Dynamic resilience for biological wastewater treatment processes: Interpreting data for process management and the potential for knowledge discovery

Abstract

Climate change, population growth and increasing regulation are causing wastewater treatment plants to become increasingly stressed, especially in countries like the UK, where many of these systems date back to the early part of the 20th century. Understanding resilience dynamics for these ageing wastewater assets represents a fundamental step in classifying multi-dimensional water stressors toward preventing severe pollution incidents. This paper explores the potential of a novel dynamic resilience approach to assess and predict the dynamic resilience of biological wastewater treatment based on the separation of stressor events (cause) and process stress (effect) to consider the deviation from reference conditions. The approach presented provides a fundamental link between (1) conventional activated sludge modelling methodologies, (2) actual biological wastewater process instrument data (potential for knowledge discovery) and (3) the characterisation of dynamic resilience in wastewater treatment processes. Results first present the dynamic resilience approach by modelling simulated shock flow conditions on an activated sludge plant, then incorporates ten years of wastewater process instrument data to demonstrate the actual dynamic resilience. The aim is to represent the “dynamic resilience” as self-ordering windows, a visual knowledge base (three dimensional, heat map), which operational staff can easily interpret. The outcomes presented suggest that such an approach is feasible and has the potential for real-time identification of conditions that result in pollution incidents based on actual historical process instrument data (knowledge discovery). Also, the methods presented could be extended to develop an improved understanding of wastewater system resilience under a range of future stressor scenarios.