CONTROL ENGINEERING LABORATORY

Mechanistic modelling of pulp and paper mill wastewater treatment plants

Jukka Keskitalo and Kauko Leiviskä

Report A No 41, January 2010

University of Oulu Control Engineering Laboratory Report A No 41, January 2010

Mechanistic modelling of pulp and paper mill wastewater treatment plants

Jukka Keskitalo and Kauko Leiviskä

University of Oulu, Control Engineering Laboratory Abstract: This report provides a brief review on characteristics of pulp and paper wastewaters, activated sludge processes for wastewater treatment and the state of the art Activated Sludge Models (ASM). Literature on calibration of the ASMs and on application examples is reviewed more thoroughly. Additionally, a case study on mechanistic modelling and model calibration of pulp and paper industry wastewater treatment plant is presented. The widely used ASMs have been developed mainly for modelling the treatment of municipal wastewater. Mechanistic modelling of activated sludge treatment of pulp and paper mill wastewater requires some special considerations, as the wastewater is nutrient deficient. Calibration of the ASMs remains the weakest link in activated sludge modelling, as the models are complex and usually overparameterised to a given problem. In the case study, results of a measurement campaign from a pulp mill wastewater treatment plant are presented. Oxygen uptake rate (OUR) measurements and conventional wastewater analyses were made with sludge and wastewater sampled from the treatment plant. The results were utilised in calibrating a modified ASM no. 1 for the treatment plant and for wastewater characterisation. The model performance was validated by running a simulation with ten months of influent process data from the mill databases as inputs to the model. Model predictions of effluent quality were then compared to measured values. The measured and simulated values were in good agreement for most of the simulation period. This report is an extension to journal article by Keskitalo et al. [1]. This report provides more background information and sensitivity analysis of the model to better justify the calibration procedure. Keywords: pulp and paper wastewater, model calibration, modified activated sludge model no. 1, computer simulation, full-scale WWTP ISBN 978-951-42-6110-7 University of Oulu ISSN 1238-9390 Control Engineering Laboratory P.O. Box 4300 FIN-90014 University of Oulu

1 Introduction...................................................................................................................... 1 2. Review ............................................................................................................................ 2

2.1 Pulp and paper mill effluents .................................................................................... 2 2.2 Activated sludge process........................................................................................... 3 2.3 Activated Sludge Models .......................................................................................... 5 2.4 Calibration of the Activated Sludge Models............................................................. 6

2.4.1 Systematic experimental and experience-based ASM calibration protocols ..... 6 2.4.2 Systematic protocols for assessing parameter identifiability and calibration of parameters of the ASMs.............................................................................................. 7 2.4.3 Examples on ASM calibration ........................................................................... 9 2.4.4 Discussion on the different approaches to ASM calibration ........................... 12

3. Case study on activated sludge modelling .................................................................... 13 3.1 Materials & Methods .............................................................................................. 13

3.1.1 Plant & data description................................................................................... 13 3.1.2 Analytical work................................................................................................ 14 3.1.3 Model structure ................................................................................................ 16 3.1.4 Sensitivity analysis........................................................................................... 18 3.1.5 Wastewater characterisation ............................................................................ 20 3.1.6 Calibration procedure....................................................................................... 22

3.2 Results & Discussion .............................................................................................. 23 3.2.1 OUR measurements and wastewater analyses................................................. 23 3.2.2 Sensitivity analysis........................................................................................... 26 3.2.3 Wastewater characterisation ............................................................................ 27 3.2.4 Calibration........................................................................................................ 29 3.2.5 Simulation ........................................................................................................ 30

4. Conclusions................................................................................................................... 35 5. References..................................................................................................................... 36

SYMBOLS AND ABBREVIATIONS

AOX Adsorbable organically bound halogen ASM Activated Sludge Model ASM1 Activated Sludge Model No. 1 ASM2 Activated Sludge Model No. 2 ASM2d Activated Sludge Model No. 2d ASM3 Activated Sludge Model No. 3 Bio-P Biological phosphorus removal BOD Biological oxygen demand COD Chemical oxygen demand CSTR Continuous stirred-tank reactor DO Dissolved oxygen FIM Fischer information matrix IWA International Water Association MLSS Mixed liquor suspended solids N Nitrogen OUR Oxygen uptake rate P Phosphorus PAO Phosphorus accumulating organism SA Sensitivity analysis SI Soluble inert material SND Soluble biodegradable organic nitrogen SNH Ammonium nitrogen SNI Inert soluble nitrogen SNO Nitrate nitrogen SOUR Specific oxygen uptake rate SP Soluble biodegradable phosphorus SS Suspended solids SS Readily biodegradable substrate XI Particulate inert material XNB Active mass nitrogen XNI Inert particulate nitrogen XNP Nitrogen in products arising from biomass decay XPB,1 Minimum phosphorus in active biomass XPB,2 Additional phosphorus in active biomass XPD Particulate organically bound phosphorus XPP Phosphorus in products arising from biomass decay XS Slowly biodegradable substrate VSS Volatile suspended solids WWTP Wastewater treatment plant YH Heterotrophic yield coefficient δmsqr Local sensitivity measure

1

1 INTRODUCTION

Modelling activated sludge wastewater treatment has been extensively studied since publication of the first Activated Sludge Model, the ASM1, in 1987. Modelling has become an accepted practice in treatment plant design, teaching and research. Even though the Activated Sludge Models were introduced for modelling municipal wastewater treatment, the models have been successfully applied for modelling the treatment of some industrial wastewaters. This report attempts to extend activated sludge modelling and model calibration to pulp and paper wastewaters, which has not yet received much attention. The report begins with an introduction to the characteristics of pulp and paper wastewaters, activated sludge process, the state of the art activated sludge models and model calibration. The state of the art models are complex and usually overparameterised to a given problem [2]. Calibration of the models remains the weakest link in activated sludge modelling [3]. A case study of modelling pulp and paper industry wastewater treatment plant with a modified ASM no. 1 is presented. The model is calibrated with a simplified calibration protocol utilising results from oxygen uptake rate (OUR) measurements. The model is used for simulating the full-scale pulp mill wastewater treatment plant. Simulation results are compared to measured process data. This report is based on the same process data and experimental results as [1]. The report complements the article by providing more background information and application of sensitivity analysis of the model to better justify the calibration procedure.

2

2. REVIEW

2.1 Pulp and paper mill effluents

Even though the pulp and paper making industry remains a major consumer of freshwater globally, its emissions to waterways have been significantly reduced. For example, BOD7 emissions from the industry in Finland have been reduced by about 90% since the 1970 while the production of paper and board has become threefold and production of pulp has doubled. Emissions of adsorbable organically bound halogens have also been decreased to a fraction of their past levels. Similar trend, however, has not been seen for nutrients nitrogen and phosphorus. [4] Challenges still remain in reducing incidental discharges, which have become a significant fraction of the total discharges. Disturbances in upstream production processes cause changes in wastewater quality and volume, which may further disturb the external biological wastewater treatment and cause violations of effluent quality limits. [5] Effluents from pulp and paper mills contain wood components, chemicals used in the processes and their reaction products, fillers and auxiliary chemicals. Effluent compositions vary considerably and only part of the effluent components have been identified so far. Most of the contaminants are in solid form but some are colloidal or dissolved. Effluents from chemical pulping are highly coloured due to the dissolved lignin. Untreated effluents contain components such as fatty acids and resin acids, which are toxic to aquatic life. Levels of nitrogen and phosphorus in pulp and paper mill effluents are usually low compared to municipal sewage. [4] The different components in wastewater are lumped together in commonly used measurements. The most common measurements are biological oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), phosphorus (P), nitrogen (N), adsorbable organically bound halogen (AOX), chloro-organics, colour and toxicity [4]. These measurements are used in design and operation of wastewater treatment plants and in defining water discharge limits. As for the individual compounds, the measured characteristics of wastewater are different depending on the type of the mill. Typical wastewater loads from several types of mills before external treatment are given in Table 1.

3

Table 1. Typical wastewater loads from different types of mills before external treatment [4]. Effluent

volume [m3/t] COD [kg/t]

BOD [kg/t]

Suspended solids [kg/t]

Nitrogen [g/t]

Phosphorus [g/t]

Sulphate pulp, unbleached

20-60 20-30 5-10 12-15 200-400 80

Sulphate pulp, conventional bleaching

60-100 60-120 18-25 12-18 300-500 120

Sulphite pulp, conventional bleaching

150-200 60-100 30-40 20-40 100-200 60

Thermomechanical pulp, unbleached

6-15 40-80 15-25 10-30 100-200 70

Thermomechanical pulp, peroxide bleached

6-15 60-100 20-40 10-30 200-300 100

Fine paper, coated 30-50 10-20 3-8 10-20 50-100 5 Newsprint 10-25 2-4 1-3 5-10 10-20 5 Tissue 20-40 3-6 1-3 5-10 50-80 8

2.2 Activated sludge process

The external treatment of pulp and paper mill wastewaters begins commonly with primary treatment stages including solids removal, neutralisation, cooling and equalisation [6]. Possible processes for solids removal are sedimentation and flotation. High solids removal over 80% is achieved in primary treatment. However, the insufficient removal of organic material requires secondary treatment. [7] The most commonly applied secondary treatment methods for pulp and paper effluents are biological treatment in activated sludge process or aerated lagoon. The use of aerated lagoons has recently declined due their lower treatment performance. [6] Activated sludge process is available in numerous configurations from simple designs for organic material removal to more complex configurations, which are required for biological nutrient removal [8]. Emissions to waterways from pulp and paper industries are dominated by organic material [6]. Therefore simple process designs without biological nutrient removal are common in the industry. The basic activated sludge process consists of an aerated reactor where the sludge is kept in suspension, sedimentation tank for liquid-solids separation and recycle stream for returning separated solids back to the reactor [8]. The process is illustrated in Figure 1. Microorganisms which perform the treatment are kept in the suspension along with other solid material from the influent wastewater. The microorganisms oxidise dissolved and particulate carbonaceous organic material found in wastewater to produce energy and new cells. Nitrogen and phosphorus nutrients are needed for cell growth. [8]

4

Primary clarifier Aeration basin Secondary clarifier

Influent

Primary sludge Return activated sludge

Biological sludge

Effluent

Figure 1. Complete mix activated sludge process. All processes for biological nitrogen removal require an aerobic zone for nitrification and an anoxic zone to complete the nitrogen removal by denitrification. The processes are grouped according to the location of the anoxic zone to preanoxic, postanoxic and simultaneous nitrification-denitrification processes. The preanoxic process is used most often. In the preanoxic process, nitrate is fed to the anoxic reactor by internal recycle from the aerobic reactor and by return activated sludge from the secondary clarifier. Electron donor in nitrification is biodegradable COD in the influent wastewater. The preanoxic process is illustrated in Figure 2. In the postanoxic process, nitrate is reduced in the anoxic reactor where very little biodegradable COD remains in the nitrified effluent. Denitrification can be achieved by endogenous respiration if retention time is sufficiently long, or by addition of external carbon source such as methanol. [8]

