Abstract

Plant-wide modelling can be considered an appropriate approach to represent the current complexity in water resource recovery facilities, reproducing all known phenomena in the different process units. Nonetheless, novel processes and new treatment schemes are still being developed and need to be fully incorporated in these models. This work presents a short chronological overview of some of the most relevant plant-wide models for wastewater treatment, as well as the authors' experience in plant-wide modelling using the general model BNRM (Biological Nutrient Removal Model), illustrating the key role of general models (also known as supermodels) in the field of wastewater treatment, both for engineering and research.

LIST OF ACRONYMS

     
  • AD

    Anaerobic digestion

  •  
  • ADM

    Anaerobic Digestion Model

  •  
  • AnMBR

    Anaerobic membrane bioreactor

  •  
  • AOO

    Ammonium oxidizing organisms

  •  
  • A/O

    Anoxic/Aeration system

  •  
  • ASDM

    Activated Sludge-Digestion Model

  •  
  • ASM

    Activated Sludge Models

  •  
  • BNRM

    Biological Nutrient Removal Model

  •  
  • BOD

    Biological oxygen demand

  •  
  • BSM1

    Benchmark Simulation Model

  •  
  • BVSS

    Biodegradable volatile suspended solids

  •  
  • CAS

    Conventional activated sludge

  •  
  • CBIM

    Continuity-Based Model Interface Methodology

  •  
  • CFD

    Computational fluid dynamics

  •  
  • COD

    Chemical oxygen demand

  •  
  • COST

    European Cooperation in Science and Technology

  •  
  • DESASS

    Design and Simulation of Activated Sludge Systems

  •  
  • DO

    Dissolved oxygen concentration

  •  
  • FISH

    Fluorescence in-situ hybridization

  •  
  • GAO

    Glycogen-accumulating organisms

  •  
  • GHG

    Greenhouse gas

  •  
  • IWA

    International Water Association

  •  
  • LCA

    Life cycle assessment

  •  
  • MBR

    Membrane bioreactor

  •  
  • NOO

    Nitrite oxidizing organisms

  •  
  • OUR

    Oxygen uptake rate

  •  
  • PAO

    Polyphosphate-accumulating organisms

  •  
  • PCM

    Plant-wide modelling

  •  
  • PC-PWM

    Physico-chemical PCM

  •  
  • SBR

    Sequencing batch reactor

  •  
  • SHARON

    Single reactor system for High activity Ammonium Removal Over Nitrite

  •  
  • SRO

    Sulphate reducing organisms

  •  
  • SRT

    Sludge retention time

  •  
  • STP

    Standard Temperature and Pressure

  •  
  • STR

    Scientific and Technical Report

  •  
  • TAN

    Total ammonium nitrogen

  •  
  • VSS

    Volatile suspended solids

  •  
  • WRRF

    Water resource recovery facilities

  •  
  • WWTP

    Wastewater treatment plant

INTRODUCTION

Wastewater treatment modelling

In the wastewater treatment field, mathematical models are useful tools for research and development, as well as for design and optimization of the different processes involved. Mathematical modelling efforts are highly stimulated by different social, economic and environmental factors, such as the more and more stringent legislation, the urgent need for water recycling and carbon footprint reduction and the importance of general cost savings and public profile issues, among others. These factors force a move towards a more sustainable wastewater treatment design, where wastewater must turn into a source of resources such as reclaimed water, bioenergy and bioproducts (i.e. nutrients, biosolids). This paradigm shift requires the integration of sustainable processes in future water resource recovery facilities (WRRFs) (Batstone et al. 2015; Robles et al. 2018). In this respect, mathematical modelling plays a key role in the incorporation of the circular economy principles in the wastewater treatment sector.

This work presents a short overview of some of the most relevant plant-wide models for wastewater treatment, as well as the authors' experience in plant-wide modelling using the general model BNRM (Biological Nutrient Removal Model). The paper aims to illustrate the key role of plant-wide models in the field of wastewater treatment, both for engineering and research.

Initially, wastewater treatment modelling focused on the biochemical processes taking place either on the water line or the sludge line. The Activated Sludge Models (ASM, Henze et al. 2000) and the Anaerobic Digestion Model (ADM1, Batstone et al. 2002) introduced the use of the Gujer or Petersen table (stoichiometric matrix) and are still today the most widely used tools for modelling activated sludge processes and anaerobic digestion (AD) processes, respectively. More recently, modelling efforts were focused on plant-wide modelling and aimed at simulating the whole plant, taking into account the effect of side-streams on mainstream. In this respect, a higher descriptive capacity of the whole wastewater treatment system can only be achieved if physico-chemical and chemical processes are also taken into account. For instance, a proper pH calculation has proven to be necessary since it affects the stoichiometry and kinetics of biological (nitrification/denitrification) and chemical processes (phosphorus precipitation, gas solubility, etc.). Gas transfer processes also determine the effectivity of aeration, which involves a significant energy consumption and affects the carbon footprint estimation of WRRFs.

Plant-wide models

Plant-wide models have been developed following two different approaches: the interfaces approach and the general approach (also known as supermodel approach). The interfaces approach consists of connecting existing standard models by means of an interface between units and their models. Copp et al. (2003) and Nopens et al. (2009) defined ASM1-ADM1 interfaces, whereas Vanrolleghem et al. (2005) developed the Continuity-Based Model Interface Methodology (CBIM) proposing a procedure to connect any standard model. Dedicated tools have also been developed and widely adapted, such as the COST/IWA Benchmark Simulation Model No.1 (BSM1) (Copp 2002; Jeppsson & Pons 2004), the BSM1_LT (Rosen et al. 2004), the BSM2 (Jeppsson et al. 2006; Nopens et al. 2010), the BSM2G (Flores-Alsina et al. 2011) and the BSM-MBR (Maere et al. 2011). They consist of a standardized simulation procedure for control strategies design in WWTP and their evaluation in terms of effluent quality and operational cost. The main advantage of using an interface-based approach with respect to other integrated methodologies such as general models is that the original model structure can be used, and there is thus no need for state variable representation in all process units with the resulting increased use of computational power, model complexity and adverse model stability characteristics (Grau et al. 2009).

On the other hand, the general approach makes use of a single model to describe key processes taking place in a WWTP. A single set of state variables is used, which includes the components of all processes involved. Therefore, different groups of microorganisms (e.g. aerobic, anaerobic and facultative) are considered in all treatment units and their growth will be determined by the environmental conditions. In this case, the user does not need to decide which model should be applied for each system. In general models, there is a common characterization of the state of the process and the explicit calculation of pH is required as well. With higher computational costs, general models have become more and more feasible due to advances in computer technology. There are significant and successful plant-wide models following the general approach in literature. For instance, the general Activated Sludge-Digestion models (ASDM) implemented in BioWin (EnviroSim Associates Ltd) (Jones & Takácks 2004), the Biological Nutrient Removal Model (BNRM) (Seco et al. 2004; Barat et al. 2013; Durán et al. 2017), the plant-wide modelling methodology proposed by Grau et al. (2007), the plant-wide mass balance based steady-state WWTP model proposed by Ekama (2009) or the Sumo©, Mantis2 and Mantis3 models incorporated in the Sumo© and GPS-X software, respectively.

