A framework for model-based assessment of resilience in water resource recovery facilities against power outage.

Current practice to enhance resilience in Water Resource Recovery Facilities (WRRFs) is to ensure redundancy or back-up for most critical equipment (e.g. pumps or blowers). Model-based assessment allows evaluation of different strategies for quantitatively and efficiently enhancing resilience and justifying the allocation of resources. The goal of this study is to provide guidance for the development of tailored deterministic models of full-scale WRRFs. A framework for model-based resilience assessment is proposed that provides guidance on data collection, model selection, model calibration and scenario analysis. The framework is embedded into the Good Modeling Practice (GMP) Unified Protocol, providing a new application for resilience assessment and an initial set of stressors for WRRFs. The usefulness of the framework is illustrated through a resilience assessment of the WRRF of Girona against power outage. Results show that, for the Girona facility, limited energy back-up can cause non-compliance of WRRF discharge limits in the case of a blower power shut-down of 6 h, and around 12 h when the blower shut-down is also combined with a shut-down of the recirculation pumps. The best option to enhance resilience would be increasing the power back-up by 218%, which allows the plant to run with recirculation pumps and blowers at minimum capacity. In such a case, resilience can be further enhanced by manipulating the air supply valves to optimise the air distribution, to balance oxygen needs in each reactor with the overall system pressure. We conclude that, with industry consensus on what is considered an acceptable level of resilience, a framework for resilience assessment would be a useful tool to enhance the resilience of our current water infrastructure. Further research is needed to establish if the permit structure should accommodate levels sof functionality to account for stress events.

Long-term simulation of a full-scale EBPR plant with a novel metabolic-ASM model and its use as a diagnostic tool.

This study evaluates the predictive capacity of the META-ASM model, a new integrated metabolic activated sludge model, in describing the long-term performance of a full-scale enhanced biological phosphorus removal (EBPR) system that suffers from inconsistent performance. In order to elucidate the causes of EBPR upsets and troubleshoot the process accordingly, the META-ASM model was tested as an operational diagnostic tool in a 1336-day long-term dynamic simulation, while its performance was compared with the ASM-inCTRL model, a version based on the Barker & Dold model. Overall, the predictions obtained with the META-ASM without changing default parameters were more reliable and effective at describing the active biomass of polyphosphate accumulating organisms (PAOs) and the dynamics of their storage polymers. The primary causes of the EBPR upsets were the high aerobic hydraulic retention times (HRTs) and low organic loading rates (OLRs) of the plant, which led to periods of starvation. The impact of these factors on EBPR performance were only identified with the META-ASM model. Furthermore, the first signs of process upsets were predicted by variations in the aerobic PAO maintenance rates, suggesting that the META-ASM model has potential to provide an early warning of process upset. The simulation of a new viable operational strategy indicated that troubleshooting the process could be achieved by reducing the aerated volume by switching off air in the first half of the aeration tank. In this new strategy, the META-ASM model predicted a simultaneous improvement in the biological phosphorus (P) and nitrogen (N) removal due to the enhancement of the hydrolysis and fermentation of the mixed liquor sludge in the new unaerated zone, which increased the availability of volatile fatty acids (VFAs) for PAOs. This study demonstrates that the META-ASM model is a powerful operational diagnostic tool for EBPR systems, capable of predicting and mitigating upsets, optimising performance and evaluating new process designs.

A novel metabolic-ASM model for full-scale biological nutrient removal systems.