Primary clarifier

Anoxic

Secondary clarifier

Influent

Primary sludge Return activated sludge

Biological sludge

Effluent Aerobic

Internal recycle

Figure 2. Preanoxic process for biological nitrogen removal. Biological phosphorus removal (Bio-P) can be accomplished by an anaerobic zone followed by an aerobic zone. Phosphorus removal is initiated in the anaerobic zone where phosphorus accumulating organisms (PAOs) convert volatile fatty acids to internal carbon storage products with energy obtained from releasing stored polyphosphates. Under aerobic conditions where external biodegradable substrate is less available PAOs utilise the storage products and take up polyphosphates. Phosphorus removal is achieved by wasting the phosphorus rich sludge from aerobic zone. Bio-P can be combined with the various biological nitrogen removal mechanisms. [8]

5

2.3 Activated Sludge Models

The most widely used model for describing biological wastewater treatment processes is the International Water Association (IWA) (formerly IAWQ and IAWPRC) Activated Sludge Model No. 1 (ASM1) published in 1987. The goal in the model development was to develop the simplest model capable of describing carbon oxidisation, nitrification and denitrification accurately. [9] The ASM3 was also developed to describe biological nitrogen removal. The main differences between the two models are the role of storage polymers and the change of growth-decay-growth model to growth-endogenous respiration model in the ASM3. The changes were made to correct defects which were recognised in the usage of the ASM1 and to improve parameter identifiability. The ASM2 was developed to include description of biological phosphorus removal in the ASM1. The description of biological phosphorus removal was not complete in ASM2. Therefore the model was improved in ASM2d. [10] In the ASM1, the carbonaceous material is divided into readily biodegradable substrate (SS), slowly biodegradable substrate (XS), soluble inert material (SI) and particulate inert material (XI), heterotrophic biomass (XB,H), autotrophic biomass (XB,A) and inert particulate products arising from biomass decay (XP). The total nitrogen is also divided into different state variables. It is hypothesised that the SS is directly oxidised by microorganisms while the XS has to first undergo hydrolysis. The decay of microorganisms produces XP, but also XS according to the death-regeneration hypothesis. [11] The process rates are modelled using Monod or first-order kinetics. [9] The model contains a total of eight processes with 19 parameters affecting 13 state variables. ASM2 is more complex than the ASM1 and has additional components and processes to describe biological phosphorus removal. In addition to heterotrophic and nitrifying organisms ASM2 has phosphorus-accumulating organisms. ASM2 also describes chemical precipitation of phosphorus. High molecular weight compounds must undergo hydrolysis before being utilised by microorganisms as in ASM1. However, in ASM2 hydrolysis processes depend on the electron acceptor conditions. [11] Growth-decay-growth model of ASM1 was not changed until ASM3. ASM2d has an important extension to ASM2: denitrifying phosphorus accumulating organisms [11]. The ASM2 contains 19 processes affecting 19 state variables and the ASM2d contains 21 processes affecting 19 state variables. In ASM3 the decay process of ASM1 is replaced with the concept of endogenous respiration. The flows of COD in the growth and decay of heterotrophic and nitrifying bacteria are clearly separated in ASM3. As the processes are less interrelated, identifiability of the model should be better. ASM3 includes cell internal storage compounds and associated storage processes. All substrate must be stored before being utilised. [11] ASM3 with description of biological phosphorus removal was published in [12]. The ASM3 contains 12 processes affecting 13 state variables. The ASM3 with bio-P contains 23 processes with 32 parameters affecting 17 state variables.

6

The full description of the original Activated Sludge Models ASM1, ASM2, ASM2d and ASM3 including model equations and default parameters can be found in [11]. Numerous modifications to the original ASMs have been published. One example of a modified ASM is the Lindblom’s model for nutrient deficient wastewaters [13]. Lindblom’s model is described in more detail in Chapter 3.1.3 and is used in this work for modelling a full-scale pulp mill wastewater treatment plant.

2.4 Calibration of the Activated Sludge Models

The parameter sets of the ASMs are not universal as most applications require adjusting the model parameters according to the characteristics of the treatment plant [2]. It has been even stated that calibration of the models is strictly required prior to application [3]. Calibration of the ASMs has traditionally been based on ad-hoc approaches and expert knowledge of the modeller. An overview of the methodologies that have been applied in calibration of the ASMs can be found in [14]. Systematic experience based protocols have been proposed lately in attempt to create a standard protocol for calibration of the ASMs [15-18]. There have also been attempts to automate the calibration procedure by developing approaches based on systems analysis [19-21]. These approaches address the problem of poor identifiability of the ASMs by analysing the practical identifiability of the models to find identifiable subsets of parameters. Optimisation algorithms can be then applied to tune the parameters of the identifiable subsets. Despite all the effort and numerous publications on calibrating the ASMs, none of the proposed calibration protocols has established status as the standard calibration protocol.

2.4.1 Systematic experimental and experience-based ASM calibration protocols

Systematic protocols have been proposed for calibrating the ASMs, which remains the weakest link in modelling the activated sludge treatment [3]. Four such protocols are documented in the literature: The Dutch Foundation of Applied Water Research (STOWA) calibration protocol [15], BIOMATH calibration protocol [16], HSG-guideline [17] and Water Environment Research Foundation (WERF) calibration protocol [18]. A comparison of strengths and weaknesses of the four systematic calibration protocols STOWA, BIOMATH, HSG and WERF was made in [3]. It was pointed out that the protocols have a lot in common: they all start with a definition of the objective of the calibration, emphasise the importance of collecting data and verifying quality of the data and have similar validation step. However, the protocols differ in the design of the measurement campaigns, the choice of experimental methods and the calibration of parameter subsets. The protocols have mainly been developed for modelling full-scale municipal WWTPs and may therefore not be directly applicable for industrial WWTPs. The STOWA protocol [15] is based on the experience gathered from modelling full-scale wastewater treatment plants in the Netherlands. The aim of the protocol is to be easy to use and minimise the effort on sampling and testing, but provide reliable results. The

7

influent characterisation is based on filtering methods and long-term BOD measurements. Parameter calibration is a stepwise procedure where predefined parameter subsets are manually adjusted in a predefined order. It is emphasised that if major adjustments in the parameter values are necessary, a structural error in the model should be suspected. The BIOMATH protocol [16] has been developed at the BIOMATH research unit in Ghent University, Belgium. The protocol aims to combine the state of the art methodologies for calibration of different processes at the wastewater treatment plants to achieve good prediction capability under variable process conditions. The protocol contains a number of dedicated lab-scale experiments for determining parameter values. It is, however, emphasised that only experiments which provide data for influential parameters, as determined by sensitivity analysis, should be performed. The calibration procedure has two steps: steady-state calibration and dynamic calibration. Parameters having most effect on the long-term behaviour of the model are calibrated in the steady-state calibration with averaged process data. Finally, parameters which are found to be influential on the important model outputs are calibrated with dynamic process data. It is recognized that the calibration is most often performed manually due to the over parameterisation of the ASMs, which yields problems with parameter optimisation algorithms. The WERF protocol [18] was developed based on North American experiences to provide methods and guidance for calibration of the ASMs to municipalities and consulting engineers. The protocol gives excellent advice on specific tasks such as influent characterisation and estimation of nitrification and denitrification parameters. Documentation of the protocol lists several case studies which are helpful for engineers applying the protocol. However, the protocol does not discuss modelling the secondary clarifiers. Moreover, it lacks a clear structure of the complete modelling and calibration procedure. The WERF protocol also suggests the use of sensitivity analysis to determine the most influential parameters. The HSG-guideline [17] has been developed at Austrian, Swiss and German universities with the aim of producing high quality results. HSG-guideline emphasises the importance of carefully documenting the simulation study to allow comparison and reproducibility of results. The guideline does not require using any particular bioprocess model. Therefore it does not give systematic instructions on performing the calibration. However, it does suggest using sensitivity analysis to determine influential parameters. The HSG-guideline differs from the other protocols because it is not a calibration protocol. It is rather a general guideline which when followed should ensure the quality of the simulation study.

2.4.2 Systematic protocols for assessing parameter identifiability and calibration of parameters of the ASMs

The activated sludge models are practically unidentifiable for two reasons: the available data is insufficient in quality and quantity, and the model structure does not allow identification of unique values for all parameters [22]. Lack of availability and reliability of sensors for online measurements have been limiting the model identifiability [9,23].

8

Even though the performance and reliability of online sensors have improved substantially, the new sensors have not received widespread acceptance at the existing treatment plants [24]. Even if high quality data were available it wouldn’t solve the problem of poor identifiability. The activated sludge models contain non-measurable parameters and state variables. Error in one coefficient may be compensated when determining value for another coefficient and different parameters sets may produce identical results. [9] In most applications, only a small subset of parameters is chosen for calibration due to the poor identifiability of ASM parameters from data [2]. Expert knowledge was and is still used in different steps of the modelling process including the choice of which parameters should be calibrated and how they are calibrated [22]. However, it is possible to apply systematic approaches for selecting identifiable parameter subsets for given model structure and data. Two such approaches can be found in the literature: an approach based on the Fischer Information Matrix (FIM) [19, 21] and an approach based on identifiability measures which combine information on parameter sensitivity and interdependencies [20, 2]. Both approaches use local sensitivity analysis for ranking parameter importance and initial selection of parameter subsets for identifiability analysis. The approach for selecting identifiable subsets of ASM parameters proposed in [19] uses criteria calculated from the Fischer Information Matrices. The model outputs and a priori parameter values are selected first. Then a sensitivity analysis is performed to find a reduced set of parameters which show most sensitivity in the selected outputs. FIM is computed for all subsets of parameters smaller or equal to the size of the reduced set. In practice the exponentially increasing computational effort limits the analysis of large subsets. Subsets of parameters are then ranked by criterion based on the condition number or the determinant of the FIM. The approach proposed in [20] and [2] uses sensitivity analysis for quantifying the influence of individual parameters on selected outputs and collinearity measures for identifiability of parameter subsets. Influential parameters are selected to subsets which are then analysed for parameter interdependencies as quantified by the collinearity measures. The approach aims to find the largest identifiable subset of parameters which can be uniquely identified from the available data. Both approaches are local analyses as the sensitivity functions are calculated at specific parameter values [35]. The sensitivity analysis methodology is presented in detail in Chapter 3.1.4. Even though full search of the parameter space is not made, the local analysis is promising when parameter values leading to acceptable model output are known [20]. This is especially the case with the ASMs, as parameter values for various process configurations are available in the literature. Additionally, the computational cost may prohibit the application of regional methods [20]. As only a subset of all model parameters are adjusted, fixing the parameters a priori which are not included in the identifiable subset can potentially bring bias in the parameter estimates [20].

