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Related Concept Videos

Multiple Pipe Systems01:21

Multiple Pipe Systems

752
Multipipe systems consist of complex configurations of interconnected pipes designed to transport fluids efficiently across intricate networks. They are essential in engineering applications requiring precise control over flow distribution, pressure, and head loss. They are categorized into series, parallel, loop, and network configurations, each distinguished by unique flow characteristics and applications.
Series Configuration
In a series configuration, fluid flows sequentially from one pipe...
752

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Digital Twin Platform for Water Treatment Plants Using Microservices Architecture.

Carlos Rodríguez-Alonso1,2, Iván Pena-Regueiro1, Óscar García1

  • 1ESIT-Escuela Superior de Ingeniería y Tecnología, UNIR-International University of La Rioja, Av. de la Paz 137, 26006 Logroño, Spain.

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Digital Twins optimize wastewater treatment plants for better water quality and resource management. This framework uses edge computing and machine learning for efficient operation and maintenance, addressing water scarcity challenges.

Keywords:
Digital TwinHMIartificial intelligenceedge computingmicroserviceswater treatment plant

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Area of Science:

  • Environmental Engineering
  • Computer Science
  • Water Management

Background:

  • Climate change and societal growth exacerbate water scarcity and quality issues, leading to widespread diseases.
  • Digitalization and Digital Twins offer potential solutions for optimizing water resource management and infrastructure.
  • Wastewater treatment plants face challenges in efficient operation and maintenance due to complex systems.

Purpose of the Study:

  • To present a framework for developing a Digital Twin platform for wastewater treatment plants.
  • To optimize resource management and infrastructure within the water cycle.
  • To enhance operational efficiency and maintenance processes in wastewater treatment.

Main Methods:

  • Development of a Digital Twin platform utilizing a microservices architecture.
  • Implementation of edge computing for optimized performance.
  • Integration of machine learning, process modeling, simulation, and Building Information Modeling (BIM) data.

Main Results:

  • A novel framework for a Digital Twin platform tailored for wastewater treatment plants.
  • Optimized design for edge computing implementation, enhancing processing capabilities.
  • Leveraging diverse data sources including BIM for informed decision-making.

Conclusions:

  • The proposed Digital Twin framework offers a viable solution for optimizing wastewater treatment plant operations.
  • Edge computing and machine learning integration enable efficient resource management and predictive maintenance.
  • Digitalization of water infrastructure is crucial for addressing water scarcity and improving public health.