetics similar to that used in ASM1 and ASM2d is used. Both the ordinary heterotrophic organism and fermentative organism are considered to participate in the hydrolysis process

3. Anoxic hydrolysis: This hydrolysis process is active under anoxic conditions. The oxygen saturation term in aerobic hydrolysis rate expression is replaced with an oxygen inhibition term. A saturation term is added to the rate expression. The specific hydrolysis rate is reduced by anoxic hydrolysis reduction factor .

4. Anaerobic hydrolysis: This hydrolysis process is active only under anaerobic conditions. The rate expression contains oxygen and NO_X inhibition terms. The specific hydrolysis rate is reduced by anaerobic hydrolysis reduction factor .

5. Ammonification: The ammonification process converts soluble organic nitrogen to ammonia nitrogen. Both the ordinary heterotrophic organism and fermentative organism are considered to participate in the ammonification process. The kinetics of ammonification is similar to that provided in ASM1.

6. Growth on fermentable substrate (ss) using O₂ as electron acceptor: The process of heterotrophic growth takes place under aerobic conditions. The reaction rate for this process is formulated considering the concept of multi substrate kinetics outlined in ASM2d model. The growth rate is considered proportional to the ratio of fermentable substrate to total soluble substrate available to heterotrophic biomass. The main difference in the growth stoichiometry of different biomass is the uptake of N, P, Ca, Mg, K, anions, and cations during the biomass growth. In this implementation, it is assumed that concentration of Ca, Mg and K is non-limiting during growth. If required, the saturation terms for each micronutrient can be added easily in the model equations. The growth kinetics does not use the alkalinity saturation function as it is planned to add a pH inhibition term in the growth kinetics at a later date.

7. Growth on acetate (sac) using O₂ as electron acceptor: The process of aerobic heterotrophic growth on acetate is similar to the process 6, except that the growth rate is proportional to the ratio of acetate concentration to the total soluble substrate available to heterotrophic biomass.

8. Growth on propionate (spro) using O₂ as electron acceptor: The process of aerobic heterotrophic growth on acetate is similar to the process 6, except that the growth rate is proportional to the ratio of propionate concentration to the total soluble substrate available to heterotrophic biomass.

9. Growth on fermentable substrate (ss) using NO₃ as electron acceptor: This process of heterotrophic growth takes place in the presence of NO₃-N. The stoichiometry of this process is developed by considering partial reduction of NO₃-N to NO₂-N. The rate expression for the process uses an inhibition term for oxygen and a saturation term for the NO₃-N. The growth rate is also considered proportional to the ratio of fermentable substrate to total soluble substrate available to heterotrophic biomass. The reaction rate expression also reduces the amount of heterotrophic biomass by the fraction of NO₃-N to total NO_X nitrogen available in the system. It is assumed that when the heterotrophic biomass is converting NO₃-N to NO₂-N, it is not participating in the conversion of NO₂-N to N₂ gas.

10. Growth on acetate (sac) using NO₃ as electron acceptor: The process of heterotrophic growth on acetate using NO₃-N is similar to the process #9, except that the growth rate is proportional to the ratio of acetate concentration to the total soluble substrate (ss+sac+spro) available to heterotrophic biomass.

11. Growth on propionate (spro) using NO₃ as electron acceptor: The process of heterotrophic growth on acetate using NO₃-N is similar to the process #9, except that the growth rate is proportional to the ratio of propionate concentration to the total soluble substrate (ss+sac+spro) available to heterotrophic biomass.

12. Growth on fermentable substrate (ss)using NO₂ as electron acceptor: This process of heterotrophic growth takes place in the presence of NO₂-N. The stoichiometry of this process is developed by considering reduction of NO₂-N to N₂. The rate expression for the process uses an inhibition term for oxygen and a saturation term for the NO₂-N. The growth rate is also considered proportional to the ratio of fermentable substrate to total soluble substrate (ss+sac+spro) available to heterotrophic biomass. The reaction rate expression also reduces the amount of heterotrophic biomass by the fraction of NO₂-N to total NO_X nitrogen available in the system. It is assumed that when the heterotrophic biomass is converting NO₂-N to N₂, it is not participating in the conversion of NO₃-N to NO₂-N gas.

13. Growth on acetate (sac) using NO₂ as electron acceptor: The process of heterotrophic growth on acetate using NO₂-N is similar to the process #12, except that the growth rate is proportional to the ratio of acetate concentration to the total soluble substrate (ss+sac+spro) available to heterotrophic biomass.

14. Growth on propionate (spro) using NO₂ as electron acceptor: The process of heterotrophic growth on acetate using NO₂-N is similar to the process #12 except that the growth rate is proportional to the ratio of propionate concentration to the total soluble substrate (ss+sac+spro) available to heterotrophic biomass.

15. Decay of heterotrophs: The process rate of heterotrophic decay is modeled similar to that in ASM2d model. The main difference in the stoichiometry of decay reaction is the production of N, P, Ca, Mg, K, anion and cation according to the biomass composition

Processes mediated by autotrophic organisms

The model considers two-step conversion of NH₃-N to NO₃-N. The two steps are mediated by ammonia oxidizer and nitrite oxidizer sequentially.

16. Growth of ammonia oxidizer: The process of growth of ammonia oxidizer oxidizes NH₃-N to NO₂-N in the presence of oxygen. The reaction rate uses ammonia and oxygen saturation terms. The stoichiometry of the process is developed based on the conversion of NH₃-N to NO₂-N.

17. Growth of nitrite oxidizer: The process of growth of ammonia oxidizer oxidizes NO₂-N to NO₃-N in the presence of oxygen. The reaction rate uses NO₂-N and oxygen saturation terms. The stoichiometry of the process is developed based on the conversion of NO₂-N to NO₃-N.

18. Decay of ammonia oxidizer: The process rate of ammonia oxidizer decay is modelled similar to that in the ASM2d model.

19. Decay of nitrite oxidizer: The process rate of nitrite oxidizer decay is modelled similar to that in the ASM2d model.

Processes mediated by phosphate accumulating organisms (PAO)

The processes mediated by PAO are based on the ASM2D model. Three new processes describing the storage of PHA on propionate, growth of PAO on PHA using NO₂ as electron acceptor and anoxic storage of XPP using NO₂ are added to the processes mediated by PAO.

20. Storage of PHA by PAO using acetate: The rate expression for PHA storage by PAO using acetate as used in ASM2d is modified to include the effect of propionate, another VFA used by PAO. The rate expression is modified by assuming that PAO can utilize acetate and propionate simultaneous in proportion to the availability of each substrate

21. Storage of PHA by PAO using propionate: This is a new process and describes the storage of PHA by PAO using propionate. The kinetics and stoichiometry of the process is based on concepts used in describing process #20.

22. Growth of PAO on PHA using O₂ as electron acceptor: The stoichiometry and rate expression for this process is the same as ASM2d.

23. Storage of XPP on PHA using O₂ as electron acceptor: The stoichiometry and rate expression for this process is the same as ASM2d.

24. Growth of PAO on PHA using NO₃ as electron acceptor: This process of PAO growth takes place in the presence of NO₃-N. The stoichiometry of this process is developed by considering partial reduction of NO₃-N to NO₂-N. The rate expression for the process uses an inhibition term for oxygen and a saturation term for the NO₃-N. The reaction rate expression also reduces the amount of PAO biomass by the fraction of NO₃-N to total NO_X nitrogen available in the system. It is assumed that when the PAO biomass is converting NO₃-N to NO₂-N, it is not participating in the conversion of NO₂-N to N₂ gas.

25. Storage of XPP on PHA using NO₃ as electron acceptor: In this process the storage of poly-phosphate takes place while the stored PHA compounds are oxidised using NO₃-N as an electron acceptor. Only partial reduction of NO₃-N to NO₂-N is considered in process stoichiometry formulation. Similar to the process #24, a reduction factor equal to the ratio of NO₃-N to NO_X-N concentration is applied to account for fraction of total biomass mediating this process.

26. Growth of PAO on PHA using NO₂ as electron acceptor: This process of PAO growth takes place in the presence of NO₂-N. The stoichiometry of this process is developed by considering partial reduction of NO₂-N to N₂-N. The rate expression for the process uses an inhibition term for oxygen and a saturation term for the NO₂-N. The reaction rate expression also reduces the amount of PAO biomass by the fraction of NO₂-N to total NO_X nitrogen available in the system. It is assumed that when the PAO biomass is converting NO₂-N to N₂-N, it is not participating in the conversion of NO₃-N to NO₂-N.

27. Storage of XPP on PHA using NO₂ as electron acceptor: In this process the storage of poly-phosphate takes place while the stored PHA compounds are oxidised using NO₂-N as an electron acceptor. The conversion of NO₂-N to N₂ is considered in process stoichiometry formulation. Similar to the process #26, a reduction factor equals to the ratio of NO₂-N to NO_X –N concentration is applied to account for fraction of total biomass mediating this process.

28. Decay of PAO: The stoichiometry and rate expression for this process is same as ASM2d, except that alkalinity saturation term is not used.

29. XPP lysis: The stoichiometry and rate expression for this process is same as ASM2d, except that alkalinity saturation term is not used.

30. PHA lysis: The stoichiometry and rate expression for this process is same as ASM2d, except that alkalinity saturation term is not used.

Processes mediated by methylotrophs

A single population of methylotrophs, which degrades methanol, a single carbon substrate is incorporated in the model. Four reaction processes are considered for this biomass.

31. Growth of methylotrophs on methanol using O₂ as electron acceptor: The stoichiometry and kinetics of the process is developed using the principles of heterotrophic growth. The rate expression for growth of methylotrophs includes oxygen saturation and a methanol saturation term. The biomass yield on methanol is reported to be much lower than other carbon sources. Therefore, a different yield coefficient is used in the process stoichiometry.

