Real-Time Control of Urban Water Cycle under Cyber-Physical Systems Framework

Abstract

The urban water cycle (UWC), which is composed of the water supply system (WSS) and urban drainage system (UDS), is a critical infrastructure required for the functioning of urban society. Considering the growing pollution and subsequent water scarcity caused by increasing urbanization and climate change, efficient UWC management is required to maintain resource sustainability and environmental protection. Cyber-physical systems (CPSs) provide a technological suite for the efficient management of critical systems. To exploit advantages of CPS for UWC, this paper proposes a CPS-based management framework enabling supervision, subsystem interoperability, and integrated optimization of UWC: (1) Firstly, clear definitions are provided to demonstrate that UWC systems can be considered as CPSs. (2) A multi-layer CPS-based supervision framework is presented afterwards, conceptually dividing the physical UWC and its digital counterpart into Supervision&Control, Scheduling, Digital Twin, and Water Users and Environment four layers. (3) The information flows that interact with each layer, as well as a key aspect of CSP operation, namely the interoperability among subsystems in the context of UWC, are also addressed. (4) To demonstrate advantages of supervision and interoperability of subsystems under the CPS framework, an integrated optimizer based on model predictive control (MPC) is applied and compared against the individual control of each system. A real case study of the WSS and UDS in Barcelona UWC is applied in order to validate the proposed approaches through virtual reality simulations based on MATLAB/SIMULIN and EPA-SWMM.

CLAP: A Cross-Layer Analytic Platform for the Correlation of Cyber and Physical Security Events Affecting Water Critical Infrastructures

“…Multi-layer cyber-physical system (CPS)-based management framework [31] Enables supervision, subsystem interoperability, and integrated optimization of urban water cycle, offering significant improvements in resource sustainability and environmental protection in the overall water cycle.…”

An MPC-Enabled SWMM Implementation of the Astlingen RTC Benchmarking Network

“…By utilizing the internal model over the prediction horizon Hp, the cost function of the MPC can be written as in Equation (14), where X 2 R is the weighted quadratic norm X T RX, while the predicted objectives z and overflow volumes, given by Equations (15) and (16), were derived from the internal model and propagation through the predicted volumes and delays. The constraints of the internal model can similarly be collected into a single matrix inequality given by Equation (17). The matrices Ψ, Φ, Θ, and Γ define the influence of the initial volume, the predicted control q u , inflow w, and CSO q w on the objectives, respectively.…”

“…For example, the hydrological module of the Simba# simulator has previously been used to demonstrate the base case (BC) of locally controlled throttle settings, as well as the global rule-based equal-filling-degree (EFD) approach [13]. MPC has been widely investigated for UDS optimization solutions [1,3,4,7,[9][10][11][14][15][16][17], but it is difficult to find a contribution with clear definition of the internal MPC model, as well as the core implementation principles [11]. Therefore, the extension of the benchmark model for MPC application and testing can support the development of these techniques.…”

Integrated pollution-based real-time control of sanitation systems

“…However, the high computation load and potential "premature" problem prevent its usage into large scale systems. Rule-based control is a widely used RTC strategy for SSs in practice (Schütze, et al, 2003;Schütze, et al, 2017;Sun, et al, 2020a;Sun, et al, 2020b) because of its simplicity of defining rules (in the format of "if-then-else") without modelling requirement (Elbys, et al, 2018;García, et al, 2015;Klepiszewski and Schmitt, 2002). Considering quality of the rule-based control is highly depended on experience of the person who defines the rules, and the optimal control solutions cannot be guaranteed.…”