It has to be stressed that under both approaches (the interfaces approach and the general approach) continuity equations need to be fulfilled in every process so that mass and charges balances are met.

Current research on plant-wide models

As WRRFs increase in complexity, more complete and reliable plant-wide models are needed, able to reproduce the behaviour of the whole system. Novel processes are still being developed for water resource recovery (membrane-based processes, microalgae cultivation, etc.), but also mature and established technologies are being integrated in novel treatment schemes in order to achieve energy-positive WRRFs (Solon et al. 2019a). On the other hand, greater understanding of the hydrodynamics or the microbiological and biochemical fields have led to the development of the so-called computational fluid dynamics (CFD) models (Rehman et al. 2017) and metabolic models (Lopez-Vazquez et al. 2009), respectively.

Currently, plant-wide modelling efforts are focused on integrating different model extensions to better reproduce the phenomena occurring in wastewater treatment and incorporate the new concepts and technologies that are emerging under the umbrella of the circular economy. For instance, the last extensions of BSM2 are focused on modelling phosphorus plant-wide, a common goal within the scientific community mainly due to the issue of phosphate rock depletion. Flores-Alsina et al. (2015) proposed a plant-wide aqueous phase chemistry module describing pH variations and ion speciation/pairing in wastewater treatment process models whereas Kazadi Mbamba et al. (2016) developed a physico-chemistry framework. Afterward, Solon et al. (2017) integrated both extensions and also developed a new set of biological and physico-chemical process models to describe the required tri-phasic compound transformations and the close interlinks between phosphorus, sulphate and iron cycles. These extensions have been validated and then applied to optimize the chemical phosphorus removal in wastewater treatment systems (Kazadi Mbamba et al. 2019). On the other hand, the last extension of the general model proposed by Grau et al. (2007) incorporated a physico-chemical plant-wide framework (Lizarralde et al. 2015) which has been applied to optimize the phosphorus management strategies in Sur WWTP (Madrid, Spain) (Lizarralde et al. 2019) and to quantitatively assess the energy demand and resource recovery of different WRRF configurations (Fernández-Arévalo et al. 2017).

On the other hand, a plant-wide modelling approach which takes into account greenhouse gases (GHG) has become a common goal among researchers in the quest to reduce the carbon footprint of WRRFs (Mannina et al. 2016). Flores-Alsina et al. (2011) proposed a model called BSM2G which includes the estimation of the potential on-site and off-site sources of GHG emissions. This extension was then applied, for instance, to show the importance of adding GHG emissions as key performance evaluation criteria in WRRFs (Flores-Alsina et al. 2013). On the other hand, Mannina et al. (2019) proposed a plant-wide model for carbon and energy footprint which quantifies direct and indirect GHG emission related to biological and physical processes.

In summary, literature in the field shows an increasing and successful progress in plant-wide modelling, which can – and should – support the transition of WWTPs into WRRFs (Pretel et al. 2016b; Solon et al. 2019b), in order to facilitate water and nutrient recycling and carbon footprint reduction, but also general cost savings and compliance to new legislation. Table 1 shows a summary of the above presented plant-wide models, developed and applied during the last two decades. Due to the complexity of the models, their application is usually carried out by means of different software tools. Table 2 shows a summary of the simulation platforms commercially available (sometimes free of charge). These tools present a library of different models the user chooses from or implement their own models. At times, they include sewer networks or river quality models.

Table 1

Overview of some plant-wide models for wastewater treatment

Plant-wide modelReferenceType
BSM2 Jeppsson et al. (2006), Nopens et al. (2010)  Interfaces 
BSM-MBR Maere et al. (2011)  
BSM2G Flores-Alsina et al. (2011)  
Extended BSM2 a plant-wide aqueous phase chemistry module describing pH variations and ion speciation/pairing Flores-Alsina et al. (2015)  
Extended BSM2 a modular physicochemistry framework (PCF) Kazadi Mbamba et al. (2016)  
Extended BSM2 from Flores-Alsina et al. (2015) and Kazadi Mbamba et al. (2016) and new set of biological and physico-chemical process models (P, Fe and S cycles) Solon et al. (2017)  
Mantis2 and its extension Mantis3 Propietary model from Hydromantis, Environmental Software Solutions Inc. General 
Sumo© models In-house developed at Dynamita 
The general Activated Sludge-Digestion Model ASDM Propietary model from Envirosim 
Biological Nutrient Removal Model (No.1, No.2, No.2S) Seco et al. (2004), Barat et al. (2013), Durán et al. (2017)  
Plant-wide mass balance based steady-state WWTP model Ekama (2009)  
The plant-wide modelling methodology (PWM) Grau et al. (2007)  
Physico-chemical plant-wide modelling (PC-PWM) methodology for incorporating physico-chemical transformations into multiphase wastewater treatment process models Lizarralde et al. (2015)  
A plant-wide wastewater treatment plant model for carbon and energy footprint Mannina et al. (2019)  
Plant-wide modelReferenceType
BSM2 Jeppsson et al. (2006), Nopens et al. (2010)  Interfaces 
BSM-MBR Maere et al. (2011)  
BSM2G Flores-Alsina et al. (2011)  
Extended BSM2 a plant-wide aqueous phase chemistry module describing pH variations and ion speciation/pairing Flores-Alsina et al. (2015)  
Extended BSM2 a modular physicochemistry framework (PCF) Kazadi Mbamba et al. (2016)  
Extended BSM2 from Flores-Alsina et al. (2015) and Kazadi Mbamba et al. (2016) and new set of biological and physico-chemical process models (P, Fe and S cycles) Solon et al. (2017)  
Mantis2 and its extension Mantis3 Propietary model from Hydromantis, Environmental Software Solutions Inc. General 
Sumo© models In-house developed at Dynamita 
The general Activated Sludge-Digestion Model ASDM Propietary model from Envirosim 
Biological Nutrient Removal Model (No.1, No.2, No.2S) Seco et al. (2004), Barat et al. (2013), Durán et al. (2017)  
Plant-wide mass balance based steady-state WWTP model Ekama (2009)  
The plant-wide modelling methodology (PWM) Grau et al. (2007)  
Physico-chemical plant-wide modelling (PC-PWM) methodology for incorporating physico-chemical transformations into multiphase wastewater treatment process models Lizarralde et al. (2015)  
A plant-wide wastewater treatment plant model for carbon and energy footprint Mannina et al. (2019)  

PLANT-WIDE MODELLING USING BNRM

Model description

The Biological Nutrient Removal Model No.1 (BNRM1) for dynamic simulation of WWTPs was described by Seco et al. (2004). The physical, chemical and biological processes included were, respectively, settling and clarification processes (flocculated settling, hindered settling and thickening), volatile fatty acids elutriation and gas–liquid transfer; acid–base processes (equilibrium conditions are assumed); organic matter, nitrogen and phosphorus removal, acidogenesis, acetogenesis and methanogenesis. One of the most important advantages of this model was that no additional analysis with respect to ASM2d was required for wastewater characterization. Thus, the usual physiochemical parameters determined in a WWTP were enough to determine the model components.