32. Growth of methylotrophs on methanol using NO₃ as electron acceptor: The methylotrophs can use NO₃-N as terminal electron acceptor to oxidize methanol. The stoichiometry of this process is developed by considering partial reduction of NO₃-N to NO₂-N. The rate expression for the process uses an inhibition term for oxygen and a saturation term for the NO₃-N. The reaction rate expression also reduces the amount of biomass mediating the reaction by the fraction of NO₃-N to total NO_X nitrogen available in the system. It is assumed that when the methylotroph biomass is converting NO₃-N to NO₂-N, it is not participating in the conversion of NO₂-N to N₂ gas.

33. Growth of methylotrophs on methanol using NO₂ as electron acceptor: This represents the second step of denitrification. The stoichiometry of this process is developed by considering partial reduction of NO₂-N to N₂-N. The rate expression for the process uses an inhibition term for oxygen and a saturation term for the NO₂-N. The reaction rate expression also reduces the amount of biomass mediating the reaction by the fraction of NO₂-N to total NO_X nitrogen available in the system. It is assumed that when the methylotroph biomass is converting NO₂-N to N₂-N, it is not participating in the conversion of NO₃-N to NO₂-N.

34. Decay of methylotrophs: Decay of methylotrophs is modelled using first order reaction rate, with respect to the biomass concentration.

Processes mediated by Anaerobic Microorganisms

The Mantis2 model includes processes mediated by anaerobic microorganisms. These processes present the key transformations observed in strict anaerobic environment like anaerobic digester and other modifications of anaerobic treatment technology. These processes are adapted from the anaerobic digestion model developed at University of Cape Town (UCTADM1). The process rate and stoichiometry were converted from molar units to COD units. The process stoichiometry was also modified to include the mass balances for phosphorus, Ca, Mg, K, cation and anion species. The scheme of anaerobic biodegradation assumes that the anaerobic biomass decay takes place according to the decay processes listed above. The slowly degradable substrate is then hydrolyzed anaerobically according to process 4. The resulting soluble fermentable substrate is then converted to CH₄ and H₂ by the processes described below.

35. Growth of fermentive bacteria at low H₂: This process models the fermentation by acidogens under low H₂ partial pressure. The process stoichiometry is developed based on a conversion of model fermentable substrate (glucose) to acetic acid, H₂ and CO₂. The kinetic expression for the process uses a H₂ inhibition term to reduce the reaction rate as the partial pressure of H₂ increases.

36. Growth of fermentive bacteria at high H₂: This process models the fermentation by acidogens under high H₂ partial pressure. The process stoichiometry is developed based on a conversion of model fermentable substrate (glucose) to acetic acid, propionic acid, H₂ and CO₂. The kinetic expression for the process uses a H₂ saturation term to account for increased rate at higher partial pressure of H₂

37. Decay of fermentive biomass: The stoichiometry and kinetic expression for the decay process of fermentative biomass is based on the same principles as for other biomass types.

38. Growth of acetogens on propionate: This process (Acetogenesis) is the process whereby the acetogens convert propionic acid under low hydrogen partial pressure. The kinetic rate expression for the process includes a H₂ inhibition term and a propionic acid saturation term.

39. Decay of acetogens: The stoichiometry and kinetic expression for the decay process of fermentative biomass is based on the same principles as for other biomass types.

40. Growth of hydrogenotrophic methanogens: In this process hydrogenotrophic methanogens grow by converting H₂ and CO₂ to CH₄. The stoichiometry of the process is based on the chemical reaction describing this process. The kinetic expression for the process uses a hydrogen saturation term.

41. Decay of hydrogenotrophic methanogens: The stoichiometry and kinetic expression for the decay process of fermentative biomass is based on the same principles as for other biomass types.

42. Growth of Acetoclastic methonegens: In this process the acetic acid is converted to methane and CO₂ by acetoclastic methanogens. The kinetic expression of the process includes an acetate saturation term.

43. Decay of acetoclastic methanogens: The stoichiometry and kinetic expression for the decay process of fermentative biomass is based on the same principles as for other biomass types.

Processes mediated by anaerobic autotrophic microorganisms

A new type of anaerobic autotrophic biomass type is included in Mantis2 to perform anaerobic ammonium oxidation (ANAMMOX) process. The process stoichiometry is described in Strous et al. (1998) as below.

1NH₄⁺ + 1.32 NO₂^- + 0.066 HCO₃^- + 0.13 H⁺ à 1.02 N₂ + 0.26 NO₃^- + 0.066 CH₂O_0.5N_0.15 +2.03 H₂O

According to the above reaction, a mole of ammonia-N is oxidized to 1.02 mole of N₂ using 1.32 mole of nitrite-N. The reaction also produces 0.26 mole of nitrate-N and leads to growth of autotrophic biomass.

To model this process, two processes are added in Mantis2.

44. Growth of anammox microorganism: The stoichiometry factors to express the yield of biomass, NO₂-N consumption and N₂ and NO₃^- are expressed in terms of per unit NH₄⁺-N oxidized. The reaction rate expression uses an oxygen inhibition function and NO₂-N and NH₃-N saturation functions. Total (ionized + non-ionized) concentration of both substrates is used in the kinetic expression.

45. Decay of anammox microorganism: Decay of anammox microorganisms is modelled using first order reaction rate with respect to the biomass concentration.

Chemical precipitation processes

The precipitation reactions in Mantis2 are adapted from Musvoto et al. 2000 with some modifications. In addition to the processes of precipitation of AlPO₄ and FePO₄ which are included in ASM2d, five commonly observed precipitation processes in wastewater treatment are included in the model. The key differences from Musvoto et al. 2000 are: 1) the effect of ion-pair is neglected in the calculations; 2) the kinetic rate equations use the approach suggested in ASM2d with a few modifications and 3) the mole-based stoichiometry is replaced with mass based stoichiometry for maintaining model consistency. The kinetic expression for the precipitation reactions uses the concentration of participating ions, rate of precipitation and solubility product of the precipitate. The kinetic rate expression is formulated such that the precipitation and dissolution reactions can be modeled by a single equation.

46. Precipitation/dissolution of CaCO₃: CaCO₃ is assumed to precipitate as calcite. The calcite precipitation is assumed to take place in the presence of Ca²⁺ and CO₃^2- ions in the solution. The concentration of CO₃^2- is estimated by using the soluble inorganic carbon and the pH of the system.

47. Precipitation/dissolution of MgNH₄PO₄.6H₂O (struvite): Struvite is the most commonly observed precipitate in the digester supernatant. The precipitation of struvite takes place if the Mg²⁺, NH₄⁺ and PO₄^3- species are present in the solution. The concentrations of NH₄⁺ and PO₄^3- are estimated based on the solution pH.

48. Precipitation/dissolution of MgHPO₄.3H₂O (newberyite): This is another precipitate of magnesium with phosphate which is normally observed at lower pH. For this precipitate, Mg²⁺ and HPO₄^2- ionic species are required. The concentration of HPO₄^2- ion is estimated based on pH of the solution.

49. Precipitation/dissolution of Ca₃(PO₄)₂ (ACP): As the numbers of water molecules associated with the precipitate are variable, an anhydrous form is considered in expressing the mass concentration of the precipitate. It is indicated that this is probably the least stable precipitate among the possible precipitates and transforms into more stable forms with time. The Ca²⁺ ion and PO₄^3- species are required in precipitation process.

50. Precipitation/dissolution of MgCO₃: MgCO₃ (magnesite) is another precipitate of magnesium that is included in the model. The precipitation reaction requires Mg²⁺ and CO₃^2- ionic species for its formation.

51. Precipitation/dissolution of AlPO₄: The precipitation of AlPO₄ is required to model the metal precipitation of phosphorous in the plant. The stoichiometry of the process is similar to ASM2d; however, the kinetic expression is modified to include the solubility product of the precipitate.

52. Precipitation/dissolution of FePO₄: The precipitation of FePO₄ is required to model the metal precipitation of phosphorous in the plant. The stoichiometry of the process is similar to ASM2d; however, the kinetic expression is modified to include the solubility product of the precipitate.

Gas liquid transfer processes

Similar to the gas liquid transfer of oxygen, four additional processes as below are added in the model. The gas-liquid transfer is modeled using a mass transfer constant (K_La) and the gas saturation concentration at the given temperature and pressure. The mass transfer constant for each gas can be correlated to the oxygen mass transfer using gas diffusivities. For simplification, fractional factors are applied to the mass transfer constant of oxygen to obtain respective mass transfer constant.

53. Gas liquid transfer of CO₂: Process describes the stripping/absorption of CO₂ from/to the liquid. Since the concentration of CO₂ in liquid depends on the pH, this process is very sensitive to the pH of the solution.

54. Gas-liquid transfer of N₂: Process describes the stripping/absorption of N₂ from/to the liquid.

55. Gas-liquid transfer of CH₄: Process describes the stripping/absorption of CH₄ from/to the liquid.

56. Gas-liquid transfer of H₂: Process describes the stripping/absorption of H₂ from/to the liquid.

Algebraic pH Solver

In the Mantis2 model, the pH is estimated in all the unit processes. The dissociation reactions for acid and bases are much faster than the other processes used in the model, therefore, algebraic form of equations are chosen over the differential form for expressing acid/base dissociation reactions. The pH in each unit process is estimated by solving a set of algebraic equations which include a charge balance equation along with the equilibrium equations for each ionic species. Although temperature dependency of the dissociation constant is considered in the model, ionic activity corrections are not applied in the equations. For a typical wastewater, the ionic activity correction may not be important, but at higher ionic concentrations the model pH values should be used with appropriate caution.

The Mantis2 model uses the dissociation equations for: carbonic acid (diprotonic), phosphoric acid (triprotonic), ammonium (monoprotonic), acetic acid (monoprotonic), propionic acid (monoprotonic), nitrous acid (monoprotonic). The charge balance equation to solve for [H⁺] concentration is prepared using the ionized species of weak acid/base and strong anions (NO₃^-, sana) and strong cations (Ca²⁺, Mg²⁺, K⁺, scat).