9

2.4.3 Examples on ASM calibration

Some examples from the literature are presented here on calibrating the ASMs for full-scale municipal and industrial wastewater treatment plants Even though calibration approaches based on systems analysis have been developed, most case-studies and practical applications of modelling full-scale WWTPs with the activated sludge models presented in literature apply experience-based and ad-hoc methods for model calibration. Actually, it appears that in the field of activated sludge modelling the only studies using the systematic methods for assessing parameter identifiability and parameter calibration are the studies which demonstrate these methods. In [25], Pardo et al. applied the ASM3 and the STOWA protocol for modelling an activated sludge plant removing organic matter and nitrogen from oil refinery effluents. Influent wastewater was characterised according to the STOWA protocol. Calibration was performed utilising steady-state data and laboratory scale batch tests and by manual parameter adjustment to fit modelled MLSS, effluent COD and nitrogen balance to measured data. No results from dynamic simulations were presented. Methanol is dosed as an external carbon source to denitrification in order to achieve very low nitrate levels in the effluent. The calibrated model was used to study scenarios leading to decreased methanol consumption while maintaining current discharge levels. Hulle et al. [26] applied an extended ASM1 and the BIOMATH calibration protocol for modelling an activated sludge plant treating chemical industry wastewater. Aims of the study were to demonstrate the capability of the BIOMATH protocol in modelling industrial wastewater treatment and use the calibrated model for optimisation of the studied treatment plant. Respirometric batch tests were used in determining central parameters of the model. Additionally, three parameters were manually adjusted to improve the fit of model response to data. The model was validated by running a dynamic simulation with operational data from the WWTP. Finally, the model was used for optimisation of operating strategies of the WWTP and investigation of the impact of production schedules on the WWTP effluent discharges. Vandekerckhove et al. [27] modelled a food industry WWTP with the ASM1. Model parameters were not calibrated, but wastewater characterisation was made according to combination of the STOWA protocol and operator experience. Performance of the model was validated by running a simulation with full-scale treatment plant data. The performance was found to be sufficient even though the model was not calibrated. The model was then used to evaluate options for physical treatment plant upgrade. In [28], a model calibration procedure was proposed, which later became known as the BIOMATH calibration protocol [16]. The procedure was evaluated by calibrating ASM1 for a municipal-industrial WWTP. The calibrated model was to be used in process optimisation focusing on improvement of capacity for nitrogen removal. Laboratory scale experiments were used to estimate values for parameters which were thought to be significant. Local sensitivity analysis was performed for the calibrated model to verify that the calibrated parameters were indeed significant. Tracer tests were performed to

10

characterise hydraulics of the WWTP. Simulation results provided a decent agreement when compared to plant effluent data. Barañao and Hall calibrated ASM3 for an activated sludge plant treating mechanical pulp and paper mill effluents in [29]. The most significant parameters, as determined by a non-specified method of sensitivity analysis, were calibrated with results from respirometric batch experiments and conventional analyses. The calibrated model was able to fit the measured oxygen uptake rate (OUR) curves. Simulation results with full-scale process data were not presented. Nuhoglu et al. [30] modelled an activated sludge plant treating municipal wastewater with the ASM1. Respirometric batch experiments were used in wastewater characterisation. Four of the ASM1 parameters were manually adjusted from their original values to improve the model fit to process data from the WWTP. The full-scale treatment plant was simulated with the model for a period of 42 days, and the simulation results were in good agreement with the measured values. Satoh et al. [31] applied ASM2 for modelling nutrient removal in pilot scale activated sludge processes treating municipal wastewater. Characterisation of wastewater was chosen rather arbitrarily. Five of the model parameters were manually adjusted to improve the model fit to experimental data. Simulated nutrient and COD profiles in the processes were compared to experimental results, which were in good agreement for the calibrated model. Koch et al. presented calibration and simulation results of ASM3 for Swiss municipal wastewater treatment plants in [32]. Most influential parameters, as determined from local sensitivity functions, were calibrated manually by fitting the model to experimental results of respirometric batch experiments with different substrates under aerobic and anoxic conditions. The same set of parameters was used for simulation of four full-scale and pilot plants. Fit of the simulation results to full-scale plant data was further improved by adjusting some of the parameters. In [33], Wichern et al. reported on their experiences on modelling three full-scale municipal wastewater treatment plants in Germany with ASM3 combined with Bio-P module. Readily degradable COD was determined with respirometric experiments. Parameters calibrated in [32] were used initially. Few of the parameters were manually adjusted for each treatment plant model to increase the correspondence between full-scale simulation results and measured data. In [22], Sin et al. proposed an approach to automate the ASM calibration procedure which is usually carried out manually. The approach was applied for re-calibration of ASM2d for municipal WWTP performing nutrient removal. The model was already calibrated with the experience-based BIOMATH protocol. It should be noted that the available data was of unusually high quality: for example nitrogen measurements were available every five minutes during the 86 day period. The approach begins with selecting the identifiable subset of parameters either by identifiability analysis or expert

11

knowledge. Then a distribution for the parameter values has to be provided. The novel aspect of this approach is the application of Monte Carlo procedure for model parameter estimation. Latin hypercube sampling is used to pick values from the parameter space and a simulation is run with the sampled values. The parameter set which results in the lowest objective function value is chosen. The proposed approach basically does parameter optimisation by covering the parameter space systematically. It would be interesting to see how the Monte Carlo optimisation would compare to evolutionary computing approaches in the same problem. Weijers and Vanrolleghem [19] presented a procedure for systematic selection of identifiable parameters in calibrating the ASMs. The procedure was briefly introduced in Chapter 2.4.2. The procedure was applied on modelling a carousel type wastewater treatment plant with the ASM1. The chosen a priori parameter set consisted of ASM1 default values and values from earlier manual calibration. The most sensitive parameters were first selected by using a local sensitivity analysis. All subsets of two to eight parameters were evaluated with the proposed identifiability criteria to find the best identifiable subset for each size. Identifiability of the subsets was tested with simulated output data and real data. For simulated data with and without noise the parameters were identified accurately. Parameter subsets up to size of five were possible to estimate from real data. However, no simulation results were presented to show the performance of the calibrated model. Finally, the effect of the a priori parameter values was evaluated by sampling the whole parameter space. It was concluded that the a priori selection did not affect the results too strongly. In [2], Brun et al. applied the method of systematic selection and tuning of ASM parameter sets first introduced in [20] for ASM2d. This method was also briefly introduced in Chapter 2.4.2. The modelled process was an experimental lane of a WWTP performing organic carbon, nitrogen and phosphorus removal from municipal wastewater. The process was equipped with four online sensors for measuring phosphate and ammonia in the reactors. The identifiability study started with quantifying uncertainty in the a priori parameter set, which was chosen as the ASM2d default parameters. Also certain parameters were excluded from the study, as they were considered to be better estimated from other experiments. This prior analysis relies heavily on expert judgement. Parameter significance was quantified with a local sensitivity analysis around the a priori parameter values. The identifiability measures, the collinearity index and the determinant measure, were calculated for parameter subsets of different sizes. Subsets up to size of nine parameters were considered identifiable. However, three more parameters were fixed and the final set, which was estimated from the available data, had six parameters. Comparison of simulation results and measured data was presented and it showed good agreement. Finally, the effect of fixing certain parameter values was studied. It was found that the estimated parameter values depend strongly on the values of the fixed parameters, proving that the estimated parameter values were reasonable rather than true or unique. Despite its shortcomings, the identifiability analysis methodology presented in [20] has proven to be a useful tool in analysis of large deterministic models. The methodology has been applied in studies of models other than the ASMs in environmental and chemical engineering [34-37].

12

Machado et al. [21] proposed a procedure for systematic selection and calibration of parameter subsets of the ASMs. The procedure is based on the works of Weijers and Vanrolleghem [19] and Brun et al. [20] but it has some improvements. The procedure was applied on modelling a pilot plant performing nitrogen and phosphorus removal from synthetic wastewater. Local sensitivity analysis is used for initial parameter ranking as in the two previously described methods. Parameter subset selection uses the highest ranking parameters as seeds to which parameters are added one at a time. Identifiability of the subsets is quantified by criteria calculated from the FIM. The procedure was tested on a limited set of data but the results were promising. Improvements over the previously proposed identifiability analyses are the decreased computation time, as it is not necessary to explore all parameter subsets under a certain size, and the decreased need for subjective expert judgement. Ruano et al. [38] evaluated parameter subset selection used in experience-based and systems analysis based approaches with the methods of [20]. It was found that the parameters ranking highest in sensitivity analysis were often omitted from the identified parameter set. Experience-based choices for identifiable subsets were either too optimistic, resulting in poorly identifiable sets, or too pessimistic, resulting in loss of available information. On the other hand, the applied systematic identifiability analysis method suffered from heavy computational demand, rendering it practically useless for analysis of large parameter subsets.

2.4.4 Discussion on the different approaches to ASM calibration

Accurate process data with high sampling frequency has to be available for purely process data based ASM identification protocols to make sense. If monitoring of the process conditions is very infrequent, and quality of influent and effluent are measured and recorded only to satisfy the authorities, even small parameter subsets cannot be identified accurately from the data. The quality of ASM calibration would be poor in such a case. For these reasons the experience based calibration protocols described in Chapter 2.4.1 utilise dedicated laboratory experiments to estimate model parameters and to complement the available process data. In successful calibration studies with process data based systems analysis methods the process data has been of exceptionally high quality. The earliest study on systematic selection of identifiable parameter subsets, [19], used effluent and influent data from two day measurement campaign where the data had been sampled every two hours. In [2] online measurements of phosphate and ammonia were available from several compartments of the treatment plant. Three months of process data with ammonium, nitrate and dissolved oxygen (DO) measurements at every five minutes coupled with intensive measurement campaign were utilised in [22] and [38]. Purely process data based ASM parameter identification methods are unlikely to get much success outside academic studies unless online instrumentation is implemented on a larger scale at WWTPs.

13

3. CASE STUDY ON ACTIVATED SLUDGE MODELLING

Modelling of biological wastewater treatment has been an increasingly popular topic for research since the publication of ASM1. Activated Sludge Models have been mainly developed for modelling activated sludge plants treating municipal wastewater and therefore may not be directly applicable to industrial WWTPs [10]. However, ASM models with appropriate modifications or extensions have been found useful in modelling and simulation of biological treatment of industrial wastewaters [26, 39-40]. Despite the abundance of literature on activated sludge modelling, pulp and paper wastewater treatment modelling has not received much attention. The objective of the study presented here was to calibrate a modified ASM1 for an activated sludge plant treating pulp mill wastewater and validate the model utilising a long-term simulation of a full-scale WWTP. The primary interest in modelling was to achieve an accurate model of COD and nutrient removal. Applied model is a modified ASM1 introduced by Lindblom [13]. The model is simplified by omitting the biological nitrogen removal by the processes of nitrification and denitrification. Since the wastewater is known to be nutrient deficient, growth limiting effects on heterotrophic bacteria from nitrogen and phosphorus are included in the model. The model was calibrated for the wastewater treatment plant of Stora Enso Fine Paper Oulu pulp mill. Wastewater characterization and model parameter calibration were simple without extensive analytical work. OUR measurements and routinely measured process data were used for these purposes. All modelling and simulation studies were made with data from Stora Enso Fine Paper Oulu pulp mill WWTP. Measurements and wastewater characterisation results from Stora Enso Fine Paper Nymölla pulp and paper mill WWTP are provided for comparison.

3.1 Materials & Methods

3.1.1 Plant & data description

All modelling and simulations in this report were made with the wastewater treatment plant of Stora Enso Fine Paper Oulu bleached kraft pulp mill. The wastewater treatment plant is an aerobic activated sludge plant designed for removal of suspended solids and organic carbonaceous material. Wastewater is treated in primary sedimentation before the biological treatment. Primary treatment stages also include pH control, cooling and nitrogen dosing. Nutrients dosing is not actively controlled according to the carbonaceous load to the treatment plant, which results in occasionally too low and too high nutrient to carbon ratios in the wastewater. The activated sludge plant treats an average of 32 000 m3 wastewater per day. Other technical information and wastewater characteristics can be found in Tables 2 and 3. More detailed description of the treatment plant can be found in [41].

