Water resource recovery facilities face challenges with increasingly stringent effluent demands, complexity and demand for capacity increasing investments. Emerging technologies such as digital twins could alleviate these problems but require high frequency influent data. This work presents a method for utilising measurements in the primary clarifier effluent with a model of the processes between the influent and primary clarifier effluent to predict influent orthophosphate load for a plant with considerable internal load. Five functions for describing daily load variations were tested and compared for accuracy and computational time. All functions were shown to reproduce the measured primary effluent orthophosphate concentration with high accuracy, although the function based on four normal distributions was deemed the most suitable due to its short computational time, realistic influent concentration variations and accurate estimated primary effluent orthophosphate concentration. Validation of the optimised influent concentrations shows that it follows similar patterns but might overpredict the afternoon load, which could be due to deviating daily patterns by inhabitants during the COVID-19 pandemic (although this requires further investigation). The presented methodology can be extended also to estimate influent COD-fractions, automate plant calibration and optimise plant performance.

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.

In Sweden, phosphorus is commonly removed from municipal wastewater treatment by chemical precipitation (CP). Recently, such alternatives as enhanced biological phosphorus removal (EBPR) have garnered interest due to the increased risk of chemical shortage. In this study, a life cycle assessment (LCA) was performed to compare EBPR and CP in three scenarios: 1) baseline – precipitation chemicals available, 2) stricter effluent requirements – precipitation chemicals available, and 3) chemical shortage – no precipitation chemicals available. Data acquisition that was based on dynamic process simulation was useful, yielding more site-specific results, in contrast to standard literature values. The results indicated substantial differences in greenhouse gas emissions between configurations (around three times higher methane emissions for EBPR compared to CP configurations – although this finding requires further validation). These differences suggest that different emission factors for EBPR and CP should be considered. Furthermore, it is suggested to include waterline methane emissions, at least when the configuration incorporates anaerobic reactors in the water line. Further validation of emissions is necessary, especially for EBPR plants with side-stream hydrolysis and digester reject water treatment. The LCA results showed a similar overall environmental impact for both configurations, but the results of individual impact categories differed. EBPR caused greater climate impact due to the larger direct emissions of methane. Toxicity was more important for CP, based on the inherent heavy metal content in precipitation chemicals. Freshwater eutrophication was similar for both configurations, assuming that precipitation chemicals were available. However, if the recipient is sensitive, implementing EBPR reduces the freshwater eutrophication potential by 75% during a chemical shortage, and should be considered. 

Digital twins (DT) for water resource recovery facilities (WRRF) are different from regular process models. They require 1) a physical plant twin; 2) automatic data exchange with the real plant; 3) possibility to dynamically update models when or if required. Their use has the potential to improve understanding of plant behaviour and unmeasured variables; move towards proactive decision making at the plants when including influent forecasts; improve data quality control when comparing simulation results to measured values; and be used for predictive maintenance. The model used in a DT can be mechanistic (i.e., describing underlying mechanisms/physics), data driven (empirical, based on observed relationships between variables) or a combination of both (hybrid model). Most of the commercially available (mechanistic) wastewater process simulators include the option to use them in (near) real time as digital twins. Fault detection is important for DTs to avoid use of faulty input data. Methods range from dimensional reduction techniques to process models and statistical control charts. Automated methods for gap filling and corrections of sensor values based on laboratory measurements can be used to correct faulty data. Forecasts of influent flow rate and concentration of pollutants can be useful for optimization and “what if”-scenarios. Forecast models can be data driven (e.g., many examples with time series models and artificial neural networks available in the literature) or detailed mechanistic models. Common for most examples is that weather forecasts (temperature and precipitation) are used, and the model accuracy of course depend on the quality of the forecast. Automatic calibration can be used for both data driven/hybrid models (i.e., re-training) and mechanistic models. For mechanistic models, examples in the literature include simple changing of measured influent fractions or settler solids separation efficiency to global optimization of multiple variables over a plant-wide model. Automatic calibration can be done at fixed intervals or based on performance evaluation. Model predictive control (MPC) has been widely studied in simulated settings, with few real examples for WRRFs. For digital twins, the possibility to combine a mechanistic model with influent forecasts and numerical optimisation for, e.g., setpoints over a future time interval to achieve a certain goal is promising. The faster control applications can then be handled using regular PID-controllers. Few examples of implemented digital twins for WRRFs have so far been published in the literature. Here, one example of a digital twin is presented. It includes automatic data transfer, automatic calibration, and forecasts, but is (at the time of writing based on the available literature) only used as an advisory tool and not for direct control. Digital twins of water resource recovery facilities are complex with many different parts and models that work together. They can be used for fault detection, predictions, and optimization/control. This report summarizes some of the components that can be used to build digital twins, which ones to include of course depends on the scope and goals of the specific project. In all cases, the flow of data from collection to use must be well designed to avoid unnecessary interruptions in operation.

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.

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

Uppvärmning av tappvarmvatten utgör lejonparten av den totala energianvändningen i den urbana vattencykeln, upp till 90 procent. Uppskattningar visar att 780 till 1 150 kWh per person och år används i svenska hushåll i form av varmvatten. Denna energi hamnar huvudsakligen i avloppsvattnet. Variationerna i varmvattenanvändning är stora och det går att göra besparingar på brukarnivå. Ändå finns det stor potential för energieffektivisering genom värmeåtervinning ur avloppsvatten med värmeväxlare och värmepumpar.

Heating of tap water makes up the lion share of the total energy used in the urban water cycle, up to 90 %. Estimates show that 780 to 1,150 kWh per person and year is used in Sweden for heating water. This energy mainly ends up in the sewers. Even if variations in energy use for this purpose are large and savings are possible, wastewater heat recovery, using heat exchangers or heat pumps, has a large potential.

This is an internal report in the research project Sustainability Analysis of Wastewater (WW) HeatRecovery (WWHR) - Hållbarhetsanalys av värmeåtervinning ur avloppsvatten (HÅVA), in Swedish - coordinated by the Division of Industrial Electrical Engineering and Automation at Lund University,Lund, Sweden. Key partners in the project are RISE Research Institutes of Sweden, the wastewaterutilities VA Syd, Tekniska Verken in Linköping and Käppalaförbundet, and the real estate companyStångåstaden.

In the project a system-wide sustainability analysis will be performed using process models. The modelwill include components from the origin of domestic wastewater in buildings through WWHR units andsewers to the impact of temperature changes on the wastewater treatment plant (WWTP). Theliterature review on WWHR identified wastewater characteristics as a key variable for the model(Arnell et al., 2017). This document contains a description of a stochastic model for generatingwastewater from households over the course of one day, which was calibrated based onmeasurements from a case study in Linköping, Sweden, and validated with literature data.