Selenium and Sulfur (MANTIS2SLIB)

Introduction

The Mantis2S model is an extension of the Mantis2 model including sulfur oxidation/reduction reactions and selenium reduction reactions. The Mantis2S model contains all the processes modelled in the Mantis2 model, plus 29 additional processes such as sulfate reduction, selenium reduction, gas transfer of H₂S, metal sulfide precipitation, etc.

Mantis2S Model Components

The Mantis2S model contains all the key processes in the Mantis2 model, with the addition of:

· Anaerobic reduction of sulfate to hydrogen sulfide with sulfate reducing bacteria

· Aerobic growth of sulfur oxidizers on hydrogen sulfide, sulfur, and sulfite

· Anoxic growth of sulfur oxidizers on hydrogen sulfide (two-step denitrification)

· Precipitation of metal sulfides

· Anaerobic reduction of selenate and selenite to elemental selenium

· Anaerobic degradation of methanol to methane with methylotrophic methanogens

· Mass balance for sulfur and selenium

The sulfur and selenium modelling structure in the Mantis2S model is based on the following references:

1. Biological Sulphate Reduction with primary sludge in an UASB – Development of a kinetic model (Poinapen and Ekama, 2010)

2. Laboratory-Scale Continuous Reactor for Soluble Selenium Removal Using Selenate-Reducing Bacterium, Bacillus sp. SF-1 (Fujita et al., 2002)

3. Kinetic study on biological reduction of selenium compounds (Takada et al., 2008)

4. Operational studies using FGD (flue-gas desulphurization) and mining wastewater

State Variables

The Mantis2S model contains the 52 states in the Mantis2 model and 20 additional states. The additional states are outlined in the sections below.

Soluble States

As in Mantis2, the soluble states in the model can be classified into four categories: Soluble inert organic; Soluble biodegradable organics; Soluble inorganic ions; and Dissolved gases. There are no differences between Mantis2 and Mantis2S in the Soluble inert organic and Soluble biodegradable organics categories. The additional states in the Soluble inorganic ions and Dissolved gases categories are described in following sections.

Soluble Inorganic Ions

In addition to the soluble inorganic ions considered in Mantis2, the Mantis2S model considers the following soluble inorganic states:

· selenate selenium (sselna)

· selenite selenium (sselni)

· sulfate sulfur (sso4)

· sulfite sulfur (sso3)

· soluble heavy metal (shme)

These inorganic states were added to appropriately describe the sulfur and selenium transformations, inorganic precipitation, and pH changes across the various unit processes in WRRF.

Two oxidized forms of soluble selenium, (i.e., soluble selenate and selenite) are considered in the model. This choice is necessary to model the two-step selenium reduction process. Two oxidized forms of soluble sulfur, (i.e., soluble sulfate and sulfite) are considered in the model. This choice is necessary to model the three-step sulfur oxidation process from hydrogen sulfide (considered a dissolved gas) to sulfate (particulate elemental sulfur is the additional intermediary form of sulfur).

Timeline Description automatically generated with medium confidence

Figure 3‑1 – Sulfur and Selenium Transformations in Mantis2s.

Dissolved Gases

In addition to the dissolved gases considered in Mantis2, the Mantis2S model considers soluble sulfide sulfur (stssul). This gas is relevant as it is the final product of the sulfur reduction process as well as substrate for the sulfur oxidation process. Like the other soluble gases, a gas-liquid transfer equation is used to model the dissolution/stripping of soluble sulfide sulfur in the unit processes.

Particulate States

As in Mantis2, the particulate states in the model are classified into four categories: Particulate Inert Organics; Particulate Organic Substrate/Storage; Active Biomass; and Particulate Inorganic. There are no differences between Mantis2 and Mantis2S in the Particulate Inert Organics category. The additional states in the Active Biomass; Particulate Organic Substrate/Storage and Particulate Inorganic categories are described in the following sections.

Active Biomass

In addition to the active biomass considered in Mantis2, the Mantis2S model considers the following biomass species to model the sulfur and selenium processes:

· selenium-reducing biomass (xbsel)

· methylotrophic selenium-reducing biomass (xbsemet)

· propionate degrading sulfate-reducing biomass (xbsr1)

· hydrogen utilizing sulfate-reducing biomass (xbsr2)

· acetate utilizing sulfate-reducing biomass (xbsr3)

· methanol utilizing sulfate-reducing biomass (xbsr4)

· sulfur-oxidizing biomass (xbsox)

· methylotrophic methanogens (xbmem)

The selenium-reducing and methylotrophic selenium-reducing biomass are considered to model the conversion of selenate and selenite to elemental selenium. The propionate degrading, hydrogen utilizing, acetate utilizing and methanol utilizing sulfate reducing biomass are considered to model sulfate reduction to sulfide. The sulfur oxidizing biomass is considered to model the aerobic/anoxic oxidation of the sulfur species. The methylotrophic methanogens are considered to model methanogenesis with methanol as the carbon source.

Particulate Organic Substrate/Storage

In addition to the particulate organic substrate considered in Mantis2, the Mantis2S model considers slowly biodegradable substrates 1,2 and 3 (xs1, xs2, xs3). Slowly biodegradable substrate 1 is analogous to the slowly biodegradable substrate state from the Mantis2 model, but slowly biodegradable substrates 2 and 3 are different. Slowly biodegradable substrates 2 and 3 are only considered in the hydrolysis reactions (they are not formed via biomass decay). This allows the user to partition the influent slowly biodegradable COD into different types based on biodegradability rates by adjusting the hydrolysis rates of slowly biodegradable substrate 2 and 3.

Particulate Inorganic

In addition to the particulate inorganic species considered in Mantis2, the Mantis2S model considers the following particulate inorganic states:

· elemental selenium (xse0)

· elemental sulfur (xsul0)

· particulate heavy metal sulfide (xhmes)

· iron sulfide (xfes)

The particulate elemental selenium state is the product of the selenium reduction reactions, and the particulate elemental sulfur state is involved in the sulfur oxidation and reduction reactions. The particulate heavy metal sulfide and iron sulfide states are included as sulfide precipitants.

Composite variables

Mantis2S uses a well-defined stoichiometry for each state variable to estimate the composite variables. The state variables and their relationships to the composite variables are shown in the Composite Variables in MANTIS2SLIB section of CHAPTER 4.

Processes

The Mantis2S model includes all the processes in the Mantis2 model and some additional processes. The following processes from the Mantis2 model are modelled identically in the Mantis2S model:

· Adsorption/Enmeshment

· Processes mediated by heterotrophic organisms

· Processes mediated by autotrophic organisms

· Processes mediated by phosphate accumulating organisms (PAO)

· Processes mediated by Anaerobic Microorganisms

· Processes mediated by anaerobic autotrophic microorganisms

· Processes mediated by methylotrophs

The Mantis2S model includes these additional processes:

· Processes mediated by sulfate reducing biomass (SRB)

o Growth of propionate degrading SRB using SO₄ as an electron acceptor: This process takes place in the presence of SO₄-S. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SO₄-S and propionate.

o Growth of hydrogen utilizing SRB using SO₄ as an electron acceptor: This process takes place in the presence of SO₄-S. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SO₄-S and hydrogen.

o Growth of acetate utilizing SRB using SO₄ as an electron acceptor: This process takes place in the presence of SO₄-S. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SO₄-S and acetate.

o Growth of methanol utilizing SRB using SO₄ as an electron acceptor: This process takes place in the presence of SO₄-S. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SO₄-S and methanol.

o Decay of SRB (all types): The process rate of SRB decay is modeled the same as biomass decay in the Mantis2 model – first order decay with different rates for aerobic, anoxic, and anaerobic conditions.

· Processes mediated by sulfur-oxidizing biomass

o Growth on hydrogen sulfide using O₂ as an electron acceptor: This process oxidizes hydrogen sulfide to elemental sulfur in the presence of oxygen. The rate expression for the process uses saturation terms for hydrogen sulfide and oxygen.

o Growth on elemental sulfur using O₂ as an electron acceptor: This process oxidizes elemental sulfur to sulfite in the presence of oxygen. The rate expression for the process uses saturation terms for elemental sulfur and oxygen.

o Growth on sulfite using O₂ as an electron acceptor: This process oxidizes sulfite to sulfate in the presence of oxygen. The rate expression for the process uses saturation terms for sulfite and oxygen.

o Growth on hydrogen sulfide using NO₃ as an electron acceptor: This process oxidizes hydrogen sulfide to sulfate in the presence of nitrate. The rate expression for the process uses saturation terms for hydrogen sulfide and nitrate and an inhibition term for oxygen.

o Growth on hydrogen sulfide using NO₂ as an electron acceptor: This process oxidizes hydrogen sulfide to sulfate in the presence of nitrite. The rate expression for the process uses saturation terms for hydrogen sulfide and nitrite and an inhibition term for oxygen.

o Decay of sulfur-oxidizing biomass:The process rate of sulfur-oxidizing biomass decay is modeled the same as biomass decay in the Mantis2 model – first order decay with different rates for aerobic, anoxic, and anaerobic conditions.

