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

Vanninfrastruktur i både urbane og industrielle miljøer står overfor nye utfordringer som fører til nye databehandlings- og kontrollsystemer som er i stand til å sikre visse vannparametere og kostnadseffektive løsninger. Vanndistribusjonsnettverk må for eksempel optimalisere pumpedriften for å minimere energikostnadene og er sårbare for utilsiktede forurensningssøl, som kan kompromittere innbyggernes liv. Aquaponics industrianlegg, som kombinerer akvakultur med hydroponics for å symbiotisk oppdrette planter og fisk, utgjør et annet eksempel, som lover generering av bærekraftig mat dersom vann- og klimaparametere kontrolleres intelligent. Dessverre klarer ikke toppmoderne verktøy innen engineering å møte disse kravene. INDURB introduserer en sammenhengende og tverrfaglig tilnærming som utnytter nyere fremskritt innen datavitenskap, optimalisering og maskinlæring for å utvikle smarte sanse- og kontrollmekanismer for vanninfrastruktur. Nøkkeloppgaver til systemet vårt inkluderer a) pålitelig kontroll av vannparametere og optimalisering av variabler i smarte vannnettverk; b) forbedret beslutningstaking og fremtidig planlegging for tjenesteoperasjoner; og c) interaksjon med sluttbrukerne. Fra et vitenskapelig perspektiv har vi utviklet algoritmer for dataforbehandling for tidsjustering av tidsserier, læringsavhengighetsdatamodeller fra de observerte tidsseriene, læring av normale operasjonsmodeller og forutsigelse og oppdagelse av hendelser samt design av dyp forsterkning læringsbasert kontroll metoder. Vi har også implementert det overordnede systemet og nettverksarkitekturen for inkrementell online datainnsamling, analyse og relatert maskinlæring. Eksempler på påvirkning i applikasjoner er: a) styring av pumper i vanndistribusjonsnett for å tilfredsstille brukerens vannbehov og opprettholde en balanse mellom trykkene, redusere også kostnadene ved vedlikehold, b) estimering av strømningsdataverdier i vannnettet på steder der det ikke er noen sensorer eller det er feil, c) maksimere planteveksten samtidig som velvære for fiskene i Aquaponics-planter sikres. Dette arbeidet er gjort i samarbeid med partnerne NIVA, Feedback Aquaculture og Kristiansand kommune.

- Design of overall system architecture for online data acquisition of Water Distribution Networks, including the necessary protocols for real-time analysis of data, with an online incremental data acquisition. In the case of Aquaponics, we have also developed a data pipeline involving data pre-processing, dimensionality reduction, feature selection, and prediction capabilities. - Design of online learning of causal (spatial and temporal) dependencies from multiple streaming data time-series, which are dynamic and non-stationary. This includes online kernel-based learning algorithms for both linear and non-linear vector autoregressive models, where in the latter case, the high dimensionality of the inference problem is tackled by using a Fourier-based random feature approximation. These data-driven model learning algorithms have been tested for data from Water Networks, allowing to perform both prediction of dynamics and data imputation. - For scenarios where the data samples from different sensors are not aligned in time, we have designed zero-delay reconstruction of correlated multivariate sensor time series under certain smoothness constraints, associated to the water network data. We learn about the space of signals of interest and optimise the acquisition process at hand to improve the accuracy of the reconstruction under zero-delay processing requirements. This allows resampling and aligning temporal samples simultaneously across multiple time series. - Design of an estimation algorithm to approximate the posterior distribution between states and observations for complex dynamic systems, such as the water quality variables in the Aquaponics systems, and considering also an stochastic risk measure based on the variance of the estimated values, which is also part of the optimisation criterion. - Design of a framework for data-driven adaptive control based on safe deep reinforcement to manage the variables in safety-critical environments, e.g. Aquaponics (maximise plant growth and quality while keeping a healthy fish), providing partial information about potential risks to enhance the training for the control agents. - Design of graph filtering techniques for different inference tasks, such as least squares estimation, denoising, consensus, and change-point or anomaly detection. First, the design of distributed robust graph filters against quantization noise and stochastic errors; second, the design of graph shift operators to maximise the efficiency of the associated inference tasks; third, the joint design of network protocols and graph filters for distributed implementation over a wireless sensor network. - Design of reinforcement learning algorithms for data-driven cost-efficient pump control in water distribution networks. In addition to minimising the energy costs, we have also considered balancing the pressures across the water network. This leads to minimising water losses due to leakage events and b) maintenance costs.

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.