However, this model did not consider nitrite and failed to accurately simulate the AD because precipitation processes were not considered. Therefore, an extension was proposed and named Biological Nutrient Removal Model No.2 (BNRM2) (Barat et al. 2013). This extension comprised the components and processes required to simulate nitrogen removal via nitrite and the formation of the solids most likely to precipitate in anaerobic digesters (struvite, amorphous calcium phosphate, hidroxyapatite, newberite, vivianite, strengite, variscite, and calcium carbonate). Apart from nitrite oxidizing organisms (NOO), two groups of ammonium oxidizing organisms (AOO) were considered since different sets of kinetic parameters had been reported for the AOO present in activated sludge systems and SHARON (Single reactor system for High activity Ammonium Removal Over Nitrite) reactors.

The latest extension to the BNRM2, called BNRM2S, includes the activity of the sulphate reducing organisms (SRO) and was validated with a pilot-scale anaerobic membrane bioreactor under steady-state and dynamic conditions (Durán et al. 2017).

The collection model BNRM is implemented in the simulation software DESASS (Ferrer et al. 2008) for steady-state and dynamic modelling. DESASS is linked with the geochemical model MINTEQA2 for equilibrium speciation calculations (Allison et al. 1991; EPA 2006). The solution procedure implemented in the software consists of a sequential iteration among the differential equations for the kinetic governed processes and the algebraic equations for the equilibrium governed processes. The section below ‘Full-scale model applications’ shows a compilation of experiences where the modelling results were obtained with this software, illustrating the potential of plant-wide modelling in research and development as well as in design of new plants or optimization of existing ones.

Wastewater characterization

Although the BNRM considers key physical, chemical and biological processes taking place in WWTPs, the required wastewater characterization is similar to the one for Activated Sludge Model No. 2d (Henze et al. 2000). Thus, the needed analyses are the following: COD (total and soluble fraction), BODlim (total and soluble fraction), nitrogen (total and soluble fraction), ammonium, nitrite, nitrate, phosphorus (total and soluble fraction), orthophosphate, volatile fatty acids, pH, alkalinity and different ions such as sulphate, calcium, potassium and magnesium.

Model calibration

Accurate model predictions require a proper calibration of the model parameters. Model calibration can be carried out by fitting model predictions to dynamic experimental data (on-line calibration) or with laboratory experiments (off-line calibration). The IWA STR on Guidelines for using ASMs presents a procedure for on-line calibration (Rieger et al. 2012). The drawback of this kind of calibration for the BNRM is that, due to the high number of parameters included and given a set of experimental data, different sets of parameter values will be able to reproduce the dynamic system performance, although not all of them will necessarily be able to predict plant performance when operating conditions are changed. For this reason, we recommend identifying the high influence model parameters (a small variation in these parameters leads to significant variations in model predictions) and calibrating them with off-line laboratory experiments isolating the activity of each microorganism group. Values obtained with this method are more reliable since they are obtained with experiments carried out under different conditions (substrate, inhibitors or oxygen concentration). With this philosophy, Penya-Roja et al. (2002) developed an off-line calibration methodology for heterotrophic, autotrophic and polyphosphate accumulating organisms. The developed methodology consists in isolating specific processes for these bacterial groups and it is mainly based on oxygen uptake rate (OUR) measurements. The methodology was upgraded by Jimenez et al. (2011, 2012) to estimate the model parameters related to the two bacterial groups involved in the nitrification process (AOO and NOO).

These kinds of respirometric experiments provide information about the maximum bacterial activity under certain conditions, including biomass concentration of the different bacterial groups. In order to determine the maximum growth rate for each of these groups (in time−1 units) it is important to determine their concentration. Borrás (2008) developed a methodology to estimate the concentrations of PAO, GAO, AOO, NOO, methanogens and SRO in an activated sludge sample. This methodology is based on determining the percentage of viable bacteria (obtained by means of the LIVE/DEAD® BacLightTM Bacterial Viability Kit) and the percentage of each specific group over the whole bacteria in terms of area using fluorescence in-situ hybridization (FISH), a molecular cytogenetic technique. Knowing the suspended COD concentration of the sample, the concentration (in COD units) of each specific bacterial group can be estimated from the results obtained with the FISH.

Other specific calibration methodologies can be found in literature, such as that proposed by Claros et al. (2011) for AOO r-strategists, since it is known that the growth rate of AOO in a SHARON reactor (r-strategists species) depends on free ammonia (FA) concentration whereas the growth rate of AOO in activated sludge systems (k-strategists species) depends on total ammonium nitrogen (TAN) concentration. It should be noted that in the case of off-line calibration it is still a challenge to reach consensus regarding the methodologies to be used.

Literature on off-line calibration procedures for anaerobic digestion processes is scarce. Durán (2013) developed an off-line procedure to calibrate the high influence parameters of other anaerobic microorganisms such as sulphate reducing bacteria. One of the reasons for the predominance of on-line procedures for model calibration could be that no equivalent parameter to the OUR measurement (reliable and easily obtained with cheap and robust sensors) can be used for off-line experiments. Another reason might be the difficulty in isolating the activity of different bacterial groups, which is a current challenge regarding model calibration.

Model validation

Model validation consists of verifying the ability of a calibrated model to reproduce the observed system under different operating conditions. Once the model has been validated, it can be used reliably for predicting plant performance. It is important that the model is successful under changing conditions with small variations in parameter values; that is, without the need to recalibrate too often when applied under changed conditions. If a parameter needs to be tuned and the new value is too different from the originally calibrated one, this is an indication of the existence of different considerations not included in the model (inhibition, interaction with other microorganisms, not enough specialization in the specification of the organisms' groups, etc.). A compromise needs to be met between the accuracy of the model (in the sense of detailed description of organisms and processes) and the stability of the parameters. In this sense, metabolic processes have a considerable amount of constant parameters, since all stoichiometry is calculated based on the metabolism of the organisms and kinetic parameters are practically constant. In this kind of model, the need for calibration is drastically reduced. Their difficulty comes from the complexity in defining the equations for processes that are at times complicated to describe, which remains a current challenge in model development. In metabolic models the trade-off is between parameter calibration and the complexity of the model. The benefit is a very robust model that, once validated, renders very trustworthy simulations.

Regarding the model under study, different examples of BNRM validation can be found in literature. Serralta et al. (2004) demonstrated the model's capability to predict the pH variations taking place in an A/O SBR system; Barat et al. (2011) showed the model's capability to predict the variations in potassium, magnesium and calcium concentrations in an A/O SBR jointly with precipitation and redissolution processes; Durán et al. (2017) showed that the model was able to reproduce the performance of an anaerobic membrane bioreactor (AnMBR) pilot plant (effluent composition, biomass wasted and biogas production) in different steady- and non-steady-state periods.