· Processes mediated by selenium-reducing biomass

o Growth on fermentable substrate using SeO₄ as an electron acceptor: This process takes place in the presence of SeO₄-Se. The stoichiometry of this process was developed by considering the reduction of SeO₄-Se to SeO₃-Se. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SeO₄-Se and fermentable substrate. The growth rate is also considered proportional to the ratio of fermentable substrate to total soluble substrate (ss+sac+spro) available to the selenium-reducing biomass.

o Growth on acetate using SeO₄ as an electron acceptor: This process takes place in the presence of SeO₄-Se. The stoichiometry of this process was developed by considering the reduction of SeO₄-Se to SeO₃-Se. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SeO₄-Se and acetate. The growth rate is also considered proportional to the ratio of acetate to total soluble substrate (ss+sac+spro) available to the selenium-reducing biomass.

o Growth on propionate using SeO₄ as an electron acceptor: This process takes place in the presence of SeO₄-Se. The stoichiometry of this process was developed by considering the reduction of SeO₄-Se to SeO₃-Se. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SeO₄-Se and propionate. The growth rate is also considered proportional to the ratio of propionate to total soluble substrate (ss+sac+spro) available to the selenium-reducing biomass.

o Growth on fermentable substrate using SeO₃ as an electron acceptor: This process takes place in the presence of SeO₃-Se. The stoichiometry of this process was developed by considering the reduction of SeO₃-Se to elemental Selenium. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SeO₃-Se and fermentable substrate. The growth rate is also considered proportional to the ratio of fermentable substrate to total soluble substrate (ss+sac+spro) available to the selenium-reducing biomass.

o Growth on acetate using SeO₃ as an electron acceptor: This process takes place in the presence of SeO₃-Se. The stoichiometry of this process was developed by considering the reduction of SeO₃-Se to elemental Selenium. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SeO₃-Se and acetate. The growth rate is also considered proportional to the ratio of acetate to total soluble substrate (ss+sac+spro) available to the selenium-reducing biomass.

o Growth on propionate using SeO₃ as an electron acceptor: This process takes place in the presence of SeO₃-Se. The stoichiometry of this process was developed by considering the reduction of SeO₃-Se to elemental Selenium. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SeO₃-Se and propionate. The growth rate is also considered proportional to the ratio of propionate to total soluble substrate (ss+sac+spro) available to the selenium-reducing biomass.

o Decay of selenium-reducing biomass:The process rate of selenium-reducing biomass decay is modeled the same as biomass decay in the Mantis2 model – first order decay with different rates for aerobic, anoxic, and anaerobic conditions.

· Processes mediated by methylotrophic selenium-reducing biomass

o Growth on methanol using SeO₄ as an electron acceptor: This process takes place in the presence of SeO₄-Se. The stoichiometry of this process was developed by considering the reduction of SeO₄-Se to SeO₃-Se. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SeO₄-Se and methanol.

o Growth on methanol using SeO₃ as an electron acceptor: This process takes place in the presence of SeO₃-Se. The stoichiometry of this process was developed by considering the reduction of SeO₃-Se to elemental Selenium. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and saturation terms for SeO₃-Se and methanol.

o Decay of methylotrophic selenium-reducing biomass: The process rate of methylotrophic selenium-reducing biomass decay is modeled the same as biomass decay in the Mantis2 model – first order decay with different rates for aerobic, anoxic, and anaerobic conditions.

· Processes mediated by methylotrophic methanogens

o Growth on methanol: This process takes place in anaerobic conditions. The rate expression for the process uses inhibition terms for nitrate/nitrate and oxygen and a saturation term for methanol.

o Decay of methylotrophic methanogens:The process rate of selenium-reducing biomass decay is modeled the same as biomass decay in the Mantis2 model – first order decay with different rates for aerobic, anoxic, and anaerobic conditions.

· Chemical precipitation processes

o The Mantis2S model considers all the chemical precipitation processes considered in the Mantis2 model, with the addition of iron sulfide and heavy metal sulfide precipitation. The new precipitation/dissolution processes in the Mantis2S model follow the same structure as the processes in the Mantis2 model.

o Precipitation/dissolution of iron sulfide: The precipitation of iron sulfide takes place if S^2- and Fe³⁺ are present in the solution. The concentrations of S^2- and Fe³⁺ are estimated based on the solution pH.

o Precipitation/dissolution of heavy metal sulfide: The precipitation of heavy metal sulfide takes place if S^2- and soluble heavy metals are present in the solution. The concentration of S^2- is estimated based on the solution pH.

· Gas liquid transfer processes

o The Mantis2S model considers all the gas liquid transfer processes considered in the Mantis2 model, with the addition of gas liquid transfer of hydrogen sulfide. The new gas liquid transfer process in the Mantis2S model follows the same structure as the processes in the Mantis2 model.

o Gas liquid transfer of hydrogen sulfide: The process describes the stripping/absorption of H₂S from/to the liquid.

Algebraic pH Solver

The Mantis2S model uses the same algebraic pH solver as the Mantis2 model.

Carbon Footprint – Carbon, Nitrogen, Phosphorus, pH (MANTIS3LIB)

The amount of GHG emission from a wastewater treatment plant depends on the choices of treatment processes, operational strategies, energy sources, energy and material consumption. To minimize the GHG emission from the wastewater treatment plants, it is important to characterize the sources of emissions and evaluate available process and operational alternatives for mitigation. In this development, a carbon footprint estimation model (Mantis3) was developed in the GPS-X simulation platform. This development is expected to provide a comprehensive tool to the process engineers to optimize wastewater treatment process design and operation in the light of minimizing the carbon footprint of the plant.

Global Settings and Database

The global settings for Green House Gas Emission estimation are available in System > Input Parameters > Carbon Footprint menu. The global model parameters that can be changed are as below:

1. CO₂ equivalents for methane and nitrous oxide

2. Region selection

3. % non-fossil carbon in the influent wastewater

The menu also provides access to following three databases that are used in the models.

1. Regional emission factor for electricity generation database

2. Carbon emission associated with different fuels database

3. Calorific value of fuels database

The type of fuel used is selected inside an individual unit process for which the carbon emission factor and the calorific value of the fuel is obtained from the database.

Summary of Scopes

The carbon emissions were classified in three categories of Scope-1, Scope-2 and Scope-3 according to IPCC (2006). The sources of carbon emissions and offsets in each category are as shown in Table 3‑1Table 3‑1 - Emission Sources in each .

Table 3‑1 - Emission Sources in each Category

Category	Emission Sources	Offsets
Scope 1	Process emissions · CO₂ from aerobic, anoxic and anaerobic processes · CH₄ from anaerobic processes · N₂O from nitrification and denitrification On-site fuel consumption emissions · Fuel usage for heating	Process offsets · CO₂ emission due to non-fossil carbon · CH₄ capture offsets · CH₄ heat recovery offsets · CH₄ electricity generation offsets
Scope 2	Emissions due to energy consumption · Pumping energy · Blower energy · Miscellaneous energy (mixing, raking etc.)	None considered presently
Scope 3	Emissions due to Consumables · Chemical manufacturing · Material replacement (membrane, media) manufacturing · Transportation	None considered presently

The GHG emissions and offsets for each scope are reported by the model according to the framework shown in Figure 3‑2.

Figure 3‑2 - Hierarchal Estimation Scheme for the Scope 1 (Scheme Applicable to other Scopes as well)

Process Water Treatment Library (PROCWATER)

Introduction

The Process Water Library focuses on simulating the process water treatment systems which require modelling the inorganic interactions among different inorganic compounds in process water. The library allows the user to model the performance of commonly found unit processes in water treatment system. The list of the available unit processes includes different sources of raw water (river, lake, ground, municipal, brackish and sea), chemical dosing objects (acid feed, alkali feed, nutrients, flocculants, and polymers), equalization tank, sedimentation basin, bioreactor, neutralization tank, lime softening, dissolved air floatation, cation exchange, anion exchange, Reverse Osmosis, decarbonation, evaporator, cooling tower, boiler, and others.

The model includes the inorganic soluble compounds like Mg, Ca, K, Cl, HCO₃, Cl₂, Cu, Fe (II), F, HSO₃, Mn, Na, SO₄, S, Zn, SiO₂, PO₄, NH₃, NO₃, NO₂, and inorganic precipitates of Ca, Mg, Fe, and silica. The model also allows modeling biological degradation of organic compounds, nitrification, and denitrification. The model uses equilibrium chemistry and ionic balance equations to predict the pH in different streams of water in the plant. For every stream, important operational parameters like alkalinity, hardness, Langelier stability index (LSI), Ryznar stability index (RSI), Puckorius stability index (PS), Ionic strength, conductivity, resistivity, osmotic pressure, Turbidity and Color are calculated.

Model State Variables

The model state variables are the conserved variables which are used in the mass balance equations for each unit process. These variables are calculated for all the streams in a model layout.

The list of the state variables used in the model is as shown in State Variables section in this chapter. The model contains most important soluble inorganic compounds, selected inorganic precipitates, organic compounds, dissolved gases, biomass, and pathogens.

Water Composite Variables

The composite variables are calculated by manipulating the model state variables. For example, two model states of “x” and “y” could be added to result in a useful composite value “z”. The list of Composite Variables estimated in the model and their relationships to the state variables are shown in the Composite Variables in PROCWATERLIB section of CHAPTER 4.

A definition of the calculation procedure for each composite variable is provided below.

pH

The pH in each flow stream is estimated based on a charge balance equation, which is solved iteratively for H⁺ ion concentration.

Equation 3‑1

The concentrations of total anions [A_T]e and [C_T]e is estimated by adding equivalent concentration of all the strong/weak anions and cations respectively. The equivalent concentration of an ion is estimated usingfollowing expression.

Equation 3‑2

Where:

= equivalent concentration of ion, Mole/L

= concentration of ion, Mole/L

= charge on ion, -

The dissociated ionic concentrations of weak base/acid are estimated using ionic equilibrium equations based on dissociation coefficients. The dissociation equations used for estimating the concentration of ionic species arising from dissociation of mono-protonic, diprotonic and triprotonic acids are described below.

1. For acid with one step dissociation (Mono-protonic acid):

Dissociation coefficient = from Equation 3‑3.

Equation 3‑3

Equation 3‑4

Equation 3‑5

Equation 3‑6

2. For acid with two step dissociation (di-protonic acid):

First dissociation coefficient = from Equation 3‑7.

Equation 3‑7

Second dissociation coefficient = from Equation 3‑8.