14

Table 2. Technical information of the wastewater treatment plant at Stora Enso Fine Paper, Oulu. Number Area (m2) Volume (m3) Primary clarifiers 1 1963 9100 Equalization basins 1 11 000 Activated sludge basins 1 3400 25 000 Secondary clarifiers 1 2827 12 150

Table 3. Characteristics of the influent, effluent and discharge limits at the wastewater treatment plant of Stora Enso Fine Paper Oulu. Values are averages over the simulation period. Aeration

influent Effluent Discharge limits

(kg/d) Flow (m3/d) 32 000 32 000 COD (mg/l) 1167 541 45 000 BOD7 (mg/l) 255 14 Tot-N (mg/l) 6.6 2.9 500 Tot-P (mg/l) 1.7 0.7 55 Temperature (ºC) 39

The primary clarifier was not included in this study. Therefore all samples and influent data were collected from the aeration basin influent. Effluent data refers to the secondary clarifier effluent discharged to the sea. Process data from 1 November 2007 to 4 September 2008 was used for the simulations in this study. The influent process data consists of flow rates of influent wastewater, return sludge and wasted sludge, and of total COD, total nitrogen and total phosphorus concentrations. The effluent process data consists of total COD, total nitrogen and phosphorus, and soluble nitrogen and phosphorus concentrations. Flow rates in the data are daily average values. Total COD concentrations are analysed from 24 hour composite samples five times a week for the influent wastewater and daily for the effluent. Nitrogen and phosphorus concentrations are analysed weekly from samples combined from the 24 hour composite samples. Activated sludge and wastewater samples from the WWTP of Stora Enso Nymölla mill were also analysed to characterise the wastewater. At Nymölla mill, activated sludge plant treats on average a total of 81 000 m3 wastewater per day from sulphite pulp mill and paper mill producing office and graphic papers. The WWTP is an activated sludge plant with two aerated 70 500 m3 basins and three secondary clarifiers with a volume of 10 000 m3 each. Influent wastewater is pretreated in primary clarifiers and dosed with additional nitrogen and phosphorus nutrients. Part of the wastewater from bleaching is pretreated in ultrafiltration before activated sludge treatment.

3.1.2 Analytical work

Oxygen uptake rate (OUR) measurements were made with sludge and wastewater from the Stora Enso Fine Paper Oulu pulp mill and Nymölla mill wastewater treatment plants. The OUR measurements were made as described in [42] and [43]. Activated sludge was

15

sampled from the aeration basin and wastewater from the aeration basin influent. The measurements in Oulu were made between 8 January and 9 February 2009 once or twice a week. Due to some problems with the OUR equipment useful results weren’t obtained until 21.1. With each sample the OUR measurements were made both with the wastewater and potassium acetate as carbon sources. For some measurements a third reactor and oxymeter were used to validate the results of the OUR measurement with wastewater as the carbon source. Specific oxygen uptake rate (SOUR) was calculated by dividing the OUR values with the suspended solids (SS) or volatile suspended solids (VSS) concentration in the reactor. Each sample was analysed also for soluble and total COD, suspended solids and soluble and total phosphorus concentrations. More details on the performed analyses are found in [1]. Dates and analyses made on a given day can be found in Table 4. At the beginning of the measurement campaign there were problems with the equipment and dilution of the sludge. Only measurements which went through without problems are given in Table 4. Table 4. Measurement dates and measurements made on samples from Stora Enso Oulu mill WWTP. ww = wastewater. OUR,

ww OUR, ww in another

reactor

OUR, acetate

Total COD, sludge

Soluble COD, sludge

Total COD, ww

Soluble COD, ww

SS, sludge

SS, ww

21.1.2009 x x x x x x x x 22.1.2009 x x x x x x x x 26.1.2009 x x x x x x x x x 28.1.2009 x x x x x x x x x 2.2.2009 x x x x x x x x x 4.2.2009 x x x x x x x x x 9.2.2009 x x x x x x x x x Total

orto-P, ww

Soluble orto-P, ww

Soluble orto-P, sludge

Total P, ww

Soluble P, ww

Soluble P,

sludge

Total N, ww

Soluble N, ww

Ammonium N, ww

21.1.2009 x x x 22.1.2009 x x x x x x 26.1.2009 x x x 28.1.2009 x x x x x x 2.2.2009 x x x 4.2.2009 x x x 9.2.2009 x x x

Sludge and wastewater from Nymölla mill were sampled on only two occasions, 19.11.2008 and 4.6.2009. Dates and analyses made on each day can be found in Table 5.

16

Table 5. Measurement dates and measurements made on samples from Stora Enso Nymölla mill WWTP. ww = wastewater. OUR, ww OUR, acetate OUR,

methanol OUR,

ethanol OUR, 2/3 acetate

+ 1/3 ethanol OUR,

glucose 19.11.2008 x x 4.6.2009 x x x x x x

Total COD, sludge

Soluble COD, sludge

Total COD, ww

Soluble COD, ww

SS, sludge SS, ww

19.11.2008 x x x x x x 4.6.2009 x x x x x x

3.1.3 Model structure

The original Activated Sludge Models were developed with the aim of being able to describe the biological removal of organic carbon and nitrogen from municipal wastewater, and later also biological removal of phosphorus. Municipal wastewater has much higher concentrations of nutrients than is required for the growth of heterotrophic bacteria oxidising organic carbon. Therefore growth limiting effects of nitrogen and phosphorus were not included in the original ASMs. However, in wastewaters from pulp and paper mills the nutrient concentrations are much lower. In many cases it is necessary to add nutrients to achieve complete removal of biodegradable organic carbon. A deterministic model for the biological treatment of nutrient deficient wastewaters should include mechanisms for growth limiting effects of nutrients. An example of such model was presented by Lindblom in [13]. The model presented in [13] has both simplifications and extensions to the original ASM1. Since the model aims to describe only the removal of organic carbon, the model was simplified by removing the biological nitrogen removal processes of nitrification and denitrification and their associated state variables. The model was extended to include description of reduction in sludge production by higher order organisms called protozoa. Even though the presence and significance of protozoa in activated sludge systems is well established [44], this extension should be considered highly experimental as the mechanisms have not been validated. Moreover, OUR measurements and available process data do not have information on dynamics of these processes further deteriorating identifiability of the model. All processes and state variables associated with higher order organisms were omitted from the model applied in this work. As the nitrification process is not included in the model [13] the state variable for nitrate and nitrite nitrogen (SNO) of the original ASM1 was removed. The active mass nitrogen (XNB), nitrogen in products arising from biomass decay (XNP), inert soluble nitrogen (SNI) and inert particulate nitrogen (XNI) are explicitly included as state variables in the model unlike in the ASM1. The original ASM1 did not have any state variables for phosphorus. The modified ASM1 has state variables for soluble biodegradable phosphorus (SP), particulate organically bound phosphorus (XPD), phosphorus in products arising from biomass decay (XPP) and two state variables for phosphorus in active biomass (XPB,1 & XPB,2). XPB,1 is the minimum phosphorus content in active biomass and XPB,2 is additional

17

variable phosphorus content which is part of the biomass only when excess phosphorus is present. Inert soluble and particulate phosphorus are not explicitly included as state variables. However, they are taken into account in wastewater characterisation. In many cases DO is an important factor limiting the treatment performance in an activated sludge process, because oxygen is required for aerobic degradation of organic material and endogenous respiration. DO is modelled as a state variable in the ASMs and it influences the growth rate of heterotrophic bacteria. Volumetric mass transfer coefficient is used for modelling aeration of the activated sludge basins. DO concentration at the studied treatment plant was in the range of 3-6 mg l-1 throughout the simulated period. DO concentrations in the range of 1.5-2.0 mg l-1 are considered to be sufficient and DO concentrations above 4 mg l-1 do not improve operation of the treatment plant significantly [8]. Therefore in this work DO was not a limiting factor for utilisation of substrate in the simulations. When applying the activated sludge models, the aeration basin is modelled as a continuous stirred-tank reactor (CSTR) or as a series of CSTRs depending on the tank configuration. Component balances for the liquid phase over each CSTR are written for every state variable in the model. Component balances for the liquid phase over each CSTR are written as

( ) iiinii RCC

VQ

dtdC

+−= , (1)

where dCi is the concentration of the ith component in mg l-1, Q is the flow rate in m3 d-1, V is the reactor volume in m3 and Ri is the reaction rate of the ith component in mg (l d)-1. Reaction rates Ri in the component balances for each state variable are obtained from the ASM model matrix as

(2) ∑=

⋅=n

jjiji ryR

1,

where yj,i is the stoichiometric coefficient of the ith component for the jth process, rj is the rate equation for the jth process and n is the number of processes. The stoichiometric coefficients for each state variable and rate equations for each process of the applied model are given in Appendix 1. Complete set of parameter values for the model is given in Appendix 2. The model equations are not described in detail in this report. For complete description of the equations, see the original work [13] or for example [45]. Variable nitrogen uptake Nitrogen is added to the influent of the studied treatment plant to avoid nutrient deficiency in biological removal of organic carbon. However, occasionally the required nitrogen dosage is exceeded or disturbances in the upstream processes cause higher than required concentrations of nitrogen in the influent wastewater. Simulation results in Chapter 3.2.5 show that with no other mechanism for nitrogen removal than the growth

18

of heterotrophic biomass, the model predicts higher than measured concentration of nitrogen in the effluent. An experiment was made with a modification in the model to allow some variance in the nitrogen content of heterotrophic biomass when excess nitrogen is present to see if it could improve the model of nitrogen removal. Similar mechanism for additional phosphorus uptake is already included in the model. However, such a mechanism for nitrogen is not supported by the literature. Some justification for the mechanism is given by the fact that default nitrogen content of active biomass in ASM1 is rather high, 0.086 gN (gCOD)-1, compared to the default value of the modified model of 0.05 gN (gCOD)-1. Modelling the secondary settler A model of the secondary settler is required for simulating a complete activated sludge plant. Most activated sludge plant configurations also include a primary settler. Primary settler was not included in the treatment plant model in this work, as all the used influent wastewater measurements were made from the aeration basin influent. Secondary settler was modelled according to the Benchmark Simulation Model no. 1 (BSM1) by the IWA Task Group on Benchmarking of Control Strategies for WWTPs [46].

