Totally decentralised and digital twin based automation of water treatment and supply  

Aalto University

Water supply systems are critical infrastructures facing challenges like environmental uncertainties and security threats. Traditional centralized automation systems are vulnerable to single points of failure and cyber-attacks. To address these issues, there's a growing need for decentralized automation architectures. Inspired by swarm intelligence principles, these architectures distribute decision-making across multiple nodes, enhancing system resilience and responsiveness. Key advancements driving this shift include 5G/6G connectivity, digital twins, and AI. These technologies enable real-time data exchange, predictive maintenance, and autonomous decision-making, essential for ensuring the integrity and safety of water supply systems. Additionally, the project aims to enhance water security by integrating decentralized monitoring with digital twins to proactively detect and mitigate threats at both cyber and physical system levels. Ultimately, this project seeks to revolutionize water treatment automation, bolstering the resilience and security of water supply systems to safeguard public health and societal well-being.

Addressing resilience and safety criticality of water supply systems, this project will propose and validate a novel automation architecture of water treatment automation that is based on the concept of decentralised swarm intelligence. This new automation approach will rely on the latest technology advancements, such as 5G/6G connectivity, digital twins and artificial intelligence. One of the targets is to guarantee the level of water security making it impossible to conduct adversary attacks against the society. In this project, we will investigate the concept of distributed cyber-physical security in which decentralised nodes monitor the situation independently, compare the observations with the digital twins, thus preventing any attacks not only on the cyber level, but also on the physical system level.

Task 1.5, 3.1, 3.3, 4.3

Urban nature based water systems, modeling, management

An ideal candidate for this position would have a strong background in automation software and hardware and experience of developing and using simulation software. Graduates from electrical engineering, automation systems engineering, software engineering programs and computer science are encouraged to apply.

VTT Technical Research Centre of Finland Ltd

Dept. Electrical Engineering and Automation / School of Engineering / Aalto University

Prof. Valeriy Vyatkin

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Markku Kylänpää, Senior Scientist