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.

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.

The influence of aeration diffuser system design on electricity usage, effluent water quality, and life-cycle cost in biological wastewater treatment was investigated. A plant-wide model was implemented, and simulations were carried out with different process configurations and aeration systems. Model-aided design of new aeration diffuser systems could significantly decrease electricity usage and life-cycle cost while at the same time avoiding negative effects on the treatment performance. The optimum distribution of diffuser systems in tanks in series was found to be influenced by process configuration, volumetric loading rate, temperature, and the internal recirculation flow rate. Compared with a conventional design approach, increasing the number of diffusers, up to a critical point, led to higher energy efficiency and lower life-cycle cost. This was despite an increasing limitation of the minimum airflow rate, leading to dissolved oxygen levels significantly exceeding control targets. Aeration systems optimized by simulations were found to, independently of process configuration, exhibit 20% lower electricity usage and 16%-18% lower life-cycle costs compared with systems designed based on a more conventional approach typically applied in practice.

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. © 2022 The Authors

Benchmarking has been a useful tool for unbiased comparison of control strategies in wastewater treatment plants (WWTPs) in terms of effluent quality, operational cost and risk of suffering microbiology-related total suspended solids (TSS) separation problems. This chapter presents the status of extending the original Benchmark Simulation Model No 2 (BSM2) towards including greenhouse gas (GHG) emissions. A mathematical approach based on a set of comprehensive models that estimate all potential on-site and off-site sources of COinf2/inf, CHinf4/inf and Ninf2/infO is presented and discussed in detail. Based upon the assumptions built into the model structures, simulation results highlight the potential undesirable effects on increased GHG emissions when carrying out local energy optimization in the activated sludge section and/or energy recovery in the anaerobic digester. Although off-site COinf2/inf emissions may decrease in such scenarios due to either lower aeration energy requirement or higher heat and electricity production, these effects may be counterbalanced by increased Ninf2/infO emissions, especially since Ninf2/infO has a 300-fold stronger greenhouse effect than COinf2/inf. The reported results emphasize the importance of using integrated approaches when comparing and evaluating (plant-wide) control strategies in WWTPs for more informed operational decision-making. 

Around 90% of the energy requirement for urban water systems management is for heating domestic tap water. In addition, the energy content of wastewater is mainly in the form of heat (85%). Hence, there is an obvious interest in recovering a large portion of this heat. However, city-wide scenario analyses that evaluate heat recovery at various locations while considering impacts on wastewater treatment plant (WWTP) performance are currently very limited. This study presents a comprehensive model-based city-wide evaluation considering four different heat recovery locations (appliance, household, precinct and WWTP effluent) for a Swedish city with varying degrees of implementation using an uncertainty-based approach. Results show that heat recovery at the appliance level, with heat exchangers installed at 77% of the showers at domestic households, leads to a mean energy recovery of 127 MWh/day with a 0.25 °C reduction in mean WWTP inlet temperature compared to the default case without heat recovery. The highest mean temperature reduction compared to the default case is 1.5 °C when heat is recovered at the precinct level for 77% of the domestic wastewater flow rate. Finally, the impact on WWTP nitrification capacity is negligible in this case due to its large existing capacity and design. © 2022 The Authors

Rapporten ska ge VA-branschen en kunskapsbas för arbetet med digitalisering inom vatten och avlopp. Den beskriver potentialen och pekar ut framgångsfaktorer för omställningen. Den tar också upp utmaningar med kompetensförsörjning, datahantering och cybersäkerhet. En inspirationskatalog ger tio exempel på lyckade digitala tillämpningar ur verkligheten.

The report provides a knowledge base on the digital transformation in the water industry, its visionand potential. Key success factors are pointed out and challenges with workforce competence,data management and cybersecurity is outlined. A catalogue with ten examples of successful digitalapplications is provided for inspiration.

Rapporten går igenom olika hållbarhetsmål som VA-branschen bör förhålla sig till. Den tar upp VA-organisationers arbete med Svenskt Vattens Hållbarhetsindex (HBI) och hur det förhåller sig till det globala hållbarhetsarbetet via Agenda 2030 och FN:s 17 hållbarhetsmål. HBI täcker in flera av de globala målen, men det finns möjlighet för VA-organisationer att utöka sitt hållbarhetsarbete. Rapporten ger förslag till fördjupat arbete med hållbarhetsfrågor.

The report contains a presentation of different sustainability goals that the water and wastewater sector needs to consider. Water utilities’ work in Sweden with sustainability index (HBI) and its relation to the SDGs is shown. HBI is covering several of the SDGs but the water and wastewater sector can both deepen and broaden its sustainability work; suggestions how are given in the report.

The vast majority of the energy consumed for urban water services is used to heat tap water. Heat recovery from wastewater is consequently an area of rapidly growing concern, both in research and by commercial interest, promoting the path towards a circular economy. To facilitate a system-wide evaluation of heat recovery from wastewater, this paper compares two one-dimensional models (mechanistic and conceptual) that can describe wastewater temperature dynamics in sewer pipe systems. The models are applied to successfully predict downstream wastewater temperature for sewer stretches in two Swedish cities (Linköping and Malmö). The root mean squared errors for the mechanistic model (Linköping Dataset1 – 0.33 °C; Linköping Dataset2 – 0.28 °C; Malmö – 0.40 °C) and the conceptual model (Linköping Dataset1 – 0.32 °C; Linköping Dataset2 – 0.20 °C; Malmö – 0.44 °C) indicate that both models have similar predictive capabilities, encouraging the use of conceptual models to reduce data requirements and model calibration efforts. Both models are freely distributed and can be easily integrated with wastewater generation and treatment models to facilitate system-wide wastewater temperature dynamics analysis. © 2021 The Authors.

Wastewater heat recovery upstream of wastewater treatment plants (WWTP) poses a risk to treatment performance, i.e. the biological processes. In order to perform a sustainability analysis, a detailed prediction of the temperature dynamics over the WWTP is needed. A comprehensive set of heat balance equations was included in a plant-wide process model and validated for the WWTP in Linköping, Sweden, to predict temperature variations over the whole year in a temperate climate. A detailed model for the excess heat generation of biological processes was developed. The annual average temperature change from influent to effluent was 0.78°C with clear seasonal variations, wherein 45% of the temperature change arose from processes other than the activated sludge unit. To address this, plant-wide energy modelling was necessary to predict in-tank temperature in the biological treatment steps. The energy processes with the largest energy gains were solar radiation and biological processes, while the largest losses were from conduction, convection, and atmospheric radiation. Tanks with large surface areas showed a significant impact on the heat balance regardless of biological processes. Simulating a 3°C lower influent temperature, the temperature in the activated sludge unit dropped by 2.8°C, which had a negative impact on nitrogen removal