3.1.4 Sensitivity analysis

Sensitivity analysis (SA), which studies the “sensitivity” of the system outputs to changes in the parameters, inputs or initial conditions, can be divided into local and global approaches. Local sensitivity analysis is restricted to effects of small changes of parameters while global methods analyse the effects of simultaneous, order of magnitude changes. [47] The local approach of evaluating sensitivity functions at a specific location in parameter space has proven successful when systems are operated around predefined parameter values and when there are known values which produce acceptable output. [20] These conditions are true for the ASMs for which there are plenty of calibrated and tested parameter sets available in the literature. Sensitivity analysis of mathematical models may be carried out for a number of reasons. SA can be used to provide information on which parameters require more research, which parameters are insignificant and can be eliminated and which parameters and inputs correlate most with the outputs. [48] For large scale nonlinear environmental models the calibration is a task to find physically acceptable parameter values that describe the data adequately. The main tool for identifiability analysis, which attempts to provide insight on these adequate values, is sensitivity analysis. [20] We will present the local sensitivity analysis methodology used in identifiability studies of Brun et al. [20, 2] and Weijers and Vanrolloghem [19]. The most complete and unambiguous description of the methodology is found in [37], and is reproduced here. The dimensionless scaled sensitivity functions in both approaches are

19

( ) ( )

0

,, 0

0,

θθθθθ

θ=

∂∂

=j

i

i

jji

tysc

ts (3)

where jiy θ∂∂ / denotes the derivative of a model variable with respect to the parameter iy

jθ evaluated at a specific point 0θ in the parameter space, j0θ is the nominal value of the perturbed variable and is a scale factor representing typical magnitudes of the output . In the original description [20, 2], an a priori measure of the reasonable range of

isc

iy

jθ is used in (3) instead of j0θ . Different methods and practical issues on approximating the partial derivatives in Equation (3) are discussed in [47] and reproduced here. Finite difference approximation is the simplest method to approximate the partial derivatives. Finite difference approximation is given by the equation

( ) ( )

j

jijji

j

i tytyyθ

θθθθ Δ

−Δ+≈

∂∂ ,,

. (4)

where jθΔ is the change of the parameter value ξθθ jj =Δ (5) where ξ is the defined perturbation factor. Perturbation factor has to be small, but in practice the numerical accuracy of the solution limits the choice. Finite difference approximation requires solution of the model with the nominal parameter values and p solutions with the perturbed values where p is the number of parameters. The calculated sensitivities belong to the ( 2/ )θθ Δ+ parameter set. If sensitivities around the nominal values are required, the central difference formula should be used. The central difference formula is given by

( ) ( )

j

jjijji

j

i tytyyθ

θθθθθ Δ

Δ−−Δ+≈

∂∂

2,,

. (6)

The sensitivity functions in the central difference formula can also be used to obtain information on quality of the sensitivity calculation. The central difference formula requires 2p solutions. Sensitivity measures can be calculated to quantify the influence of a single parameter on the model outputs [37]. Sensitivity measure originally proposed in [20] can be calculated as

20

n

s jmsqrj =δ (7)

where js is the norm of the vector sj constructed by concatenating the sensitivity function vectors of each variable for the parameter j and n is the total number of measurements. Therefore the sensitivity measure (7) sums the influence of a certain parameter on all the outputs included in the sensitivity analysis.

3.1.5 Wastewater characterisation

Only total concentrations of COD and nutrients are measured routinely at the treatment plants. For modelling purposes and for running simulations, the total COD and nutrient concentrations of influent wastewater have to be divided into different biodegradable and inert fractions. In the ASM1 and the modified ASM1 applied in this work biodegradable COD is divided into easily (SS) and slowly biodegradable (XS) fractions, and inert COD is divided into soluble (SI) and particulate inert (XI) fractions. Physical-chemical or biological methods can be used for characterising wastewater. Typically a combined approach is applied. Respirometric experiments are often used for biological characterisation. [14] Different methods for wastewater characterisation have been proposed, but there is no established protocol for conducting the experiments and interpreting the results. In recent published studies on modelling full-scale activated sludge plants the exact characterisation protocol varies from one study to another [26-30]. In this report COD characterisation results from two different approaches are presented. Substrate consumption is difficult to measure directly, but oxygen consumption can be calculated from OUR measurements. Yield coefficient for heterotrophic bacteria is required for calculating the amount of substrate consumed from the amount of oxygen consumed. Therefore determination of heterotrophic yield coefficient is the key for calculating different COD fractions. The two approaches presented here differ in the way how heterotrophic yield coefficients are determined. In both approaches readily and slowly biodegradable COD fractions are calculated from the results of OUR measurements. Area under the OUR curve is integrated to get oxygen consumption from oxidation of external substrate as displayed in Figure 3. Exogenous respiration rate is obtained by subtracting endogenous respiration rate from the OUR. Readily and slowly biodegradable COD are calculated with

( )⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

−= ∫

fint

exH

dtrY

S01

10 (8)

where S(0) is the readily or slowly biodegradable substrate initially present in the wastewater, YH is the heterotrophic yield coefficient, tfin is the end point of integration where the substrate is completely oxidized and rex is the exogenous respiration rate for

21

readily or slowly biodegradable substrate [14]. Lower limit of the definite integral in (8) is the time instant where wastewater is added to the endogenous sludge.

Figure 3. An example of calculating oxygen consumption associated with easily and slowly biodegradable substrate from OUR measurement. Division of total exogenous OUR into oxidisation of SS and XS is slightly different from what was used in COD fractionation in the journal paper [1] based on the same material. In [1], the division was made at the same time instant but with a vertical line. The approach used in this report is better, as according to the model XS is consumed from the moment of wastewater addition by the hydrolysis process. Thus the division of biodegradable COD into SS and XS fractions is slightly different between [1] and this report. Since direct measurement of SI is not possible, SI is often estimated by the soluble COD in effluent or as 90% of the effluent COD [14,16]. Because only total COD measurements are available in the process data from the studied treatment plant, SI is estimated as 90% of the effluent COD. SI fractions for the days when the OUR measurements were made cannot be calculated from the plant data due to unreliability of influent measurements. However, COD measurements made from the influent grab samples used for OUR measurements can be used instead. Estimating heterotrophic yield coefficient from biodegradable COD in wastewater Heterotrophic yield coefficient YH can be calculated from Equation (9) when the biodegradable COD in wastewater, CODDegradable is known.

( )

Degradable

Degradable

COD

dttrCODY

t

ex

H

∫−= 0 (9)

CODDegradable is estimated as the COD concentration in the filtered wastewater subtracted with the inert soluble COD [14]. ( )trex is the exogenous respiration rate and t is the time instant when the exogenous substrate is completely oxidized. Lower limit of the definite integral is the time instant where wastewater is added to the endogenous sludge. With the

22

estimate of YH from Equation (9) readily and slowly biodegradable fractions can be calculated from Equation (8). Estimating heterotrophic yield coefficient from OUR measurements with reference substrate Heterotrophic yield coefficient, which is required in calculating COD fractions from OUR data, can be estimated by making OUR measurements with a reference substrate which is known to be 100% biodegradable. This procedure is described in [43]. Knowing the exact amount of biodegradable substrate overcomes the problem of estimating CODDegradable in Equation (9). This approach, however, is not without problems. Potassium acetate, which was used as reference substrate in the OUR measurements with samples from Oulu mill, may not be the best choice for sludge from the WWTP of the kraft pulp mill. Acetate gave a very low response in the OUR experiments compared to the wastewater from the pulp mill. Therefore it doesn’t represent the easily biodegradable part of the wastewater, and the yield coefficient values calculated for it are not likely to be same for the actual wastewater. There is also evidence that yield coefficients for acetate and wastewater differ because of different storage phenomena [49-50]. In future work on OUR measurements of pulp and paper mill wastewater different reference substrates will be tried to find the most suitable one. Characterisation of wastewater nutrient content Nutrients in the wastewater also have different biodegradable and inert fractions which have to be determined for simulating the treatment plant. In the applied model, wastewater nitrogen is fractioned into ammonium nitrogen (SNH), soluble biodegradable organic nitrogen (SND), soluble inert nitrogen (SNI) and particulate inert nitrogen (XNI). Phosphorus in wastewater is fractioned into soluble biodegradable phosphorus (SP) and particulate biodegradable phosphorus (XPD). Inert phosphorus is not included as a state variable, but it is taken into account by removing the presumed inert fraction from the biodegradable phosphorus. Division of nutrients into soluble and particulate fractions can be made directly from the measurements. SNH can be determined from a direct measurement. Fractions of inert nutrients cannot be determined from simple nutrient concentration measurements. Therefore they are adjusted from the default values provided in [13] in steady-state calibration described in Chapter 3.1.6.

3.1.6 Calibration procedure

The calibration strategy applied in this work attempts to utilise results from simple OUR measurements and available process data to calibrate activated sludge models for long-term simulation of full-scale activated sludge plants. It is recognised that there exists several comprehensive calibration protocols for the activated sludge models. However, they either require dedicated laboratory experiments, which are often very time consuming, difficult to carry out or require special equipment or high quality frequently sampled process data from online instruments.

23

As complete parameter sets of activated sludge models are unidentifiable [22], only parameters having most influence on studied model outputs are calibrated. Parameter significance is ranked using sensitivity analysis. However, identifiability of parameters selected for calibration is not analysed in this work. Optimisation of parameters is possible to automate, but in this work it is done manually. For successful numerical optimisation identifiability of the selected parameter subset would have to be analysed and ensured to obtain unique and physically meaningful parameter values. In this work ideas of steady-state and dynamic model calibration from the BIOMATH calibration protocol [28, 16] are loosely followed. Separate steady-state and dynamic calibration levels are also introduced in the original report on ASM2 [11]. The activated sludge models contain parameters which have effect on the long-term behaviour of the treatment plant model and parameters which determine the response of the model to short-term disturbances. When running a simulation with real process data the models should be capable of describing both long-term trends and transient behaviour. As in the steady-stage calibration stage of the BIOMATH calibration protocol, also in this work process data is averaged and model is calibrated to fit effluent quality and sludge production data. After steady-state calibration, the model is used to simulate OUR experiments and parameters are again calibrated to fit the simulated and measured OUR data. OUR measurements give information on response of the activated sludge to short-term disturbances. Kinetic parameters, which are not well identified from steady-state data, can therefore be estimated utilising the OUR experiments.

3.2 Results & Discussion

3.2.1 OUR measurements and wastewater analyses

OUR measurements were made with sludge sampled from the treatment plants of Stora Enso Fine Paper Oulu and Nymölla mill. Wastewater samples and reference substrates were used for the measurements. Results of the OUR measurements are shown in Figures 4 and 5. Measurement dates and substrates used can be seen from the figures. Results of conventional wastewater measurements are given in Tables 6 and 7. From the Figures 4 and 5 it can be seen that in all cases the wastewater COD was easily degraded and the wastewater was not toxic for the sludge. OUR for wastewater from both Oulu and Nymölla has high initial peak corresponding to the easily biodegradable COD. The slow decline to endogenous level after the initial peak corresponds to the slowly biodegradable COD. Even though potassium acetate should be very easily degradable substrate, it yielded much lower SOUR than the wastewater in measurements with sludge from Oulu mill. Volatile fatty acids are not present in significant concentrations in sulphate pulp mill effluent, and more suitable reference substrates should be explored. On the other hand, sludge acclimatised to sulphite pulp mill effluent as in the case of Nymölla responds very well to acetate. Even higher SOUR is obtained with a mixture of acetate and ethanol. This behaviour has been observed elsewhere [51] and is a result of more bacterial groups becoming active. The mixture of acetate and ethanol represents

24

sulphite pulp mill wastewater well, and can be used as a reference substrate in OUR measurements with sulphite pulp mill WWTP sludge and wastewater.

