Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID ▲	Type	Year	Authors	Title	Venue/Journal	DOI	Abstract
publications-4741	article	2019	Short, Michael and Short, Michael and Twiddle, J.A. and Twiddle, John and Twiddle, J.A.	An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment.	Sensors	10.3390/s19173781	This paper is concerned with the implementation and field-testing of an edge device for real-time condition monitoring and fault detection for large-scale rotating equipment in the UK water industry. The edge device implements a local digital twin, processing information from low-cost transducers mounted on the equipment in real-time. Condition monitoring is achieved with sliding-mode observers employed as soft sensors to estimate critical internal pump parameters to help detect equipment wear before damage occurs. The paper describes the implementation of the edge system on a prototype microcontroller-based embedded platform, which supports the Modbus protocol; IP/GSM communication gateways provide remote connectivity to the network core, allowing further detailed analytics for predictive maintenance to take place. The paper first describes validation testing of the edge device using Hardware-In-The-Loop techniques, followed by trials on large-scale pumping equipment in the field. The paper concludes that the proposed system potentially delivers a flexible and low-cost industrial digitalization platform for condition monitoring and predictive maintenance applications in the water industry.
publications-4742	article	2022	Qiu, Hao and Qiu, Hao and Feng, Yixiong and Feng, Yixiong and Feng, Yixiong and Hong, Zhaoxi and Li, Kangjie and Li, Kangjie and Tan, Jianrong and Tan, Jianrong	Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration	Scientific Reports	10.1038/s41598-022-14835-1	Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriented value chain collaboration. Existing deviation prediction methods are mainly used for assembly quality control, which concentrate in the product design and assembly stage. However, the actual assembly deviations generated in the service stage can be used to guide the equipment maintenance and tolerance design. In this paper, a high-fidelity prediction and privacy-preserving method is proposed based on the observable assembly deviations. A hierarchical graph attention network (HGAT) is established to predict the assembly feature deviations. The hierarchical generalized representation and differential privacy reconstruction techniques are also introduced to generate the graph attention network model for assembly deviation privacy-preserving. A derivation gradient matrix is established to calculate the defined modified necessary index of assembly parts. Two privacy-preserving strategies are designed to protect the assembly privacy of node representation and adjacent relationship. The effectiveness and superiority of the proposed method are demonstrated by a case study with a four-column hydraulic press.
publications-4743	article	2024	Schroer, HW and Just, CL	Feature Engineering and Supervised Machine Learning to Forecast Biogas Production during Municipal Anaerobic Co-Digestion.	ACS ES&T engineering	10.1021/acsestengg.3c00435	Municipalities with excess anaerobic digestion capacity accept offsite wastes for co-digestion to meet sustainability goals and create more biogas. Despite the benefits inherent to co-digestion, the temporal and compositional heterogeneity of external waste streams creates operational challenges that lead to upsets or conservative co-digestion. Given the complex microbial bioprocesses occurring during anaerobic digestion, prediction and modeling of the outcomes can be challenging, and machine learning has the potential to improve understanding and control of co-digestion processes. Biogas flows are a surrogate for process health, and here, we predicted biogas production from historical data collected by a water resource recovery facility (WRRF) during normal operation. We tested a daily lab and operational data set (n = 1089 after cleaning) and a minute-by-minute supervisory control and data acquisition (SCADA) operational data set (n = 491,761 after cleaning) to determine if forecasting biogas flow for a 24 h time horizon is feasible without collecting additional data. We found that a multilayer perceptron (MLP) neural network model outperformed tree-based and multiple linear regression models. Using a high-resolution SCADA data set for the first time, we showed that MLP neural networks could predict biogas production with an adjusted coefficient of determination (R2) of 0.78 and a mean absolute percentage error of 13.4\% on a holdout test set. Adding daily laboratory analyses to the model did not appreciably improve the prediction of biogas flows. Feature engineering was essential to an accurate prediction, and 11 of the 15 most important features in the SCADA model were calculated from raw SCADA outputs. In summary, this paper demonstrates that minute-scale SCADA information collected at a municipal co-digestion facility can forecast biogas production, as a first step toward a digital twin model, without additional data collection.Β© 2023 The Authors. Published by American Chemical Society.
