Critical review of membrane bioreactor models – Part 1: Biokinetic and filtration models

Bioresource Technology 122 (2012) 95–106

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Critical review of membrane bioreactor models – Part 1: Biokinetic and filtration models W. Naessens, T. Maere, I. Nopens ⇑ BIOMATH, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium

h i g h l i g h t s " Individual MBR biokinetics and filtration models were critically reviewed. " Modelling studies mainly focused on knowledge development. " More process model validation is required, including experimental data collection. " Future modelling studies should focus on model application, preferably at full scale. " Future modelling studies should make use of good modelling practice.

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Article history: Available online 25 May 2012 Keywords: Modelling MBR Activated sludge models Filtration Good modelling practice

a b s t r a c t Membrane bioreactor technology exists for a couple of decades, but has not yet overwhelmed the market due to some serious drawbacks of which operational cost due to fouling is the major contributor. Knowledge buildup and optimisation for such complex systems can significantly benefit from mathematical modelling. In this paper, the vast literature on modelling MBR biokinetics and filtration is critically reviewed. It was found that models cover the wide range of empirical to detailed mechanistic descriptions and have mainly been used for knowledge development and to a lesser extent for system optimisation/control. Moreover, studies are still predominantly performed at lab or pilot scale. Trends are discussed, knowledge gaps identified and interesting routes for further research suggested. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Understanding and optimizing a system as complex as a membrane bioreactor (MBR) is difficult and time-consuming. It is composed of many subprocesses that are highly coupled as shown in Fig. 1. Next to the biokinetic processes for bioconversion of pollutants, the separation process takes place as well as hydrodynamic flows that develop both in the bioreactor and membrane module. All of the above are driven by system inputs and interact: biological compounds are produced, interfering with the filtration process (arrow 1); the membrane retains compounds depending on the membrane pore size (either stemming from the influent or produced by biomass), leading to upconcentration, influencing aeration and biological processes (arrow 2); aeration provides dissolved oxygen (DO) for aerobic reactions and recirculation flows affect mixed liquor suspended solids (MLSS) concentrations and ⇑ Corresponding author. E-mail addresses: [email protected] (W. Naessens), thomas.maere@ ugent.be (T. Maere), [email protected] (I. Nopens). 0960-8524/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biortech.2012.05.070

gradients impacting conversion biokinetics (arrow 3); aeration and crossflow are applied for fouling reduction (arrows 4); hydrodynamics (mixing) affects bioreactor homogeneity (dead zones, recirculation, short circuiting) (arrow 5); particle size distribution (PSD) is affected by aeration (both biological and membrane) and mixing, both causing shear-induced (de) flocculation (arrows 6); PSD affects hydrodynamics (through rheology) (arrow 7), filtration behaviour (arrow 8) and biokinetic conversion rates (arrow 9); process variables are passed onto the control layer which returns a set of control actions (arrows 10). The latter will influence the operational cost of the entire system. The above illustrates that a thorough understanding of MBR systems is not straightforward and a classic approach of only performing physical experiments would soon become very expensive. Especially the strong process interactions hamper a straightforward analysis, since a change in one parameter can affect multiple processes, which may obscure the influences on the separate processes (Drews, 2010). An alternative resides in mathematical modelling, which is a powerful tool when studying complex systems. Here, the ‘virtual’ route is followed, i.e. virtual experiments (or simulations) are used

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Fig. 1. Illustration of the complex interactions between different processes in an MBR as well as the interaction with the control layer and costs.

to study the system. The latter exploits computational power and, hence, allows to perform many virtual experiments in a short time frame. This is clearly advantageous over tedious lab experiments, especially for slow processes like bioprocesses. However, two important aspects should not be forgotten in this context: (1) the process needs to be converted into a mathematical model (model building) and (2) every model needs experimental calibration/validation not to lose its realism. Performing the former in a proper and sound way makes it an elaborate task. When a model needs to be built from scratch for a new or unexplored technology, the goal of the modelling process is system analysis (build knowledge about the process). The typical steps required in a model building exercise are summarized in Fig. 2. The first important step is to define the goal of the modelling study. This step is often forgotten

Fig. 2. Different steps in any model building exercise needed to develop a validated model ready for further application.

and results in bad or useless models (e.g. overly complex models for the objective or overly simple models not able to answer the raised question). Next important cornerstones are collecting a priori knowledge (e.g. existing system knowledge, existing models, etc.) and experimental data (e.g. studies in literature, historical data, newly collected data) about the studied system. Next, one needs to decide on the model framework (e.g. mechanistic versus empirical models, type of equations – algebraic, ordinary or partial differential equations, type of empirical models, system boundaries, amount of details to be included, etc.). Finally, two more steps are required which might have to be taken in different sequence depending on the case. If several candidate models are still available, model selection techniques might need to be used. When the model structure is known, one can continue with calibration (i.e. confronting the model with experimental data). Some model selection techniques require calibration prior to the selection step, others do not (Dochain and Vanrolleghem, 2001). Once parameter estimation (or calibration) has been successfully performed, a validation step is still required. This tests whether the model is able to predict data sets other than the one used in the model calibration and is a measure for the predictive power of the model. If this validation fails, one needs to go back to previous steps. If successful, the model is ready to be used. Clearly, this process of model building is iterative and can be time consuming. Explaining the modelling process in more detail is outside the scope of this review. The interested reader is referred to specific literature on the topic. Excellent text books are available on the matter dedicated to wastewater treatment (Dochain and Vanrolleghem, 2001; Gujer, 2008). Another application of modelling is system optimisation. The prerequisite is that a validated model was developed through the above-described procedure. If this is the case, the advantage of

