The future of WRRF modelling - outlook and challenges

Towards good modelling practice for parallel hybrid models for wastewater treatment processes

This study explores various approaches to formulating a parallel hybrid model (HM) for Water and Resource Recovery Facilities (WRRFs) merging a mechanistic and a data-driven model. In the study, the HM is constructed by training a neural network (NN) on the residual of the mechanistic model for effluent nitrate. In an initial experiment using the Benchmark Simulation Model no. 1, a parallel HM effectively addressed limitations in the mechanistic model's representation of autotrophic bacteria growth and the data-driven model's incapability to extrapolate. Next, different versions of a parallel HM of a large pilot-scale WRRF are constructed, using different calibration/training datasets and different versions of the mechanistic model to investigate the balance between the calibration effort for the mechanistic model and the compensation by the NN component. The HM can improve predictions compared to the mechanistic model. Training the NN on an independent validation dataset produced better results than on the calibration dataset. Interestingly, the best performance is achieved for the HM based on a mechanistic model using default (uncalibrated) parameters. Both long short-term memory (LSTM) and convolutional neural network (CNN) are tested as data-driven components, with a CNN HM (root-mean-squared error (RMSE) = 1.58 mg NO3-N/L) outperforming an LSTM HM (RMSE = 4.17 mg NO3-N/L).

A full-scale operational digital twin for a water resource recovery facility-A case study of Eindhoven Water Resource Recovery Facility

Digital transformation for the water sector has gained momentum in recent years, and many water resource recovery facilities modelers have already started transitioning from developing traditional models to digital twin (DT) applications. DTs simulate the operation of treatment plants in near real time and provide a powerful tool to the operators and process engineers for real-time scenario analysis and calamity mitigation, online process optimization, predictive maintenance, model-based control, and so forth. So far, only a few mature examples of full-scale DT implementations can be found in the literature, which only address some of the key requirements of a DT. This paper presents the development of a full-scale operational DT for the Eindhoven water resource recovery facility in The Netherlands, which includes a fully automated data-pipeline combined with a detailed mechanistic full-plant process model and a user interface co-created with the plant's operators. The automated data preprocessing pipeline provides continuous access to validated data, an influent generator provides dynamic predictions of influent composition data and allows forecasting 48 h into the future, and an advanced compartmental model of the aeration and anoxic bioreactors ensures high predictive power. The DT runs near real-time simulations every 2 h. Visualization and interaction with the DT is facilitated by the cloud-based TwinPlant technology, which was developed in close interaction with the plant's operators. A set of predefined handles are made available, allowing users to simulate hypothetical scenarios such as process and equipment failures and changes in controller settings. The combination of the advanced data pipeline and process model development used in the Eindhoven DT and the active involvement of the operators/process engineers/managers in the development process makes the twin a valuable asset for decision making with long-term reliability. PRACTITIONER POINTS: A full-scale digital twin (DT) has been developed for the Eindhoven WRRF. The Eindhoven DT includes an automated continuous data preprocessing and reconciliation pipeline. A full-plant mechanistic compartmental process model of the plant has been developed based on hydrodynamic studies. The interactive user interface of the Eindhoven DT allows operators to perform what-if scenarios on various operational settings and process inputs. Plant operators were actively involved in the DT development process to make a reliable and relevant tool with the expected added value.

Roles of high/low nucleic acid bacteria in flocs and probing their dynamic migrations with respirogram

Bacterial migration is crucial for the stability of activated sludge but rarely reported. The static distribution was explored by changes in bacteria concentration with extracellular polymeric substances (EPS) extractions. Next, denitrification and aeration were conducted as normal running conditions for examining the bacterial migration between floc-attached and dispersed growth. Above observations were further explored by conducting copper ion (Cu2+) shock as an extreme running condition. After extracting EPS, low nucleic acid (LNA) bacteria migrated from the sludge to the supernatant primarily, and high nucleic acid (HNA) bacteria remained in the residual sludge, suggesting that HNA bacteria mainly distributed inside the sludge while LNA bacteria outside the sludge. During the denitrification process, LNA bacteria migrated out of flocs, which increased by 6.94 × 106 events/mL in the supernatant. During the feast phase of aeration, LNA bacteria grew attached to flocs, causing the increased flocs diameter from 45.60 to 47.40 μm. During the following aerobic famine phase, LNA bacteria grew dispersedly, but HNA bacteria remained unchanged. However, a further severe famine phase drove HNA bacteria to be dispersed, breaking flocs with the decreased diameter from 48.10 to 46.50 μm. When the Cu2+ shock was employed, LNA and HNA bacteria increased but the LNA/HNA ratio decreased in the supernatant, indicating more HNA bacteria migrating to the dispersed phase. From a structural perspective, HNA bacteria distributed inside the sludge and functioned as the backbone of flocs, undertaking the maintenance of flocs stability primarily; while LNA bacteria distributed outside the sludge and functioned as filling materials, having a secondary influence on flocs stability. These processes were also probed by respirogram exactly, correlating the system-scale measurement and microscale migrations and providing an early warning signal under abnormal circumstances. The processed HNA-backbone theory is promising for regulating the stability of activated sludge based on bacterial migrations.