Figure 4. Measured OUR and SOUR of samples from Stora Enso Fine Paper Oulu pulp mill wastewater treatment plant during 21.1 – 9.2.2009.

25

Figure 5. Measured OUR and SOUR of samples from Stora Enso Fine Paper Nymölla pulp and paper mill wastewater treatment plant on 19.11.2008 and 4.6.2009. Table 6. Results of COD and suspended solids measurements. Total COD,

sludge [mg/l]

Soluble COD, sludge

[mg/l]

Total COD, wastewater

[mg/l]

Soluble COD, wastewater

[mg/l]

Suspended solids, sludge

[g/l]

Suspended solids,

wastewater [g/l] Nymölla: 19.11.2008 7290 420 1920 1830 6.3 0.08 4.6.2009 8700 600 2310 2195 6.2 0.111 Oulu: 8.1.2009 4770 1286 4.4 12.1.2009 5500 1190 1090 6.2 0.4 16.1.2009 6150 1330 1210 5.4 0.14 21.1.2009 5900 415 1360 1200 6.2 0.12 22.1.2009 5810 380 1280 1140 5.8 0.10 26.1.2009 5831 369 1031 947 5.8 0.10 28.1.2009 5864 411 1008 953 6.2 0.13 2.2.2009 5709 325 883 834 6.2 0.10 4.2.2009 5681 379 1170 1046 6.4 0.12 9.2.2009 5629 383 998 974 6 0.13

26

Table 7. Results of nutrient measurements. Total

orto-P, ww

[mg/l]

Soluble orto-P,

ww [mg/l]

Soluble orto-P, sludge [mg/l]

Total P, ww

[mg/l]

Soluble P, ww [mg/l]

Soluble P, sludge [mg/l]

Total N, ww [mg/l]

Soluble N, ww [mg/l]

Ammonium N, ww [mg/l]

8.1.2009 2.45 12.1.2009 3.2 1.49 1.7 0.18 16.1.2009 2.42 1.26 0.33 21.1.2009 2.45 1.7 0.4 22.1.2009 2.32 1.1 0.33 2.66 1.18 0.37 26.1.2009 2.72 1.1 0.37 28.1.2009 3.53 1.18 0.39 4.5 1.4 0.12 2.2.2009 2.57 1.12 0.39 4.2.2009 2.61 1.21 0.36 9.2.2009 2.83 1.52 0.35

3.2.2 Sensitivity analysis

Sensitivity analysis of model parameters was run to screen out most important parameters for model calibration. Only kinetic and stoichiometric coefficients of the activated sludge model were included in the analysis. Sensitivity of wastewater characterisation on the model outputs was not studied even though characterisation certainly has a lot of influence on the model behaviour. Including wastewater characterisation would have been problematic, as change in a fraction of a component has to be compensated by changes in other fractions to make the sum of fractions one. Some stoichiometric model parameters such as iXP and iXB also affect wastewater characterisation. Their effect on characterisation was not included in the sensitivity analysis for the reason above. Sensitivity analysis was run for the three different simulations and data: steady-state and OUR experiment simulations in the model calibration and simulation with the full process data. In model calibration, steady-state simulation is run first to calibrate parameters responsible for long-term behaviour of the treatment plant model. For steady-state simulation, sensitivity was calculated at the end of the simulation. For sensitivity analysis of the OUR experiment, simulation results were sampled every five minutes for sensitivity calculation. Finally, for the sensitivity analysis of the full process data, simulation results were sampled once a day for sensitivity calculation. Sensitivity was calculated for outputs total COD, soluble N and soluble P. The choice of perturbation factor in the sensitivity analysis is rather challenging as the perturbation factor determines quality of the sensitivity function. Perturbation factor should approach zero, but in practice precision of calculations limits the choice. [47] Sensitivity analysis was run with perturbation factors 0.05, 0.01, 0.005 and 0.001. For simulation with the long-term full process data, perturbation factor of 0.01 yielded reasonable results for all model parameters. Smaller values produced “noise” in the results which could be seen by obviously insignificant parameters getting high sensitivity measures. Steady-state simulation was less sensitive to the choice of perturbation factor and the value of 0.01 was used. OUR experiment simulation required higher perturbation factor of 0.05 to get reasonable results.

27

δmsqr is a local measure of parameter sensitivity. Therefore sensitivities are calculated at initial parameter values and after calibration. Results of the sensitivity analysis are given in Table 8. Table 8. Results of sensitivity analysis.

Long-term simulation,

initial parameter values

Long-term simulation, calibrated

parameter values

Steady-state simulation,

initial parameter values

Steady-state simulation, calibrated

parameter values

OUR experiment simulation, initial parameter values

OUR experiment simulation, calibrated

parameter valuesPara-meter

δmsqr Para-meter

δmsqr Para-meter

δmsqr Para-meter

δmsqr Para-meter

δmsqr Para-meter

δmsqr

YH 6.07 YH 6.94 YH 2.615 YH 3.931 YH 0.897 YH 0.914 iXB 1.55 iXB 1.52 iXP 0.544 iXP 0.650 μH 0.300 KS 0.168 bH 1.25 bH 1.20 iXB 0.453 iXB 0.568 kh 0.116 kh 0.126 iXP 0.96 iXP 0.98 fP 0.436 fP 0.536 bH 0.090 μH 0.122 fP 0.71 fP 0.47 bH 0.375 bH 0.503 iXPB,1 0.082 KP,2 0.109

iXPB,1 0.54 iXPB,1 0.43 iXPP 0.144 iXPP 0.164 KX 0.078 iXPB,1 0.105 iXPB,2 0.36 iXPB,2 0.36 iXPB,1 0.098 iXPB,2 0.150 KS 0.072 KNH 0.093 iXPP 0.26 iXPP 0.24 iXPB,2 0.082 iXPB,1 0.116 KP,2 0.065 bH 0.089 KP,1 0.25 ka 0.22 KPi 0.013 KPi 0.028 KPi 0.055 fP 0.080 KP,2 0.19 KX 0.21 kh 0.004 kh 0.013 iXB 0.049 ka 0.065 ka 0.19 kh 0.17 KX 0.003 KX 0.010 iXPB,2 0.037 KPi 0.064 KPi 0.17 KPi 0.13 μH 0.002 μH 0.004 iXPP 0.035 KX 0.056 KS 0.16 KOH 0.10 KS 0.002 KS 0.004 KOH 0.034 iXB 0.041

YH is obviously the most important parameter in the model. It is the most influential parameter in all three sensitivity analyses by large margin to the second. As expected, in the steady-state simulation the most influential parameters are stoichiometric parameters, which determine long-term behaviour of the treatment plant. Kinetic parameters affect simulated OUR the most. The model also contains parameters which have very little influence on the inputs. Even though the model contains a large parameter set, it is necessary to consider only the most influential parameters for calibration. Choice of parameters for calibration is discussed in Chapter 3.2.4.

3.2.3 Wastewater characterisation

Characterisation of wastewater total COD was calculated with the two methods presented in Chapter 3.1.5. Results for wastewater characterisation when the heterotrophic yield coefficient was estimated by the biodegradable COD in wastewater are presented in Table 9 and wastewater characterisation with heterotrophic yield coefficient from OUR measurements with reference substrate is presented in Table 10. Reliability of YH estimates from OUR measurements with reference substrate is questionable, as discussed earlier. However, YH estimates with the two methods are rather close. As process data was not available from Stora Enso Nymölla, COD fractionation was made only with reference substrate.

28

Table 9. COD fractionation results with YH estimated by the biodegradable COD in wastewater. YH Fraction of SS Fraction of XS Fraction of SI Fraction of XI Oulu: 21.1.2009 0.749 0.158 0.473 0.252 0.118 22.1.2009 0.761 0.176 0.472 0.243 0.109 26.1.2009 0.664 0.341 0.303 0.275 0.082 28.1.2009 0.745 0.303 0.360 0.282 0.055 2.2.2009 0.705 0.405 0.244 0.296 0.056 4.2.2009 0.744 0.264 0.408 0.222 0.106 9.2.2009 0.730 0.514 0.207 0.255 0.024 Mean 0.730 0.309 0.352 0.261 0.079 Standard deviation

0.033 0.126 0.106 0.025 0.035

Table 10. COD fractionation results with YH estimated with reference substrate. YH Fraction of SS Fraction of XS Nymölla: 19.11.2008 0.77 0.128 0.191 4.6.2009 0.80 0.134 0.414 Oulu: 21.1.2009 0.729 0.146 0.436 22.1.2009 0.744 0.165 0.441 26.1.2009 0.752 0.461 0.410 28.1.2009 0.771 0.337 0.401 2.2.2009 0.731 0.444 0.268 4.2.2009 0.749 0.270 0.417 9.2.2009 0.760 0.579 0.233

Characterisation of wastewater nutrient content Division of nutrients into soluble and particulate fractions was made directly with the measurements given in Table 7. SNH, a fraction of total soluble nitrogen, was also determined with a direct measurement. Measurements for inert nutrient fractions were not available. Therefore inert fractions were adjusted in the steady-state calibration stage to get an agreement between measured and simulated values. Characterisation of wastewater nutrient content is given in Table 11. Table 11. Wastewater nutrient characterisation. Fraction

of SP Fraction of XPD

Fraction of SNH

Fraction of SND

Fraction of SNI

Fraction of XND

Fraction of XNI

Oulu: 21.1.2009 0.694 0.306 22.1.2009 0.444 0.556 26.1.2009 0.404 0.596 28.1.2009 0.334 0.666 0.027 0.267 0.017 0.506 0.183 2.2.2009 0.436 0.564 4.2.2009 0.464 0.536 9.2.2009 0.537 0.463 Mean 0.473 0.527 Standard deviation

0.115 0.115

29

Wastewater characterisation in the long-term simulation Wastewater composition is known to vary and therefore it is also varying in terms of state variables of the model used in wastewater characterisation. Very little is known on the exact characteristics of the wastewater outside the measurement campaign except for the important SI fraction. SI fraction can be estimated from the effluent COD concentration available in process data as described in Chapter 3.1.5. The mean COD fractions from the measurement campaign in Table 9 are not directly applicable for use in the long-term simulation, because SI fraction was estimated to have a mean value of 0.261 with a standard deviation of 0.021 during the measurement campaign and a mean value of 0.420 with a standard deviation of 0.121 over the whole simulation period. Wastewater was more biodegradable during the measurement campaign than on the long term average. Due to these reasons SI fraction of 0.420 was used in the simulations. Changing the SI fraction led to the need of redefining also other wastewater COD fractions. The mean value of XI fraction from the measurement campaign was assumed to be sufficiently representative of the long-term average, and was used in the simulation. The remaining 50.2% COD after these two fractions is biodegradable. It was divided between the two biodegradable fractions, SS and XS, in proportion to their contributions to biodegradable COD during the measurement campaign. This choice is rather arbitrary but adequate considering that the retention time is sufficiently long for complete hydrolysis of XS in the simulation. The final SS, XS, SI and XI fractions used in the simulation were 0.235, 0.267, 0.420 and 0.079, respectively. Mean values of wastewater nutrient fractions over the measurement campaign shown in Table 11 were used in the simulation.

