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.

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.

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.

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.

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.

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.

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 (Q), chemical oxygen demand (COD), ammonium (N-NH) and total suspended solids (TSS), in addition to two climatic attributes, average temperature and daily mean precipitation rates (P) from the plant catchment area, are evaluated in this study.