Equation 3‑8

Equation 3‑9

Equation 3‑10

Equation 3‑11

Equation 3‑12

Equation 3‑13

3. For acid with three step dissociation (tri-protonic acid):

First dissociation coefficient = from Equation 3‑14

Equation 3‑14

Second dissociation coefficient = from Equation 3‑15

Equation 3‑15

Third dissociation coefficient = from Equation 3‑16

Equation 3‑16

Equation 3‑17

Equation 3‑18

Equation 3‑19

Equation 3‑20

Equation 3‑21

Equation 3‑22

Equation 3‑23

The dissociation coefficients for different weak acids/bases are estimated using the following equation.

Equation 3‑24

Where:

: Acid dissociation coefficient, -

: empirical constants, -

T: Temperature in, K

The coefficient values used in estimating the dissociation constants are part of the Global settings in GPS-X.

The dissociation constant estimation equation is temperature is temperature dependent. In situations where the temperature dependency is not clear, only the value of first temperature independent coefficient us used. In the present implementation of pH estimation, the ionic activity corrections are not applied to the ionic concentrations. For dilute water, this simplification does not affect the calculation results, however, for concentrated streams like RO brine, the estimated pH may be as reliable.

Total Dissolved Solids

The Total Dissolved Solids (TDS) are estimated using the following expression.

Equation 3‑25

Where:

: TDS conversion factor for the constituent for soluble state, mg/state-unit

: Constituent state, state-unit/L

Alkalinity

The model estimates Caustic Alkalinity, p- Alkalinity and M- Alkalinity (Alkalinity) considering carbonic acid species. The equations used in estimating these Alkalinity variables is as shown below:

Equation 3‑26

Equation 3‑27

Equation 3‑28

All alkalinity numbers are estimated as mgCaCO₃/L.

The program also estimates total alkalinity of the solution by adding the M-Alkalinity contributions from all the other weak acids/bases. The total alkalinity is estimated using the following expression.

Equation 3‑29

Where:

n: number of weak acid and bases in system

The other weak acid/bases contributing to total alkalinity are H₃PO₄, NH₃, HF, H₃SiO₂, H₂S and HNO₂.

Hardness

The model estimates Total Hardness, carbonate hardness and non-carbonate hardness. Following expressions are used.

Equation 3‑30

Equation 3‑31

Equation 3‑32

Where:

: Total hardness, mgCaCO₃/L

: Carbonate hardness, mgCaCO₃/L

: non-Carbonate hardness, mgCaCO₃/L

Stability Indices

Several stability indices are calculated in the model to provide indication about the corrosion and or scaling potential in water. The following indices are estimated.

1) Langelier stability index (LSI)

2) Puckorius stability index (PSI)

3) Ryznar stability index (RSI)

The calculation method for each of the indices is presented below.

Langelier stability index (LSI)

The LSI is estimated as below:

Equation 3‑33

Where:

LSI: Langelier Stability Index, -

pH: pH of water, -

: Langelier stability pH, -

The Langelier stability pH is estimated using the following expressions.

Equation 3‑34

Equation 3‑35

Equation 3‑36

Equation 3‑37

Equation 3‑38

Where:

TDS: Total Dissolved Solids, mg/L

: Temperature, ^oC

: Calcium concentration, mgCaCO₃/L

: M-Alkalinity, mgCaCO₃/L

Table 3‑2 below describes the corrosion and scaling properties of water depending on the values of LSI. Negative values are indicative of corrosion, while positive values lead to scaling.

Table 3‑2 - Corrosion and Scaling Properties Linked to LSI.

Langelier Saturation Index	Description	General Recommendation
-5	Severe Corrosion	Strong Corrosion Inhibitor Treatment Required
-3	Moderate Corrosion	Strong Corrosion Inhibitor Treatment Required
-2	Moderate Corrosion	Corrosion Inhibitor Treatment Required
-1	Mild Corrosion	Corrosion / Scale Treatment Required
-0.5	None- Mild Corrosion	Corrosion / Scale Treatment Required
0	Near Balanced	Balanced Treatment Required
0.5	Some Faint Coating	Scale preventive Treatment Required
1	Mild Scale Coating	Scale preventive Treatment Required
2	Mild to Moderate Coatings	Scale preventive Treatment Required
3	Moderate Scale Forming	Strong Scale Preventive Treatment Required
4	Severe Scale Forming	Strong Scale Preventive Treatment Required

Ryznar stability index (RSI)

Ryznar saturation index (RSI) was developed from empirical observations of corrosion rates and film formation in steel mains. It is defined as below:

Equation 3‑39

Where:

: pH stability index as calculated in the LSI estimation

For 6,5 < RSI < 7 water is approximately at saturation equilibrium with calcium carbonate. For RSI > 8 water is under saturated and, therefore, would tend to dissolve any existing solid CaCO₃. For RSI < 6,5 water tends to be scale forming. The RSI >> 8 mild steel corrosion becomes an increasing problem.

Puckorius stability index (PSI)

The PSI index is calculated in a manner similar to the Ryznar stability index. Puckorius uses an equilibrium pH rather than the actual system pH to account for the buffering effects:

Equation 3‑40

Where:

: M-Alkalinity, mgCaCO₃/L

: pH stability index as calculated in the LSI estimation

Ionic Strength

The ionic activity in water is estimated by using the following expression.

Equation 3‑41

Where:

: Ionic Strength, Mole/L

: Concentration of the i^th ionic species, Mole/L

z_i: Charge on ion, -

Ion Activity Coefficients

The ion activity coefficients are estimated using three different approaches reported for different ionic strength water. The expressions used are as shown in table below (Stumm & Morgan 1996).

Table 3‑3 - Activity Coefficient for Individual Ions

Approximation	Equation	Applicability
Debye-Huckel		<10^-2.3 [M]
Guntelberg		<10^-1 [M]
Davies		<0.5 [M]

Conductivity

The conductivity is estimated using the following expression.

Equation 3‑42

Where:

: conductivity, S/m

: Specific conductivity, S.L/m. Mole

: Molar concentration of ionic species, Mole/L

: Temperature correction factor, -

The specific conductivity of the ionic species used in the model are determined through literature review. The values of the specific conductivity are available in the Global menu of the model. As the values of specific molar conductivities are specified at 25^oC, a temperature correction factor is applied to estimate the conductivity at temperature other than 25^oC. Following expression is used for temperature correction.

Equation 3‑43

Where:

: Temperature correction coefficient = 1.02, -

Tc: Temperature, ^oC

Resistivity

The resistivity is the reciprocal of conductivity and is estimated as below:

Equation 3‑44

Where:

: Resistivity, m/S

: conductivity, S/m

Osmotic pressure

The osmotic pressure is estimated using the following expression.

Equation 3‑45

Where:

π: Osmotic pressure, kPa

C_T: Total molar concentration of ionic species, Mole/L

Tc: Temperature, ^oC

R: Universal gas constant, Pa.L.K-1.mol-1

Turbidity

Following turbidity relationship is used.

Equation 3‑46

Where:

: Turbidity, NTU

: Calibration factor, a value of 0.5 is used by default

: Calibration factor, as value of 1.0 is used by default

: TSS concentration in water, mg/L

: Colloidal organic concentration, mg/L

Depending on the size distribution of TSS, the value of empirical constant k₁ may vary. The colloidal fraction can also have a significant influence on the measured turbidity values. The colloidal organic matter may be measured by the difference in the COD content from the filtrates of glass filter (1.5 micron) and 0.45-micron filter.

Color

Color is often caused by dissolved organic matter, e.g., humic and fulvic acids (organic decomposition products from vegetation). It can also be a result of impurities of minerals such as iron and manganese.

So, an empirical relationship is used between the color and Si (inert soluble material). The inert soluble material (Si) is considered as surrogate for humic and fulvic acid presence in water. Following expression is used.

Equation 3‑47

Where:

Co : Color

Petrochemical Wastewater Library (MANTISIWLIB)

Introduction

MantisIW is a dynamic-mechanistic model of the industrial activated sludge process. It is based on calculating dynamic mass balances for chemical oxygen demand (COD), nitrogen, phosphorus, and sulfur around completely mixed, or plug-flow activated sludge reactors. The general form of the mass balance equation is as follows:

Rate of Accumulation

Rate of Input

Rate of Output

Rate of Production or Consumption by Reaction or Physical Transformation

To calculate the production/consumption terms, the model utilizes aspects of the ASM1 (Henze et al., 2000), ASM2d (Henze et al., 2000), Mantis2 (Hydromantis, 2019), and Baker (1994) biological models along with a revised categorization of the influent COD and added biological and physical transformations. The model has 52 components or state variables, for which mass balances are written, and 90 transformation processes that are incorporated into the production/consumption terms.

MantisIW Model Components

In MantisIW, the model components or state variables are categorized as follows:

Existing Mantis2 influent COD state variables

· Readily biodegradable substrate

· Slowly biodegradable substrate

· Soluble inert organic matter

· Particulate inert organic matter

· Colloidal substrate

New organic influent COD state variables

· Organic solvents

o Short-chain

o Halogenated

· Aliphatic compounds

o Short-chain

o Long-chain

· Aromatic compounds

o Mono-cyclic

o Poly-cyclic

o Halogenated

· Phenolic compounds

· Other industrial compounds

o Soluble

o Adsorbed

The COD categorization was developed by considering the physical, chemical and biological properties (biodegradability, adsorbability and volatility of different organic compounds). Consideration was also given to the classes of compounds that are normally tracked in industrial WWTPs due to health, safety and environmental concerns. The new influent COD variables are further defined below and in Table 1, with accompanying typical and representative compounds in terms of biodegradability, adsorbability and volatility.

Dissolved Oxygen

Dissolved oxygen is required for aerobic biomass growth and prediction of aeration requirements.