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computational power can be exploited depending on the complexity of the model, as thorough analysis, usually based on a multitude of simulations, becomes nearly impossible for very intensive calculations. If one simulation is reasonably fast, several analysis tools are available for the modeller. Sensitivity analysis can be used to pinpoint which process or operational parameters impact output variables of interest. This can help in optimizing the system as it reveals the most influential control handle(s) to improve the system. The analysis can be performed locally, based on a linear approximation of the sensitivity function in one point in the parameter space, or globally, based on more sophisticated techniques and yielding information over the entire parameter space. The latter approach uses computationally intensive methods based on Monte Carlo techniques (Saltelli et al., 2008). Scenario analysis is used to explore scenarios in a virtual manner, without the risk of upsetting the real system. A number of operational degrees of freedom (e.g. sludge retention time (SRT), recycle flows, controller setpoints, etc.) is selected and ranges are defined, leading to a multidimensional grid with a dynamic simulation performed at every grid point. User-defined evaluation criteria are used to find an optimal point of operation for the system. An example of such an approach is illustrated in Nopens et al. (2007). Uncertainty analysis has recently gained interest in general wastewater treatment literature, due to the fact that many modellers tend to fall in love with their model and interpret numbers coming out of a model as being the ’truth’. However, one should always be aware of the fact that any model is a simplification of reality and that many sources of uncertainty exist: model structure (e.g. not including an important process will result in model structure uncertainty), model parameters (e.g. every parameter estimation should include confidence intervals which are a measure for the uncertainty of that value), experimental data (e.g. every measurement is accompanied by a measurement error introduced by the sampling, sample handling, device, analysis procedure). All this uncertainty will propagate through the model and result in model output uncertainty. Different methods exist to compute output uncertainty: Monte Carlo based techniques (Benedetti et al., 2010; Saltelli et al., 2008) or techniques borrowed from hydrology like generalised likelihood uncertainty estimation (GLUE) (Beven and Binley, 1992; Mannina et al., 2010), among others. These techniques are again highly computational intensive. Finally, process control is typically used to maintain the system at its optimal operational point. The latter can be achieved by one or more controllers that use a measurement of a process variable as input and compute a control action that is introduced to the system through an actuator (e.g. pump, valve, timer, etc.). In order to develop controllers, models can be used to test their appropriateness prior to implementing them in reality. Control models are further discussed in Section 4. From the above, it is clear that modelling serves many different purposes. The purpose of this paper is to provide a review of the literature with respect to modelling of MBRs, making clear where different studies have added value to process knowledge, how they can be useful for application (e.g. optimisation for design or operation, control) and what their flaws and pitfalls are. By doing so, gaps in model development and application will be pointed out and directions for future research and application with respect to modelling MBRs will be given. The different individual processes taking place in an MBR were used as the basis for the structure of the review (boxes with solid lines in the processes section of Fig. 1): biokinetic models, filtration models, hydrodynamic models. Additionally, integrated models are discussed, which can include any combination of the interactions 1–10 indicated in Fig. 1. Each section consists of (1) a general introduction on the type of model, (2) a review of the different literature models to date and (3) discussion and perspectives.

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2. Modelling of biological processes 2.1. Introduction Application of mass balances to conventional activated sludge (CAS) systems has led to the family of activated sludge models (ASM1, 2(d) and 3). These were built to model different system configurations and differ in the number of components and processes accounted for (Henze et al., 2000). ASM models are widely accepted in literature and have been frequently applied to CAS for system analysis and optimisation. During the last decade they were also applied to MBR. 2.2. Use of biokinetic models for knowledge buildup 2.2.1. Review by Fenu et al. (2010a) A recent thorough review on modelling of biokinetic processes with special regard to MBR specificities was conducted by Fenu et al. (2010a). It would be redundant to repeat the content of this exhaustive work. The main conclusions are briefly summarized and will be topped with the recent activities in literature using a similar breakdown of contributions in unmodified and modified model applications, the latter indicating that additional compounds and/or processes have been added. With regard to unmodified ASMs, Fenu et al. (2010a) stated that nitrification parameters were most affected by differences between CAS and MBR, albeit depending on hydrodynamic and operational conditions, whereby it was recommended to determine the kinetic parameters as a coherent set for each new study. Denitrification rates and parameters seemed to be similar in MBR versus CAS, except for the dissolved oxygen half-saturation constant K OH , describing oxygen poisoning of anoxic zones. This was attributed to the small floc size in MBR which could ease the oxygen mass transfer. For biological phosphorus removal, data and literature were insufficient to base any conclusions on them. Perspectives and points of attention included the influent characterization and fate of inorganic compounds, especially regarding systems operating under high solids retention time (SRT). For the nitrification–denitrification kinetics no further research was deemed necessary, whereas the opposite was suggested for Bio-P kinetics. With respect to modified ASMs, Fenu et al. (2010a) concluded that the use of an ASM expansion with the EPS/SMP concept is justifieif following objectives are pursued: (1) linking biology with fouling, (2) soluble chemical oxygen demand (COD) predictions in bulk and (3) modelling high SRT cases. The reason is that, if not necessary, the concept creates difficulties in the calibration of newly introduced parameters. Future research needs concerned the hydrolysis, membrane retention and impact of disturbances on SMP and the overparameterisation of SMP/EPS models. In general, more full-scale studies using ASM models were considered necessary to rule out differences between lab and pilot level versus full scale. 2.2.2. Unmodified activated sludge model application to MBR since Fenu et al. (2010a) Despite consensus on nitrification–denitrification kinetics at lab scale, further studies were conducted in this area. Baek et al. (2009) used lab-scale data to calibrate a slightly reduced ASM1 model and found similar differences in calibrated kinetic parameters as in previous studies (Fenu et al., 2010a). However, the authors also calibrated stoichiometric parameters, which is not considered to be good modelling practice (Rieger et al., 2012). Despite this, the authors found the model useful to better understand the mechanisms and kinetics of the high SRT MBR process. Verrecht et al.

