Hybrid modelling of water resource recovery facilities: status and opportunities.

Mathematical modelling is an indispensable tool to support water resource recovery facility (WRRF) operators and engineers with the ambition of creating a truly circular economy and assuring a sustainable future. Despite the successful application of mechanistic models in the water sector, they show some important limitations and do not fully profit from the increasing digitalisation of systems and processes. Recent advances in data-driven methods have provided options for harnessing the power of Industry 4.0, but they are often limited by the lack of interpretability and extrapolation capabilities. Hybrid modelling (HM) combines these two modelling paradigms and aims to leverage both the rapidly increasing volumes of data collected, as well as the continued pursuit of greater process understanding. Despite the potential of HM in a sector that is undergoing a significant digital and cultural transformation, the application of hybrid models remains vague. This article presents an overview of HM methodologies applied to WRRFs and aims to stimulate the wider adoption and development of HM. We also highlight challenges and research needs for HM design and architecture, good modelling practice, data assurance, and software compatibility. HM is a paradigm for WRRF modelling to transition towards a more resource-efficient, resilient, and sustainable future.

Hybrid differential equations: Integrating mechanistic and data-driven techniques for modelling of water systems.

Mathematical modelling is increasingly used to improve the design, understanding, and operation of water systems. Two modelling paradigms, i.e., mechanistic and data-driven modelling, are dominant in the water sector, both with their advantages and drawbacks. Hybrid modelling aims to combine the strengths of both paradigms. Here, we introduce a novel framework that incorporates a data-driven component into an existing activated sludge model of a water resource recovery facility. In contrast to previous efforts, we tightly integrate both models by incorporating a neural differential equation into an existing mechanistic ODE model. This machine learning component fills in the knowledge gaps of the mechanistic model. We show that this approach improves the predictive capabilities of the mechanistic model and is able to extrapolate to unseen conditions, a problematic task for data-driven models. This approach holds tremendous potential for systems that are difficult to model using the mechanistic paradigm only.

Model-based control for a demand-driven biogas production to cover residual load rises.

The development of systems for energy storage and demand-driven energy production will be essential to enable the switch from fossil to renewable energy sources in future. To cover the residual load rises, a rigorous dynamic process model based on the Anaerobic Digestion Model No. 1 (ADM1) was applied to analyse the flexible operation of biogas plants. For this, the model was optimised and an operational concept for a demand-driven energy production was worked out. Different substrates were analysed, both by batch fermentation and Weende analysis with van Soest method, to determine the input data of the model. The lab results show that the substrates have got different degradation kinetics and biogas potentials. Finally, the ADM1 was extended with a feeding algorithm which is based on a PI controller. Essential feeding times and quantities of available substrates were calculated so that a biogas plant can cover a defined energy demand. The results prove that a flexible operation of biogas plants with a feeding strategy is possible.

Arsenic contamination in rainwater harvesting tanks around Lake Poopó in Oruro, Bolivia: An unrecognized health risk.

Drinking water sources used by largely rural and indigenous communities around Lake Poopó in the Bolivian Altiplano are impacted by drought and a combination of natural and anthropogenic mining-related contaminants putting the long-term health and sustainability of these communities at risk. As an alternative drinking water source, 18 rainwater harvesting tanks connected to corrugated iron roofs, each with a first-flush system, were installed in 5 communities around the lake. The water quality of these tanks was monitored over 22 months and compared to alternative unprotected surface and groundwater sources the communities previously relied upon. The rainwater quality was found to be within the Bolivian and World Health Organization (WHO) limits, except for elevated arsenic concentrations two times the recommended health limit (0.01 mg/L). Tracing arsenic concentrations through the rainwater flow-path showed that the elevated arsenic concentrations result from mineral dust particles entering the system when rainwater interacts with the roof catchment, with arsenic leaching out. A leaching test showed that 24 h of contact time between 200 mL of water and <1 g of roof dust is enough to raise the arsenic levels of the water above the Bolivian and WHO limit. Currently, no other research exists evaluating the quality of harvested rainwater in the Bolivian Altiplano for human consumption or the source of arsenic in harvested water. This represents a significant knowledge gap for future development practitioners and programs addressing water security around Lake Poopó and the wider region. As a result, it is strongly recommended to include arsenic as a standard parameter in water quality monitoring of rainwater harvesting projects, especially in active mining regions, and to optimize strategies to minimize roof dust from entering the collection system.