This study demonstrates that META-ASM, a new integrated metabolic activated sludge model, provides an overall platform to describe the activity of the key organisms and processes relevant to biological nutrient removal (BNR) systems with a robust single-set of default parameters. This model overcomes various shortcomings of existing enhanced biological phosphorous removal (EBPR) models studied over the last twenty years. The model has been tested against 34 data sets from enriched lab polyphosphate accumulating organism (PAO)-glycogen accumulating organism (GAO) cultures and experiments with full-scale sludge from five water resource recovery facilities (WRRFs) with two different process configurations: three stage Phoredox (A2/O) and adapted Biodenitro™ combined with a return sludge sidestream hydrolysis tank (RSS). Special attention is given to the operational conditions affecting the competition between PAOs and GAOs, capability of PAOs and GAOs to denitrify, metabolic shifts as a function of storage polymer concentrations, as well as the role of these polymers in endogenous processes and fermentation. The overall good correlations obtained between the predicted versus measured EBPR profiles from different data sets support that this new model, which is based on in-depth understanding of EBPR, reduces calibration efforts. On the other hand, the performance comparison between META-ASM and literature models demonstrates that existing literature models require extensive parameter changes and have limited predictive power, especially in the prediction of long-term EBPR performance. The development of such a model able to describe in detail the microbial and chemical transformations of BNR systems with minimal adjustment to parameters suggests that the META-ASM model is a powerful tool to predict and mitigate EBPR upsets, optimise EBPR performance and to evaluate new process designs.

Modelling gas-liquid mass transfer in wastewater treatment: when current knowledge needs to encounter engineering practice and vice versa.

Gas-liquid mass transfer in wastewater treatment processes has received considerable attention over the last decades from both academia and industry. Indeed, improvements in modelling gas-liquid mass transfer can bring huge benefits in terms of reaction rates, plant energy expenditure, acid-base equilibria and greenhouse gas emissions. Despite these efforts, there is still no universally valid correlation between the design and operating parameters of a wastewater treatment plant and the gas-liquid mass transfer coefficients. That is why the current practice for oxygen mass transfer modelling is to apply overly simplified models, which come with multiple assumptions that are not valid for most applications. To deal with these complexities, correction factors were introduced over time. The most uncertain of them is the α-factor. To build fundamental gas-liquid mass transfer knowledge more advanced modelling paradigms have been applied more recently. Yet these come with a high level of complexity making them impractical for rapid process design and optimisation in an industrial setting. However, the knowledge gained from these more advanced models can help in improving the way the α-factor and thus gas-liquid mass transfer coefficient should be applied. That is why the presented work aims at clarifying the current state-of-the-art in gas-liquid mass transfer modelling of oxygen and other gases, but also to direct academic research efforts towards the needs of the industrial practitioners.

The future of WRRF modelling - outlook and challenges.

The wastewater industry is currently facing dramatic changes, shifting away from energy-intensive wastewater treatment towards low-energy, sustainable technologies capable of achieving energy positive operation and resource recovery. The latter will shift the focus of the wastewater industry to how one could manage and extract resources from the wastewater, as opposed to the conventional paradigm of treatment. Debatable questions arise: can the more complex models be calibrated, or will additional unknowns be introduced? After almost 30 years using well-known International Water Association (IWA) models, should the community move to other components, processes, or model structures like 'black box' models, computational fluid dynamics techniques, etc.? Can new data sources - e.g. on-line sensor data, chemical and molecular analyses, new analytical techniques, off-gas analysis - keep up with the increasing process complexity? Are different methods for data management, data reconciliation, and fault detection mature enough for coping with such a large amount of information? Are the available calibration techniques able to cope with such complex models? This paper describes the thoughts and opinions collected during the closing session of the 6th IWA/WEF Water Resource Recovery Modelling Seminar 2018. It presents a concerted and collective effort by individuals from many different sectors of the wastewater industry to offer past and present insights, as well as an outlook into the future of wastewater modelling.

Ammonia-based aeration control with optimal SRT control: improved performance and lower energy consumption.

Ammonia-based aeration control (ABAC) is a cascade control concept for controlling total ammonia nitrogen (NHx-N) in the activated sludge process. Its main goals are to tailor the aeration intensity to the NHx-N loading and to maintain consistent nitrification, to meet effluent limits but minimize energy consumption. One limitation to ABAC is that the solids retention time (SRT) control strategy used at a water resource recovery facility (WRRF) may not be consistent with the goals of ABAC. ABAC-SRT control is a strategy for aligning the goals of ammonia-based aeration control and SRT control. A supervisory controller is used to ensure that the SRT is always optimal for ABAC. The methodology has the potential to reduce aeration energy consumption by over 30% as compared to traditional dissolved oxygen (DO) control. Practical implementation aspects are highlighted for implementation at full scale, such as proper selection of the set point for the supervisory controller, proper calculation of the rate of change in sludge inventory, using a mixed liquor suspended solids (MLSS) controller, and tuning of the controllers. In conclusion, ABAC-SRT is a promising approach for coordinated control of SRT, total ammonia nitrogen, and dissolved oxygen in the activated sludge process that balances both treatment performance and energy savings.

