AbstractWater Resource Recovery Facilities (WRRF) are becoming vulnerable to new factors that generate increasingly dominant stressors. These novel stressors have emerged rapidly, such as climate change and the COVID-19 pandemic, causing dynamic and environmentally damaging WRRF process failures. Also, the safety factors used to increase the ‘resilience’ of UK wastewater systems (some dating back to the 18th century) has, in some cases, been eliminated. As a result, severe pollution incidents have increased in the UK (>400 serious pollution incidences occurred between 2015 and 2021).
Without the funding and expert knowledge available to evaluate these dynamic, novel and rapidly emerging stressors, UK water companies have failed to secure the long-term ‘resilience’ of their assets and infrastructure. Existing WRRF resilience approaches focus on theoretical scenarios outside of actual operational WRRF data. This can lead to a detached view of resilience, with numerous iterations required to simulate scenarios and can also be computationally intensive. Therefore, monitored WRRF performance variables should be investigated in spatial and temporal dimensions to be analysed as 'dynamic resilience'. It may then be possible to shift ‘resilience’ simulations to a real-time system that evaluates the dynamics of resilience in response to novel ‘dynamic’ stressors.
This research proposes the novel concept of ‘dynamic resilience’ for WRRF systems and processes, which separates stressors from ‘process stresses’ to quantify each within discrete and global process boundaries. The concept of ‘process stress’ was first verified in a survey of international wastewater professionals and then tested using Monte Carlo simulations (case study 1). Two further case studies were performed using actual WRRF data to visualise ‘dynamic resilience’ as a contoured heat map or Self Ordering Window (SOW). These case
studies demonstrate that any WRRF system with suitable data logging frequencies could incorporate the principles of ‘dynamic resilience’.
Overall the methodologies presented in this Thesis demonstrate the possibility of moving toward real-time ‘dynamic resilience’ observations. These methods could also be applied to high-value product streams such as oil, gas and chemical industries, where increasing ‘resilience’ could reduce system downtime and consequential losses.
|Date of Award||6 Feb 2023|
|Supervisor||John Williams (Supervisor), Djamila Ouelhadj (Supervisor) & Cleasby Barry (Supervisor)|