Implementation of Digital Twins at Water Resource Recovery Facility

Hanna Molin –

DIVISION of Industrial Electrical Engineering and Automation

Implementation of digital twins at water resource recovery facilities Identification and description of challenges and enablers for the use of real-time process models for decision making within the water and wastewater sector and other relevant industries

The core of developing and using digital twins is data. However, data alone will not be sufficient. One must go from data, to information, to knowledge to ‘intelligence’. While data is retrieved by quantitative or qualitative measurements from sensors or equipment in the WRRF, information is what can be obtained from the data through data analysis. Piecing together different information and insight to the mechanisms and processes in the WRRF gives knowledge. When the knowledge is used to develop new ideas and perspectives, it is commonly said to be ‘intelligent’, albeit that the term intelligent can be somewhat misleading (Therrien et al., 2020).
Data is collected from sensors, actuators and other process equipment, as well as from laboratory tests. As the data sources may have different interfaces and supported protocols, means to gather the data is needed to increase the connectivity (Tao et al., 2018). Internet of Things (IoT), or rather Industrial IoT (IIoT), are important technologies to achieve this. IIoT is regarded as a key enabler for digitalization within the industry as it enables the connectivity between devices and thereby facilitating data collection, exchange and analysis (Malakuti & Gruener, 2018).

Key enablers
• Communication platforms and protocols
• IT infrastructure
• Cyber security
• IoT, IIoT
• Big data technologies
• Storage capacity
• Cloud, edge or fog computing
• Modelling and simulation tools/platforms
• Data science
• Computer science
• Artificial intelligence (Machine learning, Deep learning)
• Visualization and graphical user interface
• Transparency and interpretability

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