| publications-5081 |
Conference paper |
2023 |
Mad Said S.H.B.; Mokhti R.M.B.M.; Arumugam S.B.; Kok K.H.B.; Sidek R.B.; Ow J.B.; Ahmad M.A. |
Machine Learning Algorithm Autonomously Steered a Rotary Steerable System Drilling Assembly Delivering a Complex 3D Wellbore in Challenging Downhole Drilling Environment: A Case Study, Malaysia |
Society of Petroleum Engineers - ADIPEC, ADIP 2023 |
10.2118/216308-MS |
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Deviated oil and gas wellbores were drilled by competent and experienced directional driller (DD) to deliver the wellbore's trajectory on target and on time. The variable human factor and rapid changes of various surface and downhole parameters present challenges for consistent performance every time. A dynamic physics-based machine learning (ML) algorithm leverages on digital twin data analytics and real-time downhole measurement to autonomously steer Rotary-Steerable System (RSS) drilling assembly was employed. This case study describes the methods, the algorithm learning, assimilates the data in real-time, and autonomously steers. Operator's in-field redevelopment plan consists of drilling two oil producers and one water injector side-tracking from donor wells. Due to congested nature of the platform with many producing wells through multi-stacked reservoirs, the wellbore profiles are complex 3D to tap into the various reservoir target sands and avoiding close approach to nearby drilled well. Meticulous well engineering and Bottom-Hole-Assembly (BHA) analysis were performed during the pre-drill planning stage to ascertain the directional performance of the RSS BHA, sensitivity study, offset directional performance and risks were thoroughly assessed. The autonomous steering algorithm models the directional performance tapping into the vast database of digital twin, expected directional performance, evaluates past yields, projecting ahead and constantly adjusting parameters such as steering aggressiveness, dogleg severity, turn rate, whilst staying within safety margin of anti-collision if any to deliver the wellbore to target. The speed of the computation from downhole sensor measurement, coupled with high-speed telemetry of the data to the surface allows for systematic increased in speed of real-time data processed, culminating to ML autonomous steering for RSS BHA to deliver a smoother wellbore that is not possibly with human manual calculation. The complex 3D well profile entails building wellbore angle from 40 degrees to 80 degrees before dropping to 67 degrees while turning azimuth from 190 degrees to 85 degrees with 3.5 degree per 30 meters dogleg severity. After initially side-tracking performed manually by the Directional Drillers at the rig site, autonomous steering algorithm was executed to directionally drill the wellbore through all the planned geological targets till well's total depth. 1224 meters were successfully drilled in autonomous mode without any DD intervention for 3 days of drilling with average 30-45 meters per hours rate-of-penetration. This resulted in 97 percent of wellbore autonomously steered and placed optimally through all planned geological targets, with 32 percent faster drilling compared to offsets. Eighty-six autonomous closed loop steering command flawlessly executed downlink saved fourteen hours of rig time, eliminating invisible loss time translating to faster on bottom drilling. The digital transformation with advances in ML and artificial intelligence, provided impetus drilling automation, to a paradigm shift on how we traditionally drill directional wellbores. Β© 2023, Society of Petroleum Engineers. |
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| publications-5082 |
Conference paper |
2023 |
Liu C.; Liu J.; Jia N.; Wang Y. |
Constructing Waterborne Big Data Platform from the Perspective of Resilient Transportation |
7th IEEE International Conference on Transportation Information and Safety, ICTIS 2023 |
10.1109/ICTIS60134.2023.10243995 |
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Based on the latest research progress of digital twin channel and intelligent channel, as well as the guidance for the waterborne big data platform construction, this study explores the construction of a waterborne big data platform from the perspective of resilient transportation. In view of the new aim of developing an intelligent and safe national transportation network unveiled by the Guidelines on Developing National Comprehensive Transport Network and the trunk-branch layout of inland water transportation, it is proposed that the resilience of water transport on the one hand is reflected in the role that the water channel system plays in the resilience of the entire transportation network, and on the other hand, it is reflected in the capabilities of safety early warning, prevention and control, emergency response and recovery of the water channel itself. Therefore, the basic functions and core theories of the resilient transportation module for the waterborne big data platform are proposed, and the data acquisition methods are briefly described. Β© 2023 IEEE. |
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| publications-5083 |
Conference paper |
2023 |
Abhijith G.R.; Steffelbauer D.B.; Ostfeld A. |
Toward Digital Twins for Emerging Contaminants in Water Distribution Systems |
World Environmental and Water Resources Congress 2023: Adaptive Planning and Design in an Age of Risk and Uncertainty - Selected Papers from World Environmental and Water Resources Congress 2023 |
10.1061/9780784484852.096 |