Full-scale model applications

WWTP design, upgrade and optimization are among the most important applications of mathematical models in wastewater treatment. Mathematical models allow comparing the results obtained for different treatment schemes, different operating conditions, variable influent wastewater composition, etc. and therefore selecting the best alternative. The application of the BNRM to different full-scale WWTPs is presented below. Examples are given of simulation results in quantitative (flows, concentrations, etc.) but also qualitative terms (development of strategies, schemes and decision support).

Design of a conventional WWTP

The WWTP in Sevilla (Spain) went out to public tender, in which some criteria for the characteristics of the plant were included. The treatment flow of this plant is 100,000 m3/d. The BNRM was applied to design all the elements of the plant. Simulations rendered information on dimensions of the different treatment units, effluent quality, aeration needs, sludge production, FeCl3 needs, biogas production, NaOH and MgCl2 addition for struvite recovery, as well as operational parameters for the activated sludge reactor and anaerobic digestion. An alternative solution to the proposed design criteria was also developed (Figure 1). This alternative solution was based on reducing sludge retention time (SRT), enhancing biological phosphorus removal, rearranging the sludge line to reduce uncontrolled precipitation problems and recovering phosphorus as struvite. A struvite crystallization unit was designed in order to recover the phosphorus from the reject water in the form of a slow-release fertilizer. Simulation results show that around 50% of the influent phosphorus would be recovered and 4.8 t/d of struvite would be produced.

Figure 1

Flow diagram of the base solution (above) and alternative solution (below).

Figure 1

Flow diagram of the base solution (above) and alternative solution (below).

Figure 2

Treatment scheme of Denia WWTP: (a) original, (b) upgraded.

Figure 2

Treatment scheme of Denia WWTP: (a) original, (b) upgraded.

Design of an AnMBR-based WWTP

The WWTP in Santa Rosa (Spain) was upgraded in 2016 with an AnMBR in order to demonstrate this technology as a sustainable alternative for sewage treatment. The plant was designed for treating 18 m3/d at ambient temperature: 15 °C in winter and 25 °C in the summer season and with ground-buried reactors. Modelling results under different operating and environmental conditions lead to the recommendation of operating at an SRT of 60 days, for which a biogas production depending on temperature was estimated: 1.34 or 1.70 m3/d (with a methane content around 74%) was expected when operating at 15 or 25 °C, respectively. Methane yield resulted in circa 160 and 200 STP LCH4/kg COD being removed at 15 °C and 25 °C, respectively. It is important to point out that sulphur concentration in the influent oscillated around 65 mg S/L, affecting therefore methanization of organic matter due to the competition between SRO and methanogens, which could be reproduced by the model. The effluent quality parameters were also evaluated by simulation. The simulations revealed that the permeate could be used for fertigation purposes due to its ammonium and phosphate concentrations, while COD, BOD and SS were far below the discharge limits. Moreover, low amounts of waste sludge were achieved, this sludge already being stabilised. Specifically, 0.127 and 0.115 kg VSS (volatile suspended solids) per m3 of treated water were produced with a biodegradable volatile suspended solids (BVSS) content of 32.3 and 21.5% when operating at 15 °C or 25 °C, respectively. The application of the plant-wide model also allowed prediction of the behaviour of the new plant in the event of polluting load increase or wastewater flow increase.

Revamp of a WWTP by including an AnMBR

Currently, the urban WWTP in Torrent (Spain) cannot treat all the incoming wastewater flow and therefore a new installation needs to be built to increase the treatment capacity from 6,000 to 18,000 m3/d. Since agricultural activity in the area has a demand of 6,000 m3/d of water for irrigation, an AnMBR system of this capacity was deemed appropriate and therefore designed. The modelling results revealed the production of a high quality effluent, which complies with solids and organic matter content discharge limits and presents nutrient concentrations for fertigation that allow for savings in the use of inorganic fertilizers. It will be possible to treat the effluent in the conventional activated sludge system in periods without agricultural need. The interconnection of the streams with a plant-wide model made it possible to simulate the whole new system proposed.

Upgrade of a conventional WWTP

The plant-wide model was used to simulate different options for upgrading the Denia WWTP (Spain). This WWTP treats around 18,000 m3/d and was initially designed for organic matter removal and nitrification. The biological treatment consisted of a conventional activated sludge process where primary and excess sludge were aerobically digested (Figure 2). The decision to upgrade the WWTP was made in order to meet the European Commission requirements for total nitrogen and phosphorus in sensitive areas and solve the existing odour problems caused by insufficient stabilization of the excess sludge. Different scenarios were simulated and the results are to be used to support the decisions related to the WWTP upgrade. The modifications carried out in the treatment scheme consisted of operation under extended aeration conditions, converting the biological reactors and the aerobic digesters into one plug-flow biological reactor, converting the old primary settlers into anoxic reactors, and removing phosphorus by chemical precipitation. Moreover, simulations of significant ammonium and COD peak loads showed that increasing the anoxic zone would reduce sludge flotation problems. Therefore, an impeller was installed in the first part of the biological reactor to avoid suspended solids sedimentation when the air control valve was closed in order to increase the anoxic volume. The plant modifications proposed were successfully implemented (Seco et al. 2005).

Upgrade of a conventional WWTP for P recovery

In WWTP with biological P removal it becomes very interesting to enhance P recovery and minimize uncontrolled P precipitation. For this, a modification in the sludge line was proposed after a simulation study and tested in different full-scale applications (Tarragona, Calahorra and Murcia-Este WWTPs). The simulations evaluated the potential P recovery by mixing the thickened sludges in a mixing chamber before the anaerobic digestion and pumping the mix towards the primary thickener, therefore obtaining an overflow stream highly enriched in orthophosphate available for its recovery. Figure 3(a) shows the schematic description of the simulated sludge line configuration and Figure 3(b) shows the concentration of orthophosphate in the overflow stream, estimated at different operational conditions in Murcia-Este WWTP. The details of the simulation and optimization work in the Tarragona WWTP can be found in Ruano et al. (2012) while Martí et al. (2017) describe the case of Calahorra WWTP. This configuration allows recovery of up to 40% of the incoming phosphorus and considerably reduces the uncontrolled phosphorus precipitation in digesters, pipes, centrifuges and other equipment.

Figure 3

(a) Schematic representation of the sludge line configuration simulated (b) concentration of phosphorus in the primary thickener overflow at different operational conditions: primary sludge flow (Qps) (blue line into the primary thickener) and elutriation flow (Qelut) (green line from the primary thickener to the P-release tank). The full colour version of this figure is available in the online version of this paper, at http://dx.doi.org/10.2166/wst.2020.056.

Figure 3

(a) Schematic representation of the sludge line configuration simulated (b) concentration of phosphorus in the primary thickener overflow at different operational conditions: primary sludge flow (Qps) (blue line into the primary thickener) and elutriation flow (Qelut) (green line from the primary thickener to the P-release tank). The full colour version of this figure is available in the online version of this paper, at http://dx.doi.org/10.2166/wst.2020.056.