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002-2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to cost-effectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

Energy and material refineries of future: Wastewater treatment plants

There have been many important milestones on humanity's long journey towards achieving environmental sanitation. In particular, the development of the activated sludge system can be claimed to be one of the most groundbreaking advances in the protection of both public health and the wider ecosystem. The first wastewater treatment plants (WWTPs) were developed over a century ago and were soon configured for use with activated sludge. However, despite their long history and service, conventional activated sludge (CAS) plants have become an unsustainable method of wastewater treatment. In addition, conventional WWTPs are intensive energy-consumers and at best allow only very limited material recovery. A paradigm shift to convert existing WWTPs into more sustainable facilities must therefore be considered necessary and to this end the wastewater biorefinery (WWBR) concept may be considered a solution that maximizes both energy and material recovery, in line with the circular economy approach.

Techno-economic analysis of sidestream ammonia removal technologies: Biological options versus thermal stripping

Over the past twenty years, various commercial technologies have been deployed to remove ammonia (NH₄-N) from anaerobic digestion (AD) liquors. In recent years many anaerobic digesters have been upgraded to include a pre-treatment, such as the thermal hydrolysis process (THP), to produce more biogas, increasing NH₄-N concentrations in the liquors are costly to treat. This study provides a comparative techno-economic assessment of sidestream technologies to remove NH₄-N from conventional AD and THP/AD dewatering liquors: a deammonification continuous stirred tank reactor (PNA), a nitrification/denitrification sequencing batch reactor (SBR) and thermal ammonia stripping process with an ammonia scrubber (STRIP). The SBR and PNA were based on full-scale data, whereas the STRIP was designed using a computational approach to achieve NH₄-N removals of 90-95%. The PNA presented the lowest whole-life cost (WLC) over 40 years, with £7.7 M, while the STRIP had a WLC of £43.9 M. This study identified that THP dewatering liquors, and thus a higher ammonia load, can lead to a 1.5-3.0 times increase in operational expenditure with the PNA and the SBR. Furthermore, this study highlighted that deammonification is a capable and cost-effective nitrogen removal technology. Processes like the STRIP respond to current pressures faced by the water industry on ammonia recovery together with targets to reduce nitrous oxide emissions. Nevertheless, ammonia striping-based processes must further be demonstrated in WWTPs and WLC reduced to grant their wide implementation and replace existing technologies.

Hybrid modelling of water resource recovery facilities: status and opportunities

Mathematical modelling is an indispensable tool to support water resource recovery facility (WRRF) operators and engineers with the ambition of creating a truly circular economy and assuring a sustainable future. Despite the successful application of mechanistic models in the water sector, they show some important limitations and do not fully profit from the increasing digitalisation of systems and processes. Recent advances in data-driven methods have provided options for harnessing the power of Industry 4.0, but they are often limited by the lack of interpretability and extrapolation capabilities. Hybrid modelling (HM) combines these two modelling paradigms and aims to leverage both the rapidly increasing volumes of data collected, as well as the continued pursuit of greater process understanding. Despite the potential of HM in a sector that is undergoing a significant digital and cultural transformation, the application of hybrid models remains vague. This article presents an overview of HM methodologies applied to WRRFs and aims to stimulate the wider adoption and development of HM. We also highlight challenges and research needs for HM design and architecture, good modelling practice, data assurance, and software compatibility. HM is a paradigm for WRRF modelling to transition towards a more resource-efficient, resilient, and sustainable future.