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(2010) modelled a community-scale MBR for reuse and used ASM2d to account for phosphate accumulating organism (PAO) activity, despite the lack of an anaerobic reactor. The calibrated model was able to predict MLSS and effluent nutrient concentrations accurately and was successfully used for optimizing the plant. The model parameters did not need any calibration when using the model corrections proposed by Gernaey and Jørgensen (2004) to make biomass decay electron acceptor (NO3, O2) dependent and modelling the process dynamically instead of steady state. However, the authors concluded that the modelling of biological phosphorus (Bio-P) removal needed more attention. Two full-scale calibration studies for MBRs were reported by Fenu et al. (2010b) and Delrue et al. (2010). The former authors used ASM2d to account for PAO activity even though an anaerobic reactor was not present, similar to Verrecht et al. (2010). Default values were adopted except for K NH and K OA , the nitrification related halfsaturation coefficients for ammonium (NH4) and oxygen, which were lowered. Bio-P removal could not be modelled successfully. It must be noted that the focus of Fenu et al. (2010b) was on energy prediction, rather than effluent nutrient predictions, unlike Delrue et al. (2010), and their calibration efforts were also not as extensive. The latter authors achieved a successful ASM1 calibration despite difficulties with the configuration and operation of the aeration system. This lead to many different operational cases that also induced simultaneous nitrification and denitrification (SNDN), making the calibration of oxygen transfer difficult. A calibrated set of parameters was nevertheless proposed, but validation was not very successful, mainly due to the fact that the dominant aeration configuration had changed during the validation period. The calibrated parameter set followed the conclusions by Fenu et al. (2010a), with the exception of a significantly larger value for the half-saturation coefficient for nitrate (K NO ), caused by the SNDN and non-ideal mixing. The authors concluded that every full-scale MBR case is likely to exhibit some specific problems, but their effort could be used as starting point for future modelling projects. A significant number of articles were dedicated to the specific SNDN process invoked by low DO conditions. Sarioglu et al. (2008) performed a long-term pilot study and modelled their experimental findings. They found that diffusion through biomass exerted a significant effect on system performance and introduced additional switching functions next to other model (ASM1) changes, including the dependency of biomass decay to the electron acceptor and the incorporation of the endogenous decay concept (ASM3), to account for nitrification–denitrification kinetics under different DO concentrations. Full nitrification (and reduced denitrification) was observed above DO levels of 1.5 mg O2 L1, whereas the opposite was true for DO levels of 0.3– 0.6 mg O2 L1. They could predict the observed behaviour by increasing several half-saturation constants (K NO ; K NH ; K OH ; K OA ) and proposed a new set of values valid for these systems. Sarioglu et al. (2009b) extended the work to a pilot system without an anoxic reactor and found that the same model modification for SNDN was valid to predict the observations. The used parameter set differed only with respect to the autotrophic growth rate (lA ) due to nitrification inhibition and the oxygen half-saturation coefficients for heterotrophs and autotrophs (K OH ; K OA ) which were claimed dependent of MLSS concentrations. The latter was further investigated by Sarioglu et al. (2009a). He et al. (2009) investigated the impact of DO, food to microorganism ratio (F/M), carbon to nitrogen ratio (C/N) and pH on the performance of SNDN on lab scale and concluded on the basis of a simplified model without detailed calibration that K NO needed to be increased as well. Hocaoglu et al. (2011) modelled a pilot-scale MBR operated as a sequencing batch reactor (SBR) treating very strong blackwater at low DO conditions. They used a strongly adapted ASM1 model similar to Sarioglu

et al. (2008) but particularly extended with ASM2d features to accommodate the special influent characteristics. Similar to Delrue et al. (2010) they adapted heterotrophic yield (Y H ) to be dependent on electron acceptor conditions (as in ASM3). Due to the many changes it becomes difficult to compare the kinetic parameters between studies but, strikingly, all above-mentioned half-saturation coefficients were calibrated to low values, e.g. Delrue et al. (2010) except for K NO or Fenu et al. (2010b) for K NH and K OA . The reason might lie in the nitrification inhibition that was experienced and the atypical influent, but the authors also explicitly mentioned their moderate MLSS concentration. Insel et al. (2011) developed a functional relationship between MLSS (which is in fact related to apparent viscosity), K OA and K OH based on literature data and used the model of Hocaoglu et al. (2011) to determine optimal DO setpoints for SNDN at different sludge concentrations. Sarioglu et al. (2011) used this gathered knowledge to develop a design procedure for MBRs experiencing SNDN, which is a distinctly different use of models compared to the previous studies, i.e. understanding observed process behaviour or process optimisation. Jimenez et al. (2010) partly addressed the need for better understanding the impact of disturbances at the influent side as stipulated by Fenu et al. (2010a) by investigating the impact of primary sedimentation and screens on MBR performance. The ASM model was helpful to understand phenomena related to the use of presettled or screened sewage, but the model was not able to predict the differences between short and long SRT. A possible solution might have been the addition of a slow hydrolysis of inert organic matter (Fenu et al., 2010a), but the authors also suggested to use a more complex ASM-SMP model. As most previous studies showed, the effect of DO on biological treatment performance is high. As such, the modelling of oxygen transfer should be dealt with carefully. However, the way the aeration process is modelled is often not clearly described and calibration results to DO measurements are often lacking. Fenu et al. (2010b) and Delrue et al. (2010) used dedicated protocols to measure the standard aeration efficiency for coarse and fine bubble aeration, but did not convincingly show good model performance for (dynamic) DO concentrations after calibration. Sarioglu et al. (2008, 2009a,b) showed satisfactory results regarding pseudo steady-state DO concentrations at different sludge concentrations using a mechanistically based approach, but did not explain in detail how the aeration was modelled or calibrated. Hocaoglu et al. (2011) and Insel et al. (2011) used the concept of a volumetric oxygen transfer coefficient under process conditions (akLa) with the standard model incorporated in ASM, but did not show detailed results. Finally, Verrecht et al. (2010) employed a mechanistically based approach which is more clearly described and slightly updated in Maere et al. (2011). The model incorporates differences in diffuser type (coarse and fine bubble) and adverse aeration efficiency effects at elevated MLSS by relating the a-factor to MLSS (as many studies do). Though seemingly valid for an individual MBR, recent research suggests that mixed liquor volatile suspended solids (MLVSS) might be a better indicator (Henkel et al., 2009). Parameter values were chosen from literature and the model was confronted with the measured airflow rates and DO concentrations, as did Sarioglu et al. (2008, 2009a,b). Only the fouling factors of the diffusers needed calibration. Results were satisfactory, but not shown for DO. To allow model predictions, the dynamic aeration controller (as present in reality) was added after the biokinetic calibration and was tuned to fit the measurements, to avoid that deviations of the simulated control actions from the real system would influence the calibration results. As mentioned before, the model was successfully used for optimisation.

