6,631 research outputs found
Digital twin reference model development to prevent operators' risk in process plants
In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms
Optimizing vertical farming : control and scheduling algorithms for enhanced plant growth
L’agriculture verticale permet de contrôler presque totalement les conditions pour croître
des plantes, qu’il s’agisse des conditions météorologiques, des nutriments nécessaires à la
croissance des plantes ou même de la lutte contre les parasites. Il est donc possible de
trouver et de définir des paramètres susceptibles d’augmenter le rendement et la qualité des
récoltes et de minimiser la consommation d’énergie dans la mesure du possible. À cette fin,
ce mémoire présente des algorithmes d’optimisation tels qu’une version améliorée du recuit
simulé qui peut être utilisée pour trouver et donner des lignes directrices pour les paramètres
de l’agriculture verticale. Nous présentons égalementune contribution sur la façon dont les
algorithmes de contrôle, p. ex. l’apprentissage par renforcement profond avec les méthodes
critiques d’acteurs, peuvent être améliorés grâce à une exploration plus efficace en prenant
en compte de l’incertitude épistémique lors de la sélection des actions. cette contribution
peut profiter aux systèmes de contrôle conçus pour l’agriculture verticale. Nous montrons
que notre travail est capable de surpasser certains algorithmes utilisés pour l’optimisation et
le contrôle continu.Vertical farming provides a way to have almost total control over agriculture, whether it be
controlling weather conditions, nutrients necessary for plant growth, or even pest control. As
such, it is possible to find and set parameters that can increase crop yield, and quality, and
minimize energy consumption where possible. To that end, this thesis presents optimization
algorithms such as an enhanced version of Simulated Annealing that can be used to find and
give guidelines for those parameters. We also present work on how real-time control algorithms such as Actor-Critic methods can be made to perform better through more efficient
exploration by taking into account epistemic uncertainty during action selection which can
also benefit control systems made for vertical farming. We show that our work is able to
outperform some algorithms used for optimization and continuous control
Microservices’ libraries enabling server-side business logic visual programming for digital twins
Memristor Emulator Circuit Design and Applications
This chapter introduces a design guide of memristor emulator circuits, from conceptual idea until experimental tests. Three topologies of memristor emulator circuits in their incremental and decremental versions are analysed and designed at low and high frequency. The behavioural model of each topology is derived and programmed at SIMULINK under the MATLAB environment. An offset compensation technique is also described in order to achieve the frequency-dependent pinched hysteresis loop that is on the origin and when the memristor emulator circuit is operating at high frequency. Furthermore, from these topologies, a technique to transform normal non-linear resistors to inverse non-linear resistors is also addressed. HSPICE numerical simulations for each topology are also shown. Finally, three real analogue applications based on memristors are analysed and explained at the behavioural level of abstraction
Digital Twins: How Far from Ideas to Twins?
As a bridge from virtuality to reality, Digital Twin has increased in
popularity since proposed. Ideas have been proposed theoretical and practical
for digital twins. From theoretical perspective, digital twin is fusion of data
mapping between modalities; from practical point of view, digital twin is
scenario implementation based on the Internet of Things and models. From these
two perspectives, we explore the researches from idea to realization of digital
twins and discuss thoroughly
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
Laser-directed-energy deposition (DED) offers advantages in additive
manufacturing (AM) for creating intricate geometries and material grading. Yet,
challenges like material inconsistency and part variability remain, mainly due
to its layer-wise fabrication. A key issue is heat accumulation during DED,
which affects the material microstructure and properties. While closed-loop
control methods for heat management are common in DED research, few integrate
real-time monitoring, physics-based modeling, and control in a unified
framework. Our work presents a digital twin (DT) framework for real-time
predictive control of DED process parameters to meet specific design
objectives. We develop a surrogate model using Long Short-Term Memory
(LSTM)-based machine learning with Bayesian Inference to predict temperatures
in DED parts. This model predicts future temperature states in real time. We
also introduce Bayesian Optimization (BO) for Time Series Process Optimization
(BOTSPO), based on traditional BO but featuring a unique time series process
profile generator with reduced dimensions. BOTSPO dynamically optimizes
processes, identifying optimal laser power profiles to attain desired
mechanical properties. The established process trajectory guides online
optimizations, aiming to enhance performance. This paper outlines the digital
twin framework's components, promoting its integration into a comprehensive
system for AM.Comment: 12 Pages, 10 Figures, 1 Table, NAMRC Conferenc
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