Optimization of the Anaerobic-Anoxic-Oxic Process by Integrating ASM2d with Pareto Analysis of Variance and Response Surface Methodology

Wastewater treatment plants (WWTPs) are high-energy-consuming units. Reasonable operation strategies can enable WWTPs to meet discharge standards while reducing the operating cost. In this study, the activated sludge model 2d (ASM2d), Pareto analysis of variance (ANOVA), and response surface methodology (RSM) were jointly used to simulate and optimize the operation of a lab-scale anaerobic-anoxic-oxic (AAO) reactor. The optimization objective was to determine the optimal design and operational parameters (DOPs) that could enhance both pollutant removal and energy saving. The DOPs that had significant influence on the optimization objective, such as sludge retention time (SRT), dissolved oxygen (DO), and the ratio of biodegradable chemical oxygen demand to total nitrogen (BCOD/TN), were identified by Pareto ANOVA. The optimal DOPs with SRT of 15 days, DO concentration of 0.5 mg/L, and BCOD/TN of 5.21 were determined by RSM. Under the optimal conditions, the removal efficiencies of NH4+-N, total nitrogen (TN), and total phosphorus (TP) were 96.2%, 76.8%, and 92.8%, respectively, and the annual operating cost was $26.4. Furthermore, this combination of DOPs was validated using a pilot-scale AAO system. The TN and TP removal efficiencies were improved by 11.0% and 5.0%, respectively, and the annual operating cost could be reduced by 15.0%. Overall, this study confirmed that the method integrating ASM2d with Pareto ANOVA and RSM was effective in optimizing wastewater treatment processes.

Roadmapping the Transition to Water Resource Recovery Facilities: The Two Demonstration Case Studies of Corleone and Marineo (Italy)

The current exploitation of freshwater, as well as the significant increase in sewage sludge production from wastewater treatment plants (WWTPs), represent nowadays a critical issue for the implementation of sustainable development consistent with the circular economy concept. There is an urgent need to rethink the concept of WWTPs from the conventional approach consisting in pollutant removal plants to water resource recovery facilities (WRRFs). The aim of this paper is to provide an overview of the demonstration case studies at the Marineo and Corleone WRRFs in Sicily (IT), with the final aim showing the effectiveness of the resources recovery systems, as well as the importance of plant optimization to reduce greenhouse gas (GHG) emissions from WRRFs. This study is part of the H2020 European Project “Achieving wider uptake of water-smart solutions—Wider-Uptake”, which final aim is to demonstrate the water-smart solution feasibility in the wastewater sector. The main project goal is to overcome the existing barriers that hamper the transition to circularity through the implementation of a governance analysis tool. The preliminary actions in the two demonstration cases are first presented, while, subsequently, the water-smart solutions to be implemented are thoroughly described, highlighting their roles in the transition process. The achieved preliminary results underlined the significant potential of WRRF application, a great chance to demonstrate the feasibility of innovative solutions in the wastewater sector to overcome the existing social, administrative and technical barriers.

Considering syntrophic acetate oxidation and ionic strength improves the performance of models for food waste anaerobic digestion

Current mechanistic anaerobic digestion (AD) models cannot accurately represent the underlying processes occurring during food waste (FW) AD. This work presents an update of the Anaerobic Digestion Model no. 1 (ADM1) to provide accurate estimations of free ammonia concentrations and related inhibition thresholds, and model syntrophic acetate oxidation as acetate-consuming pathway. A modified Davies equation predicted NH₃ concentrations and pH more accurately, and better estimated associated inhibitory limits. Sensitivity analysis results showed the importance of accurate disintegration kinetics and volumetric mass transfer coefficients, as well as volatile fatty acids (VFAs) and hydrogen uptake rates. In contrast to the default ADM1, the modified ADM1 could represent methane production and VFA profiles simultaneously (particularly relevant for propionate uptake). The modified ADM1 was also able to predict the predominant acetate-consuming and methane-producing microbial clades. Modelling results using data from reactors dosed with granular activated carbon showed that this additive improves hydrogen uptake.

An adaptive real-time grey-box model for advanced control and operations in WRRFs

Grey-box models, which combine the explanatory power of first-principle models with the ability to detect subtle patterns from data, are gaining increasing attention in wastewater sectors. Intuitive, simple structured but fit-for-purpose grey-box models that capture time-varying dynamics by adaptively estimating parameters are desired for process optimization and control. As an example, this study presents the identification of such a grey-box model structure and its further use by an extended Kalman filter (EKF), for the estimation of the nitrification capacity and ammonia concentrations of a typical Modified Ludzack-Ettinger (MLE) process. The EKF was implemented and evaluated in real time by interfacing Python with SUMO (Dynamita™), a widely used commercial process simulator. The EKF was able to accurately estimate the ammonia concentrations in multiple tanks when given only the concentration in one of them. In addition, the nitrification capacity of the system could be tracked in real time by the EKF, which provides intuitive information for facility managers and operators to monitor and operate the system. Finally, the realization of EKF is critical to the development of future advance control, for instance, model predictive control.