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Emerging contaminants (ECs) are natural or manufactured chemical compounds that are hard to remove through water treatment; hence, they accumulate in the environment. Such contaminants have already been detected in wastewater, aquatic environments, and water distribution systems (WDSs). Consequently, researchers are developing sensors tailored explicitly to new contaminants. By combining those new sensors with real-time simulation models, digital twins are within reach to assess system-wide water quality. In the future, such twins will build the cornerstone for early warning or real-time control systems concerning these new pollutants. However, realistic simulation tools competent enough to create such digital twins are lacking, mainly because of two reasons: (1) hydraulic models are unable to account for the spatiotemporal dynamics of customer demand, and (2) water quality models are not equipped to simulate the fate and transport of ECs. Our work aims to close this gap by proposing a novel way to model water quality that combines realistic water demand models (i.e., by using the stochastic water demand end-use model, pySIMDEUM), hydraulic solvers (i.e., using the object-oriented Python NETwork analysis tool, OOPNET), and water quality solvers (i.e., using EPyT-C). Extending the state-of-the-art for hydraulic and water quality modeling by incorporating the water demand stochasticity and the uncertainties associated with the imperfect understanding of the formation and transmission of ECs in WDSs is expected to advance the digital twins technology for detecting ECs' formation and evaluating health-related exposure risks in WDSs. Β© World Environmental and Water Resources Congress 2023.All rights reserved |
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| publications-5084 |
Conference paper |
2023 |
Logothetis I.; Mari I.; Vidakis N. |
Towards a Digital Twin Implementation of Eastern Crete: An Educational Approach |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
10.1007/978-3-031-43401-3_17 |
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In the age of digitalization, modern technologies have multiplied, changing how businesses work across a wide range of industries and organizations. Technologies under the extended reality (XR) umbrella can provide immersive user experiences, connectivity, and data collecting from devices and sensors via the Internet of Things (IoT), and digital twins (DT) allowing consumers to test their products and services in a safe virtual environment. This work describes an early use of digital twin technology to model and simulate regions, cultural buildings, fauna, and water-related circumstances within Sitia’s Geopark. This paper presents 3D maps in AR while allowing users to interact with virtual items using various techniques. Additionally, digital reconstruction of cultural buildings and mechanisms are included in this study, which enables users to experience how they were and operated when in use. The result of this study is an application that aims to familiarize students with the topography, surface, and underground of a Plateau that simulates real-life conditions of a Plateau in the Sitia Geopark area. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
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| publications-5085 |
Book chapter |
2023 |
Wang L.; Lian G.; Harris Z.; Horler M.; Wang Y.; Chen T. |
The controlled environment agriculture: a sustainable agrifood production paradigm empowered by systems engineering |
Computer Aided Chemical Engineering |
10.1016/B978-0-443-15274-0.50345-0 |
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Controlled environment agriculture (CEA) has some clear advantages over traditional farming, such as: reliable and consistent production capability; efficiency in water and space use; reducing the use and runoff of fertiliser and pesticides; etc. As such CEA can greatly benefit from the CAPE (computer-aided process engineering) approach – cross-fertilization of these two apparently distinct areas may result in new methods and applications to improve CEA and process engineering, with potentially significant contribution to circular economy. In this paper, we discuss several important aspects of CEA drawing from our own experiences in aquaculture and aeroponics, including product development, process design and process operation, and the potential contribution of CAPE. Finally, we postulate a systems platform for CEA, aiming to foster a long-lasting partnership between the two scientific communities. © 2023 Elsevier B.V. |
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| publications-5086 |
Conference paper |
2023 |
Gao S.; Han P.; Gansel L.C.; Li G.; Zhang H. |
Real-Time Prediction of Fish Cage Behaviors under Varying Currents using Deep Neural Network |
Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 |
10.1109/ICIEA58696.2023.10241403 |
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This paper presents a Deep Neural Network (DNN) model for rapid and low-cost prediction of fish cage behavior under varying currents. We employ a numerical model of the fish cage created in Orcaflex and a set of current profiles from the water surface to the bottom of the cage (0-30 m). A DNN model is trained on a subset of simulated results and evaluated on a separate dataset. Our findings demonstrate that the DNN model can provide real-time, model-free predictions of fish cage behavior comparable to those of the simulator, with improved computational efficiency and robustness. The method is demonstrated to be suitable for digital twin applications, offering near-instant updates on cage behavior and valuable insights for ensuring the safety and stability of fish cage structures in challenging ocean environments. Β© 2023 IEEE. |
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| publications-5087 |
Article |
2023 |
SkΓ΅kala J.; Awty-Carroll K.; Menon P.P.; Wang K.; Lessin G. |
Future digital twins: emulating a highly complex marine biogeochemical model with machine learning to predict hypoxia |
Frontiers in Marine Science |
10.3389/fmars.2023.1058837 |
