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IKTPLUSS-IKT og digital innovasjon

Data-driven cyber-physical networked systems for autonomous cognitive control and adaptive learning in industrial & urban water environments

Alternative title: Datadrevne Cyber-fysiske nettverkssystemer for autonome kognitiv kontroll og adaptiv læring i industrielle og urbane vannmiljøer

Awarded: NOK 16.0 mill.

Water infrastructures in both urban and industrial environments face new challenges that prompt novel autonomous control and data processing systems capable of ensuring certain water parameters and cost-efficient solutions. For example, water distribution networks need to optimize pumping operations and are vulnerable to accidental contamination spills, which can compromise the lives of inhabitants. Aquaponics industrial plants, which combine aquaculture with hydroponics to symbiotically raise plants and fish, constitute another example, which promise the generation of sustainable food if water and climate parameters are intelligently controlled. Unfortunately, state-of-the-art tools in engineering are unable to meet these demands. INDURB introduces a cohesive and multidisciplinary approach that leverages recent advances in machine learning, deep learning and data science to develop smart sensing and control mechanisms for water infrastructures. The work plan includes the design and implementation of cyber-physical systems (CPSs) for autonomous control and data processing in urban water distribution networks and Aquaponics plants. Key tasks of the designed CPS include a) reliable control of water parameters and optimization of smart valves and pumps; b) improved decision making and future planning for service operations; and c) real-time bidirectional interaction with the end-users, including water utilities, food and water consumers. During this period, we have developed algorithms for learning adaptive multi-variate data models from the observed time-series, learning normal operational models and predicting and detecting critical events as well as the initial design of deep reinforcement learning based control methods. Examples of this are control of the pumps in water distribution network in order to satisfy the user water demand or maximizing plant growth while ensuring the wellbeing of the fishes in an Aquaponics plant. At this point, we have finalized the overall networking infrastructure design to support all the necessary intelligence. The merits of the designed CPSs will be demonstrated by means of two pilot deployments in a real water distribution network and a real Aquaponics factory. These tasks will be accomplished in cooperation with the partners NIVA, which is a world-wide leader in water technology; the Municipality of Kristiansand, which pursues multiple smart city objectives; and FAqua, which is a pioneer company in Aquaponics technology.

The main objective of INDURB is the design and implementation of a data-driven networked cyber-physical system (CPS) for autonomous control and continuous learning from data in both industrial and urban water environments, which will be represented by Aquaponics industrial plants and urban Water Distribution Networks (WDN). These networked systems are composed of a large number of wirelessly interconnected components with local data processing and control capabilities over large geographic areas or with high spatial densities. The malfunctioning of one component, either due to accidents or intentional attacks, may have devastating effects. Unfortunately, currently existing scientific and engineering methods do not consider smart sensing and data analytics components, and proper integration methods to offer timely detection, cognitive control and adaptive learning and therefore, are very conservative and highly sub-optimal. INDURB offers a new complete multidisciplinary approach, providing the following features: a) a highly reliable and low-cost water health protection with respect to both chemical and microbiological contamination, predicting and reacting through actuation (chemical dosage, smart valves and pumps), ensuring that the water parameters are within the correct limits adapting to the application demands; b) improved decision making and future planning for service operations; and c) a real-time bidirectional interaction between our CPS and the end-users (water utilities, food and water consumers), which will also provide data into the system, interplaying with cost-benefit trade-offs and allowing an automatic end-user complaint surveillance to address water quality problems and improve satisfaction. The results will be demonstrated for both scenarios, by means of a real WDN pilot and a real Aquaponics fish factory, in cooperation with partners NIVA (world wide leader in water technology), Municipality of Kristiansand (smart city objective) and FAqua.


IKTPLUSS-IKT og digital innovasjon