Mathematically formulated key performance indicators for design and evaluation of treatment trains for resource recovery from urban wastewater

While urban wastewater infrastructure is aging and no longer adequate, climate change and sustainability are urging the transition from pollution management to resource recovery. Lacking evidence-based quantitative evaluation of the potential benefits and consequences of resource recovery from wastewater hinders the negotiation amongst stakeholders and slows down the transition. This study proposes mathematical formulations for technical, environmental, economic, and social key performance indicators (KPIs) that can be used to quantify the benefits and the risks of resource recovery. The proposed formulations are derived from the literature and validated with stakeholders. Each KPI is mathematically formulated at treatment train level by considering: (1) the characteristics of individual unit processes (UPs) in the treatment train (TT), (2) the context in which the TT is installed, and (3) the resources to be recovered. The mathematical formulations of the KPIs proposed in this study enable a transparent, consistent and informative evaluation of existing treatment trains, as well as support the (computer aided) design of new ones. This could aid the transition from urban wastewater treatment to resource recovery from urban wastewater.

Economic Model Predictive Control for optimal struvite recovery

Resource recovery from municipal wastewater has been a prime focus for a decade. Although several recovery processes already exist in the market today, the high cost of material, inherent disturbance in the influent quality, lack of real time monitoring of critical parameters, and lack of a robust automation system may result in suboptimal performance. This work attempts to construct a model based predictive control for optimal operation of a struvite recovery unit in a full scale WRRF. A multi-parameter based predictive control has been developed by implementing an Economic Model Predictive Controller (EMPC) for optimal dosing of magnesium hydroxide in a struvite recovery unit. The EMPC used customized objective function for real-time optimization of performance and economical parameters of the crystallization unit. The effectiveness of the proposed EMPC controller is verified through tests conducted on the Benchmark Simulation Model No. 2 (BSM2d.). The results obtained from the simulator-based evaluation of EMPC demonstrate a significant improvement in resource recovery at reduced operational costs. The economic advantages of implementing an EMPC compared to proportional and constant magnesium dosage has also been enumerated.

A critical review of the data pipeline: how wastewater system operation flows from data to intelligence

Faced with an unprecedented amount of data coming from evermore ubiquitous sensors, the wastewater treatment community has been hard at work to develop new monitoring systems, models and controllers to bridge the gap between current practice and data-driven, smart water systems. For additional sensor data and models to have an appreciable impact, however, they must be relevant enough to be looked at by busy water professionals; be clear enough to be understood; be reliable enough to be believed and be convincing enough to be acted upon. Failure to attain any one of those aspects can be a fatal blow to the adoption of even the most promising new measurement technology. This review paper examines the state-of-the-art in the transformation of raw data into actionable insight, specifically for water resource recovery facility (WRRF) operation. Sources of difficulties found along the way are pinpointed, while also exploring possible paths towards improving the value of collected data for all stakeholders, i.e., all personnel that have a stake in the good and efficient operation of a WRRF.

When the fourth water and digital revolution encountered COVID-19

The ongoing COVID-19 pandemic is, undeniably, a substantial shock to our civilization which has revealed the value of public services that relate to public health. Ensuring a safe and reliable water supply and maintaining water sanitation has become ever more critical during the pandemic. For this reason, researchers and practitioners have promptly investigated the impact associated with the spread of SARS-CoV-2 on water treatment processes, focusing specifically on water disinfection. However, the COVID-19 pandemic impacts multiple aspects of the urban water sector besides those related to the engineering processes, including sanitary, economic, and social consequences which can have significant effects in the near future. Furthermore, this outbreak appears at a time when the water sector was already experiencing a fourth revolution, transitioning toward the digitalisation of the sector, which redefines the Water-Human-Data Nexus. In this contribution, a product of collaboration between academics and practitioners from water utilities, we delve into the multiple impacts that the pandemic is currently causing and their possible consequences in the future. We show how the digitalisation of the water sector can provide useful approaches and tools to help address the impact of the pandemic. We expect this discussion to contribute not only to current challenges, but also to the conceptualization of new projects and the broader task of ameliorating climate change.

