| publications-4941 |
Article |
2024 |
Wahab N.H.A.; Hasikin K.; Lai K.W.; Xia K.; Bei L.; Huang K.; Wu X. |
Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices |
PeerJ Computer Science |
10.7717/PEERJ-CS.1943 |
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Background: Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology: Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results: The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies’ flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions: Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors. © 2024 Abd Wahab et al. Distributed under Creative Commons CC-BY 4.0. All Rights Reserved. |
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| publications-4942 |
Article |
2024 |
Kim M.-G.; Bartos M. |
A digital twin model for contaminant fate and transport in urban and natural drainage networks with online state estimation |
Environmental Modelling and Software |
10.1016/j.envsoft.2023.105868 |
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Increased pollutant loads caused by urbanization and climate change have led to widespread impairment of surface water systems. To better manage these threats, water managers are seeking digital twins that combine online models with sensor data to respond to water quality hazards in real-time. This study introduces Pipedream-WQ, a new model for contaminant transport in drainage networks that combines a novel implicit solver for the unsteady advection–reaction–diffusion (ARD) equation with an efficient data assimilation scheme based on Kalman Filtering. We show that this solver reliably reproduces analytical solutions to the ARD equation in steady conditions, and accurately captures unsteady contaminant transport behavior in a complex drainage network. Furthermore, we show that online sensor data assimilation enables better estimation of contaminant concentrations at ungauged locations compared to a model-only approach. This model will enable improved pollutant tracking and source identification, and active water quality management through real-time control of hydraulic infrastructure. © 2023 The Authors |
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| publications-4943 |
Review |
2024 |
Girotto C.D.; Piadeh F.; Bkhtiari V.; Behzadian K.; Chen A.S.; Campos L.C.; Zolgharni M. |
A critical review of digital technology innovations for early warning of water-related disease outbreaks associated with climatic hazards |
International Journal of Disaster Risk Reduction |
10.1016/j.ijdrr.2023.104151 |
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Water-related climatic disasters pose a significant threat to human health due to the potential of disease outbreaks, which are exacerbated by climate change. Therefore, it is crucial to predict their occurrence with sufficient lead time to allow for contingency plans to reduce risks to the population. Opportunities to address this challenge can be found in the rapid evolution of digital technologies. This study conducted a critical analysis of recent publications investigating advanced technologies and digital innovations for forecasting, alerting, and responding to water-related extreme events, particularly flooding, which is often linked to disaster-related disease outbreaks. The results indicate that certain digital innovations, such as portable and local sensors integrated with web-based platforms are new era for predicting events, developing control strategies and establishing early warning systems. Other technologies, such as augmented reality, virtual reality, and social media, can be more effective for monitoring flood spread, disseminating before/during the event information, and issuing warnings or directing emergency responses. The study also identified that the collection and translation of reliable data into information can be a major challenge for effective early warning systems and the adoption of digital innovations in disaster management. Augmented reality, and digital twin technologies should be further explored as valuable tools for better providing of communicating complex information on disaster development and response strategies to a wider range of audiences, particularly non-experts. This can help to increase community engagement in designing and operating effective early warning systems that can reduce the health impact of climatic disasters. Β© 2023 The Authors |
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| publications-4944 |
Article |
2024 |
Ali Z.A.; Zain M.; Hasan R.; Al Salman H.; Alkhamees B.F.; Almisned F.A. |
Circular Economy Advances with Artificial Intelligence and Digital Twin: Multiple-Case Study of Chinese Industries in Agriculture |
Journal of the Knowledge Economy |
10.1007/s13132-024-02101-w |
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The population growth is drastically surging in demand for food and water and uplifting consumption and waste resulting in overburden of society and the environment. Urgent actions are required to address these emerging global issues. Therefore, adopting a circular economy (CE) is essential to sustain the consumption rate and accommodate the ever-increasing demand. Moreover, the CE practices accelerate the progress on sustainable development. From this perspective, digital technologies are playing driving roles in the successful implementations of CE practices and achievements of the United Nations’ (UN) sustainable development goals (SDGs). Among various emerging digital technologies, artificial intelligence (AI) and digital twin ((DT) are the promising ones. This paper aims to understand and explore how both technologies facilitate the CE transitions and attain SDGs in the agriculture domain. To this end, we provide insights into the concepts of CE, AI, and DT with preliminary and current research status. This research evaluates the contributions of global organizations for CE transitions. We elaborate on the significant contributions of AI and DT in the transition towards CE and identify some challenges that hinder the adoption of these technologies. Besides expanding knowledge, concise multiple case studies are also presented as evidence to depict how companies in China are deploying these technologies to digitize various operations and create solutions for waste management, sustainable resource consumption, renewal energy, water conservation, etc. Findings reveal that these companies successfully attain many SDGs of 1, 2, 6, 7, 9, 11, 12, 13, 14, 15, and 17. This paper enormously contributes to the emerging research domain of integrating CE, AI, DT, and agriculture. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. |