publications-4744	article	2024	Molin, H and WΓ¤rff, C and Lindblom, E and Arnell, M and Carlsson, B and Mattsson, P and BΓ¤ckman, J and Jeppsson, U	Automated data transfer for digital twin applications: Two case studies.	Water environment research : a research publication of the Water Environment Federation	10.1002/wer.11074	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.Β© 2024 The Author(s). Water Environment Research published by Wiley Periodicals LLC on behalf of Water Environment Federation.
publications-4745	article	2022	Blanco-GΓ³mez, Pablo and Blanco-GΓ³mez, Pablo and JimΓ©nez-GarcΓa, JosΓ© Luis and JimΓ©nez-GarcΓa, JosΓ© Luis and Cecilia, Jose M. and Cecilia, JosΓ© M.	Low-cost automated GPS, Electrical Conductivity and Temperature sensing device (EC+T Track) and Android platform for water quality monitoring campaigns	HardwareX	10.1016/j.ohx.2022.e00381	Environmental and water quality monitoring are of utmost interest in a context where land use changes, uncontrolled agricultural practices, human settlements, tourism and other activities affect a watershed and condition the usage of their surface waters. Such is the case of Mar Menor lagoon in Southeast of Spain, where the EU H2020 SMARTLAGOON project stands and is implementing an intelligent environmental infrastructure and modelling that will let the construction of a digital twin of the lagoon. Performing environmental monitoring is expensive and the number of sampling locations is typically limited by the budget. For this reason, we have developed a low-cost monitoring system that can be integrated in a small-sized buoy and attached to fishing and recreational boats allowing citizens to gather water quality information - i.e. electrical conductivity and temperature - with the use of their smartphones. The usage of such devices leads to key stakeholder engagement and citizen science activities that could enrich and ease the data gathering process.
publications-4746	article	2024	Secci, D and Saysel, AK and Uygur, Δ° and YoloΔ_x009f_lu, OC and Zanini, A and Copty, NK	Modeling for sustainable groundwater management: Interdependence and potential complementarity of process-based, data-driven and system dynamics approaches.	The Science of the total environment	10.1016/j.scitotenv.2024.175491	Groundwater systems are vast natural water reservoirs used to support human water demands and ecosystem services. Various modeling approaches have been developed to help manage these complex highly-dynamic systems. This paper discusses the strengths and limitations of three modeling approaches, namely: process-based, data-driven and system dynamics modeling. For demonstration purposes, the three modeling approaches are applied to the Konya Closed Basin, a large agricultural region with semi-dry climate located in central Turkey. Process-based modeling is grounded in the theory-based representation of the governing processes but is somewhat limited by the computational effort and the difficulty of defining the required input parameters that characterize the heterogeneous aquifer system. Process-based models are shown to be powerful tools for resource management purposes provided climatic and water demand scenarios are accurately defined. Data-driven models are efficient tools for the management of groundwater resources but are highly dependent on the availability of large training data sets encompassing the spectrum of possible system responses. The high efficiency of surrogate modeling approaches makes them ideal tools for incorporation into applications such as real-time decision support systems and digital twin platforms. System dynamics modeling examines the groundwater exploitation problem within a socio-economic context that involves multiple stakeholders and their decision making. It combines groundwater flow models with socio-economics and endogenous decision rules to conduct scenario analysis and support policy development. The analyses and model demonstrations presented in this paper underscore the interconnectedness and complementarity of these three modeling approaches and the need for more integrated use of these modeling approaches for enhanced multi-sectoral management of groundwater systems.Copyright Β© 2024 Elsevier B.V. All rights reserved.
