No abstract

Data scarcity can be considered as the main limitation for a more widespread utilization of mathematical models in the design, optimization and control of biological nutrient removal activated sludge systems (BNRAS). High cost and demanding workload related to experimental data and sufficient sampling campaigns make the data collection process an unpleasant necessity for managing stakeholders in modelling projects. Complicated use of online-sensors leading to frequent erroneous readings and dynamic nature of wastewater treatment processes can intensify the data scarcity problems. This paper investigates the influence of data scarcity on the development and calibration of wastewater treatment plant (WWTP) models. A straightforward methodology is proposed to address the challenges associated with data quality and quantity problems in modelling of a BNRAS in the largest Italian WWTP located in Castiglione, Italy. The plant operational modes, weather condition and sensor performance during the sampling campaigns were the main sources of the data scarcity. Influent, biokinetic, aeration, hydraulic and transport, clarifier, energy consumption and effluent sub-models were calibrated by use of the proposed extensive step-wise calibration process. The Monte Carlo analysis was performed to quantify the uncertainty of the modelling results. The proposed methodology could be implemented in engineering practice to develop and calibrate the WWTP models while it increases the awareness about modelling robustness and its characterized uncertainty to avoid bad modelling practice.

This study develops an accurate numerical tool for investigating optimal retrofit configurations in order to minimize wave overtopping from a vertical seawall due to extreme climatic events and under changing climate. A weakly compressible smoothed particle hydrodynamics (WCSPH) model is developed to simulate the wave-structure interactions for coastal retrofit structures in front of a vertical seawall. A range of possible physical configurations of coastal retrofits including re-curve wall and submerged breakwater are modelled with the numerical model to understand their performance under different wave and structural conditions. The numerical model is successfully validated against laboratory data collected in 2D wave flume at Warwick Water Laboratory. The findings of numerical modelling are in good agreement with the laboratory data. The results indicate that recurve wall is more effective in mitigating wave overtopping and provides more resilience to coastal flooding in comparison to base-case (plain vertical wall) and submerged breakwater retrofit.

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 WRRF 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.