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The Machine learning (ML) revolution is becoming established in oceanographic research, but its applications to emulate marine biogeochemical models are still rare. We pioneer a novel application of machine learning to emulate a highly complex physical-biogeochemical model to predict marine oxygen in the shelf-sea environment. The emulators are developed with intention of supporting future digital twins for two key stakeholder applications: (i) prediction of hypoxia for aquaculture and fisheries, (ii) extrapolation of oxygen from marine observations. We identify the key drivers behind oxygen concentrations and determine the constrains on observational data for a skilled prediction of marine oxygen across the whole water column. Through this we demonstrate that ML models can be very useful in informing observation measurement arrays. We compare the performance of multiple different ML models, discuss the benefits of the used approaches and identify outstanding issues, such as limitations imposed by the spatio-temporal resolution of the training/validation data. Copyright Β© 2023 SkΓ΅kala, Awty-Carroll, Menon, Wang and Lessin. |
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| publications-5088 |
Review |
2023 |
Gao L.; Zhang L.; Hong Y.; Chen H.-X.; Feng S.-J. |
Flood hazards in urban environment |
Georisk |
10.1080/17499518.2023.2201266 |
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Apart from the estimation of magnitudes of precipitation, floods and storm surges, modelling of storm water flows in a densely populated urban area is required for designing coping strategies and making decisions. Incorporating surface runoff and conduit flow modelling capabilities has enabled the prediction of urban flood hazards. This study synthesises methodologies for simulating flood processes and evaluating flood hazards in urban environment. Existing models and their associated uncertainties are summarised, and state-of-the-art techniques to build up a numerical model for simulating urban floods and the applications to specific cases are illustrated. A schematic framework for urban flood hazard prediction is proposed, within which multi-source observation retrieval, physics-based modelling, parameter optimisation, uncertainty estimation, model-observation fusion, evaluation of compound effects of multiple factors and digital twin techniques are included. The major challenges and uncertainties in flood process modelling originate from input data, model structures, validation processes and compounding effects. Multidisciplinary techniques for estimating the input data and enhancing the efficiency and accuracy of the flood evaluation should be developed. Great efforts are needed in understanding the process-dependent indicators, coupled modelling and data-model assimilation. Determining the probability of compound floods and understanding the driving factors are also essential for evaluating flood risks. Β© 2023 Informa UK Limited, trading as Taylor & Francis Group. |
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| publications-5089 |
Conference paper |
2023 |
Xiao Y.; Zhuang Z.; Wang J.; Yu T. |
Intelligent Control Strategy of Air Handling Unit Based on Digital Twins |
Building Simulation Conference Proceedings |
10.26868/25222708.2023.1454 |
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In this paper, a digital twin air handling unit system was proposed by building a real-time monitoring system for the air handling unit, using TRNSYS to build a digital model of the air handling unit, to achieve the goal of the lowest energy consumption of the system through the intelligent control strategy with the linkage control algorithm between air handling unit’s fan-pump and room cooling load. The performance of this digital twin system was evaluated through practical cases to show that: compared with the traditional fixed air volume and fixed water volume, the energy consumption using the variable air volume air conditioning system by the optimal control strategy saves 56% of the energy in summer under high cooling load conditions (100% of the peak load) and 69% under medium cooling load conditions (75% of the peak load). © 2023 IBPSA.All rights reserved. |
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| publications-5090 |
Conference paper |
2023 |
Pisani J.; Cavone G.; Giarre L.; Pascucci F. |
Industrial Control Systems attack detection by hybrid digital twin |
2023 European Control Conference, ECC 2023 |
10.23919/ECC57647.2023.10178183 |
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The massive digital transformation of industrial control systems, based on tight integration between information and telecommunication technologies, has enlarged the attack surfaces of the industrial systems, thus increasing the need to protect the operational and production processes. Information Technology tools are currently not able to guarantee confidentiality, integrity, and availability in the industrial domain, therefore it is of paramount importance to understand the interaction of the physical components with the information networks. This paper aims to provide a novel Intrusion Detection System for identifying cyber-attacks in cyber-physical systems: the key novel idea is to consider a virtual model for both the plant and the controller to detect attacks. In fact, most of the approaches presented in literature rely on a virtual model representing only the plant, while in this contribution we consider the plant and the controller as a system to be replicated in the digital twin and resulting in a hybrid automata. This allows the detection of attacks to the actuators, that otherwise cannot be revealed. Advantages of this approach are demonstrated by exploiting data from a water distribution system. Β© 2023 EUCA. |
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