Dynamic air supply models add realism to the evaluation of control strategies in water resource recovery facilities.

This paper introduces the application of a fully dynamic air distribution model integrated with a biokinetic process model and a detailed process control model. By using a fully dynamic air distribution model, it is possible to understand the relationships between aeration equipment, control algorithms, process performance, and energy consumption, thus leading to a significantly more realistic prediction of water resource recovery facility (WRRF) performance. Consequently, this leads to an improved design of aeration control strategies and equipment. A model-based audit has been performed for the Girona WRRF with the goal of providing a more objective evaluation of energy reduction strategies. Currently, the Girona plant uses dissolved oxygen control and has been manually optimised for energy consumption. Results from a detailed integrated model show that the implementation of an ammonia-based aeration controller, a redistribution of the diffusers, and the installation of a smaller blower lead to energy savings between 12 and 21%, depending on wastewater temperature. The model supported the development of control strategies that counter the effects of current equipment limitations, such as tapered diffuser distribution, or over-sized blowers. The latter causes an intermittent aeration pattern with blowers switching on and off, increasing wear of the equipment.

Towards advanced aeration modelling: from blower to bubbles to bulk.

Aeration is an essential component of aerobic biological wastewater treatment and is the largest energy consumer at most water resource recovery facilities. Most modelling studies neglect the inherent complexity of the aeration systems used. Typically, the blowers, air piping, and diffusers are not modelled in detail, completely mixed reactors in a series are used to represent plug-flow reactors, and empirical correlations are used to describe the impact of operating conditions on bubble formation and transport, and oxygen transfer from the bubbles to the bulk liquid. However, the mechanisms involved are very complex in nature and require significant research efforts. This contribution highlights why and where there is a need for more detail in the different aspects of the aeration system and compiles recent efforts to develop physical models of the entire aeration system (blower, valves, air piping and diffusers), as well as adding rigour to the oxygen transfer efficiency modelling (impact of viscosity, bubble size distribution, shear and hydrodynamics). As a result of these model extensions, more realistic predictions of dissolved oxygen profiles and energy consumption have been achieved. Finally, the current needs for further model development are highlighted.

Development of a model for activated sludge aeration systems: linking air supply, distribution, and demand.

During the design of a water resource recovery facility, it is becoming industry practice to use simulation software to assist with process design. Aeration is one of the key components of the activated sludge process, and is one of the most important aspects of modelling wastewater treatment systems. However, aeration systems are typically not modelled in detail in most wastewater treatment process modelling studies. A comprehensive dynamic aeration system model has been developed that captures both air supply and demand. The model includes sub-models for blowers, pipes, fittings, and valves. An extended diffuser model predicts both oxygen transfer efficiency within an aeration basin and pressure drop across the diffusers. The aeration system model allows engineers to analyse aeration systems as a whole to determine biological air requirements, blower performance, air distribution, control valve impacts, controller design and tuning, and energy costs. This enables engineers to trouble-shoot the entire aeration system including process, equipment and controls. It also allows much more realistic design of these highly complex systems.

Ammonia-based feedforward and feedback aeration control in activated sludge processes.

Aeration control at wastewater treatment plants based on ammonia as the controlled variable is applied for one of two reasons: (1) to reduce aeration costs, or (2) to reduce peaks in effluent ammonia. Aeration limitation has proven to result in significant energy savings, may reduce external carbon addition, and can improve denitrification and biological phosphorus (bio-P) performance. Ammonia control for limiting aeration has been based mainly on feedback control to constrain complete nitrification by maintaining approximately one to two milligrams of nitrogen per liter of ammonia in the effluent. Increased attention has been given to feedforward ammonia control, where aeration control is based on monitoring influent ammonia load. Typically, the intent is to anticipate the impact of sudden load changes, and thereby reduce effluent ammonia peaks. This paper evaluates the fundamentals of ammonia control with a primary focus on feedforward control concepts. A case study discussion is presented that reviews different ammonia-based control approaches. In most instances, feedback control meets the objectives for both aeration limitation and containment of effluent ammonia peaks. Feedforward control, applied specifically for switching aeration on or off in swing zones, can be beneficial when the plant encounters particularly unusual influent disturbances.