Optimization of an industrial WWTP

Plant-wide models can also be applied to simulate treatment processes of industrial wastewaters. In these cases, the steps of wastewater characterization and parameter calibration take a crucial role. Several complete analytical campaigns are required for wastewater characterization and values from literature cannot be adopted. Model parameter values should be obtained with off-line calibration methodologies to detect bacterial inhibitions. Table 3 shows, as an example, the values obtained for the high influence model parameters in the WWTP of a petrochemical company, quite different from the typical values for urban WWTPs. This showed that wastewater characteristics influence the activity of microorganisms to a large degree.

Table 3

Values of the main model parameters calibrated for the industrial wastewater and the reference ones for sewage proposed in BNRM1 (Seco et al. 2004)

Model parameterCalibratedDefault
YOHO Yield for heterotrophic biomass 0.38 0.63 
μOHO,Max (d−1Maximum heterotrophic growth rate 1.04 
bOHO (d−1Heterotrophic decay rate 0.18 0.4 
KF,OHO (mg DQO·l−1Saturation coefficient for fermentable matter 17.19 
ημOHO,Ax3 Correction factor for anoxic conditions 0.05 0.43 
μAOO,Max (d−1Maximum autotrophic growth rate 0.2 
bAOO (d−1Autotrophic decay rate 0.05 0.15 
KNH,AOO (mg N·l−1Saturation coefficient for ammonium 0.38 
Model parameterCalibratedDefault
YOHO Yield for heterotrophic biomass 0.38 0.63 
μOHO,Max (d−1Maximum heterotrophic growth rate 1.04 
bOHO (d−1Heterotrophic decay rate 0.18 0.4 
KF,OHO (mg DQO·l−1Saturation coefficient for fermentable matter 17.19 
ημOHO,Ax3 Correction factor for anoxic conditions 0.05 0.43 
μAOO,Max (d−1Maximum autotrophic growth rate 0.2 
bAOO (d−1Autotrophic decay rate 0.05 0.15 
KNH,AOO (mg N·l−1Saturation coefficient for ammonium 0.38 

Figure 4 shows the oxygen uptake rate values recorded at different substrate concentrations for heterotrophic and autotrophic bacteria. Very high substrate concentrations (higher than usual for urban WWTPs) are required for heterotrophic bacteria to reach their maximum activity. Maximum activity of autotrophic bacteria is relatively low but is reached at low ammonium concentrations.

Figure 4

OUR values obtained at different substrate concentrations for (a) heterotrophic bacteria, (b) autotrophic bacteria.

Figure 4

OUR values obtained at different substrate concentrations for (a) heterotrophic bacteria, (b) autotrophic bacteria.

Development of control strategies

Control system design, calibration and validation can be supported by plant-wide models, since it is possible to reproduce the response of the operational units to the performed actions. For instance, plant-wide models allow taking into account the effect of dewatering and supernatant streams recycling to the mainline, affecting the virtual nitrogen loading rate. For this, Ruano et al. (2017) used the simulation software DESASS (Ferrer et al. 2008), the IWA BSM1 (Alex et al. 2008) as a working scenario and the software LoDif Biocontrol® (Ferrer et al. 2011) in order to design, calibrate and validate control strategies for optimal nitrogen removal (minimized energy consumption) in activated sludge systems. Figure 5 shows a schematic representation of the development procedure for these controllers to be implemented in full-scale WWTPs.

Figure 5

Schematic representation of the development procedure for the controllers to be implemented in WWTPs.

Figure 5

Schematic representation of the development procedure for the controllers to be implemented in WWTPs.

An example of simulation results from one of the designs carried out in the study is shown in Figure 6. The dissolved oxygen concentration (DO) through a plug-flow reactor was controlled by changing the DO setpoints through time. When the aeration capacity was sufficient, the DO concentration oscillated near the established DO set points. The pattern of the DO set points showed similarities with the dynamics in ammonium concentration, mainly as a result of the information obtained from the pH sensors that were used to modify the DO set point. Suitable overall process performance was achieved, resulting in enhanced nitrogen removal efficiencies. Moreover, compared to the baseline scenario, the controller significantly reduced the energy demand. Specifically, power requirements were reduced from approximately 0.13–0.10 kWh per m3 of treated water.

Figure 6

Evolution of: (a) DO set point (R2_DO3sp) and ammonium concentration in the outlet of the aerobic reactor (NH4-N effluent). R2_DO3 is the measured DO concentration in the reactor lane 2; and (b) inputs to the controller (Moving Average of pH difference (R2_pH difference MA), cumulative DO error in the third aerated chamber over ten (R2_CDO3/10), DO (R2_DO3) and DO set point (R2_DO3sp) in last aerated chamber).

Figure 6

Evolution of: (a) DO set point (R2_DO3sp) and ammonium concentration in the outlet of the aerobic reactor (NH4-N effluent). R2_DO3 is the measured DO concentration in the reactor lane 2; and (b) inputs to the controller (Moving Average of pH difference (R2_pH difference MA), cumulative DO error in the third aerated chamber over ten (R2_CDO3/10), DO (R2_DO3) and DO set point (R2_DO3sp) in last aerated chamber).

Other extensions for plant-wide modelling

A filtration model was also included in the collection model BNRM in order to allow simulation of a wider spectrum of processes. Specifically, a model was proposed for immersed MBRs taking into account the effect of biogas sparging and back-flushing on cake detachment, as well as the risk of forming irreversible fouling. This specific model was validated in an AnMBR system equipped with industrial-scale membranes in the short (Robles et al. 2013a) and the long term (Robles et al. 2013b) and used for control purposes, showing that it is possible to efficiently maintain low fouling rates by the application of an upper layer fuzzy-logic controller. In addition, this model was applied to optimise the performance of an AnMBR at pilot scale, obtaining energy savings of up to 25%. A model-based optimization method was also applied to improve the performance of AnMBRs (Robles et al. 2014, 2018).

Regarding integration of energy and environmental aspects on the modelling target, Pretel et al. (2016a) extended the collection model BNRM with a plant-wide energy model, which was validated in an AnMBR system treating sewage at steady- and unsteady-conditions. The results indicated that the model was capable of reproducing energy variations even when operating at dynamic conditions (i.e. variations in ambient temperature and/or inflow temperature). Pretel et al. (2016b) combined this model with life cycle assessment (LCA) for comparing different treatment technologies. In this case, the conclusion could be achieved that an AnMBR combined with a CAS-based post-treatment results in significant reductions in different environmental impact categories mainly due to reduced power requirements.

SUMMARY AND FUTURE PERSPECTIVES IN WASTEWATER TREATMENT MODELLING

After the development and wide spread of biochemical models to describe separately the most relevant processes in wastewater treatment, the field has evolved in the last decades in the direction of creating plant-wide models that are able to reproduce the increasing complexity of the plants as a whole. These models take cost into account, as well as a variety of processes such as chemical equilibria, oxygen transfer, greenhouse gas generation, etc. and they intend to be widely and easily applicable. They have a key role in process design, optimization and control. The viability of applying a plant-wide model increases with advances in computer technology and the development of simulation platforms. The major role of these plant-wide models has been shown in this work with a series of case studies where WWTP simulation studies were performed applying the BNRM model on the DESASS platform.

