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The provision of self-cleaning velocities has been shown to reduce the risk of discolouration in water distribution networks (WDNs). Despite these findings, control implementations continue to be focused primarily on pressure and leakage management. This paper considers the control of diurnal flow velocities to maximize the self-cleaning capacity (SCC) of WDNs. We formulate a new optimal design-for-control problem where locations and operational settings of pressure control and automatic flushing valves are jointly optimized. The problem formulation includes a nonconvex objective function, nonconvex hydraulic conservation law constraints, and binary variables for modelling valve placement, resulting in a nonconvex mixed integer nonlinear programming (MINLP) optimization problem. Considering the challenges with solving nonconvex MINLP problems, we propose a heuristic algorithm which combines convex relaxations (with domain reduction), a randomization technique, and a multi-start strategy to compute feasible solutions. We evaluate the proposed algorithm on case study networks with varying size and degrees of complexity, including a large-scale operational network in the UK. The convex multi-start algorithm is shown to be a more robust solution method compared to an off-the-shelf genetic algorithm, finding good-quality feasible solutions to all design-for-control numerical experiments. Moreover, we demonstrate the implemented multi-start strategy to be a fast and scalable method for computing feasible solutions to the nonlinear SCC control problem. The proposed method extends the control capabilities and benefits of dynamically adaptive networks to improve water quality in WDNs., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bradley Jenks, Filippo Pecci, and Ivan Stoianov report financial support was provided by Engineering and Physical Sciences Research Council. Bradley Jenks reports financial support was provided by Bristol Water Plc, Analytical Technology Inc, and Imperial College London’s Skempton Scholarship., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

We advance a novel computational theory of the hippocampal formation as a hierarchical generative model that organizes sequential experiences, such as rodent trajectories during spatial navigation, into coherent spatiotemporal contexts. We propose that the hippocampal generative model is endowed with inductive biases to identify individual items of experience (first hierarchical layer), organize them into sequences (second layer) and cluster them into maps (third layer). This theory entails a novel characterization of hippocampal reactivations as generative replay: the offline resampling of fictive sequences from the generative model, which supports the continual learning of multiple sequential experiences. We show that the model learns and efficiently retains multiple spatial navigation trajectories, by organizing them into spatial maps. Furthermore, the model reproduces flexible and prospective aspects of hippocampal dynamics that are challenging to explain within existing frameworks. This theory reconciles multiple roles of the hippocampal formation in map-based navigation, episodic memory and imagination., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This paper investigates control and design-for-control strategies to improve the resilience of sectorized water distribution networks (WDN), while minimizing pressure induced pipe stress and leakage. Both evolutionary algorithms (EA) and gradient-based mathematical optimization approaches are investigated for the solution of the resulting large-scale non-linear (NLP) and bi-objective mixed-integer non-linear programs (BOMINLP). While EAs have been successfully applied to solve discrete network design problems for large-scale WDNs, gradient-based mathematical optimization methods are more computationally efficient when dealing with large search spaces associated with continuous variables in optimal network control problems. Considering the advantages of each method, we propose a sequential hybrid method for the optimal design-for-control of large-scale WDNs, where a refinement stage relying on gradient-based mathematical optimization is used to solve continuous optimal control problems corresponding to design solutions returned by an initial EA search. The proposed method is applied to compute the Pareto front of a bi-objective design-for-control problem for the operational network BWPnet, where we consider reopening closed connections between isolated supply areas. The results show that the considered design-for-control strategy increases the resilience of BWPnet while minimizing pressure induced leakage. Moreover, the refinement stage of the proposed hybrid method efficiently improves the coarse approximation computed by the initial EA search, returning a continuous and even Pareto front approximation., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Monitoring the quality of drinking water from the treatment plant to the consumers tap is critical to ensure compliance with national standards and/or WHO guideline levels. There are a number of processes and factors affecting the water quality during transmission and distribution which are little understood. A significant obstacle for gaining a detailed knowledge of various physical and chemical processes and the effect of the hydraulic conditions on the water quality deterioration within water supply systems is the lack of reliable and low-cost (both capital and O & M) water quality sensors for continuous monitoring. This paper has two objectives. The first one is to present a detailed evaluation of the performance of a novel in-pipe multi-parameter sensor probe for reagent- and membrane-free continuous water quality monitoring in water supply systems. The second objective is to describe the results from experimental research which was conducted to acquire continuous water quality and high-frequency hydraulic data for the quantitative assessment of the water quality changes occurring under steady and unsteady-state flow conditions. The laboratory and field evaluation of the multi-parameter sensor probe showed that the sensors have a rapid dynamic response, average repeatability and unreliable accuracy. The uncertainties in the sensor data present significant challenges for the analysis and interpretation of the acquired data and their use for water quality modelling, decision support and control in operational systems. Notwithstanding these uncertainties, the unique data sets acquired from transmission and distribution systems demonstrated the deleterious effect of unsteady state flow conditions on various water quality parameters. These studies demonstrate: (i) the significant impact of the unsteady-state hydraulic conditions on the disinfectant residual, turbidity and colour caused by the re-suspension of sediments, scouring of biofilms and tubercles from the pipe and increased mixing, and the need for further experimental research to investigate these interactions; (ii) important advances in sensor technologies which provide unique opportunities to study both the dynamic hydraulic conditions and water quality changes in operational systems. The research in these two areas is critical to better understand and manage the water quality deterioration in ageing water transmission and distribution systems., (Copyright © 2011 Elsevier Ltd. All rights reserved.)