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2.2.3. Modified activated sludge model application to MBR since Fenu et al. (2010a) In general, the work described in literature on the extension of ASM models with regard to SMP dynamics can be broken down in two parts: stand-alone SMP models and further integrated ASMSMP models. The former only comprises of one study by Menniti and Morgenroth (2010), which focused on the appropriateness of three different literature models in their ability to describe two distinct biomass associated products (BAP) production mechanisms: floc erosion and biomass decay (including predation), both shown to be important. This was based on experimental data collected under specific conditions of shear and predation, accompanied by specific short-term batch experiments. Only the model of Aquino and Stuckey (2008) (of the models that were tested) was able to describe the observations. Further observations included the fact that SMP produced during increased predation were slowly degradable, while eroded floc-associated EPS were rapidly degradable. This was based on specific oxygen uptake rate (OUR) tests. The slower degradable fraction of SMP will obviously dominate the bulk phase SMP concentration as they accumulate. For integrated ASM-SMP models, it is remarkable that almost all new developments are based on ASM3. Tian et al. (2011a) performed a comprehensive lab-scale study including thorough experimental data collection both in batch (calibration) and continuous systems (validation). Dedicated batch experiments were set up to measure biomass associated products (BAP) and utilisation associated products (UAP) of SMP (from both heterotrophs and autotrophs separately) using sludge that was adapted to the SRT and hydraulic retention time (HRT) conditions of the validation step. In all batch experiments, BAP and UAP were measured in time with different techniques: COD concentration, protein and carbohydrate concentrations, excitation-emission matrix fluorescence, Fourier transform infrared spectroscopy (FTIR) and gel-permeating chromatography (GPC). This lead to a vast amount of information and, hence, knowledge buildup. From the latter, modifications of ASM3 were proposed by adding 2 states (BAP and UAP) and several processes. The model was calibrated in a stepwise manner based on the different experimental data sets. These were used to separately calibrate the submodels for BAP and UAP. Model calibration was accompanied by a quality check based on the Fisher Information Matrix (FIM) which is regarded as good modelling practice. Subsequently, a model validation was performed based on a continuous lab-scale MBR operated at the same HRT/SRT of the model calibration. Only a steady-state validation was performed, although a dynamic validation would have been more informative. Finally, the model was used in a simulation study to determine optimal operating conditions for minimizing total SMP production, which resulted in an SRT of 40 d at an HRT of 8 h. The authors warned that this result should be interpreted with care as the potential difference in fouling potential of BAP and UAP was not accounted for. Further study into the transfer of the results to larger scaled MBRs and real wastewater is recommended, but the work can form a solid basis for further research. Janus and Ulanicki (2010) came to exactly the same conclusion. They used ASM3 extended with the model of Laspidou and Rittmann (2002) and applied it to experimental data from literature which is limited to measurements of substrate, EPS, SMP and total biomass concentrations – hence, less extensive compared to Tian et al. (2011a). The authors preferred ASM3 over ASM1, but also briefly tested the extended ASM1-SMP model version. They found a good agreement for both models after calibration but did not perform a validation. They also used the calibrated ASM3 model for investigating the impact of operational conditions (SRT, MLSS and temperature) and compared these with the results found by Jiang et al. (2008). The major trends were similar but the magnitude of the values was found to be different. Finally, a sensitivity analysis for the

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parameters in the SMP submodel was performed. Major conclusions drawn by the authors were: fouling strength of different types of SMP (UAP, BAP) should be considered when optimizing; floc size distribution is not accounted for; impact of disturbances are not considered in the model; deficiencies of ASM3 are inherited by the extended model; proposed calibrated kinetic and stoichiometric parameters should be handled with care. These partially overlap with conclusions of Tian et al. (2011a). Finally, Paul and Hartung (2008) compared the ASM1 model extended with SMP of Lu et al. (2001) with the ASM3 model extended with SMP of Oliveira-Esquerre et al. (2006) by testing both models’ performance on a limited data set collected from an industrial-scale MBR. The latter model seemed to perform better for filtered COD prediction, but was way off for NH4. Compared to the other studies, this study seems premature and the conclusions should be handled with care. Moreover, Janus and Ulanicki (2010) stated that both models suffer from large defects, making them inappropriate for use. The studies of Sarioglu et al. (2008, 2009a,b) and Hocaoglu et al., 2011 which have been previously mentioned also incorporated SMP, albeit in a simpler way based on the model of Orhon et al. (1989). However, the focus of those studies was not on SMP modelling, so they will not be further discussed here. Fenu et al., 2011 employed the ASM2d-SMP model of Jiang et al. (2008), corrected for some issues, to model the MBR part of a fullscale municipal hybrid MBR. Batch experiments were used to determine the kinetics for UAP and BAP, but the protocol for UAP (Jiang et al., 2008) was considered invalid due to storage phenomena (ASM3) and ill represented degradation kinetics. Instead, UAP parameters were fitted. UAP were found markedly predominant to BAP and the SMP rejection rate of the membranes was found susceptible to influent dynamics. Employing a fixed SMP retention factor (as most studies do) would thus appear inappropriate. The model was fairly able to describe SMP, COD, NH4 and NO3 measurements on a daily basis, but detailed dynamics and results were not shown. Based on their findings, the authors concluded that the modelling of SMP did not improve the predictions for nutrient removal, sludge production or energy consumption. They also questioned the use of modelling SMP since the measurements did not appear correlated with the observed membrane fouling rates, which is still the main driver for SMP modelling. 2.3. Conclusions and perspectives With regard to modelling MBR systems with ASM, there seems to be consensus on nitrification and denitrification parameters when DO concentrations are sufficiently high and, hence, SNDN is not taking place. Lab-scale studies that are performed on the topic seem repetitive, but confirm this consensus. The extension to full scale proves more difficult as only two studies are reported. Markedly, the full-scale study employing ASM2d (Fenu et al., 2011) did not need extensive calibration, as was also the case for the community-scale ASM2d study of Verrecht et al. (2010). Both studies based their choice for ASM2d on PAO activity, despite the absence of anaerobic tanks, but concluded that more research on Bio-P removal is needed. The full-scale study of Delrue et al. (2010) was only partly successful as practical case-specific aspects complicated the model calibration, but it still deserves merit for bringing ASM to full scale. Although every full-scale effort will have its specific issues, it is recommended to further walk this route in order to pursue consensus on reported findings. Quite some effort was observed to investigate SNDN. Results indicate the need to increase half-saturation constants to allow adequate model predictions, especially at high MLSS (Insel et al., 2011; Sarioglu et al., 2009a). At low MLSS, floc size and density appear decisive factors herein, but this needs more research. The need to incorporate kinetic electron acceptor dependencies (O2, NO3) was

