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Membrane bioreactor (MBR) models are useful tools for both design and management. The system complexity is high due to the involved number of processes which can be clustered in biological and physical ones. Literature studies are present and need to be harmonized in order to gain insights from the different studies and allow system optimization by applying a control. This position paper aims at defining the current state of the art of the main integrated MBR models reported in the literature. On the basis of a modelling review, a standardized terminology is proposed to facilitate the further development and comparison of integrated membrane fouling models for aerobic MBRs.

This paper proposes a model-based methodology that allows synthesising the most appropriate strategies for optimising the operation of wastewater treatment plants (WWTPs). The methodology is applied with the aim of maximising the nitrogen removal in membrane bioreactors (MBRs). The proposed procedure is based on a systematic approach composed of four steps. First, a sensitivity analysis of the input variables is carried out in order to obtain a first assessment of the potential for operational improvements. Then, the optimum input variable values are calculated by a model-based optimisation algorithm that minimises a cost function associated with the effluent total nitrogen at different temperatures. Then, the optimum operational strategies are identified. Finally, these operational strategies form the conceptual knowledge base for designing automatic control laws. The obtained optimal control strategies have shown a significant improvement in performance in comparison with fixed operation for the studied case, reducing the total nitrogen by 40%.

Little information is available on pharmaceutical trace compounds degradation pathways in wastewater. The potential of the proteomics approach has been evaluated to extract information on activated sludge microbial metabolism in degrading a trace concentration of a pharmaceutical compound (ibuprofen). Ibuprofen is one of the most consumed pharmaceuticals, measured in wastewater at very high concentrations and, despite its high removal rates, found in different environmental compartments. Aerated and completely mixed activated sludge batch tests were spiked with ibuprofen at 10 and 1,000 μg L(-1). Ibuprofen concentrations were determined in the liquid phase: 100% removal was observed and the kinetics were estimated. The solid phase was sampled for proteomics purposes. The first objective was to apply proteomics to evaluate protein profile variations in a complex matrix such as activated sludge. The second objective was to determine, at different ibuprofen concentrations, which proteins followed pre-defined trends. No newly expressed proteins were found. Nonetheless, the obtained results suggest that proteomics itself is a promising methodology to be applied in this field. Statistical and comparative studies analyses provided, in fact, useful information on biological reproducibility and permitted us to detect 62 proteins following coherent and plausible expected trends in terms of presence and intensity change.

Key developments of instrumentation, control and automation (ICA) applications in wastewater systems during the past 40 years are highlighted in this paper. From the first ICA conference in 1973 through to today there has been a tremendous increase in the understanding of the processes, instrumentation, computer systems and control theory. However, many developments have not been addressed here, such as sewer control, drinking water treatment and water distribution control. It is hoped that this review can stimulate new attempts to more effectively apply control and automation in water systems in the coming years.

As the work of the IWA Task Group on Benchmarking of Control Strategies for wastewater treatment plants (WWTPs) is coming to an end, it is essential to disseminate the knowledge gained. For this reason, all authors of the IWA Scientific and Technical Report on benchmarking have come together to provide their insights, highlighting areas where knowledge may still be deficient and where new opportunities are emerging, and to propose potential avenues for future development and application of the general benchmarking framework and its associated tools. The paper focuses on the topics of temporal and spatial extension, process modifications within the WWTP, the realism of models, control strategy extensions and the potential for new evaluation tools within the existing benchmark system. We find that there are major opportunities for application within all of these areas, either from existing work already being done within the context of the benchmarking simulation models (BSMs) or applicable work in the wider literature. Of key importance is increasing capability, usability and transparency of the BSM package while avoiding unnecessary complexity.

Membrane filtration has gradually gained acceptance as the preferred pre-treatment for reverse osmosis (RO). In this paper, an integrated membrane bioreactor (MBR)/RO system for wastewater reuse treating real sewage water has been evaluated and the RO fouling has been characterised. The MBR achieved low values of organic matter, total nitrogen, PO(4)(3-), total organic carbon, turbidity and conductivity. Filtration with two different RO commercial membranes was performed after the MBR pre-treatment and the same average fouling rate (0.08 bar day(-1)) was noted. These results gained from the characterisation of the high quality MBR/RO permeate show its potential for water reuse. Inorganic precipitation appears to be the predominant form of fouling in the RO membranes. Calcium phosphate and alumino-silicates were identified by a scanning electron microscope combined with an energy dispersive X-ray and polysaccharides, amide and aliphatic structures were detected with attenuated total reflection infrared microspectroscopy.