Rethinking wastewater characterisation methods for activated sludge systems - a position paper.

Increasingly stringent effluent limits and an expanding scope of model system boundaries beyond activated sludge has led to new modelling objectives and consequently to new and often more detailed modelling concepts. Nearly three decades after the publication of Activated Sludge Model No1 (ASM1), the authors believe it is time to re-evaluate wastewater characterisation procedures and targets. The present position paper gives a brief overview of state-of-the-art methods and discusses newly developed measurement techniques on a conceptual level. Potential future paths are presented including on-line instrumentation, promising measuring techniques, and mathematical solutions to fractionation problems. This is accompanied by a discussion on standardisation needs to increase modelling efficiency in our industry.

Improving nutrient removal while reducing energy use at three Swiss WWTPs using advanced control.

Aeration consumes about 60% of the total energy use of a wastewater treatment plant (WWTP) and therefore is a major contributor to its carbon footprint. Introducing advanced process control can help plants to reduce their carbon footprint and at the same time improve effluent quality through making available unused capacity for denitrification, if the ammonia concentration is below a certain set-point. Monitoring and control concepts are cost-saving alternatives to the extension of reactor volume. However, they also involve the risk of violation of the effluent limits due to measuring errors, unsuitable control concepts or inadequate implementation of the monitoring and control system. Dynamic simulation is a suitable tool to analyze the plant and to design tailored measuring and control systems. During this work, extensive data collection, modeling and full-scale implementation of aeration control algorithms were carried out at three conventional activated sludge plants with fixed pre-denitrification and nitrification reactor zones. Full-scale energy savings in the range of 16-20% could be achieved together with an increase of total nitrogen removal of 40%.

Performance evaluation of fault detection methods for wastewater treatment processes.

Several methods to detect faults have been developed in various fields, mainly in chemical and process engineering. However, minimal practical guidelines exist for their selection and application. This work presents an index that allows for evaluating monitoring and diagnosis performance of fault detection methods, which takes into account several characteristics, such as false alarms, false acceptance, and undesirable switching from correct detection to non-detection during a fault event. The usefulness of the index to process engineering is demonstrated first by application to a simple example. Then, it is used to compare five univariate fault detection methods (Shewhart, EWMA, and residuals of EWMA) applied to the simulated results of the Benchmark Simulation Model No. 1 long-term (BSM1_LT). The BSM1_LT, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor and actuator faults and process disturbances in a wastewater treatment plant. The results from the method comparison using BSM1_LT show better performance to detect a sensor measurement shift for adaptive methods (residuals of EWMA) and when monitoring the actuator signals in a control loop (e.g., airflow). Overall, the proposed index is able to screen fault detection methods.

Data reconciliation for wastewater treatment plant simulation studies-planning for high-quality data and typical sources of errors.

Model results are only as good as the data fed as input or used for calibration. Data reconciliation for wastewater treatment modeling is a demanding task, and standardized approaches are lacking. This paper suggests a procedure to obtain high-quality data sets for model-based studies. The proposed approach starts with the collection of existing historical data, followed by the planning of additional measurements for reliability checks, a data reconciliation step, and it ends with an intensive measuring campaign. With the suggested method, it should be possible to detect, isolate, and finally identify systematic measurement errors leading to verified and qualitative data sets. To allow mass balances to be calculated or other reliability checks to be applied, few additional measurements must be introduced in addition to routine measurements. The intensive measurement campaign should be started only after all mass balances applied to the historical data are closed or the faults have been detected, isolated, and identified. In addition to the procedure itself, an overview of typical sources of errors is given.

Wastewater treatment models in teaching and training: the mismatch between education and requirements for jobs.