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also noticed. An issue regarding SNDN that currently receives a lot of attention in CAS systems is the production of the N2O greenhouse gas (Kampschreur et al., 2009). Emission studies on N2O from MBR systems have not been reported to date, but should receive the necessary attention as it is highly linked with energy optimisation, being exactly the issue needing optimisation. The real application of calibrated ASM models for MBR optimisation has only been reported once (Verrecht et al., 2010) and also deserves more attention as to reveal model flaws and indicate routes for further research. With regard to SMP modelling, little activity is reported. Existing stand-alone models were tested in their ability to capture specific observations but only the more complex model seemed adequate. This, however, implies the need for more extensive calibration data sets. Therefore, it is important to focus on essential aspects instead of studying details that might not occur at full scale (Fenu et al., 2011) or are artefacts of lab-scale or pilot-scale studies (Drews, 2010; Kraume et al., 2009). Regarding ASM-SMP mostly ASM3 was used, or features of it, due to limitations of ASM1. Studies like Tian et al. (2011a), coupling modelling with extensive lab-scale data collection, are encouraged but should also be extrapolated to full scale. The experimental observation techniques appear sufficient to analyse the process. Hence, applying these techniques on a broader range of operational conditions should be the next step. A remaining issue is the impact of PAOs on SMP, which has not been modelled yet. The impact of process disturbances (van den Brink et al., 2011) should be another point of attention. Finally, regarding oxygen transfer, adequate models seem to be available but a lack of detailed information and results hampers their assessment. 3. Modelling the filtration process 3.1. Introduction In many MBR modelling studies primarily focusing on the biological processes, filtration modelling is heavily simplified by regarding the membrane as a point settler or ideal separation step (complete retention of solids). However, it is important not to overlook the fouling process, especially when considering modelling for operational optimisation. Modelling the filtration process is mostly performed in a mechanistic way, using Darcy’s law and the concept of resistances in series (RIS) to describe the influence of different fouling mechanisms on the permeability of the membrane and will be reviewed here. In contrast, only little information is found on data-driven approaches for filtration modelling, notwithstanding their potential for accurate predictions and their knowledge-building properties. Furthermore, whereas mechanistic filtration models are almost exclusively based upon the same concept of resistance, data-driven models form distinctive groups according to the underlying principle. The techniques covered here are artificial neural networks (ANN), principal component analysis (PCA) and related advanced regression techniques. Each method will be briefly introduced and strengths or weaknesses will be addressed, as well as some points of attention for modelling. The applications to MBR will be discussed in function of their goal and trends towards future research will be given. 3.1.1. Resistances in series models Virtually all mechanistic filtration models use Darcy’s law of filtration as the theoretical starting point for the model equations, which directly relates membrane flux (generally a setting) to the measured transmembrane pressure (TMP), using a constant for the sludge viscosity or in best cases a temperature and/or total solids dependent parameter. Darcy’s law enables the membrane

resistance R to be calculated, which is generally thought to be the combined effect of the clean membrane resistance Rm and a number of fouling mechanisms deteriorating the filtration process. The clean membrane resistance is a time invariant characteristic of the membrane itself and is either provided by the membrane manufacturer, or can be determined using Darcy’s law in ultrapure water filtration. Separate models are required for each of the other resistance components, either based on the exact mechanism taking place near the membrane surface, or on a semi-empirical calibration basis. Although this decomposition of the resistance can be done in numerous ways, generally a distinction between cake layer and pore blocking resistance is made. Mostly, resistances due to scaling and concentration polarisation are considered negligible. 3.1.2. Artificial neural networks It is not exceptional that the basis for a data-based technique can be found in everyday biological principles. Following this observation, artificial neural networks find their origin within the mathematical representation of the brain (Rojas, 1996), where neurons are represented by nodes, each containing a mathematical function, and organised in an input, output and multiple in between hidden layers. The nodes are interconnected (as synapses within the brain): the outputs of all nodes within a single layer are weighted (xi;j ), summed, and augmented with a bias term (bj ) before the mathematical function (f) of a neuron in the next layer is applied (Eq. 1), traditionally being a sigmoid, tangent or linear transfer function.

yi ¼ f

n X

!

xi;j  xi þ bj

ð1Þ

i¼1

Since ANN require a lot of fine-tuning by the modeller, the process is briefly described here. The first task of the modeller is to define the network’s architecture, being the number of hidden layers and neurons within each layer. Most filtration studies use only one or two hidden layers, while the number of neurons within the hidden layer(s) is usually defined using a trial-and-error approach, i.e. each network architecture is tested and the performance is evaluated using an R2 or root mean squared residuals (RMSR) value. Nevertheless, better techniques are at hand, e.g. genetic algorithms (Sahoo and Ray, 2006). The next modelling step encompasses a training stage, in which a dynamic training data set is presented to the algorithm and the internal weights (the model parameters) are adjusted to match the predicted output with the measurements. The quality of the training depends on the network’s architecture and the training data; a low number of hidden layers, neurons per layer, or a small data set will typically result in a low reliability of the network, whereas a too complex architecture and large training data will result in overfitting of the data, i.e. the algorithm ‘‘learns the data by heart’’, resulting in poor performances as well (Liu et al., 2009; Sahoo and Ray, 2006). After training, a validation can be performed in which a validation data set, different from the training data set, is presented to the algorithm and an R2 and/or RMSR value is evaluated to ensure a good architecture and calibration (training), which are essential to obtain reliable results. 3.1.3. Advanced regression techniques In order to deal with ever growing data sets, several multivariate techniques can be used to reduce the variable space. Principal component analysis consists of a data transformation (an axis rotation to be more precise) in which all variables X ;j are recombined into linearly independent combinations, called principal components (PC), denoted as T ;j . The weights within this transformation are organised in loading vectors P;j , which are arbitrarily of unit length and orthogonal to each other, making them an appropriate set of new coordinate axes. The transformation, depicted in Fig. 3,

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Fig. 3. Visual representation of principal component analysis.

is conducted in such a way that the data set’s information content is concentrated within the first few PCs, enabling a data reduction. A related technique is PLS, i.e. partial least squares aka projection to latent structures, in which the variable space is also subject to a data reduction, but the main goal is to use the reduced space to predict output variables. Another type of multivariate models, called autoregressive exogenous models (ARX), rests on the main principle that an output prediction can be based on previous output and input values:

yðtÞ þ

n X

ai yðt  iÞ ¼

mþd1 X

ði¼1Þ

bj uðt  jÞ þ ðtÞ

ð2Þ

ðj¼dÞ

where y(t) is the current output, u(t) the current input, (t) represents white noise, ai and bj calibration coefficients, n and m the poles and zeros of the system respectively, d the system’s time delay. 3.2. Filtration models for MBRs 3.2.1. Resistances in series models As stated before, the traditional way of modelling the filtration process in MBRs applies the concept of resistances in series. Table 1 provides an overview of the contributions that will be discussed here, with their respective model structure. No details will be given on the mathematics behind the models, since there is a lot of variety in approaches to calculate the resistances. The interested reader is referred to the cited papers for more details. The most basic approach uses three terms to calculate the total filtration resistance, being the clean membrane resistance Rm (calculated using