Although there are few studies about clogging phenomenon in the peer-reviewed literature, it is considered one of the main operational challenges by membrane bioreactor (MBR) practitioners. This study presents data from the performance of a full-scale MBR affected by clogging, and ragging in particular. An evaluation of the efficiencies of different applied cleaning methods revealed the acid recovery cleaning to be more efficient than the basic recovery cleanings, although all maintenance cleanings were largely ineffective in recovering membrane permeability. Only declogging cleaning through the manual removal of the accumulated solids was found to be efficient, indicating that such solids were substantially unremoved by chemical cleaning. Moreover, reclogging following manual cleaning demonstrated a propensity for rapid clogging - within a period of 10 days over which the permeability returned to 68 and 88% of the pre-cleaned state. The analysis of the feedwater indicated suspended textile fibres (>70% cotton) to be present at a concentration of more than 40 mg·L(-1), ∼90% being smaller than 1 mm (0.06-0.4 mm). These small lengths of filaments evidently pass through pre-treatment and are retained on the membrane surface, forming 'rags' within the membrane module, notwithstanding the routine high quality of sludge reflected in the capillary suction time and filterability measurements. Pre-treatment improvement, manual cleaning and permeate flux reduction are the only options to minimise ragging impact over MBR performance.

The main goal of the Water Framework Directive is to achieve good chemical and ecological status of water bodies by 2015. The implementation of integrated river basin management, including sewer systems, wastewater treatment plants and receiving water bodies, is essential to accomplishing this objective. Integrated management is complex and therefore the implementation of control systems and the development of decision support systems are needed to facilitate the work of urban wastewater system (UWS) managers. Within this context, the objective of this paper is to apply integrated modelling of an UWS to simulate and analyse the behaviour of the 'Congost' UWS in Spain, and to optimize its performance against different types of perturbations. This analysis results in optimal operating set-points for each perturbation, improves river water quality, minimizes combined sewer overflows and optimizes flow lamination from storm water tanks. This is achieved by running Monte Carlo simulations and applying global sensitivity analysis. The set-points will become part of the knowledge base composed of a set of IF-THEN rules of the environmental decision support system being developed for this case study.

Although membrane bioreactors (MBRs) technology is still a growing sector, its progressive implementation all over the world, together with great technical achievements, has allowed it to reach a mature degree, just comparable to other more conventional wastewater treatment technologies. With current energy requirements around 0.6-1.1 kWh/m3 of treated wastewater and investment costs similar to conventional treatment plants, main market niche for MBRs can be areas with very high restrictive discharge limits, where treatment plants have to be compact or where water reuse is necessary. Operational costs are higher than for conventional treatments; consequently there is still a need and possibilities for energy saving and optimisation. This paper presents the development of a knowledge-based decision support system (DSS) for the integrated operation and remote control of the biological and physical (filtration and backwashing or relaxation) processes in MBRs. The core of the DSS is a knowledge-based control module for air-scour consumption automation and energy consumption minimisation.

MBR technology is currently challenging traditional wastewater treatment systems and is increasingly selected for WWTP upgrading. MBR systems typically are constructed on a smaller footprint, and provide superior treated water quality. However, the main drawback of MBR technology is that the permeability of membranes declines during filtration due to membrane fouling, which for a large part causes the high aeration requirements of an MBR to counteract this fouling phenomenon. Due to the complex and still unknown mechanisms of membrane fouling it is neither possible to describe clearly its development by means of a deterministic model, nor to control it with a purely mathematical law. Consequently the majority of MBR applications are controlled in an "open-loop" way i.e. with predefined and fixed air scour and filtration/relaxation or backwashing cycles, and scheduled inline or offline chemical cleaning as a preventive measure, without taking into account the real needs of membrane cleaning based on its filtration performance. However, existing theoretical and empirical knowledge about potential cause-effect relations between a number of factors (influent characteristics, biomass characteristics and operational conditions) and MBR operation can be used to build a knowledge-based decision support system (KB-DSS) for the automatic control of MBRs. This KB-DSS contains a knowledge-based control module, which, based on real time comparison of the current permeability trend with "reference trends", aims at optimizing the operation and energy costs and decreasing fouling rates. In practice the automatic control system proposed regulates the set points of the key operational variables controlled in MBR systems (permeate flux, relaxation and backwash times, backwash flows and times, aeration flow rates, chemical cleaning frequency, waste sludge flow rate and recycle flow rates) and identifies its optimal value. This paper describes the concepts and the 3-level architecture of the knowledge-based DSS and details the knowledge-based control module. Preliminary results of the application of the control module to regulate the air flow rate of an MBR working with variable flux demonstrates the usefulness of this approach.

