A framework for model-based assessment of resilience in water resource recovery facilities against power outage

A new framework for distributed storage tanks placement based on a resilience characteristic metric and reduced modelling

In recent years, urban flooding has been a frequent occurrence, and seriously threatens the safety of lives and properties. Rational placement of distributed storage tanks is one of the effective ways to solve urban flooding, addressing stormwater management and rainwater reuse. However, existing optimization methods (such as genetic algorithm (GA) and other evolutionary algorithms) for determining the placement of storage tanks typically have a high computational burden; as such, they can be very time-consuming, and are not conducive to energy saving, carbon reduction and work efficiency improvements. In this study, a new approach and framework based on a resilience characteristic metric (RCM) and reduced modelling requirements are proposed. In this framework, the resilience characteristic metric, which is based on the linear superposition principle of system resilience metadata, is introduced, and a small number of simulations based on a coupling of MATLAB with SWMM are used to obtain the final placement scheme of storage tanks. The framework is demonstrated and verified with two cases in Beijing and Chizhou, China, and compared with a GA. The GA requires 2000 simulations for two cases (considering the placement of 2 and 6 tanks respectively), while the proposed method needs 44 simulations for the Beijing case and 89 simulations for the Chizhou case. The results show that the proposed approach is feasible and effective, and cannot only obtain a relative better placement scheme, but also considerably reduce computational time and energy consumption. It significantly improves the efficiency of determining the placement scheme of storage tanks. This method provides a new approach for the determining better storage tank placement schemes, and is useful for informing device placement in sustainable drainage systems.

Increasing resilience through nudges in the urban water cycle: An integrative conceptual framework to support policy decision-making

Relevant challenges associated with the urban water cycle must be overcome to meet the United Nations Sustainable Development Goals (SDGs) and improve resilience. Unlike previous studies that focused only on the provision of drinking water, we propose a framework that extends the use of the theory of nudges to all stages of the overall urban water cycle (drinking water and wastewater services), and to agents of influence (citizens, organizations, and governments) at different levels of decision making. The framework integrates four main drivers (the fourth water revolution, digitalization, decentralization, and climate change), which influence how customers, water utilities and regulators approach the challenges posed by the urban water cycle. The proposed framework, based on the theory of nudges first advanced by the Nobel Prize in behavioral economics Richard H. Thaler and Cass R. Sunstein (Thaler and Sunstein, 2009), serves as a reference for policymakers to define medium- and long-term strategies and policies for improving the sustainability and resilience of the urban water cycle. Finally, we provide new insights for further research on resilience approaches to the management of the urban water cycle as an element to support the more efficient formulation of policies.

Exploring the use of water resource recovery facility instrument data to visualise dynamic resilience to environmental stressors

Water resource recovery facilities (WRRF) face increasingly dynamic stressors, such as higher rainfall intensity and extended dry periods, which can exert stress on ageing water infrastructure and processes. These events can generate process stresses, which lead to wastewater process failures which result in pollution events that could be identified from instrument data used for operational/compliance monitoring. This extraction can be performed on two levels (1) for discrete processes that generate data to monitor process control variables and (2) at the WRRF process boundary (global), which is mainly used for compliance. Both levels of data hold valuable information on the dynamic influence of environmental stressors (cause) and the resulting process stress or resilience (effect) as 'dynamic resilience'. This paper proposes a novel methodology that uses actual water company instrument data to evaluate the 'discrete' (unit processes) and 'global' (WRRF boundary) dynamic resilience of a WRRF in the south of the UK. Dynamic resilience is presented as a four-stage methodology, which; (1) cleans WRRF data and extracts a standard operating condition; (2) identifies dynamic high and low flow environmental stressor events (one in five years); (3) models the process stresses and resilience generated by the imposed dynamic stressor before; (4) generating a contoured heat map of process-related stresses or resilience as a self ordering window. These methods demonstrate the possibility of visualising the dynamics of WRRF resilience (dynamic stressors and process stresses/resilience) resulting from high and low flow dynamic environmental stressors. Despite some challenges experienced with self ordering window scaling, the results demonstrate the possibility of identifying zones of process stress and resilience. It may also be possible to expand the methods developed to incorporate storm flows and combined sewer discharges.