Darcy’s law on ultrapure water filtration), cake layer resistance Rc and pore blocking resistance Rp. Broeckmann et al. (2006) extended the available models for cake layer formation by accounting for the effect of adhesive forces between particles and membrane surface, while for pore blocking it was attempted to include the influence of the relation between membrane pore size and particle size distribution. Results showed that filtration curve slopes were predicted very well, but a constant bias between the experimental and simulated results was present, especially at higher fluxes. According to the authors, the latter was caused by pressure loss in pipes, biofilm formation and concentration polarisation; i.e. mechanisms that were not included in the model. However, the influence of the use of rather simplistic empirical backwash models should not be overlooked. As also stated by the authors, these models will need intensified research in the future. Also, the influence of cake filtration on the cake layer’s porosity, important for pore blocking, needs thorough investigation, i.e. the interaction effect between both mechanisms. A disadvantage following the extension of the models is an excess of parameters needing calibration and providing an excess of degrees of freedom, reflecting a point of attention in modelling. In this model, also the PSD was seen as a parameter, which cannot be justified on the long run since sludge characteristics can change substantially over time. As a last remark, results showed that the addition of adhesive forces mainly changed specific membrane resistance and cake thickness, but both effects counteracted on each other, almost completely eliminating the influence on Rc. Nevertheless, this is an important finding for knowledge building on fouling mechanisms.

Table 1 Overview of the different possible decompositions of the filtration resistance used in literature (Rm : clean membrane resistance, Rc : cake layer resistance, Rsta : standard pore blocking resistance, Rcom : complete pore blocking resistance, Rint : intermediate blocking resistance, Rp : pore blocking resistance, Rirr : resistance by irreversible fouling, Rcd : dynamic sludge film resistance, Rsc : stable sludge cake resistance, Rf : fouling resistance, Rb : biofilm resistance). Reference

Resistances in series decomposition

Partial resistance models

Broeckmann et al., 2006

R ¼ Rm þ Rc þ Rp þ Rirr

Rm : Darcy’s law Rc : Broeckmann et al. (2006) Rp : Broeckmann et al. (2006) Rirr : Wintgens et al. (2003)

Busch et al., 2007a

R ¼ Rm þ Rc þ Rp þ Rb

Rm : Darcy’s law Rc : Broeckmann et al. (2006) Rp : Broeckmann et al. (2006) Rb : Busch et al. (2007a)

Drews et al., 2009

R ¼ Rm þ Rc þ Rsta þ Rcom þ Rint

Rm : Darcy’s law Rc : Chudacek and Fane (1984); Elmaleh and Ghaffor, 1996 Rsta ; Rcom ; Rint : Hermia (1982)

Khan et al., 2009

R ¼ Rm þ Rc þ Rf

Rm : Darcy’s law (clean water) Rc : Darcy’s law (at end of filtration) Rf : Darcy’s law (after cake layer removal)

Li and Wang, 2006

R ¼ Rm þ Rp þ Rcd þ Rcs

Rm : not mentioned Rp : Bowen et al. (1995) Rsf : Li and Wang (2006) Rsc : Li and Wang (2006)

Ludwig et al., 2011

R ¼ Rm þ Rc þ Rf

Rm : not mentioned Rc : Ludwig et al. (2011) Rf : Geissler et al. (2005)

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The models developed for Rc and Rp by Broeckmann et al. (2006) were used by Busch et al. (2007a), who added an extensive model for biofouling resistance. Furthermore, the whole filtration model structure was combined with a rather simplistic hydrodynamic model (compared to the models discussed in part 2 of this review), and ditto module geometry assumption. Most findings are a confirmation of the working knowledge on MBR filtration: filtration is hampered by low temperature, EPS contributes strongly to the biofouling resistance Rb and high solids concentrations contribute to Rp and Rc. A sensitivity analysis confirmed that PSD and cake porosity are important for both cake filtration and pore blocking, again indicating more research is needed on the interaction of these two mechanisms. It also indicated backwash is an effective countermeasure for biofilm formation, while EPS-concentrations and its transport through the cake layer are important as a cross-linking substance. Since a biofilm acts as an additional filter, interactions with pore blocking and cake filtration should also be considered in the future. It needs to be mentioned, however, that the model was not calibrated extensively, but adopted parameter values either from literature, either estimated by experts, possibly impacting the predictive power of the model, in particular since the crucial problem of parameter calibration was already indicated by Broeckmann et al. (2006). Nevertheless, the conclusion that interaction effects between different filtration resistances (and thus fouling mechanisms) needs further research, remains valid. Another mechanistic filtration model, proposed by Li and Wang (2006), uses a sectional approach, aiming at subdividing the membrane surface as to account for the uneven distribution of both shear intensity by membrane aeration and foulant deposition. Fouling is moreover seen as a two-step process, in which a sludge cake (modelled by Rsc) is formed by the cyclic addition of the unremoved part of a sludge film (modelled by Rsf). However, this sludge film resistance was not reported to contribute significantly to the total filtration resistance. Furthermore, a number of flaws were detected which the model did not account for, while at the same time the model could be overparameterised. In addition, no validation of the sectional concept was given for the reason that spatial experimental data is difficult to obtain. All these observations may lead to the conclusion that the model is made too complex given the data availability, an often observed modelling pitfall. An empirical equation, linking fouling to flux, aeration, sludge stickiness and MLSS was provided, but did not seem valid for higher values. Interestingly, also some cross-breeding between pure mechanistic models and a more empirical approach is reported as well. Drews et al. (2009) adopted a set of submodels for each fouling mechanism from literature in an automated model recognition algorithm. Within certain parameter boundaries, each of the models is calibrated using the same data set, after which the best fitting model is selected as representing the dominant fouling type. To account for frequent changes in fouling behaviour, the data set is recursively split up, and subsequently subjected to the model selection step. This algorithm has its merit in developing a model-based control strategy aiming at decreased operating costs and increased efficiency. It is noteworthy that huge differences exist in model complexity, accuracy, usability, validity and parameterisation of literature models. In Khan et al. (2009), the concept of resistances in series is used to calculate the specific cake resistance as a basis for an empirical model linking mixing intensity with fouling rate. However, 4 different set-ups, introducing uncertainty on the results, produced an only limited amount of calibration data, and no validation is mentioned on these installations, nor on other plants. On the other hand, Ludwig et al. (2011) built a more general model, applicable to both hollow fibre and flat sheet installations, calibrated and validated on a sufficient basis on several full-scale installations. Regarding this research, only 8 parameters (5 for cake