publications-4747	article	2024	Guo, J and Yang, F and Costa, Jr OS and Yan, X and Wu, M and Qiu, H and Li, W and Xu, G	Nutrient budgets and biogeochemical dynamics in the coastal regions of northern Beibu Gulf, South China Sea: Implication for the severe impact of human disturbance.	Marine environmental research	10.1016/j.marenvres.2024.106447	This study examined the nutrient budgets and biogeochemical dynamics in the coastal regions of northern Beibu Gulf (CNBG). Nutrient concentrations varied spatially and seasonally among the different bays. High nutrient levels were found in the regions with high riverine inputs and intensive mariculture. Using a three end-member mixing model, nutrient biogeochemistry within the ecosystem was estimated separately from complex physical mixing effects. Nutrient consumption dominated in most bays in summer, whereas nutrient regeneration dominated in winter, likely due to phytoplankton decomposition, vertical mixing and desorption. Through the Land-Ocean Interaction Coastal Zone (LOICZ) model, the robust nutrient budgets were constructed, indicating that the CNBG behaved as a sink of dissolved inorganic nitrogen, phosphorus and silicon. River-borne nutrient inputs were the dominant nutrient source, while residual flows and water exchange flows transported nutrient off the estuaries. This study could help us better understand nutrient cycles and nutrient sources/sinks in the CNBG.Copyright Β© 2024 Elsevier Ltd. All rights reserved.
publications-4748	article	2022	Fu, Guangtao and Fu, Guangtao and Jin, Yiwen and Jin, Yiwen and Sun, Siao and Sun, Siao and Yuan, Zhiguo and Yuan, Zhiguo and Butler, David and Butler, David	The role of deep learning in urban water management: A critical review.	Water Research	10.1016/j.watres.2022.118973	Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world.
publications-4749	article	2023	Zuo, Shouwei and Niu, Wenchao and Chu, Shengqi and An, Pengfei and Huang, Huan and Zheng, Lirong and Zhao, Leiga and Zhang, Jing	Water-Regulated Lead Halide Perovskites Precursor Solution: Perovskite Structure Making and Breaking	The journal of physical chemistry letters	10.1021/acs.jpclett.3c00683	Identifying the impact of water on iodoplumbate complexes in various solutions is essential for linking the coordination environment of the perovskite precursor to its final perovskite solar cell (PSC) properties. In this study, we propose a digital twin approach based on X-ray absorption fine structure and molecular dynamic simulation to investigate the structure evolution of iodoplumbate complexes in precursor solutions as a function of storage time under a constant humidity environment. A full picture about what water does in the perovskite formation process is brought out, and the β€_x009c_making and breakingβ€_x009d_ role of water molecules is uncovered to link the structure of iodoplumbate complexes to its final properties. This study sheds light on a full picture about what water does in the perovskite formation process and the role of water, which will lead to developing water-involved strategies for consistent PSC fabrication under ambient conditions.
publications-4750	article	2023	Bolorinos, Jose and Mauter, Meagan S. and Rajagopal, Ram	Integrated Energy Flexibility Management at Wastewater Treatment Facilities	Environmental science & technology	10.1021/acs.est.3c00365	On-site batteries, low-pressure biogas storage, and wastewater storage could position wastewater resource recovery facilities as a widespread source of industrial energy demand flexibility. This work introduces a digital twin method that simulates the coordinated operation of current and future energy flexibility resources. We combine process models and statistical learning on 15 min resolution sensor data to construct a facility's energy and water flows. We then value energy flexibility interventions and use an iterative search algorithm to optimize energy flexibility upgrades. Results from a California facility with anaerobic sludge digestion and biogas cogeneration predict a 17\% reduction in electricity bills and an annualized 3\% return on investment. A national analysis suggests substantial benefit from using existing flexibility resources, such as wet-weather storage, to reduce electricity bills but finds that new energy flexibility investments are much less profitable in electricity markets without time-of-use incentives and plants without existing cogeneration facilities. Profitability of a range of energy flexibility interventions may increase as a larger number of utilities place a premium on energy flexibility, and cogeneration is more widely adopted. Our findings suggest that policies are needed to incentivize the sector's energy flexibility and provide subsidized lending to finance it.