Remaining challenges in the field of plant-wide modelling are, on the one hand, related to the model itself:

  • (i)

    Further extensions: newly modelled processes remain to be added as extensions in plant-wide models. In some cases, new models have been developed according to the standardized notation, which facilitates their inclusion. Some studies already show examples of the possibility of this combination, with processes such as enhanced anammox (Dorofeev et al. 2017), granular sludge reactors (Dold et al. 2018), enhanced biofilm processes (Moretti et al. 2018; Ji et al. 2019), microalgae and cyanobacteria activity (Shoener et al. 2019), autotrophic denitrification using sulfur (Liu et al. 2016), membrane contactors and degassing membranes for component separation (Nagy et al. 2019), life cycle analysis (Ontiveros & Campanella 2013) or energy balance (Drewnowski et al. 2018). Some commercial models such as BioWin, SUMO or GPS-X already include some of the most used extensions.

  • (ii)

    New pollutants: especially in the case where new legal discharge limits are established (e.g. emerging pollutants or heavy metals). Including these components in a plant-wide model will constitute a great challenge, given the high number of pollutants that could possibly be considered and the often-complex routes of degradation and interaction amongst them and other wastewater components. A considerable effort will be needed to study the fate of pollutants in each treatment unit and therefore the formation of intermediate and final compounds, some of which are pollutants as well.

On the other hand, achieving a real wide spread of plant-wide models among operators of water resource recovery facilities is a current challenge for the scientific community involved in the development of such models. The full potential of plant-wide models for designing new sustainable WRRF, as well as for optimizing existing ones, can only be achieved when these models are transferred to real application.

Regarding model calibration and validation, a consensus is needed on calibration protocols in order to minimize the variability among model parameters obtained in different studies. As commented before, the IWA STR on Guidelines for using ASMs (Rieger et al. 2012) presented a protocol for on-line calibration in the water line. There is still a need for similar standardized calibration procedures for the sludge line, in the case of off-line calibration and for plant-wide models.

Exploring the considerable amount of information currently available on the performance of full-scale implemented processes should also gain importance as a modelling tool in the near future since authors consider that big data in WRRFs is widely underutilized (Newhart et al. 2019). Although the quality of this data might be in cases questionable, the wide spread of the use of probes, for instance, can provide interesting and useful data about some of the most usual processes in a WWRF. In this respect, coupling data-driven modelling methods for plant-wide process monitoring and control with mechanistic plant-wide models will boost plant-wide optimization (Ge 2017). Other kinds of useful data that could be obtained from WWTP operators are the observed oscillations in water flow and pollutant concentrations, which can be daily, seasonal, or event-dependent such as rain or other one-time events (sporting, cultural, etc). Plant-wide models can make use of this data to develop operational strategies (rules of action) for special cases, simulating different scenarios and the plant response to possible corrective measures. In addition, integrating computational fluid dynamics models (CFD) with plant-wide models for smarter operation and optimal design still remains a big challenge.