As mathematical modeling of wastewater treatment plants has become more common in research and consultancy, a mismatch between education and requirements for model-related jobs has developed. There seems to be a shortage of skilled people, both in terms of quantity and in quality. In order to address this problem, this paper provides a framework to outline different types of model-related jobs, assess the required skills for these jobs and characterize different types of education that modelers obtain "in school" as well as "on the job". It is important to consider that education of modelers does not mainly happen in university courses and that the variety of model related jobs goes far beyond use for process design by consulting companies. To resolve the mismatch, the current connection between requirements for different jobs and the various types of education has to be assessed for different geographical regions and professional environments. This allows the evaluation and improvement of important educational paths, considering quality assurance and future developments. Moreover, conclusions from a workshop involving practitioners and academics from North America and Europe are presented. The participants stressed the importance of non-technical skills and recommended strengthening the role of realistic modeling experience in university training. However, this paper suggests that all providers of modeling education and support, not only universities, but also software suppliers, professional associations and companies performing modeling tasks are called to assess and strengthen their role in training and support of professional modelers.

Extension of ASM3 for two-step nitrification and denitrification and its calibration and validation with batch tests and pilot scale data.

Although traditionally not taken into account by most of activated sludge models the production of nitrite as an intermediate of the nitrification-denitrification processes becomes of interest in some specific plant operational situations or in case of high sensitivity of the receiving ecosystems. The Activated Sludge Model No.3 (ASM3) was therefore extended for two-step nitrification and two-step denitrification in order to better describe nitrite dynamics especially during the treatment of communal wastewater. Nitrite was included as a new model compound and as an intermediate product of biological processes, both for heterotrophic and autotrophic bacteria. Two new model compounds replace X(A), the original autotrophic biomass: Ammonium Oxidizing Bacteria, X(AOB) and Nitrite Oxidizing Bacteria, X(NOB). Growth and decay processes of nitrifiers were split into AOB and NOB processes (3 additional processes) and heterotrophic anoxic processes were also doubled in order to account for two-step denitrification (4 additional processes). Default values from literature as well as laboratory measurements were considered for the choice of kinetic and stoichiometric parameters. The model was calibrated and validated with laboratory scale tests in batch reactors and with data from an Eawag activated sludge pilot plant configured conventionally with nitrification and pre-denitrification for the treatment of communal wastewater.

Modelling nitrite in wastewater treatment systems: a discussion of different modelling concepts.

Originally presented at the 1st IWA/WEF Wastewater Treatment Modelling Seminar (WWTmod 2008), this contribution has been updated to also include the valuable feedback that was received during the Modelling Seminar. This paper addresses a number of basic issues concerning the modelling of nitrite in key processes involved in biological wastewater water treatment. To this end, we review different model concepts (together with model structures and corresponding parameter sets) proposed for processes such as two-step nitrification/denitrification, anaerobic ammonium oxidation and phosphorus uptake processes. After critically discussing these models with respect to their assumptions and parameter sets, common points of agreement as well as disagreement were elucidated. From this discussion a general picture of the state-of-the-art in the modelling of nitrite is provided. Taking this into account, a number of recommendations are provided to focus further research and development on nitrite modelling in biological wastewater treatment.

Quantifying the uncertainty of on-line sensors at WWTPs during field operation.

It remains an ongoing task to quantify the uncertainty of continuous measuring systems at WWTPs during field operation. The commonly used methods are based on lab experiments under standardized conditions and are only suitable for characterizing the measuring device itself. For measuring devices under field conditions, a knowledge of the response time, trueness and precision is equally important. A method is proposed which can be used to characterize newly installed on-line sensors or to evaluate monitoring data which may contain systematic errors. The concept is based on comparative measurements between the sensor and a reference. A linear regression is used to differentiate between trueness and precision. Various statistical tests are conducted to validate the preconditions of linear regression. The information about the trueness and precision of the measuring system under field conditions helps to adapt control strategies more effectively to the relevant processes and permits sophisticated control concepts. Moreover, the concept can help to define guidelines for evaluating the uncertainties of effluent quality monitoring to overcome the concerns about on-line sensors, improve the trust in these systems and to allow the use of continuously measuring systems for legislative purposes. The approach is discussed in detail in this paper and all statistical tests and formulas are listed in the Appendix.