scholarly journals Model Construction of Material Distribution System Based on Digital Twin

2021 ◽  
Author(s):  
Yunrui Wang ◽  
Ziqiang Jiang ◽  
Wu Yue
Keyword(s):  
Distribution System ◽  
Route Optimization ◽  
Model Construction ◽  
Control Mode ◽  
Material Distribution ◽  
Operation Mechanism ◽  
Digital Twin ◽  
Twin Model ◽  
Transmission Stage ◽  
Material Preparation

Abstract Aiming at the problems of poor periodicity of workshop material distribution, difficult prediction of station material demand time node and redundant distribution route, this paper proposes a model construction method of material distribution system based on digital twin. Build a material distribution control mode based on digital twin, and establish a full cycle material distribution mechanism on this basis to comprehensively optimize the distribution cycle from the material preparation stage, dynamic replenishment stage and data transmission stage of adjacent distribution cycles. Build the digital twin model of material distribution system, establish the material demand time node prediction operation mechanism based on LSTM, accurately predict the station material demand time node, establish the material distribution route optimization model with the lowest total cost, and optimize the AGV route. Finally, it is applied to the asynchronous card line workshop of A enterprise to verify the effectiveness of this method.

Analysing integration issues of the microgrid system with utility grid network

2021 ◽  
Vol 22 (1) ◽  
pp. 113-127
Author(s):  
Mulualem Tesfaye ◽  
Baseem Khan ◽  
Om Prakash Mahela ◽  
Hassan Haes Alhelou ◽  
Neeraj Gupta ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Distribution System ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Operating Conditions ◽  
Control Mode ◽  
Voltage Source ◽  
Modulation Scheme ◽  
Main Challenge ◽  
Utility Grid

Abstract Generation of renewable energy sources and their interfacing to the main system has turn out to be most fascinating challenge. Renewable energy generation requires stable and reliable incorporation of energy to the low or medium voltage networks. This paper presents the microgrid modeling as an alternative and feasible power supply for Institute of Technology, Hawassa University, Ethiopia. This microgrid consists of a 60 kW photo voltaic (PV) and a 20 kW wind turbine (WT) system; that is linked to the electrical distribution system of the campus by a 3-phase pulse width modulation scheme based voltage source inverters (VSI) and supplying power to the university buildings. The main challenge in this work is related to the interconnection of microgrid with utility grid, using 3-phase VSI controller. The PV and WT of the microgrid are controlled in active and reactive power (PQ) control mode during grid connected operation and in voltage/frequency (V/F) control mode, when the microgrid is switched to the stand-alone operation. To demonstrate the feasibility of proposed microgrid model, MATLAB/Simulink software has been employed. The performance of fully functioning microgrid is analyzed and simulated for a number of operating conditions. Simulation results supported the usefulness of developed microgrid in both mode of operation.

scholarly journals Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin

2021 ◽  
Vol 12 ◽  
Author(s):  
Pietro Barbiero ◽  
Ramon Viñas Torné ◽  
Pietro Lió
Keyword(s):  
Blood Pressure ◽  
Tissue Expression ◽  
Modern Medicine ◽  
Cellular Level ◽  
Graph Representation ◽  
Modular Architecture ◽  
Panoramic View ◽  
Generative Adversarial Network ◽  
Digital Twin ◽  
Twin Model

Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalized, systemic, and precise treatment plans to patients. To this purpose, we propose a “digital twin” of patients modeling the human body as a whole and providing a panoramic view over individuals' conditions.Methods: We propose a general framework that composes advanced artificial intelligence (AI) approaches and integrates mathematical modeling in order to provide a panoramic view over current and future pathophysiological conditions. Our modular architecture is based on a graph neural network (GNN) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GAN) providing a proof of concept of transcriptomic integrability.Results: We tested our digital twin model on two simulated clinical case studies combining information at organ, tissue, and cellular level. We provided a panoramic overview over current and future patient's conditions by monitoring and forecasting clinically relevant endpoints representing the evolution of patient's vital parameters using the GNN model. We showed how to use the GAN to generate multi-tissue expression data for blood and lung to find associations between cytokines conditioned on the expression of genes in the renin–angiotensin pathway. Our approach was to detect inflammatory cytokines, which are known to have effects on blood pressure and have previously been associated with SARS-CoV-2 infection (e.g., CXCR6, XCL1, and others).Significance: The graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modeling with AI. We believe that this work represents a step forward toward next-generation devices for precision and predictive medicine.

scholarly journals Analyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak

2021 ◽  
Vol 9 (1) ◽  
pp. 15-31
Author(s):  
Ali Arishi ◽  
Krishna K Krishnan ◽  
Vatsal Maru
Keyword(s):  
Raw Materials ◽  
Early Stage ◽  
Supply And Demand ◽  
Network Models ◽  
Combine System ◽  
Neural Network Models ◽  
Digital Twin ◽  
Twin Model ◽  
The Impact ◽  
Reaction Patterns

As COVID-19 pandemic spreads in different regions with varying intensity, supply chains (SC) need to utilize an effective mechanism to adjust spike in both supply and demand of resources, and need techniques to detect unexpected behavior in SC at an early stage. During COVID-19 pandemic, the demand of medical supplies and essential products increases unexpectedly while the availability of recourses and raw materials decreases significantly. As such, the questions of SC and society survivability were raised. Responding to this urgent demand quickly and predicting how it will vary as the pandemic progresses is a key modeling question. In this research, we take the initiative in addressing the impact of COVID-19 disruption on manufacturing SC performance overwhelmed by the unprecedented demands of urgent items by developing a digital twin model for the manufacturing SC. In this model, we combine system dynamic simulation and artificial intelligence to dynamically monitor SC performance and predict SC reaction patterns. The simulation modeling is used to study the disruption propagation in the manufacturing SC and the efficiency of the recovery policy. Then based on this model, we develop artificial neural network models to learn from disruptions and make an online prediction of potential risks. The developed digital twin model is aimed to operate in real-time for early identification of disruptions and the respective SC reaction patterns to increase SC visibility and resilience.

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