Current management of sanitation infrastructures (sewer systems, wastewater treatment plant, receiving water, bypasses, deposits, etc) is not fulfilling the objectives of up to date legislation, to achieve a good ecological and chemical status of water bodies through integrated management. These made it necessary to develop new methodologies that help decision makers to improve the management in order to achieve that status. Decision Support Systems (DSS) based on Multi-Agent System (MAS) paradigm are promising tools to improve the integrated management. When all the different agents involved interact, new important knowledge emerges. This knowledge can be used to build better DSS and improve wastewater infrastructures management achieving the objectives planned by legislation. The paper describes a methodology to acquire this knowledge through a Role Playing Game (RPG). First of all there is an introduction about the wastewater problems, a definition of RPG, and the relation between RPG and MAS. Then it is explained how the RPG was built with two examples of game sessions and results. The paper finishes with a discussion about the uses of this methodology and future work.

The Besòs River Basin authority is working towards the implementation of integrated river basin management, as required by the Water Framework Directive (WFD), to achieve a good ecological and chemical status of all water bodies by 2015. The studied system is constituted by two communities (La Garriga and Granollers), their corresponding draining catchments, sewer systems and two WWTPs, which discharge treated water at different locations of the same river. Within this context, the realisation of an integrated model of the river stretch and of the two WWTPs with their sewer systems and draining catchments was necessary. Such a model allows to efficiently simulate and analyse the behaviour of the integrated system and to optimize its performance holistically. In this article, a method is presented to generate rules to be implemented in a supervisory system for automatic management of the Integrated Urban Wastewater System (IUWS) in dry and storm weather. This is achieved by identifying, with the help of Monte Carlo simulations, the most performing operational parameters-according to environmental and economic criteria-for the two weather conditions.

The main objective of this paper is to evaluate the effect of filamentous bulking sludge on the predicted performance of simulated plant-wide WWTP control strategies. First, as a reference case, several control strategies are implemented, simulated and evaluated using the IWA Benchmark Simulation Model No. 2 (BSM2). In a second series of simulations the parameters of the secondary settler model in the BSM2 are automatically changed on the basis of an on-line calculated risk of filamentous bulking, in order to mimic the effect of growth of filamentous bacteria in the plant. The results are presented using multivariate analysis. Including the effects of filamentous bulking in the simulation model gives a-more realistic-deterioration of the plant performance during periods when the conditions for development of filamentous bulking sludge are favourable: compared to the reference case where bulking effects are not considered. Thus, there is a decrease of the overall settling velocity, an accumulation of the total suspended solids (TSS) in the middle layers of the settler with a consequent reduction of their degree of compaction in the bottom. As a consequence there is a lower TSS concentration in both return and waste flow, less biomass in the bioreactors and a reduction of the TSS removal efficiency. The control alternatives using a TSS controller substantially increase the food to microorganisms (F/M) ratio in the bioreactor, thereby reducing both risk and effects of bulking sludge. The effects of ammonium (NH(4)(+)), nitrate (NO(3)(-)) and reject water control strategies are rather poor when it comes to handling solids separation problems.

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Decision support systems (DSS) have generated high expectations as a tool to support activated sludge operation because of their ability to represent heuristic reasoning and to handle large amounts of qualitative, uncertain and low-accuracy data. Previous applications have been satisfactory to control simple problems, when static reasoning and literature-based solutions were enough. However to face complex operational problems with biological origin and slow dynamics (e.g. solids separation problems), it is necessary to use dynamic reasoning and apply long-term control strategies, monitoring the evolution of the process and adjusting the action plan according to the feed back of the process. This paper presents a dynamic reasoning DSS to face solids separation problems in the activated sludge system. The DSS is capable of identifying the complex problem affecting the process, determining if the current situation is new or a continuation from the previous one, assessing what is the specific cause of the situation, and recommending a long-term control strategy, which is daily adjusted according to the evolution of the process.

In this paper the development of an extension module to the IWA/COST simulation benchmark to include expert reasoning is presented. This module enables the detection of suitable conditions for the development of settling problems of biological origin (filamentous bulking, foaming and rising sludge) when applying activated sludge control strategies to the simulation benchmark. Firstly, a flow diagram is proposed for each settling problem, and secondly, the outcome of its application is shown. Results of the benchmark for two evaluated control strategies illustrate that, once applied to the simulation outputs, this module provides supplementary criteria for plant performance assessment. Therefore, simulated control strategies can be evaluated in a more realistic framework, and results can be recognised as more realistic and satisfactory from the point of view of operators and real facilities.