3.2.4 Calibration

First a simulation was run with averages of the values for influent wastewater from the measurement campaign as the inputs to the model. The simulation was run until it reached a steady-state. The steady-state simulation results were compared to values measured from the aeration basin and the effluent. Two parameters were changed to improve the agreement between modelled and measured values: YH and iXPB,2. YH was a clear choice for calibration as it was the most influential parameter in all sensitivity analyses. Sludge production as measured by suspended solids in the aeration basin and in returned sludge and COD in the treatment plant effluent were easily matched by changing YH. Agreement between modelled and measured nutrient concentrations in the effluent was improved by adjusting the inert nutrient fractions, which yielded the final nutrient characterisation in Table 11. iXPB,2 is one the many parameters influential on effluent phosphorus. It was slightly changed to improve the capability of biomass to store phosphorus. Results of the steady-state calibration are given in Table 12.

30

Table 12. Results of steady-state simulation compared to measured values. Variable Simulated steady-

state values [mg l-1] Measured

values [mg l-1] SS in aeration basin 6.11·103 6.09·103

SS in treatment plant effluent 13.97 13.86 SS in returned sludge 12.0·103 10.7·103 COD in treatment plant effluent 308.2 316.86 Total P in treatment plant effluent 1.14 0.57 Soluble P in treatment plant effluent 1.06 0.45 Total N in treatment plant effluent 2.17 2.36 Soluble N in treatment plant effluent 1.60 1.29 Two most influential kinetic parameters in the OUR simulation according to the sensitivity analysis, μH and kh, were adjusted to fit the simulated SOUR profile to the SOUR profile measured on 28.1. The simulated and measured SOUR profiles are displayed in Figure 6. All adjusted parameter values and their comparison to default values is given in Table 13.

Figure 6. The simulated and measured SOUR. Table 13. Calibrated parameters and their values. Parameter Estimated

values Original values

in [13] ASM1 default values

at 20°C [11] Unit

YH 0.72 0.67 0.67 g cell COD formed (g COD oxidised)-1

iXPB,2 0.008 0.005 - g P (g COD)-1

μH 10.2 12 6.0 d-1 kh 4.5 9.0 3.0 g slowly biodeg. COD (g cell COD d)-1

3.2.5 Simulation

The model was validated by running a long-term simulation with influent process data from a ten month period as the inputs to the model. Simulation results were then compared to the effluent process data. Comparison between the simulated and measured COD concentrations in the plant effluent is shown in Figure 7. Comparison between the simulated and measured soluble nitrogen concentrations in the plant effluent is shown in

31

Figure 8. Comparison between the simulated and measured soluble phosphorus concentrations in the plant effluent is shown in Figure 9.

Figure 7. The simulated COD in plant effluent and the measured COD in plant influent and effluent

Figure 8. The simulated soluble N in plant effluent and the measured soluble N in plant influent and effluent

32

Figure 9. The simulated soluble P in plant effluent and the measured soluble P in plant influent and effluent It can be seen that the measured and simulated values are in good agreement for most of the simulation period. However, during the peaks in the COD concentration of the influent wastewater from January to March 2008 the model predicts higher than measured effluent COD concentrations due to severe nitrogen deficiency. Correspondingly during the period of high N to COD ratio in the influent wastewater from April to May 2008 the model predicts higher than measured effluent N concentrations. It is possible that the phenomena affecting the nitrogen removal during periods of severe nitrogen deficiency or significant amounts of excess nitrogen are more complex than what is described in the model. A possible explanation for better than predicted COD removal when N to COD ratio is low in the influent wastewater is fixation of atmospheric nitrogen, which has been reported to have a considerable effect on the nitrogen balance of biological treatment of pulp and paper mill wastewater [52-54]. Better than predicted N removal during the period of high N to COD ratio may be caused by denitrification after all. Even though the treatment plant has no intentional anoxic zones, in some cases the secondary clarifier may provide an anoxic environment enabling potential for denitrification and thus reducing levels of total soluble nitrogen [55]. In that case it would be necessary to return the nitrification and denitrification processes to the model. Allowing some variance in the N uptake capability of heterotrophic biomass lowers effluent N concentration during period of high N to COD ratio, as can be seen from Figure 10. Comparison between the simulated and measured COD concentrations with variable N uptake in the model is shown in Figure 11. As seen from Figure 11, variance in the N uptake capability slightly deteriorates effluent COD prediction accuracy during period of low N to COD ratio. As the concept of variable N uptake capability of

33

heterotrophic biomass is not well supported by literature and it does not improve model predictions substantially, it should remain only as an experiment.

Figure 10. The simulated soluble N in plant effluent with variable N uptake in the model and the measured soluble N in plant influent and effluent

Figure 11. The simulated COD in plant effluent with variable N uptake in the model and the measured COD in plant influent and effluent Another very likely reason for the mismatch between the measured and simulated values is the quality of data. Calibration protocols [15-18] for the ASMs emphasise the importance of verifying the consistency of process data. In practice this means at least calculating that the mass balances match. In this work mass balances cannot be calculated as all necessary data is not available. Even if the data were available, mass balances for nitrogen are impossible to make as N2 gas from denitrification is not measured. However, the available data on nitrogen reveals problems.

34

Nitrogen is dosed to the influent wastewater as urea, and nutrient measurements are made after the dosing. Therefore the effect of urea dosage in nitrogen concentration should be seen in the measurement. However, this is not the case as can be seen Figure 12, where nitrogen dosing and measured nitrogen load are presented. Measured nitrogen load is lower than the dosed amount during the whole period. Most likely the dosed urea hasn’t dissolved and mixed properly in the neutralisation basin, causing influent N data to be unreliable.

Figure 12. Measured total nitrogen load and nitrogen dosage.

35

4. CONCLUSIONS

Wastewaters from pulp and paper industry, activated sludge treatment of wastewaters and activated sludge modelling were first briefly reviewed. Calibration of Activated Sludge Models was reviewed more thoroughly. There exist a variety of different methods and protocols for calibration of ASM parameters and characterising wastewater in terms of the model components. Experimental methods have been developed to estimate individual parameters but also experimental and experience based protocols describing the complete calibration procedure have been published. Several studies have studied the identifiability of ASMs from process data. However, none of these methods or protocols has established position as the standard protocol for ASM calibration. Activated sludge modelling of the treatment of pulp and paper mill wastewaters has not been studied yet as extensively as other common applications of activated sludge treatment. This report presents results from a brief and non-intensive measurement campaign of pulp mill wastewater and activated sludge. The results and available process data were applied to calibrate a modified ASM1. The calibrated model was used for long-term simulation of the full-scale pulp mill wastewater treatment plant. The model was able to describe the treatment plant behaviour in terms of some of the key effluent parameters, COD, nitrogen and phosphorus, when influent measurements from process data were used as inputs to the model. However, during the long simulation there are periods where the model fails to match the measured effluent quality. These periods indicate shortcomings in the model structure, but also in the available process data.

36

5. REFERENCES

1. Keskitalo J., Jansen J. la C. and Leiviskä K. (2009) Calibration and validation of a

modified ASM1 using long-term simulation of a full-scale pulp mill wastewater treatment plant. Environmental Technology, accepted for publication.

2. Brun R., Kühni M., Siegrist H., Gujer W. and Reichert P. (2002) Practical

identifiability of ASM2d parameters – systematic selection and tuning of parameter subsets. Water Research, vol. 36, 4113-4127.

3. Sin G., Van Hulle S.W.H., De Pauw D.J.W., Griensven A. and Vanrolleghem P.A.

(2005) A critical comparison of systematic calibration protocols for activated sludge models: a SWOT analysis. Water Research, vol. 39, 2459-2474.

4. Dahl O., ed. (2008) Environmental Management and Control. 2nd edition.

Papermaking Science and Technology Book 19. Paperi ja Puu Oy, Jyväskylä, Finland. 304 p.

5. Ukkonen M. (2005) Metsäteollisuuden jätevesien häiriöpäästöt ja niihin

varautuminen, Southeast Finland Regional Environment Centre publications, Valkeala, Finland.

6. The European Integrated Pollution Prevention and Control Bureau (IPPC). (2001)

Reference document on best available techniques in the pulp and paper industry. European Commission, Joint Research Centre, Institute for Prospective Technological Studies, Seville, Spain.

7. Thompson G., Swain J., Kay M. and Forster C.F. (2001) The treatment of pulp and

paper mill effluent: a review. Bioresource Technology, vol. 77, 275-286. 8. Tchobanoglous G., Burton F. and Stensel H. Metcalf & Eddy Inc. (2003) Wastewater

engineering: treatment and reuse. 4th edition. New York, McGraw-Hill. 1819 p. 9. Jeppsson U. (1996) Modelling Aspects of Wastewater Treatment Processes. Ph. D.

dissertation. Lund Institute of Technology, Department of Industrial Electrical Engineering and Automation (IEA).

10. Gernaye K.V., Loosdrecht M.C.M., Henze M., Lind M., and Jørgensen B. (2004)

Activated sludge wastewater treatment plant modelling and simulation: state of the art. Environmental Modelling and Software, vol. 19, 763-783.

11. Henze M., Gujer W., Mino T., and van Loosdrecht M.C.M., eds. (2000) Activated

Sludge Models ASM1, ASM2, ASM2d and ASM3, Scientific and Technical Report no 9. IWA Publishing, London, UK.

37

12. Rieger L., Koch G., Kühni M., Gujer W. and Siegrist H. (2001) The EAWAG Bio-P module for the Activated Sludge Model no. 3. Water Research, vol. 35, no. 16, 3887-3903.

13. Lindblom E. (2003) Dynamic modelling of nutrient deficient wastewater treatment

processes, M.Sc. thesis, Lund University. 14. Petersen B., Gernaey K., Henze M. and Vanrolleghem P.A. (2003) Calibration of

activated sludge models: a critical review of experimental designs. In: Agathos S.N. and Reineke W. (Eds.), Biotechnology for the Environment: Wastewater Treatment and Modeling, Waste Gas Handling. Kluwer Academic Publishers, Dordrecht, The Netherlands. Available at: http://biomath.ugent.be/~peter/ftp/pvr263.pdf

15. Hulsbeek J.J.W., Kruit J., Roeleveld P.J. and Van Loosdrecht M.C.M. (2002) A

practical protocol for dynamic modelling of activated sludge systems. Water Science and Technology, vol. 45, no. 6, 127-136.

16. Vanrolleghem P.A., Insel G., Petersen B., Sin G., De Pauw D., Nopens I., Dovermann

H., Weijers S. and Gernaey K. (2003) A comprehensive model calibration procedure for activated sludge models. Proceedings of WEFTEC 2003, 76th Annual Technical Exhibition and Conference, Los Angeles, CA, USA. Available at http://www.modeleau.org/GMP_TG/documents/biomath.pdf

17. Langergraber G., Rieger L., Winkler S., Alex J., Wiese J., Owerdieck C., Ahnert M.,

Simon J. and Maurer M. (2004) A guideline for simulation studies of wastewater treatment plants. Water Science and Technology, vol. 50, no. 7, 131-138.

18. Melcer H., Dold P.L., Jones R.M., Bye C.M., Takacs I., Stensel H.D., Wilson A.W.,

Sun P. and Bury S. (2003) Methods for Wastewater Characterization in Activated Sludge Modeling. Water Environment Research Foundation (WERF), Alexandria, VA, USA.

