Automated data transfer for digital twin applications: Two case studies.

Digital twins have been gaining an immense interest in various fields over the last decade. Bringing conventional process simulation models into (near) real time are thought to provide valuable insights for operators, decision makers, and stakeholders in many industries. The objective of this paper is to describe two methods for implementing digital twins at water resource recovery facilities and highlight and discuss their differences and preferable use situations, with focus on the automated data transfer from the real process. Case 1 uses a tailor-made infrastructure for automated data transfer between the facility and the digital twin. Case 2 uses edge computing for rapid automated data transfer. The data transfer lag from process to digital twin is low compared to the simulation frequency in both systems. The presented digital twin objectives can be achieved using either of the presented methods. The method of Case 1 is better suited for automatic recalibration of model parameters, although workarounds exist for the method in Case 2. The method of Case 2 is well suited for objectives such as soft sensors due to its integration with the SCADA system and low latency. The objective of the digital twin, and the required latency of the system, should guide the choice of method. PRACTITIONER POINTS: Various methods can be used for automated data transfer between the physical system and a digital twin. Delays in the data transfer differ depending on implementation method. The digital twin objective determines the required simulation frequency. Implementation method should be chosen based on the required simulation frequency.

Making waves: A vision for digital water utilities.

Digitalisation has developed over half a century and is one of the global trends defining society of today and future. Digitalisation is envisioned to help water utilities to become: i) community orientated and digitally integrated with customers and society; ii) digitally transformed end-to-end throughout the value-chain and interconnected between business units; iii) predictive & proactive, utilizing models and applications for control and decision support; iv) visually communicative with customers and society, creating customers aware of the value of water; and financially sustainable by optimal operation (OPEX), and sustainable investments (CAPEX). Digitalisation is a process for business development, where digital solutions are used for automation and innovation. Utilizing the potential of the technological innovation requires a parallel organisational transformation. Any implementation of systems or applications must be motivated in actual needs for the organisation and service delivery. Prior to any digitalisation, identifying issues and areas of improvement is essential. Starting the digital journey, motivating employees, improving the digital culture and creating acceptance of new processes are needed on all levels. Most digital applications require collection, storage, sharing and integrated analysis of large amounts of data. This includes both soft and hard digital infrastructure.

A collaborative planning process to develop future scenarios for wastewater systems.

Wastewater infrastructure has a long lifetime and is subject to changing conditions and demands. When plans are made to upgrade or build new infrastructure, transdisciplinary planning processes and a robust analysis of future conditions are needed to make sustainable choices. Here, we provide a stepwise collaborative planning process in which future scenarios are developed together with local stakeholders and expert groups. The process was implemented at one of the largest wastewater treatment plants (WWTPs) in Scandinavia. With a combination of workshops and the use of a web-based digital tool, future scenarios including flows, pollutant loads, and treatment requirements could be created. Furthermore, sustainability prioritizations affecting the WWTP, were identified. The future scenarios developed for the WWTP in the case study, predict stricter and new regulations, constant or lower future loads and ambiguous future flows. The highest ranked sustainability priority was low resource and energy consumption together with low CO2 footprint. The quantified future scenarios developed in the planning process were used as input to a process model to show the consequences they would have on the WWTP in the case study. Applying this collaborative process revealed future scenarios with many, sometimes conflicting, expectations on future WWTPs. It also highlighted needs for improvements of both the collection system and the WWTP.

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.

Modelling anaerobic co-digestion in Benchmark Simulation Model No. 2: Parameter estimation, substrate characterisation and plant-wide integration.

Anaerobic co-digestion is an emerging practice at wastewater treatment plants (WWTPs) to improve the energy balance and integrate waste management. Modelling of co-digestion in a plant-wide WWTP model is a powerful tool to assess the impact of co-substrate selection and dose strategy on digester performance and plant-wide effects. A feasible procedure to characterise and fractionate co-substrates COD for the Benchmark Simulation Model No. 2 (BSM2) was developed. This procedure is also applicable for the Anaerobic Digestion Model No. 1 (ADM1). Long chain fatty acid inhibition was included in the ADM1 model to allow for realistic modelling of lipid rich co-substrates. Sensitivity analysis revealed that, apart from the biodegradable fraction of COD, protein and lipid fractions are the most important fractions for methane production and digester stability, with at least two major failure modes identified through principal component analysis (PCA). The model and procedure were tested on bio-methane potential (BMP) tests on three substrates, each rich on carbohydrates, proteins or lipids with good predictive capability in all three cases. This model was then applied to a plant-wide simulation study which confirmed the positive effects of co-digestion on methane production and total operational cost. Simulations also revealed the importance of limiting the protein load to the anaerobic digester to avoid ammonia inhibition in the digester and overloading of the nitrogen removal processes in the water train. In contrast, the digester can treat relatively high loads of lipid rich substrates without prolonged disturbances.

Balancing effluent quality, economic cost and greenhouse gas emissions during the evaluation of (plant-wide) control/operational strategies in WWTPs.

The objective of this paper was to show the potential additional insight that result from adding greenhouse gas (GHG) emissions to plant performance evaluation criteria, such as effluent quality (EQI) and operational cost (OCI) indices, when evaluating (plant-wide) control/operational strategies in wastewater treatment plants (WWTPs). The proposed GHG evaluation is based on a set of comprehensive dynamic models that estimate the most significant potential on-site and off-site sources of CO2, CH4 and N2O. The study calculates and discusses the changes in EQI, OCI and the emission of GHGs as a consequence of varying the following four process variables: (i) the set point of aeration control in the activated sludge section; (ii) the removal efficiency of total suspended solids (TSS) in the primary clarifier; (iii) the temperature in the anaerobic digester; and (iv) the control of the flow of anaerobic digester supernatants coming from sludge treatment. Based upon the assumptions built into the model structures, simulation results highlight the potential undesirable effects of increased GHG production when carrying out local energy optimization of the aeration system in the activated sludge section and energy recovery from the AD. Although off-site CO2 emissions may decrease, the effect is counterbalanced by increased N2O emissions, especially since N2O has a 300-fold stronger greenhouse effect than CO2. The reported results emphasize the importance and usefulness of using multiple evaluation criteria to compare and evaluate (plant-wide) control strategies in a WWTP for more informed operational decision making.