Biomass Associated COD

COD variables are also included to represent biomass and include those in ASM1 plus an additional biomass type for sulfur oxidizing biomass:

· Active heterotrophic biomass

· Active ammonia oxidizing biomass

· Active nitrite oxidizing biomass

· Active sulfur oxidizing biomass

· Particulate products resulting from biomass decay

Adsorbed COD

Certain types of difficult-to-degrade COD can be adsorbed onto the surface of the heterotrophic biomass in the model including:

· Adsorbed long-chain aliphatic compounds

· Adsorbed poly-cyclic aromatic compounds

· Adsorbed halogenated aromatic compounds

· Adsorbed oil

· Adsorbed industrial compounds

Nitrogen Variables

The nitrogen variables include those in ASM1 and nitrogen gas:

· ammonia and ammonium nitrogen

· nitrate and nitrite nitrogen

· soluble organic nitrogen

· particulate organic nitrogen

· nitrogen gas

In addition, the biomass variables and the products from decay have associated nitrogen fractions.

Phosphorus Variables

Soluble phosphorus has been included as a state variable to account for possible phosphorus limitations which are common in industrial wastewater treatment. In addition, the biomass variables and the products from decay have associated phosphorus fractions.

Sulfur Variables

Following Baker (1994), the following sulfur variables have been included so that the impact of sulfur oxidation on aeration requirements can be modeled:

· Reduced sulfur compounds (e.g. sulfides, mercaptans)

· Sulfates

Inorganic Variables

Total soluble inorganic carbon is included to track potential changes in pH. Following the Mantis2 model, inert inorganic suspended solids are included so that the total suspended solids concentration can be calculated.

The model processes and process rate equations are described below. Processes and rate equations that are identical to those in Mantis2 are not described.

Aerobic and Anoxic Growth of Heterotrophs on Biodegradable Substrate

In MantisIW, heterotrophs can grow under aerobic and anoxic conditions on the following soluble substrates: readily biodegradable substrate (as in ASM1), short-chain solvents, long-chain solvents, halogenated solvents, short-chain aliphatic compounds, and mono-cyclic aromatic compounds. Switching functions have been added, as in ASM2d, to limit biomass growth if ammonium, ortho-phosphate, or alkalinity concentrations are low.

The general rate expression used for aerobic growth of heterotrophs is given below:

Equation 3‑48

where:

S_j: SSCOS, SSCLS, SHS, SALSC, SALLC, SARM, SARP, SPH,

SARH, SOIL, or SINZ

: Rate of aerobic growth of heterotrophs on the substrate S_j

: Maximum specific growth rate of S_j (1/d)

: pH inhibition factor for heterotrophs

Similarly, the general rate expression used for anoxic growth of heterotrophs with NO3 is as follows:

Equation 3‑49

where:

: Rate of anoxic growth of heterotrophs on substrate S_j

(mgCOD/L•d)

The switching function AirNo can be replaced with AllowNitDenit in the above equation if simultaneous nitrification/denitrification is being modeled. The KAD parameter (see Table 7) allows independent adjustment of denitrification without affecting the aerobic processes that use KOH.

The general rate expression used for anoxic growth of heterotrophs with NO2 is as follows:

Equation 3‑50

Adsorption of Partially Miscible Substrates and Colloidal COD

Compounds such as long-chain hydrocarbons are large molecules that are only partially miscible in water and are more difficult to degrade than compounds such as oxygenated solvents. Following Baker (1994), it is postulated that these molecules are first adsorbed onto the surface of the biomass, and then hydrolyzed and biologically degraded. The rate of adsorption, modified in MantisIW to allow for multiple types of adsorbed substrate, is defined as follows:

Equation 3‑51

where:

S_l : SALLC, SARP, SARH, SOIL, or SINZ

K_{A, l} : Adsorption rate for S_l

K_{P, l} : Adsorption partition coefficient for S_l

X_l : XALLC, XARP, XARH, or XINZ

The rate expression for adsorption of colloidal COD is defined as follows:

Equation 3‑52

where:

q_Ads : Specific adsorption rate

K_Ads : Saturation/inhibition coefficient for XS/XBH

Volatilization of Soluble Substrates

Many organic compounds are volatile and subject to surface evaporation and air stripping due to diffused aeration systems. In MantisIW, the rate of volatilization for oxygenated solvents, halogenated solvents, short-chain aliphatic compounds, long-chain aliphatic compounds, mono-cyclic aromatic compounds, poly-cyclic aromatic compounds, phenolic compounds, halogenated aromatic compounds, dispersed oil and other soluble industrial compounds is as shown below:

Equation 3‑53

where:

S_j : SSCOS, SSCLS, SHS, SALSC, SALLC, SARM, SARP, SPH, SARH,

SOIL, or SINZ

V_j : volatilization rate (gCOD/m³•d)

Aerobic Growth and Decay of Ammonia Oxidizers and Nitrite Oxidizers

These processes are modeled as in ASM1.

Aerobic Growth and Decay of Sulfur Oxidizing Organisms

Reduced sulfur compounds can be found in high concentrations in petroleum, petrochemical, and pulp and paper wastewater. Sulfur oxidizing organisms are capable of oxidizing the reduced sulfur to sulfate, which can lead to a significant oxygen demand. The following equation is used to model the rate of sulfur oxidation (growth of sulfur oxidizers) as in Baker (1994) but with added pH and phosphorus switching functions:

Equation 3‑54

The effect of sulfur oxidation on alkalinity has also been added to the model in MantisIW. Sulfur oxidizer decay is modeled as follows:

Equation 3‑55

State Variables section presents a summary of the model components or state variables including symbols and units. Mass balances are written for all of these components in the model.

Model Processes

The MantisIW model includes the following transformation processes:

· Aerobic and anoxic growth of heterotrophs on biodegradable substrate

· Adsorption of partially miscible substrate

· Volatilization of soluble substrate

· Hydrolysis of slowly biodegradable substrate, unbiodegradable residue and inert particulate

· Decay of heterotrophs

· Ammonification of soluble organic nitrogen to ammonia

· Aerobic growth and decay of nitrifiers

· Aerobic growth and decay of sulfur oxidizing organisms

· Gas transfers

The influent COD components can undergo one or more transformation processes depending on their individual characteristics. The possible transformations for each influent COD component in the model are described in Table 3‑4.

Table 3‑4 - Possible transformation for influent COD Components in MantisIW model.

Type of Organic Compound	COD Components Included	Possible Transformations
Readily biodegradable, soluble, non-volatile	· Readily biodegradable substrate	· Aerobic/anoxic heterotrophic growth
Slowly biodegradable, particulate	· Slowly biodegradable substrate	· Hydrolysis to readily biodegradable substrate
Readily biodegradable, soluble, volatile	· Short-chain solvents · Long-chain solvents · Halogenated solvents · Short-chain aliphatic compounds · Mono-cyclic aromatic compounds · Phenolic compounds	· Aerobic/anoxic heterotrophic growth · Volatilization
Partially miscible	· Long-chain aliphatic compounds · Poly-cyclic aromatic compounds · Halogenated aromatic compounds · Oil · Other industrial compounds	· Adsorption · Aerobic/anoxic heterotrophic growth · Volatilization

The following features are incorporated into the MantisIW model:

· Simultaneous nitrification/denitrification (if applicable)

· Ammonium uptake as part of biological growth

· Phosphorus uptake as part of biological growth

· pH inhibition on biological growth

The COD transformation processes in MantisIW are presented in Figure 3‑3 (overview of COD transformations) and Figure 3‑4 (overview of biological growth processes). The transformations are shown along with the required electron acceptors and nutrients.

Diagram Description automatically generated

Figure 3‑3 - Overview of COD Transformation Processes in MantisIW (Adsorption, Volatilization, and Heterotrophic Growth/Decay)

Figure 3‑4 - Overview of Biological Growth Processes in MantisIW

The model processes and process rate equations are described below. Processes and rate equations that are identical to those in Mantis2 are not described.

Aerobic and Anoxic Growth of Heterotrophs on Biodegradable Substrate

The general rate expression used for aerobic growth of heterotrophs is given below:

Equation 3‑56

where:

S_j ≡ S_SCOS , S_SCLS, S_HS, S_ALSC, S_ALLC, S_ARM , S_ARP , S_PH, S_ARH, S_OIL, or S_INZ

≡ rate of aerobic growth of heterotrophs on the substrate S_j (mg COD/L·d)

≡ maximum specific growth rate on S_j (1/d)

≡ pH inhibition factor for heterotrophs

Similarly, the general rate expression used for anoxic growth of heterotrophs with NO₃ is as follows:

Equation 3‑57

where:

≡ rate of anoxic growth of heterotrophs on substrate S_j (mg COD/L·d)

The switching function AirNo can be replaced with AllowNitDenit in the above equation if simultaneous nitrification/denitrification is being modeled. The K_AD parameter (see Table 7) allows independent adjustment of denitrification without affecting the aerobic processes that use K_OH.

The general rate expression used for anoxic growth of heterotrophs with NO₂ is as follows:

Equation 3‑58

Adsorption of Partially Miscible Substrates and Colloidal COD

Equation 3‑59

where:

S_l ≡ S_ALLC, S_ARP, S_ARH, S_OIL, or S_INZ

≡ Adsorption rate for S_l

≡ Adsorption partition coefficient for S_l

X_l ≡ X_ALLC, X_ARP, X_ARH , or X_INZ

The rate expression for adsorption of colloidal COD is defined as follows:

Equation 3‑60

where:

≡ Specific adsorption rate

≡ Saturation/inhibition coefficient for X_S/X_BH

Volatilization of Soluble Substrates

Equation 3‑61

where:

S_j ≡ S_SCOS, S_SCLS, S_HS, S_ALSC, S_ALLC, S_ARM , S_ARP , S_PH, S_ARH, S_OIL, or S_INZ

≡ volatilization rate (gCOD/m³/d)

Aerobic Growth and Decay of Ammonia Oxidizers and Nitrite Oxidizers

These processes are modeled as in ASM1.