filtration, 3 for fouling resistance) were needed to predict the filtration profile. The risk of overparameterisation was already clear from Li and Wang (2006), but also Broeckmann et al. (2006) illustrated the balance between model complexity and predictive power. Finally, a very relevant concept concerning membrane filtration that is however often forgotten, is the compaction of the filter cake and the corresponding evolution of the cake layer’s permeability. Within Zhang et al. (2011), a body-centred cubic-packing model is combined with a force balance on the individual particles, to describe cake layer filtration within a flocculants added MBR. During model building, quite some simplifications were made though, such as assuming particle sphericity, the assumption of a monodisperse suspension, use of literature constants for model parameters, and others. Nevertheless, it was found that, especially under high (stable) transmembrane pressures, an increase in the cross-flow velocity invokes decreasing fractal dimensions and decreasing aggregate sizes. Under these conditions, deposition of particles becomes more compact, resulting in higher specific cake resistances. At low cross-flow velocities however, the effect is inversed, which is of course favourable. Furthermore, it was found that a reduction in inter-aggregate porosity outweighs a reduction in intra-porosity, as far as cake collapse is concerned. Cake-porosities were estimated by the model with R2 values of 0.995. Modelled specific cake resistance values showed good agreement with experimental data, whereas neglecting cake collapse resulted in severe underpredictions. 3.2.2. Artificial neural networks Neural networks have shown to be very useful for complex, non-linear systems with a high degree of interrelatedness. Since filtration processes of all kinds fit well within this definition, neural networks are mentioned for general filtration applications like surface water filtration or filtration of industrial streams. However, applications on the filtration process within membrane bioreactors are more scarce. This is remarkable, given the huge effort but slow progress on mechanistic MBR filtration models on one hand, and the good performance of ANN used in other filtration processes on the other hand. Part of the answer lies within the fact that there is no need to separate the biological aspects from the filtration process for a black box model like neural networks; a lot of researches predict the overall MBR performance via effluent concentrations, actually being the result of the interaction of the biological and the filtration process. An example is the research of Pendashteh et al. (2011), using the organic loading rate, reaction time and total dissolved solids to predict COD, total organic carbon (TOC) and oil/ grease concentrations in the effluent of a sequencing MBR. Furthermore, this research suggested that the inspection of loadings of the different inputs could reveal the influence of certain inputs on the outputs, which can be useful to optimize the system (lowering effluent concentrations). This type of knowledge building, where final nodal weights provide information on the input–output or cause–effect relations, was already illustrated by other filtration research like Chellam (2005), where it was stated that initial permeate flux was a very important parameter, while shear rate only had a weak influence on the permeate flux. In a similar way, Liu et al. (2009) revealed that operational parameters and feed water characteristics had a similar influence on the TMP behaviour. Geissler et al., 2005 modelled a submerged hollow fibre pilot MBR, treating real wastewater using both a neural network and a semi-empirical mechanistic filtration model. Concerning the neural network, the flux was modelled using an input vector consisting of: TMP, dTMP/dt, backwash TMP, filtration time, backwash time, SRT, total suspended solids (TSS), temperature and oxygen decay rate (ODR). The trained model is used for sensitivity analysis, in order to gain insight in the effect of operational parameters on the

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output, which can finally lead to optimisation of operation. It was found that increasing the backwash flux increased the permeate flux, while increasing the TMP during filtration at first increased flux, but in the long run decreased the permeate production. Increasing the filtration time resulted in lower fluxes, while increasing backwash time only slightly increased flux. From these observations, it was concluded that the best practice would be to operate under high backwash pressures, with small backwash intervals (maximise the influence with a fixed amount of permeate). Although it was stated here that high backwash TMP and low filtration TMP would be best, recent literature suggests that in order to protect the membrane on the long term from irreversible fouling, a certain amount of reversible fouling can be desirable, achieved by temporarily increasing the flux (or TMP) after each backwash cycle, in order to form a protective cake layer. This effect was however not investigated within the approximately 300 h lasting analysis by Geissler et al. (2005). Further practical validation of the proposed optimisation is thus required, even though it seems reasonable at first sight. Although modelling MBR filtration with neural networks has not been exploited to its full potential, a lot can be learned from other filtration models. The power of black box models such as neural networks is illustrated in Liu and Kim (2008), where it outperforms all combinations of mechanistic resistance laws in a filtration experiment of sythetic wastewater. However, only certain combinations of these mechanistic models were tested, whereas a total resistance based on all mechanisms might have performed better. Furthermore, Panglisch and Keller (2011) demonstrate that neural networks can be used for control of the filtration process of sonicated secondary effluent from a wastewater treatment plant. Next to online measured process data, raw water quality parameters can be included, to predict the permeability at the end of a filtration cycle. Based on this prediction, a genetic algorithm determines the optimum filtration and backwash flux, as well as filtration and backwash duration. Other practical applications and properties of neural networks in (filtration) control systems can be found in Lennox et al. (2001), but will not be discussed here for reasons of brevity. 3.2.3. Advanced regression techniques: principal component analysis, partial least squares and autoregressive exogenous models The power of principal component analysis lies in data reduction, thereby providing a condensed visualisation of the data set and revealing hidden relationships between variables. Because MBR filtration is still regarded as a grey box system, where exact process knowledge tends to be research-dependent, PCA can deliver an increased insight in hidden process variables, by simply creating them out of the available data. As in the case for ANN, most work is performed within general filtration applications and although the technique is approximately a century old, applications to MBR are scarcely available in literature. However, a significant increase in the use of this approach can already be noticed and is expected for the future. Peiris et al., 2010b started from fluorescence excitation/emission matrices (F-EEM) to investigate the contribution of different natural organic matter (NOM) fractions to the overall retention in surface water filtration for drinking water production. The FEEM was subjected to PCA and resulted in the formation of three main PCs, corresponding to humic substances, particles/colloids and protein retention respectively, thereby decomposing the entire F-EEM into 3 main potential fouling indicators. In Peiris et al. (2010a) this analysis was taken to a next level by allocating the accumulation of each foulant to either reversible or irreversible fouling by inspecting PC score differences between retentate and permeate. It turned out colloids and particles mainly contribute to reversible fouling, while humic substances, and to a lower