Implementation of a Decision Support System for Sewage Sludge Management

In this work, a decision support system (DSS) coupled with wastewater treatment plant (WWTP) simulator tool that uses a hierarchical set of key performance indicators (KPIs) to provide an assessment of the performance of WWTP systems is presented. An assessment of different Scenarios in a real WWTP case study, each consisting of a different set of sludge line technologies and derived combinations, was successfully conducted with the developed DSS–WWTP simulator, based on Scenario simulation and hierarchical KPI analysis. The test carried out on the selected WWTP showed that although thermal valorisation and thermal hydrolysis showed similar (the best) economic viability, the latter showed additional benefits, including synergies related to improving the thermal balance of the overall WWTP even when considering other technologies. On the other hand, biogas-upgrading technologies allowed reduction of emissions, but with higher costs and thermal demands. The usage of this tool may allow the development of proposals for technological priorities as a pathway to the transition to circular economy based on the management criteria of the correspondent sanitation system.

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

A review of the literature published in 2019 on topics relating to water resource recovery facilities (WRRFs) in the areas of modeling, automation, measurement and sensors, and optimization of wastewater treatment (or water resource reclamation) is presented.

The Fourth-Revolution in the Water Sector Encounters the Digital Revolution

The so-called fourth revolution in the water sector will encounter the Big data and Artificial Intelligence (AI) revolution. The current data surplus stemming from all types of devices together with the relentless increase in computer capacity is revolutionizing almost all existing sectors, and the water sector will not be an exception. Combining the power of Big data analytics (including AI) with existing and future urban water infrastructure represents a significant untapped opportunity for the operation, maintenance, and rehabilitation of urban water infrastructure to achieve economic and environmental sustainability. However, such progress may catalyze socio-economic changes and cross sector boundaries (e.g., water service, health, business) as the appearance of new needs and business models will influence the job market. Such progress will impact the academic sector as new forms of research based on large amounts of data will be possible, and new research needs will be requested by the technology industrial sector. Research and development enabling new technological approaches and more effective management strategies are needed to ensure that the emerging framework for the water sector will meet future societal needs. The feature further elucidates the complexities and possibilities associated with such collaborations.

Application of a mathematical model to predict simultaneous reactions in anaerobic plug-flow reactors as a primary treatment for constructed wetlands

Anaerobic digestion technologies offer a set of advantages when they are implemented as a primary treatment phase prior to the use of constructed wetland systems in low cost wastewater facilities. The aim of this study is to describe a model capable of reflecting the complex functioning of anaerobic lagoons, subject to continuous flux in the feed pipe, taking into account that physicochemical properties are subject to a concentration gradient and biochemical ones to simultaneous reactions which depend on each other. Based on both Stokes and advection-diffusion-reaction equations, the proposed model includes twenty-one variables to describe hydraulic, physical, biochemical and physicochemical characteristics that take place in different points of the system and at different moments of time. Drawn up by the International Water Association, the anaerobic digestion model ADM1 is included for the purpose of incorporating the anaerobic processes in the calculation. The finite element method was used to solve the nonlinear, second order partial differential equations of the model. The calculation strategy was designed using a flowchart. Using the open-source FreeFem++ software, a simulation of the mathematical model, in bi-dimensional space, is presented to demonstrate the dynamic behaviour of the proposed model. This yields essential information about the performance of the substrate, cells, and the biochemical reaction products in each of the points within the reactor. Simulations show the potential of this methodology to carry out studies of the behaviour of each of the variables contemplated in the model, as well as comparative studies of the various possible options. In addition, this methodology can be used to help modify the behaviour of the variables based on digester geometry and the boundary values the system is subject to. From the results, it can be concluded that the proposed methodology can be a useful tool for calculating and designing the aforementioned synergistic systems of anaerobic digester plug-flow reactors and constructed wetlands.

Recovery of polyhydroxyalkanoates (PHAs) from wastewater: A review

Polyhydroxyalkanoates (PHAs) are biopolyesters accumulated as carbon and energy storage materials under unbalanced growth conditions by various microorganisms. They are one of the most promising potential substitutes for conventional non-biodegradable plastics due to their similar physicochemical properties, but most important, its biodegradability. Production cost of PHAs is still a great barrier to extend its application at industrial scale. In order to reduce that cost, research is focusing on the use of several wastes as feedstock (such as agro-industrial and municipal organic waste and wastewater) in a platform based on mixed microbial cultures. This review provides a critical illustration of the state of the art of the most likely-to-be-scale-up PHA production processes using mixed microbial cultures platform and waste streams as feedstock, with a particular focus on both, upstream and downstream processes. Current pilot scale studies, future prospects, challenges and developments in the field are also highlighted.