Aerobic Growth and Decay of Sulfur Oxidizing Organisms

Equation 3‑62

The effect of sulfur oxidation on alkalinity has also been added to the model in MantisIW. Sulfur oxidizer decay is modeled as follows:

Equation 3‑63

State Variables

Comprehensive Model Library (MANTIS2LIB)

Fifty-two (52) state variables are available in the Comprehensive Model (MANTIS2LIB) library. (Table 3‑5)

Table 3‑5- Comprehensive Model (MANTIS2LIB) Library State Variables

	State Variables	GPS-X Cryptic Symbols	Units
1.	dissolved oxygen	so	gO2/m3
2.	dissolved hydrogen gas	sh2	gCOD/m3
3.	dissolved dinitrogen gas	sn2	gN/m3
4.	dissolved methane	sch4	gCOD/m3
5.	readily degradable soluble substrate	ss	gCOD/m3
6.	acetate	sac	gCOD/m3
7.	propionate	spro	gCOD/m3
8.	methanol	smet	gCOD/m3
9.	colloidal substrate	scol	gCOD/m3
10.	soluble inert organic	si	gCOD/m3
11.	slowly biodegradable substrate	xs	gCOD/m3
12.	poly-hydroxy alkanoates in PAO	xbt	gCOD/m3
13.	unbiodegradable cell products	xu	gCOD/m3
14.	particulate inert material	xi	gCOD/m3
15.	ammonia nitrogen	snh	gN/m3
16.	nitrite	snoi	gN/m3
17.	nitrate	snoa	gN/m3
18.	soluble organic nitrogen	snd	gN/m3
19.	nitrogen in slowly biodegradable substrate	xns	gN/m3
20.	ortho-phosphate	sp	gP/m3
21	phosphorous in slowly biodegradable substrate	xps	gP/m3
22.	poly-phosphate in PAO	xpp	gP/m3
23.	heterotrophic biomass	xbh	gCOD/m3
24.	ammonia oxidizer biomass	xbai	gCOD/m3
25.	nitrite oxidizer biomass	xbaa	gCOD/m3
26.	phosphate accumulating biomass	xbp	gCOD/m3
27.	fermenting biomass	xbf	gCOD/m3
28.	acetogenic biomass	xbpro	gCOD/m3
29.	acetoclastic methanogenic biomass	xbacm	gCOD/m3
30.	hydrogenotrophic methanogenic biomass	xbh2m	gCOD/m3
31.	methylotrophic biomass	xbmet	gCOD/m3
32.	anammox biomass	xbax	gCOD/m3
33.	total soluble inorganic carbon	stic	gC/m3
34.	total soluble calcium	sca	gCa/m3
35.	total soluble magnesium	smg	gMg/m3
36.	total soluble potassium	spot	gK/m3
37.	other cation	scat	eq/m3
38.	other anion	sana	eq/m3
39.	inorganic inert particulate	xii	g/m3
40.	aluminum hydroxide	xaloh	g/m3
41.	aluminum phosphate	xalpo4	g/m3
42.	iron hydroxide	xfeoh	g/m3
43.	iron phosphate	xfepo4	g/m3
44.	calcium carbonate	xcaco3	g/m3
45.	calcium phosphate	xcapo4	g/m3
46.	magnesium carbonate	xmgco3	g/m3
47.	magnesium hydrogen phosphate (newberyite)	xmghpo4	g/m3
48.	magnesium ammonium phosphate (struvite)	xmgnh4po4	g/m3
49.	soluble component "a"	sza	notset
50.	soluble component "b"	szb	notset
51.	particulate component "a"	xza	notset
52.	particulate component "b"	xzb	notset

Comprehensive Sulphur and Selenium Model Library (MANTIS2SLIB)

Seventy-two (72) state variables are available in the Sulphur and Selenium (MANTIS2SLIB) library. (Table 3‑6)

Table 3‑6 – Sulfur and Selenium (MANTIS2SLIB) Library State Variables

	State Variables	GPS-X Cryptic Symbols	Units
1.	dissolved oxygen	so	gO2/m3
2.	dissolved hydrogen gas	sh2	gCOD/m3
3.	dissolved dinitrogen gas	sn2	gN/m3
4.	dissolved methane	sch4	gCOD/m3
5.	readily degradable soluble substrate	ss	gCOD/m3
6.	acetate	sac	gCOD/m3
7.	propionate	spro	gCOD/m3
8.	methanol	smet	gCOD/m3
9.	colloidal substrate	scol	gCOD/m3
10.	soluble inert material	si	gCOD/m3
11.	slowly biodegradable substrate 1	xs1	gCOD/m3
12.	slowly biodegradable substrate 2	xs2	gCOD/m3
13.	slowly biodegradable substrate 3	xs3	gCOD/m3
14.	poly-hydroxy alkanoates in PAO	xbt	gCOD/m3
15.	unbiodegradable cell products	xu	gCOD/m3
16.	particulate inert material	xi	gCOD/m3
17.	ammonia nitrogen	snh	gN/m3
18.	nitrite	snoi	gN/m3
19.	nitrate	snoa	gN/m3
20.	soluble organic nitrogen	snd	gN/m3
21.	nitrogen in slowly biodegradable substrate	xns	gN/m3
22.	selenate selenium	sselna	gSe/m3
23.	selenite selenium	sselni	gSe/m3
24.	elemental selenium	xse0	gSe/m3
25.	soluble sulfide sulfur	stssul	gS/m3
26.	sulfate sulfur	sso4	gS/m3
27.	sulfite sulfur	sso3	gS/m3
28.	elemental sulfur	xsul0	gS/m3
29.	particulate heavy metal sulfide	xhmes	gS/m3
30.	ortho-phosphate	sp	gP/m3
31.	phosphorus in slowly biodegradable substrate	xps	gP/m3
32.	poly-phosphate in PAO	xpp	gP/m3
33.	heterotrophic biomass	xbh	gCOD/m3
34.	ammonia oxidizer biomass	xbai	gCOD/m3
35.	nitrite oxidixer biomass	xbaa	gCOD/m3
36.	phosphate accumulating biomass	xbp	gCOD/m3
37.	selenium-reducing biomass	xbsel	gCOD/m3
38.	methylotrophic selenium-reducing biomass	xbsemet	gCOD/m3
39.	propionate degrading sulfate-reducing biomass	xbsr1	gCOD/m3
40.	hydrogen utilizing sulfate-reducing biomass	xbsr2	gCOD/m3
41.	acetate utilizing sulfate-reducing biomass	xbsr3	gCOD/m3
42.	methanol utilizing sulfate-reducing biomass	xbsr4	gCOD/m3
43.	sulfur-oxiding biomass	xbsox	gCOD/m3
44.	fermenting biomass	xbf	gCOD/m3
45.	acetogenic biomass	xbpro	gCOD/m3
46.	acetoclastic methanogenic biomass	xbacm	gCOD/m3
47.	hydrogenotrophic methanogenic biomass	xbh2m	gCOD/m3
48.	methylotrophic biomass	xbmet	gCOD/m3
49.	methylotrophic methanogenic biomass	xbmem	gCOD/m3
50.	anammox biomass	xbax	gCOD/m3
51.	total soluble inorganic carbon	stic	gC/m3
52.	total soluble calcium	sca	gCa/m3
53.	soluble heavy metal	shme	gMe/m3
54.	total soluble magnesium	smg	gMg/m3
55.	total soluble potassium	spot	gK/m3
56.	other cation	scat	eq/m3
57.	other anion	sana	eq/m3
58.	inorganic inert particulate	xii	gSS/m3
59.	aluminum hydroxide	xaloh	gAl(OH)3/m3
60.	aluminum phosphate	xalpo4	gAlPO4/m3
61.	iron hydroxide	xfeoh	gFe(OH)3/m3
62.	iron sulfide	xfes	gFeS/m3
63.	iron phosphate	xfepo4	gFePO4/m3
64.	calcium carbonate	xcaco3	gCaCO3/m3
65.	calcium phosphate	xcapo4	gCaPO4/m3
66.	magnesium carbonate	xmgco3	gMgCO3/m3
67.	magnesium hydrogen phosphate (newberyite)	xmghpo4	gMgHPO4.3H2O/m3
68.	magnesium ammonium phosphate (struvite)	xmgnh4po4	gMgNH4PO4.6H2O/m3
69.	soluble component a	sza	notset
70.	soluble component b	szb	notset
71.	particulate component a	xza	notset
72.	particulate component b	xzb	notset

Carbon Footprint – Carbon, Nitrogen, Phosphorus, pH (MANTIS3LIB)

Fifty-six (56) state variables are available in the Carbon Footprint (MANTIS3LIB) Library. (Table 3‑7).