REFERENCES

Alex
J.
Benedetti
L.
Copp
J.
Gernaey
K. V.
Jeppsson
U.
Nopens
I.
Pons
M.-N.
Rieger
L.
Rosen
C.
Steyer
J. P.
Vanrolleghem
P.
Winkler
S.
2008
Benchmark Simulation Model No. 1 (BSM1)
.
Division of Industrial Electrical Engineering and Automation, Lund University
,
Lund
,
Sweden
.
Available from: http://www.benchmarkwwtp.org (accessed July 2019)
.
Allison
J. D.
Brown
D. S.
Novo-Gradac
K. J.
1991
MINTEQA2/PRODEFA2, A Geochemical Assessment Model for Environmental Systems: Version 3.0
.
EPA/600/3- 91/021
.
USEPA
,
Washington, DC
.
Barat
R.
Serralta
J.
Ruano
M. V.
Jiménez
E.
Ribes
J.
Seco
A.
Ferrer
J.
2013
Biological nutrient removal model no. 2 (BNRM2): a general model for wastewater treatment plants
.
Water Science and Technology
67
(
7
),
1481
1489
.
Batstone
D. J.
Keller
J.
Angelidaki
I.
Kalyuzhnyi
S. V.
Pavlostathis
S. G.
Rozzi
A.
Sanders
W. T. M.
Siegrist
H.
Vavilin
V. A.
2002
Anaerobic Digestion Model No.1
.
IWA STR No.13
.
IWA Publishing
,
London
,
UK
.
Batstone
D. J.
Hülsen
T.
Mehta
C. M.
Keller
J.
2015
Platforms for energy and nutrient recovery from domestic wastewater: a review
.
Chemosphere
140
,
2
11
.
Borrás
F. L.
2008
Técnicas microbiológicas aplicadas a la identificación y cuantificación de organismos presentes en sistemas EBPR (Microbiological Techniques Applied to Identification and Quantification of Organisms Present in EBPR Systems)
.
PhD Thesis
,
Universitat Politècnica de València
,
Valencia
,
Spain
.
Claros
J.
Jiménez
E.
Aguado
D.
Ferrer
J.
Seco
A.
Serralta
J.
2011
Effect of pH and HNO2 concentration on the activity of ammonia-oxidizing bacteria in a partial nitritation reactor
.
Water Science and Technology
67
(
11
),
2587
2594
.
Copp
J. B.
2002
The COST Simulation Benchmark – Description and Simulator Manual
.
Office for Official Publications of the European Communities
,
Luxembourg
.
Copp
J. B.
Jeppsson
U.
Rosen
C.
2003
Towards an ASM1 - ADM1 state variable interface for plant-wide wastewater treatment modeling
. In:
Proceedings of the Water Environment Federation Conference (WEFTEC 2003)
,
11th–15th November 2003
,
Los Angeles, California, USA
.
Dold
P.
Alexander
B.
Burger
G.
Fairlamb
M.
Conidi
D.
Bye
C.
Du
W.
2018
Modeling full-scale granular sludge sequencing tank performance
. In:
Proceedings of the 91st Annual Water Environment Federation Technical Exhibition and Conference (WEFTEC 2018)
. pp.
3813
3826
Dorofeev
A. G.
Nikolaev
Y. A.
Kozlov
M. N.
Kevbrina
M. V.
Agarev
A. M.
Kallistova
A. Y.
Pimenov
N. V.
2017
Modeling of anammox process with the biowin software suite
.
Applied Biochemistry and Microbiology
53
(
1
),
78
84
.
Drewnowski
J.
Zaborowska
E.
Herrandez de Vega
C.
2018
Computer simulation in predicting biochemical processes and energy balance at WWTPs
. In:
1st Conference of the International Water Association IWA for Young Scientist in Poland ‘Water, Wastewater and Energy in Smart Cities’, IWA 2017
,
12–13 September 2018
,
Cracow, Poland
.
Durán
F.
2013
Modelación matemática del tratamiento anaerobio de aguas residuales urbanas incluyendo las bacterias sulfatorreductoras. Aplicación a un biorreactor anaerobio de membranas (Mathematical Model of Urban Wastewater Anaerobic Treatment Including Sulphate Reducing Bacteria. Application to an Anaerobic Membrane Bioreactor)
.
PhD Thesis
,
Universitat Politècnica de València
,
Valencia
,
Spain
.
Durán
F.
Robles
A.
Seco
A.
Ferrer
J.
Ribes
J.
Serralta
J.
2017
Modelling the anaerobic treatment of urban wastewater: application to AnMBR technology
. In:
15th IWA World Conference on Anaerobic Digestion
,
17th–20th October
,
Beijing, China
.
EPA
2006
User's manual version 4.03 2006
. .
Fernández-Arévalo
T.
Lizarralde
I.
Fdz-Polanco
F.
Pérez-Elvira
S. I.
Garrido
J. M.
Puig
S.
Poch
M.
Grau
P.
Ayesa
E.
2017
Quantitative assessment of energy and resource recovery in wastewater treatment plants based on plant-wide simulations
.
Water Research
118
,
272
288
.
Ferrer
J.
Seco
A.
Serralta
J.
Ribes
J.
Manga
J.
Asensi
E.
Morenilla
J. J.
Llavador
F.
2008
DESASS – a software tool for designing, simulating and optimising WWTPs
.
Environmental Modelling and Software
23
,
19
26
.
Ferrer
J.
Seco
A.
Ruano
M. V.
Ribes
J.
Serralta
J.
Gómez
T.
Robles
A.
2011
LoDif BioControl® Control Software, Intellectual Property
.
Main Institution: Universitat de València; Universitat Politècnica de València
.
Flores-Alsina
X.
Corominas
L.
Snip
L.
Vanrolleghem
P. A.
2011
Including greenhouse gas emissions during benchmarking of wastewater treatment plant control strategies
.
Water Research
45
(
16
),
4700
4710
.
Flores-Alsina
X.
Arnell
M.
Amerlinck
Y.
Corominas
L.
Gernaey
K. V.
Guo
L.
Lindblom
E.
Nopens
I.
Porro
J.
Shaw
A.
Snip
L.
Vanrolleghem
P. A.
Jeppsson
U.
2013
Balancing effluent quality, economic cost and greenhouse gas emissions during the evaluation of (plant-wide) control/operational strategies in WWTPs
.
Science of the Total Environment
466–467
,
616
624
.
Flores-Alsina
X.
Kazadi Mbamba
C.
Solon
K.
Vrecko
D.
Tait
S.
Batston
D. J.
Jeppsson
U.
Gernaey
K.
2015
A plant-wide aqueous phase chemistry module describing pH variations and ion speciation/pairing in wastewater treatment process models
.
Water Research
85
,
255
265
.
Ge
Z.
2017
Review on data-driven modeling and monitoring for plant-wide industrial processes
.
Chemometrics and Intelligent Laboratory Systems
171
(
2017
),
16
25
.
Grau
P.
de Gracia
M.
Vanrolleghem
P. A.
Ayesa
E.
2007
A new plant-wide modelling methodology for WWTPs
.
Water Research
41
,
4357
4372
.
Grau
P.
Copp
J.
Vanrolleghem
P. A.
Takacs
I.
Ayesa
E.
2009
A comparative analysis of different approaches for integrated WWTP modelling
.
Water Science and Technology
59
(
1
),
141
147
.
Henze
M.
Gujer
W.
Mino
T.
van Loosdrecht
M. C. M.
2000
Activated Sludge Models ASM1, ASM2, ASM2d and ASM3
.
IWA Scientific and Technical Report No.9
.
IWA Publishing
,
London
,
UK
.
Jeppsson
U.
Pons
M. N.
2004
The COST benchmark simulation model – current state and future perspective
.
Control Engineering Practice
12
(
3
),
299
304
.
Jeppsson
U.
Rosen
C.
Alex
J.
Copp
J.
Gernaey
K. V.
Pons
M.-N.
Vanrolleghem
P. A.
2006
Towards a benchmark simulation model for plant-wide control strategy performance evaluation of WWTPs
.
Water Science and Technology
53
(
1
),
287
295
.
Jiménez
E.
Giménez
J. B.
Ruano
M. V.
Ferrer
J.
Serralta
J.
2011
Effect of pH and nitrite concentration on nitrite oxidation rate
.
Bioresource Technology
102
(
19
),
8741
8747
.
Jiménez
E.
Giménez
J. B.
Seco
A.
Ferrer
J.
Serralta
J.
2012
Effect of pH, substrate and free nitrous acid concentrations on ammonium oxidation rate
.
Bioresource Technology
124
,
478
484
.
Jones
R. M.
Takácks
I.
2004
Importance of anaerobic digestion modelling on predicting waste-water treatment plants
. In:
Proceedings of Anaerobic Digestion 2004
,
24th August–2nd September 2004
.
10th World Congress
,
Montreal
,
Canada
, pp.
1371
1375
.