19. Weijers S.R. and Vanrolleghem P.A. (1997) A Procedure for selecting best

identifiable parameters in calibrating Activated Sludge Model no.1 to full-scale plant data. Water Science and Technology, vol. 36, no. 5, 69-79.

20. Brun R., Reichert P., and Künsch H.R. (2001) Practical identifiability analysis of

large environmental simulation models, Water Resources Research, vol. 37, no. 4, 1015–1030.

21. Machado V.C., Tapia G., Gabriel D., Lafuente J. and Baeza J.A. (2009) Systematic

identifiability study based on the Fisher Information Matrix for reducing the number of parameters calibration of an activated sludge model. Environmental Modelling & Software, vol. 24, 1274-1284.

38

22. Sin G., De Pauw D.J.W., Weijers S. and Vanrolleghem P.A. (2008) An efficient approach to automate the manual trial and error calibration of activated sludge models. Biotechnology and Bioengineering, vol. 100, no. 3, 516-528.

23. Dochain D., Vanrolleghem P.A. and Van Daele M. (1995) Structural identifiability of

biokinetic models of activated sludge respiration. Water Research, vol. 29, no. 11, 2571-2578.

24. Vanrolleghem P.A. and Lee D.S. (2003) On-line monitoring equipment for

wastewater treatment processes: state of the art. Water Science and Technology, vol. 47, no. 2, 1-34.

25. Pinzón Pardo A.L., Brdjanovic D., Moussa M.S., López-Vázquez C.M., Meijer

S.C.F., Van Straten H.H.A., Janssen A.J.H., Amy G. and Van Loosdrecht M.C.M. (2007) Modelling of an oil refinery wastewater treatment plant. Environmental Technology, vol. 28, 1273-1284.

26. Van Hulle S.W.H. and Vanrolleghem P.A. (2004) Modelling and optimisation of a

chemical industry wastewater treatment plant subjected to varying production schedules. Journal of Chemical Technology and Biotechnology, vol. 79, 1084-1091.

27. Vandekerckhove A., Moerman W. and Van Hulle S.W.H. (2008) Full-scale

modelling of a food industry wastewater treatment plant in view of a process upgrade. Chemical Engineering Journal, vol. 135, 185-194.

28. Petersen B., Gernaey K., Henze M. and Vanrolleghem P.A. (2002) Evaluation of an

ASM1 model calibration procedure on a municipal-industrial wastewater treatment plant. Journal of Hydroinformatics, vol. 4, 15-38.

29. Barañao P.A. and Hall E.R. (2004) Modelling carbon oxidation in pulp mill activated

sludge systems: calibration of Activated Sludge Model No 3. Water Science and Technology, vol. 50, no. 3, 1-10.

30. Nuhoglu A., Keskinler B. and Yildiz E. (2005) Mathematical modelling of the

activated sludge process – the Erzincan case. Process Biochemistry, vol. 40, 2467-2473.

31. Satoh H., Okuda E., Mino T. and Matsuo T. (2000) Calibration of kinetic parameters

in the IAWQ Activated Sludge Model: a pilot scale experience. Water Science and Technology, vol. 42, no. 3-4, 29-34.

32. Koch G., Kühni M., Gujer W. and Siegrist H. (2000) Calibration and validation of

Activated Sludge Model no 3 for Swiss municipal wastewater. Water Research, vol. 34, no. 14, 3580-3590.

39

33. Wichern M., Obenaus F., Wulf P. and Rosenwinkel K.-H. (2001) Modelling of full-scale wastewater treatment plants with different treatment processes using the Activated Sludge Model no. 3. Water Science and Technology, vol. 44, no. 1, 49-56.

34. Omlin M., Brun R. and Reichert P. (2001) Biochemical model of Lake Zürich:

sensitivity, identifiability and uncertainty analysis. Ecological Modelling, vol. 141, 105-123.

35. Reichert P. and Vanrolleghem P. (2001) Identifiability and uncertainty analysis of the

River Water Quality Model No. 1 (RWQM1). Water Science and Technology, vol. 43, no. 7, 329-338.

36. Díez M.D., Fjeld M., Andersen E. and Lie B. (2006) Validation of a compartmental

population balance model of an industrial leaching process: The Silgrain® process. Chemical Engineering Science, vol. 61, 229-245.

37. De Pauw D.J.W., Steppe K. and De Baets B. (2008) Identifiability analysis and

improvement of a tree water flow and storage model. Mathematical Biosciences, vol. 211, 314-332.

38. Ruano M.V., Ribes J., De Pauw D. and Sin G. (2007) Parameter subset selection for

the dynamic calibration of Activated Sludge Models (ASMs): experience versus systems analysis. Proceedings of the 7th International IWA Symposium on Systems Analysis and Integrated Assessment in Water Management.

39. Lee D.S., Jeon C.O., Park J.M., and Chang K.S. (2002) Hybrid neural network

modeling of a full-scale industrial wastewater treatment process. Biotechnology and Bioengineering, vol. 78, 670-682.

40. Lee D.S., Vanrolleghem P.A., and Park J.M. (2005) Parallel hybrid modeling

methods for a full-scale wastewater treatment plant. Journal of Biotechnology, vol. 115, 317-328.

41. Haka J. (2009) Aktiivilietelaitoksen mittaustiedon analysointi. Master’s Thesis.

Control Engineering Laboratory, Department of Process and Environmental Engineering, University of Oulu.

42. Hagman M. and Jansen J. la C. (2007) Oxygen uptake rate measurements for

application at wastewater treatment plants. Vatten, vol. 63, 131-138. 43. Jansen J. la C., Keskitalo J. and Juuso E. (2009) Oxygen uptake rate measurements as

a tool for deterministic modelling of pulp and paper wastewater treatment plants, Proceedings MATHMOD 09 Vienna - Full Papers CD Volume. Troch I., Breitenecker F., eds.

40

44. Eikelboom D.H. (2000) Process Control of Activated Sludge Plants by Microscopic Investigation, IWA Publishing, London, UK.

45. Holck P. (2008) Parameter Identification with Genetic Algorithms for Biological

Wastewater Treatment Process. Master’s Thesis. Control Engineering Laboratory, Department of Process and Environmental Engineering, University of Oulu.

46. Copp J.B. (ed.) (2002) The COST simulation benchmark: Description and simulator

manual, Office for Official Publications of European Communities, Luxembourg, 2002.

47. De Pauw D.J.W. and Vanrolleghem P.A. (2006) Practical aspects of sensitivity

function approximation for dynamic models. Mathematical and Computer Modelling of Dynamical Systems, vol. 12, no. 5, 395-414.

48. Hamby D.M. (1994) A review of techniques for parameter sensitivity analysis of

environmental models. Environmental Monitoring and Assessment, vol. 32, no. 2, 135-154.

49. Dircks K., Pind P.F., Mosbæk H. and Henze M. (1999) Yield determination by

respirometry – The possible influence of storage under aerobic conditions in activated sludge. Water SA, vol. 25, 69-74.

50. Carucci A., Dionisi D., Majone M., Rolle E. and Smurra P. (2001) Aerobic storage by

activated sludge on real wastewater. Water Research, vol. 35, 3833-3844. 51. Hagman M., Nielsen J.L., Nielsen P.H. and Jansen J. la C. (2006) Identification of

functional groups of denitrifiers in activated sludge. Water and Environmental Management Series, IWA Publishing.

52. Clark T.A., Dare P.H., and Bruce M.E. (1997) Nitrogen fixation in an aerated

stabilization basin treating bleached kraft mill wastewater, Water Environment Research, vol. 69, 1039-1046.

53. Bowers T.H., Reid N.M., and Lloyd-Jones G. (2008) Composition of nifH in a

wastewater treatment system reliant on N2 fixation, Applied Microbiology and Biotechnology, vol. 79, 811-818.

54. Turunen J. (2004) Typensidonta ilmasta sellu- ja paperitehtaiden

aktiivilietelaitoksissa. Vesitalous, vol. 2, 31-35. 55. Koch G., Pianta R., Krebs P. and Siegrist H. (1999) Potential of denitrification and

solids removal in the rectangular clarifier. Water Research, vol. 33, 309-318.

Appendix 1. Applied model, based on Lindblom’s modified ASM1 [13]

Appendix 2. Complete parameter set for the applied model

ISBN 978-951-42-6110-7 ISSN 1238-9390 University of Oulu Control Engineering Laboratory – Series A Editor: Leena Yliniemi 26. Paavola M, Ruusunen M & Pirttimaa M, Some change detection and time-series

forecasting algorithms for an electronics manufacturing process. 23 p. March 2005. ISBN 951-42-7662-0. ISBN 951-42-7663-9 (pdf).

27. Baroth R. Literature review of the latest development of wood debarking. August 2005. ISBN 951-42-7836. 28. Mattina V & Yliniemi L, Process control across network, 39 p. October 2005. ISBN 951-42-7875-5. 29. Ruusunen M, Monitoring of small-scale biomass combustion processes. 28 p. March

2006. ISBN 951-42-8027-X. ISBN 951-42-8028-8 (pdf). 30. Gebus S, Fournier G, Vittoz C & Ruusunen M, Knowledge extraction for optimizing monitorability and controllability on a production line. 36 p. March 2006. ISBN 951-42-9390-X 31. Sorsa A & Leiviskä K, State detection in the biological water treatment process. 53 p. November 2006. ISBN 951-42-8273-6 32. Mäyrä O, Ahola T & Leiviskä K, Time delay estimation and variable grouping using genetic algorithms. 22 p. November 2006. ISBN 951-42-8297-3 33. Paavola M, Wireless Technologies in Process Automation - A Review and an Application Example. 46 p. December 2007. ISBN 978-951-42-8705-3 34. Peltokangas R & Sorsa A, Real-coded genetic algorithms and nonlinear parameter identification. 28 p. April 2008. ISBN 978-951-42-8785-5. ISBN 978-951-42-8786-2 (pdf). 35. Rami-Yahyaoui O, Gebus S, Juuso E & Ruusunen M, Failure mode identification through linguistic equations and genetic algorithms. August 2008. ISBN 978-951-42-8849-4, ISBN 978-951-42-8850-0 (pdf). 36. Juuso E, Ahola T & Leiviskä K, Variable selection and grouping. August 2008. ISBN 978-951-42-8851-7. ISBN 978-951-42-8852-4 (pdf). 37. Mäyrä O & Leiviskä K, Modelling in methanol synthesis. December 2008. ISBN 978-951-42-9014-5 38. Ohenoja M, One- and two-dimensional control of paper machine: a literature review. October 2009. ISBN 978-951-42-9316-0 39. Paavola M & Leiviskä K, ESNA – European Sensor Network Architecture. Final

Report. 12 p. December 2009. ISBN 978-951-42-6091-9 40. Virtanen V & Leiviskä K, Process Optimization for Hydrogen Production using

Methane, Methanol or Ethanol. ISBN 978-951-42-6102-2 41. Keskitalo J & Leiviskä K, Mechanistic modelling of pulp and paper mill wastewater

treatment plants. January 2010. ISBN 978-951-42-6110-7