Table 3‑7 - Carbon Footprint (MANTIS3LIB) Library State Variables

	State Variables	GPS-X Cryptic Symbols	Units
1.	dissolved oxygen	so	gO2/m3
2.	dissolved hydrogen gas	sh2	gCOD/m3
3.	dissolved dinitrogen gas	sn2	gN/m3
4.	dissolved methane	sch4	gCOD/m3
5.	readily degradable soluble substrate	ss	gCOD/m3
6.	acetate	sac	gCOD/m3
7.	propionate	spro	gCOD/m3
8.	methanol	smet	gCOD/m3
9.	colloidal substrate	scol	gCOD/m3
10.	soluble inert material	si	gCOD/m3
11.	slowly biodegradable substrate	xs	gCOD/m3
12.	poly-hydroxy alkanoates in PAO	xbt	gCOD/m3
13.	unbiodegradable cell products	xu	gCOD/m3
14.	particulate inert material	xi	gCOD/m3
15.	ammonia nitrogen	snh	gN/m3
16.	nitrite	snoi	gN/m3
17.	nitrate	snoa	gN/m3
18.	nitric oxide-nitrogen	snrio	gN/m3
19.	nitrous oxide	snroo	gN/m3
20.	hydroxylamine	snh2oh	gN/m3
21.	nitrosyl radical	snoh	gN/m3
22.	soluble organic nitrogen	snd	gN/m3
23.	nitrogen in slowly biodegradable substrate	xns	gN/m3
24.	ortho-phosphate	sp	gP/m3
25.	phosphorous in slowly biodegradable substrate	xps	gP/m3
26.	poly-phosphate in PAO	xpp	gP/m3
27.	heterotrophic biomass	xbh	gCOD/m3
28.	ammonia oxidizer biomass	xbai	gCOD/m3
29.	nitrite oxidizer biomass	xbaa	gCOD/m3
30.	phosphate accumulating biomass	xbp	gCOD/m3
31.	fermenting biomass	xbf	gCOD/m3
32.	acetogenic biomass	xbpro	gCOD/m3
33.	acetoclastic methanogenic biomass	xbacm	gCOD/m3
34.	hydrogenotrophic methanogenic biomass	xbh2m	gCOD/m3
35.	methylotrophic biomass	xbmet	gCOD/m3
36.	anammox biomass	xbax	gCOD/m3
37.	total soluble inorganic carbon	stic	gC/m3
38.	total soluble calcium	sca	gCa/m3
39.	total soluble magnesium	smg	gMg/m3
40.	total soluble potassium	spot	gK/m3
41.	other cation	scat	eq/m3
42.	other anion	sana	eq/m3
43.	inorganic inert particulate	xii	g/m3
44.	aluminum hydroxide	xaloh	g/m3
45.	aluminum phosphate	xalpo4	g/m3
46.	iron hydroxide	xfeoh	g/m3
47.	iron phosphate	xfepo4	g/m3
48.	calcium carbonate	xcaco3	g/m3
49.	calcium phosphate	xcapo4	g/m3
50.	magnesium carbonate	xmgco3	g/m3
51.	magnesium hydrogen phosphate (newberyite)	xmghpo4	g/m3
52.	magnesium ammonium phosphate (struvite)	xmgnh4po4	g/m3
53.	soluble component "a"	sza	notset
54.	soluble component "b"	szb	notset
55.	particulate component "a"	xza	notset
56.	particulate component "b"	xzb	notset

Process Water Treatment Library (PROCWATERLIB)

Sixty-eight (68) state variables are available in the Process Water Treatment (PROCWATERLIB) Library. (Table 3‑8)

Table 3‑8 – Process Water (PROCWATERLIB) Library State Variables

	State Variables	GPS-X Cryptic Symbols	Units
1.	dissolved oxygen	so	gO2/m3
2.	dissolved nitrogen gas	sn2	gN2-N/m3
3.	nitrate	snoa	gNO3-N/m3
4.	sulfate sulfur	slso4	gSO4-S/m3
5.	soluble calcium	sca	gCa/m3
6.	soluble magnesium	smg	gMg/m3
7.	soluble potassium	spot	gK/m3
8.	other cation	scat	eq/m3
9.	other anion	sana	eq/m3
10.	sodium	sna	gNa/m3
11.	chloride	sclo	gCl/m3
12.	ferrous	sfe2	gFe2/m3
13.	manganese	smn2	gMn/m3
14.	copper	scu	gCu/m3
15.	zinc	szinc	gZn/m3
16.	nitric-nitrogen	snoi	gNO2-N/m3
17.	ammonia nitrogen	snh	gNH4-N/m3
18.	sulfurous sulfur	shso3	gHSO3-S/m3
19.	sulfide sulfur	stssul	gH2S-S/m3
20.	carbonic acid	stic	gHCO3-C/m3
21.	ortho-phosphate	sp	gHPO4-P/m3
22.	chlorine (HOCl+OCl)	scl2	gCl2/m3
23.	fluoride	sflu	gF/m3
24.	soluble silica	slsio2	gSiO2-Si/m3
25.	aluminum hydroxide	xaloh	g/m3
26.	aluminum phosphate	xalpo4	g/m3
27.	iron hydroxide	xfeoh	g/m3
28.	iron phosphate	xfepo4	g/m3
29.	calcium carbonate	xcaco3	g/m3
30.	calcium phosphate	xcapo5	g/m3
31.	magnesium carbonate	xmgco3	g/m3
32.	magnesium hydrogen phosphate (newberyite)	xmghpo4	g/m3
33.	magnesium ammonium phosphate (struvite)	xmgnh4po4	g/m3
34.	calcium sulfate	xcaso4	g/m3
35.	silica precipitate	xlsio2	g/m3
36.	magnesium silicate	xmgsio3	g/m3
37.	calcium fluoride	xcaf2	g/m3
38.	other inorganic particulate	xii	g/m3
39.	manganese oxide	xmno2	g/m3
40.	manganese hydroxide	xmnoh2	g/m3
41.	iron sulfide	xfes	g/m3
42.	magnesium hydroxide	xmgoh2	g/m3
43.	readily degradable soluble substrate	ss	gCOD/m3
44.	colloidal substrate	scol	gCOD/m3
45.	slowly biodegradable substrate	xs	gCOD/m3
46.	soluble inert material	si	gCOD/m3
47.	particulate inert material	xi	gCOD/m3
48.	unbiodegradable cell products	xu	gCOD/m3
49.	soluble biodegradable organic nitrogen	snd	gN/m3
50.	particulate biodegradable organic nitrogen	xns	gN/m3
51.	particulate biodegradable organic phosphorus	xps	gP/m3
52.	heterotrophic biomass	xbh	gCOD/m3
53.	ammonia oxidizer biomass	xbai	gCOD/m3
54.	nitrite oxidizer biomass	xbaa	gCOD/m3
55.	total coliform	sltcoli	CFU/100ml
56.	alternative disinfection benchmark - Giardia	slgiardia	no/100ml
57.	alternative disinfection benchmark - Legionella	sllegion	no/100ml
58.	alternative disinfection benchmark - Virus	slvirus	no/100ml
59.	soluble component "a"	sza	notset
60.	soluble component "b"	szb	notset
61.	soluble component "c"	szc	notset
62.	soluble component "d"	szd	notset
63.	soluble component "e"	sze	notset
64.	particulate component "a"	xza	notset
65.	particulate component "b"	xzb	notset
66.	particulate component "c"	xzc	notset
67.	particulate component "d"	xzd	notset
68.	particulate component "e"	xze	notset

Petrochemical Wastewater Library (MANTISIWLIB)

Fifty-two (52) state variables are available in the Petrochemical Wastewater (MANTISIWLIB) Library. (Table 3‑9Table 3‑8)

Table 3‑9 – Petrochemical Wastewater (MANTISIWLIB) Library State Variables

	State Variables	GPS-X Cryptic Symbols	Units
1.	dissolved oxygen	so	gO2/m3
2.	dissolved nitrogen gas	sn2	gN2-N/m3
3.	soluble inert material	si	gCOD/m3
4.	readily degradable soluble substrate	ss	gCOD/m3
5.	colloidal substrate	scol	gCOD/m3
6.	short-chain aliphatic compounds	salsc	gCOD/m3
7.	long-chain aliphatic compounds	sallc	gCOD/m3
8.	short-chain organic solvents	sscos	gCOD/m3
9.	long-chain organic solvents	slcos	gCOD/m3
10.	mono-cyclic aromatic compounds	sarm	gCOD/m3
11.	poly-cyclic aromatic compounds	sarp	gCOD/m3
12.	phenolic compounds	sph	gCOD/m3
13.	halogenated organic solvents	shs	gCOD/m3
14.	halogenated aromatic compounds	sarh	gCOD/m3
15.	dispersed oil	soil	gCOD/m3
16.	other soluble industrial compound 1	sin1z	gCOD/m3
17.	other soluble industrial compound 2	sin2z	gCOD/m3
18.	other soluble industrial compound 3	sin3z	gCOD/m3
19.	other soluble industrial compound 4	sin4z	gCOD/m3
20.	other soluble industrial compound 5	sin5z	gCOD/m3
21.	particulate inert material	xi	gCOD/m3
22.	unbiodegradable cell products	xu	gCOD/m3
23.	slowly biodegradable substrate	xs	gCOD/m3
24.	adsorbed long-chain aliphatic compounds	xallc	gCOD/m3
25.	adsorbed poly-aromatic compounds	xarp	gCOD/m3
26.	adsorbed halogenated aromatic compounds	xarh	gCOD/m3
27.	sorbed oil	xoil	gCOD/m3
28.	other adsorbed industrial compound 1	xin1z	gCOD/m3
29.	other adsorbed industrial compound 2	xin2z	gCOD/m3
30.	other adsorbed industrial compound 3	xin3z	gCOD/m3
31.	other adsorbed industrial compound 4	xin4z	gCOD/m3
32.	other adsorbed industrial compound 5	xin5z	gCOD/m3
33.	ammonia nitrogen	snh	gN/m3
34.	nitrite	snoi	gN/m3
35.	nitrate	snoa	gN/m3
36.	soluble organic nitrogen	snd	gN/m3
37.	nitrogen in slowly biodegradable substrate	xns	gN/m3
38.	ortho-phosphate	sp	gP/m3
39.	phosphorous in slowly biodegradable substrate	xps	gP/m3
40.	hydrogen sulfide	sh2s	gS/m3
41.	sulfate	sso4	gS/m3
42.	heterotrophic biomass	xbh	gCOD/m3
43.	ammonia oxidizer biomass	xbai	gCOD/m3
44.	nitrite oxidizer biomass	xbaa	gCOD/m3
45.	sulfur oxidizer biomass	xbsox	gCOD/m3
46.	total soluble inorganic carbon	stic	gC/m3
47.	total soluble calcium	sca	gCa/m3
48.	total soluble magnesium	smg	gMg/m3
49.	total soluble potassium	spot	gK/m3
50.	other cation	scat	eq/m3
51.	other anion	sana	eq/m3
52.	inorganic inert particulate	xii	g/m3