Wastewater treatment plant operators encounter complex operational problems related to the activated sludge process and usually respond to these by applying their own intuition and by taking advantage of what they have learnt from past experiences of similar problems. However, previous process experiences are not easy to integrate in numerical control, and new tools must be developed to enable re-use of plant operating experience. The aim of this paper is to investigate the usefulness of a case-based reasoning (CBR) approach to apply learning and re-use of knowledge gained during past incidents to confront actual complex problems through the IWA/COST Benchmark protocol. A case study shows that the proposed CBR system achieves a significant improvement of the benchmark plant performance when facing a high-flow event disturbance.

Classical control has serious limitations when faced with solids separation problems in the activated sludge process. Lack of knowledge about the mechanisms involved in the imbalance within the different microbiological communities implies that a general solution to these undesirable situations has not yet been provided. However, operators have to make decisions based on their experience and intuition to solve the problem (or at least to minimise the effects). The acquisition and registration of the knowledge learnt from each new experience can be decisive when solving similar problems in the future. Case-based reasoning (CBR) is an advanced technique for knowledge management in complex systems that uses past experiences to solve brand new situations. Previous simplified proposals in this field have exposed limitations, but this paper describes a new approach to CBR, considering the dynamics and the complexity of solids separation problems.

Constructed wetlands (CWs) are artificial wastewater treatment systems appropriate for small communities because of their affordability, operability and reliability. These qualities are true whenever CWs are designed and constructed properly, and as long as the necessary operation and maintenance procedures are carried out correctly. Experience shows that the operation and maintenance procedures, and the frequencies with which these procedures are carried out, differ from one CW to another. With this in mind, and along with a projected increase in CWs in Catalonia, the Catalan Water Agency (Agència Catalana de l'Aigua) has developed an Environmental Decision Support System (EDSS) which proposes guidelines for monitoring and maintenance, according to the characteristics of each CW. This EDSS was developed following a methodology based on five steps: (i) problem analysis; (ii) collecting data and knowledge acquisition; (iii) model selection; (iv) model implementation and (v) validation. This paper describes the methodology followed to build the decision support system and presents some examples of the information provided by this EDSS.

The identification of adequate wastewater treatment for small communities is a complex problem since it demands a combination of data from different sources, such as aspects of the community and landscape, the receiving environment and the available wastewater treatment technologies. The Catalan Water Agency (Agència Catalana de l'Aigua) considered using an EDSS (Environmental Decision Support System) as a tool to help water managers select the most adequate treatment for the urban wastewater of nearly 3,500 small communities in Catalonia (Spain). From that moment, EDSS was applied to all the river basins in Catalonia. In this paper the authors present the results obtained for the 76 small communities located in one of these river basins: the Fluvia River Basin. The characteristics of the community used in the reasoning process of the EDSS, the list of selected wastewater treatment alternatives, the technical environmental justification for the selected treatments and the reasons for discarding, favouring or disadvantaging them are presented. Finally, some results for the Fluvia River Basin are compared with those obtained in other Catalan river basins with different characteristics in order to evaluate which are the significant features in identifying adequate wastewater treatments.

The identification of adequate treatment for small communities is a complex problem since it makes it necessary to combine aspects of the community and landscape, the receiving environment, and the available wastewater treatment technologies. This paper presents the development and implementation of a Knowledge-Based Decision Support System (KB-DSS) to tackle this problem. Different knowledge sources have been consulted in order to make up a comprehensive and accurate knowledge base. The core of the KB-DSS embraces two objectives. The first one is to assist in the selection of the treatment level adequate to fulfil the target quality standards for the receiving environment. The second one is to select the specific type of treatment. The KB-DSS is being applied to each one of the 3,482 different small communities comprised in the Small Communities Wastewater Treatment Plan of Catalonia, grouped according to river catchments. This paper also summarizes the different steps involved in the operation of the knowledge-based DSS when solving a real case study.

Integrated operation of Wastewater Treatment Plants is still far from being solved. A reasonable proposal should link advanced and robust control algorithms to some knowledge-based techniques, allocating the detailed engineering to numerical computations, while delegating the logical analysis and reasoning to supervisory intelligent systems. This paper describes the development and implementation of a knowledge-based Hybrid Supervisory System to support the operation of a real Wastewater Treatment Plant. The system integrates different reasoning modules, overcoming the limitations in the use of each single technique, while providing an agent based architecture with additional modularity and independence. It is structured into three separated levels: data gathering, diagnosis, and decision support. The different tasks of the system are performed in a seven-step cycle: data gathering and update, diagnosis, supervision, prediction, communication, actuation, and evaluation phase. In spite of certain reservations of the scientific community about the use of these techniques, the system is successfully performing real-time support to the operation of the Granollers facility since September 1999. Results of the first four-month validation period are shown and discussed. An example of the system behavior is also shown in the paper. The conclusions indicate the key steps which are necessary to transfer the system to another facility.