Digital Twin Platform for Water Treatment Plants Using Microservices Architecture

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.