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| publications-4945 |
Book chapter |
2024 |
Fomin N.; Meshcheryakov R.V. |
Digital Twin Security of the Cyber-Physical Water Supply System |
Handbook of Digital Twins |
10.1201/9781003425724-54 |
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This chapter discusses the digital twin (DT) security of the cyber-physical water supply system. Based on the international strategies of control DTs analysis, all countries have a lot of water problems like high wear of water supply systems, security problems, anthropogenic impact on water resources and dependence on other countries. The range of CPS vulnerabilities is wider than the OSI model. To assess the vulnerabilities of the CPS, it is necessary to supplement the system elements related to the features of the CPS and important to add extra security layers: exploitation layer, intersystem layer, external layer and control layer. The calculation of risks/vulnerabilities using author’s methods, compared for analogue water supply system (AWSS) and digital water supply system (DWSS), presents the results of degree risk of each group of vulnerabilities, like β„–1 – Infrastructure level (water supply systems, equipment, water sources) or β„–2 – Software level has different levels for AWSS and DWSS. Security of DT is a complex task. By using authors’ methods of security assessment cyber-physical water supply system (CPWSS) was created as a security model of the DT CPWSS which was practically tested in the water supply company β€_x009c_Vodokanal of St. Petersburgβ€_x009d_ (St. Petersburg, Russia) – the second largest water supply company after Moscow. This approach allowed improving security systems of the water supply company. Β© 2024 selection and editorial matter, Zhihan Lyu; individual chapters, the contributors. |
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| publications-4946 |
Article |
2023 |
Gaublomme D.; Quaghebeur W.; Van Droogenbroeck A.; Vanoppen M.; De Gusseme B.; Verliefde A.; Nopens I.; Torfs E. |
A hybrid modelling approach for reverse osmosis processes including fouling |
Desalination |
10.1016/j.desal.2023.116756 |
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A novel hybrid modelling approach, combining the strengths of a mechanistic reverse osmosis (RO) model and a data-driven fouling model, is developed on a unique long-term dataset from a full-scale RO installation to predict its performance. The mechanistic solution-diffusion model describes well understood phenomena in RO such as concentration polarisation, osmotic pressure and solutes transport throughout the membrane. This solution-diffusion model is combined with a data-driven model to cover the gaps in knowledge related to fouling phenomena. Several fouling models are tested to predict the membrane resistance over time and a thorough analysis of important input features was performed. A non-linear recurrent neural network with long short-term memory (RNN-LSTM) clearly outperformed (RMSE = 1.01e13) a linear autoregressive integrated moving average with exogenous variables (ARIMAX) model (RMSE = 1.97e13) for an 8 month testing period. The best performance was achieved when including membrane cleaning as two separate features representing short and long CIPs. Moreover, recovery setpoint, concentrate flow rate, feed temperature, feed conductivity and feed calcium concentration were shown to be important model input features. Fouling sensitive parameters of the solution-diffusion model (the water permeability, the feed spacer channel height and the solute permeability - determined by a global sensitivity analysis) were made function of the output (membrane resistance) of the RNN-LSTM thus leading to a serial hybrid model. The predictions of the hybrid model showed a clear improvement for all output variables when compared to a solution-diffusion model including temperature correction. The model was developed, calibrated and trained on measurements of standard sensors and can thus be used for real-time applications such as advanced control and predictive scenario analysis in a digital twin context. Β© 2023 Elsevier B.V. |
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| publications-4947 |
Article |
2023 |
Ruangpan L.; Mahgoub M.; Abebe Y.A.; Vojinovic Z.; Boonya-aroonnet S.; Torres A.S.; Weesakul S. |
Real time control of nature-based solutions: Towards Smart Solutions and digital twins in Rangsit Area, Thailand |
Journal of Environmental Management |
10.1016/j.jenvman.2023.118389 |
