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.

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.

Digital solutions for continued operation of WRRFs during pandemics and other interruptions.

This paper includes survey results from 17 full-scale water resource recovery facilities (WRRFs) to explore their technical, operational, maintenance, and management-related challenges during COVID-19. Based on the survey results, limited monitoring and maintenance of instrumentation and sensors are among the critical factors during the pandemic which resulted in poor data quality in several WRRFs. Due to lockdown of cities and countries, most of the facilities observed interruptions of chemical supply frequency which impacted the treatment process involving chemical additions. Some plants observed influent flow reduction and illicit discharges from industrial wastewater which eventually affected the biological treatment processes. Delays in equipment maintenance also increased the operational and maintenance cost. Most of the plants reported that new set of personnel management rules during pandemic created difficulties in scheduling operator's shifts which directly hampered the plant operations. All the plant operators mentioned that automation, instrumentation, and sensor applications could help plant operations more efficiently while working remotely during pandemic. To handle emergency circumstances including pandemic, this paper also highlights resources and critical factors for emergency responses, preparedness, resiliency, and mitigation that can be adopted by WRRFs.

Application of unsupervised learning and process simulation for energy optimization of a WWTP under various weather conditions.

This paper outlines a hybrid modeling approach to facilitate weather-based operation and energy optimization for the largest Italian wastewater treatment plant (WWTP). Two clustering methods, K-means algorithm and Gaussian mixture model (GMM) based on the expectation-maximization (EM) algorithm, were applied to an extensive dataset of historical and meteorological records. This study addresses the problem of determining the intrinsic structure of clustered data when no information other than the observed values is available. Two quantitative indexes, namely the Bayesian information criterion (BIC) and the Silhouette coefficient using Euclidean distance, as well as two general criteria, were implemented to assess the clustering quality. Furthermore, seven weather-based influent scenarios were introduced to the process simulation model, and sets of aeration strategies are proposed. The results indicate that incorporating weather-based aeration strategies in the operation of the WWTP improves plant energy efficiency.

Energy optimization of a wastewater treatment plant based on energy audit data: small investment with high return.

Ambitious energy targets in the 2020 European climate and energy package have encouraged many stakeholders to explore and implement measures improving the energy efficiency of water and wastewater treatment facilities. Model-based process optimization can improve the energy efficiency of wastewater treatment plants (WWTP) with modest investment and a short payback period. However, such methods are not widely practiced due to the labor-intensive workload required for monitoring and data collection processes. This study offers a multi-step simulation-based methodology to evaluate and optimize the energy consumption of the largest Italian WWTP using limited, preliminary energy audit data. An integrated modeling platform linking wastewater treatment processes, energy demand, and production sub-models is developed. The model is calibrated using a stepwise procedure based on available data. Further, a scenario-based optimization approach is proposed to obtain the non-dominated and optimized performance of the WWTP. The results confirmed that up to 5000 MWh annual energy saving in addition to improved effluent quality could be achieved in the studied case through operational changes only.

Optimization of the wastewater treatment plant: From energy saving to environmental impact mitigation.

This paper outlines a multi-objective, integrated approach to analyze various possibilities for increasing energy efficiency of the largest Italian wastewater treatment plant (WWTP) at Castiglione Torinese. In this approach, wastewater and sludge treatment units are thoroughly investigated to find the potential ways for improving the energy efficiency of the system. Firstly, a multi-step simulation-based methodology is proposed to make a full link between treatment processes and the energy demand and production. Further, a scenario-based optimization approach is proposed to find the nondominated and optimized performance of the WWTP. The results prove a potential to save up to 5000 MWh of the annual energy consumption of the plant, in addition to improve the effluent quality through operational changes only. Even for what concerns the sludge line a model was proposed for the optimization of the energy recovery from the processes that in a WWTP are devoted to the management of sewage sludge. The obtained results show that the introduction of an advanced thickening stage and sludge pre-treatment would have a positive impact on the energy and greenhouse gas balance of the plant.

Assessment of weather-based influent scenarios for a WWTP: Application of a pattern recognition technique.

This study proposes an integrated approach by combining a pattern recognition technique and a process simulation model, to assess the impact of various climatic conditions on influent characteristics of the largest Italian wastewater treatment plant (WWTP) at Castiglione Torinese. Eight years (viz. 2009-2016) of historical influent data namely influent flow rate (Qin), chemical oxygen demand (COD), ammonium (N-NH4) and total suspended solids (TSS), in addition to two climatic attributes, average temperature and daily mean precipitation rates (PI) from the plant catchment area, are evaluated in this study. Following the outlier removal and missing-data imputation, five influent climate-based scenarios are identified by K-means clustering approach. Statistical characteristics of clustered observations are further investigated. Finally, to demonstrate that the proposed approach could improve the process control and efficiency, a process simulation model was developed and calibrated. Steady-state simulations were conducted, and the performance of the plant was studied under five influent scenarios. Further, an optimization scenario-based method was conducted to improve the energy consumption of the plant while meeting effluent requirements. The results indicate that with the adaptation of suitable aeration strategies for each of the influent scenarios, 10-40% energy saving can be achieved while meeting effluent requirements.