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extent proteins, are responsible for the irreversible part of fouling. Finally, in Peiris et al. (2011) the calculation of a specific fouling index is presented. This train of research learns that PCA can be used for knowledge building on main foulant type identification and is an advanced efficient fouling potential monitoring technique, promoted as a potential basis for fouling control strategies. More recently, Galinha et al. (2011) applied this fluorescence monitoring technique to a pilot-scale MBR and used the PLS regression method to obtain a mathematical model. Knowledge building was aimed for by examining the coefficients of the regression model. It was shown that proteins are preferentially retained by the membrane in comparison to humic substances and that although turbidity was strongly reduced by filtration, it still contributed considerably to the effluent COD. Further within the field of MBR filtration, PCA has been used by Naessens et al. (2011) for monitoring the membrane state in order to make an assessment of the prevailing fouling behaviour. TMP data of a lab-scale sidestream MBR were reorganised so that all measurements of one cycle became a single multivariate observation. These data were either first converted into a functional form, consisting of 5 deduced parameters per cycle, or either fed to PCA as such. All analyses indicated 2 PCs were adequate to describe the TMP profiles accurately. A long term analysis (ca. 6 months), including intermembrane variance, indicated that PC1 can be used as a fouling indicator, but furthermore a clear distinction between reversible and irreversible fouling was possible based on the scores of PC2. An analysis on pilot-scale data demonstrated the generic nature of the technique since similar results were obtained, but also indicated that a minimum measuring frequency is required. It was suggested by the authors that the matrix of loading vectors can be regarded as a model: future online data can be analysed using this model (i.e. principal scores can be calculated based on the already existing loading vectors) to be able to monitor fouling severeness as well as fouling type. It was also suggested that the calculated scores can be used to drive a control algorithm aiming at a reduction of fouling and thereby optimize MBR operation, which will be the subject of further work. Similar to ANN, knowledge building can be achieved by investigating the loadings of the used principal components. Within this research, it could provide an idea of the most important moments during filtration (PCA on original data set), or the most important characteristics of the TMP curve (functionised data set). The applications of PLS on filtration processes, especially MBR filtration, are more limited. An interesting contribution is made by Van den Broeck et al. (2011), where correlations are sought between different sludge related characteristics and a calculated resistance value. Using only one parameter, no clear correlations could be found, indicating sludge filterability is the result of several sludge characteristics. An empirical ratio of sludge morphology (through image analysis) and relative hydrophobicity was able to separate bad from good filterable sludge. For the latter, a PLS model was built based on relative hydrophobicity, polysaccharide fraction of SMP, protein fraction of EPS, the sludge dissociation constant (measure for floc stability) and sludge morphology. This model was able to predict the characteristic resistance value of the respective sludge sample. Tian et al., 2011b found that bulking sludge had a worse filterability than normal sludge, due to the imbalance between filamentous and floc-forming bacteria. Therefore, they constructed an ARX model able to predict the diluted sludge volume index (DSVI) (as a measure for the sludge structure), based on sludge characteristics extracted from image analysis (floc size and shape measures such as equivalent diameter, roundness, form factor, aspect ratio, etc.). The conclusions were (1) a one-parameter model has a good performance, but has low predictive power since it only uses the present values; (2) the two-parameter combinations did not yield

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positive validation results, while (3) the selected three-parameter model had a good predictive power and was validated. It has to be noted however that validation R2 values should be increased in the future (currently 65.2% and 56.9%). 3.3. Conclusions and perspectives As shown, modelling the filtration process within MBRs is mainly performed in a mechanistic way, using a resistances in series approach. However, an abundant number of ways to subdivide the total resistance in separate parts corresponding to fouling mechanisms is available, as well as quite some ways to describe the different subprocesses. Care should be taken with regard to overparameterisation correct and sufficient calibration (related to the problem of data collection) and the necessary validation before practical application can be pursued. Further research should focus on interaction effects between cake filtration, pore blocking and biofilm formation. On the other hand, several filtration applications (e.g. filtration for drinking water production) illustrate that a number of data-driven approaches can be used, to circumvent the problem of missing exact process knowledge. ANN and PCA are developing quickly, however, these techniques are far from mature to enable practical applications in MBR filtration at this point. Both mechanistic and data-driven approaches can provide process knowledge and support each other, so that hybrid techniques could emerge, in which the disadvantages of the two approaches are eliminated as much as possible. 4. Biological and filtration control models Apart from process modelling for knowledge buildup, system analysis and optimisation, it is also important to ensure the system is able to cope with disturbances, and can change its operation accordingly. To accomplish this, specific control models need to be used, which can of course be based on the process models already discussed. Ferrero et al. (2012) reviewed the MBR control systems thoroughly, and the most important conclusions will be listed here. For filtration control models a distinction is made between open and closed loop control systems, whereas the former type is typically not present in biological control. Closed loop control systems are to be preferred, since they are based on process measurements and provide a certain degree of reliability. However, also combinations are possible, where a closed loop control drives an open loop control system. TMP is mostly used as driver of the control system, but also resistances (Busch et al., 2007b), permeability (Ferrero et al., 2011) and permeate production (Huyskens et al., 2008, 2011a,b) are used. Although resistance decompositions can provide a lot of information, complexity and validation remain open issues, so it is suggested that a control system should be driven by a combination of parameters. Thereby, parameters from present instrumentation and in situ measurements are preferred. Control action time frames need more investigation at this point. Biological control is limited to only three systems. Additionally, not discussed in the review by Ferrero is the work by Chen et al. (2011), using both a support vector machine and neural network, assisted by heuristic rules to lower COD and effluent nitrogen. Control loops for CAS systems could be applicable to MBR, however, the differences between the two systems (e.g. higher MLSS concentrations) imply the need for more research to validate this statement on each control loop. Research for integrated control loops will become very important for the future, focusing on creating the best filtration conditions without hampering effluent quality. A first approach could be the integrated control of biological and membrane aeration.

Fig. 4. Example of a knowledge-based control system (Ferrero et al., 2011).

It is concluded that control models for MBR are not yet mature, due to the complexity of the fouling process and its apparent installation dependency. Promising systems are the run-to-run control proposed by Busch et al. (2007b), the previously discussed fouling mechanism detection by Drews et al. (2009), and the permeability trend approach by Ferrero et al. (2011). The latter uses the concept of a knowledge-based expert system (KBES), as is displayed in Fig. 4: process data are collected using a supervisory control and data acquisition (SCADA) system and drive a control system, which is connected to the knowledge data base. On top of this control system, a user supervision interface is generally provided.

5. Conclusions and perspectives Biokinetic and filtration models for MBRs have been critically reviewed. It is good to see that biokinetic models are starting to be applied to full scale allowing testing of hypotheses that have been put forward in the past. Once consensus is reached, it is suggested to emphasise the use of models for design/optimisation/ control instead of further development. The same holds for filtration models where many hypotheses were presented in overparameterised models that were not fully calibrated/validated. It is recommended that attention should be paid to good modelling practice and advanced data collection in both modelling areas.

Acknowledgements Thomas Maere is supported by the Institute for Encouragement of Innovation by means of Science and Technology in Flanders (IWT).

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