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The intensity and frequency of hydro-meteorological hazards have increased due to fast-growing urbanisation activities and climate change. Hybrid approaches that combine grey infrastructure and Nature-Based Solutions (NBSs) have been applied as an adaptive and resilient strategy to cope with climate change uncertainties and incorporate other co-benefits. This research aims to investigate the feasibility of Real Time Control (RTC) for NBS operation in order to reduce flooding and improve their effectiveness. The study area is the irrigation and drainage system of the Rangsit Area in Thailand. The results show that during the normal flood events, the RTC system effectively reduces water level at the Western Raphiphat Canal Station compared to the system without RTC or with additional storage. Moreover, the RTC system facilitates achieving the required minimum volume and increasing the volume in the retentions. These findings highlight the potential of using RTC to improve the irrigation and drainage system operation as well as NBS implementation to reduce flooding. The RTC system can also assists in equitable water distribution between Klongs and retention areas, while also increasing the water storage in the retention areas. This additional water storage can be utilized for agricultural purposes, providing further benefits. These results represent an essential starting point for the development of Smart Solutions and Digital Twins in utilizing Real-Time Control for flood reduction and water allocation in the Rangsit Area in Thailand. Β© 2023 The Authors |
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| publications-4948 |
Article |
2023 |
Yang W.K.; Chen Z.Y.; Wu G.S.; Xing H. |
Probabilistic model of disc-cutter wear in TBM construction: A case study of Chaoer to Xiliao water conveyance tunnel in China |
Science China Technological Sciences |
10.1007/s11431-023-2465-y |
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Disc-cutters play a crucial role in the penetration process during construction using a tunnel boring machine (TBM). Their wear status has a significant impact on working efficiency and costs that accounts for a large part of the budget. Discovering the wear pattern and characteristics of disc-cutters could provide a valuable guide for field maintenance and cutter replacement work. It also provides insight into the wear mechanism of cutters at different positions for optimizing the disc-cutter arrangement design. In this study, the cutter wears data of 34 disc-cutters over 643 days in the Chaoer River to Xiliao River water diversion tunnel was collected. The dataset contains 21862 manual readings measured from 1079 disc-cutters replaced in this project. The raw data from the hard copy version was transformed into a digital twin database by eliminating abnormal data, filling in empty values, and performing linear interpolations. It has been found that the cutters can pass statistical testing for an exponential probability distribution function with respect to the wear rate (w). The regression ratios of R 2 are essentially greater than 0.8. These findings would help estimate the future service life of a currently working cutter, which means significant savings for the costly disccutters. The application of exponential distribution has the advantage of only one shape parameter, Ξ», whose reciprocal represents both the statistical mean and standard deviation of the wear rates. It is simple and practically amenable. A preliminary study was carried out to simulate the wear process between two neighboring cutters for drafting a replacement plan for disc-cutters by the Monte Carlo method. The prediction results agreed reasonably well with the measured information. Β© 2023, Science China Press. |
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| publications-4949 |
Article |
2024 |
Bernard M. |
Using Digital Twins to Improve Pumping and Distribution System Operations |
Journal - American Water Works Association |
10.1002/awwa.2211 |
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Digital twins of water systems can give utility staff the real-time, detailed analyses they need to manage the complexities of pumping and distribution operations. Among others, the benefits yielded by investing in digital twins include cost savings, improved staff efficiency, optimal equipment operation, and the ability to quickly and accurately identify system issues. Five utility case studies illustrate how real-time data analysis effortlessly produced information that helped solve significant problems, some of which might not have been discovered otherwise. Β© 2024 American Water Works Association. |
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| publications-4950 |
Review |
2023 |
Chowdhury P.; Lakku N.K.G.; Lincoln S.; Seelam J.K.; Behera M.R. |
Climate change and coastal morphodynamics: Interactions on regional scales |
Science of the Total Environment |
10.1016/j.scitotenv.2023.166432 |
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Climate change and its impacts, combined with unchecked human activities, intensify pressures on coastal environments, resulting in modification of the coastal morphodynamics. Coastal zones are intricate and constantly changing areas, making the monitoring and interpretation of data a challenging task, especially in remote beaches and regions with limited historical data. Traditionally, remote sensing and numerical methods have played a vital role in analysing earth observation data and supporting the monitoring and modelling of complex coastal ecosystems. However, the emergence of artificial intelligence-based techniques has shown promising results, offering the additional advantage of filling data gaps, predicting data in data-scarce regions, and analysing multidimensional datasets collected over extended periods of time and larger spatial scales. The main objective of this study is to provide a comprehensive review of the existing literature, discussing both traditional methods and various emerging artificial intelligence-based approaches used in studying the coastal dynamics, shoreline change analysis, and coastal monitoring. Ultimately, the study proposes a climate resilience framework to enhance coastal zone management practices and policies, fostering resilience among coastal communities. The outcome of this study aligns with and supports particularly SDG 13 of the UN (Climate Action) and advances it by identifying relevant methods in coastal erosion studies and proposing integrated management plans informed by real-time data collection and analysis/modelling using physics-based models. Β© 2023 The Authors |
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