Kazadi Mbamba
C.
Flores-Alsina
X.
Batstone
D. J.
Tait
S.
2016
Validation of a plant-wide phosphorus modelling approach with minerals precipitation in a full-scale WWTP
.
Water Research
100
,
169
183
.
Kazadi Mbamba
C.
Lindblom
E.
Flores-Alsina
X.
Tait
S.
Anderson
S.
Saagi
R.
Batstone
D. J.
Gernaey
K. V.
Jeppsson
U.
2019
Plant-wide model-based analysis of iron dosage strategies for chemical phosphorus removal in wastewater treatment systems
.
Water Research
155
,
12
25
.
Liu
Y.
Peng
L.
Ngo
H. H.
Guo
W.
Wang
D.
Pan
Y.
Sun
J.
Ni
B.-J.
2016
Evaluation of nitrous oxide emission from sulfide- and sulfur-based autotrophic denitrification processes
.
Environmental Science & Technology
50
,
9407
9415
.
Lizarralde
I.
Fernández-Arévalo
T.
Brouckaert
C.
Vanrolleghem
P.
Ikumi
D. S.
Ekama
G. A.
Ayesa
E.
Grau
P.
2015
A new general methodology for incorporating physico-chemical transformations into multiphase wastewater treatment process models
.
Water Research
74
,
239
256
.
Lizarralde
I.
Fernández-Arévalo
T.
Manas
A.
Ayesa
E.
Grau
P.
2019
Model-based optimization of phosphorus management strategies in Sur WWTP, Madrid
.
Water Research
153
,
39
52
.
Lopez-Vazquez
C. M.
Oehmen
A.
Hooijmans
C. M.
Brdjanovic
D.
Gijzen
H. J.
Yuan
Z.
van Loosdrecht
M. C.
2009
Modeling the PAO-GAO competition: effects of carbon source, pH and temperature
.
Water Research
43
(
2
),
450
462
.
Mannina
G.
Ekama
G.
Caniani
D.
Cosenza
A.
Esposito
G.
Gori
R.
Garrido-Baserba
M.
Rosso
D.
Olsoon
G.
2016
Greenhouse gases from wastewater treatment – a review of modelling tools
.
Science of The Total Environment
254
270
.
Mannina
G.
Ferreira Rebouças
T.
Cosenza
A.
Chandran
K.
2019
A plant-wide wastewater treatment plant model for carbon and energy footprint: model application and scenario analysis
.
Journal of Cleaner Production
217
,
244
256
.
Martí
N.
Barat
R.
Seco
A.
Pastor
L.
Bouzas
A.
2017
Sludge management modeling to enhance P-recovery as struvite in wastewater treatment plants
.
Journal of Environmental Management
196
,
340
346
.
Moretti
P.
Choubert
J.-M.
Canler
J.-P.
Buffière
P.
Pétrimaux
O.
Lessard
P.
2018
Dynamic modeling of nitrogen removal for a three-stage integrated fixed-film activated sludge process treating municipal wastewater
.
Bioprocess and Biosystems Engineering
41
(
2
),
237
247
.
Newhart
K. B.
Holloway
R. W.
Hering
A. S.
Cath
T. Y.
2019
Data-driven performance analyses of wastewater treatment plants: a review
.
Water Research
157
,
498
513
.
Nopens
I.
Batstone
D.
Copp
J. B.
Jeppsson
U.
Volcke
E. I. P.
Alex
J.
Vanrolleghem
P. A.
2009
A practical ASM/ADM model interface for enhanced dynamic plantwide simulation
.
Water Research
43
,
1913
1923
.
Nopens
I.
Benedetti
L.
Jeppsson
U.
Pons
M. N.
Alex
J.
Copp
J. B.
Gernaey
K. V.
Rosen
C.
Steyer
J. P.
Vanrolleghem
P. A.
2010
Benchmark simulation model no. 2: finalisation of plant layout and default control strategy
.
Water Science and Technology
62
(
9
),
1967
1974
.
Penya-Roja
J. M.
Seco
A.
Ferrer
J.
Serralta
J.
2002
Calibration and validation of activated sludge model no. 2d for Spanish Municipal Wastewater
.
Environmental Technology
23
,
849
862
.
Pretel
R.
Robles
A.
Ruano
M. V.
Seco
A.
Ferrer
J.
2016a
A plant-wide energy model for wastewater treatment plants: application to anaerobic membrane bioreactor technology
.
Environmental Technology
37
,
2298
2315
.
Rehman
U.
Audenaert
W.
Amerlinck
Y.
Maere
T.
Arnaldos
M.
Nopens
I.
2017
How well-mixed is well mixed? hydrodynamic-biokinetic model integration in an aerated tank of a full-scale water resource recovery facility
.
Water Science and Technology
76
(
8
),
1950
1965
.
Rieger
L.
Gillot
S.
Langergraber
G.
Ohtsuki
T.
Shaw
A.
Takacs
I.
Winkler
S.
2012
Guidelines for Using Activated Sludge Models Scientific and Technical report No. 21. EWA Task Group on Good Modelling Practice. IWA Publishing Volume 11
.
Robles
A.
Ruano
M. V.
Ribes
J.
Seco
A.
Ferrer
J.
2013a
A filtration model applied to submerged anaerobic MBRs (SAnMBRs)
.
Journal of Membrane Science
444
,
139
147
.
Robles
A.
Ruano
M. V.
Ribes
J.
Seco
A.
Ferrer
J.
2013b
Mathematical modelling of filtration in submerged anaerobic MBRs (SAnMBRs): long-term validation
.
Journal of Membrane Science
446
,
303
309
.
Robles
A.
Ruano
M. V.
Ribes
J.
Seco
A.
Ferrer
J.
2014
Model-based automatic tuning of a filtration control system for submerged anaerobic membrane bioreactors (AnMBR)
.
Journal of Membrane Science
465
,
14
26
.
Rosen
C.
Jeppsson
U.
Vanrolleghem
P. A.
2004
Towards a common benchmark for long-term process control and monitoring performance evaluation
.
Water Science and Technology
50
(
11
),
41
49
.
Ruano
M. V.
Serralta
J.
Ribes
J.
Garcia-Usach
F.
Bouzas
A.
Barat
R.
Seco
A.
Ferrer
J.
2012
Application of the general model ‘biological nutrient removal model no. 1’ to upgrade two full-scale WWTPs
.
Environmental Technology
33
,
1005
1012
.
Ruano
M. V.
Robles
A.
Seco
A.
Ferrer
J.
Ribes
J.
2017
Benchmarking of control strategies implemented in a dedicated control platform for wastewater treatment processes
. In:
Proceedings of the 12th IWA Specialized Conference on Instrumentation, Control and Automation, ICA 2017
,
11th–14th June 2017
,
Quebec, Canada
.
Seco
A.
Ribes
J.
Serralta
J.
Ferrer
J.
2004
Biological nutrient removal model no. 1 (BNRM1)
.
Water Science and Technology
50
(
6
),
69
78
.
Seco
A.
Ribes
J.
Serralta
J.
Ferrer
J.
2005
Upgrading the Denia WWTP according to BNRM1 simulations
. In:
IWA Specialized Conference: Nutrient Management in Wastewater Treatment Processes and Recycle Streams
,
19th September 2005
,
Krakow, Poland
.
Serralta
J.
Ferrer
J.
Borrás
L.
Seco
A.
2004
An extension of ASM2d including pH calculation
.
Water Research
38
,
4029
4038
.
Shoener
B. D.
Schramm
S. M.
Beline
F.
Bernard
O.
Martínez
C.
Plosz
B. G.
Snowling
S.
Steyer
J.-P.
Valverde-Perez
B.
Wagner
D.
Guest
J.
2019
Microalgae and cyanobacteria modeling in water resource recovery facilities: a critical review
.
Water Research X
2
,
100024
.
Solon
K.
Flores-Alsina
X.
Kazadi Mbamba
C.
Ikumi
D.
Volcke
E. I. P.
Vaneeckhaute
C.
Ekama
G.
Vanrolleghem
P. A.
Batstone
D. J.
Gernaey
K. V.
Jeppsson
U.
2017
Plant-wide modelling of phosphorus transformations in wastewater treatment systems: impacts of control and operational strategies
.
Water Research
113
,
97
110
.
Solon
K.
Jia
M.
Volcke
E. I. P.
2019a
Process schemes for future energy-positive water resource recovery facilities
.
Water Science and Technology
79
(
7
),
1808
1820
.
Solon
K.
Jia
M.
Volcke
E. I. P.
Spérandio
M.
Van Loosdrecht
M. C. M.
2019b
Resource recovery and wastewater treatment modelling
.
Environmental Science: Water Research and Technology
5
,
631
642
.
Vanrolleghem
P. A.
Rosen
C.
Zaher
U.
Copp
J.
Benedetti
L.
Ayesa
E.
Jeppsson
U.
2005
Continuity based interfacing of models for wastewater systems described by Peterson matrices
.
Water Science and Technology
52
(
1–2
),
149
500
.