A systems-based approach to circular sludge management: Data-driven foresight, sustainability assessment, and strategic evaluation.

Sustainable sludge management in wastewater treatment plants is a critical challenge that demands strategic planning and holistic evaluation tools. This study presents a novel data-driven framework for sustainable, multifunctional circular sludge management. Unlike conventional models, the framework integrates circular planning, scenario-based foresight, a data-driven approach, and sustainability assessment to identify optimal sludge reuse pathways and treatment alternatives.

Real-time operation of municipal anaerobic digestion using an ensemble data mining framework.

This study presents a novel approach for real-time operation of anaerobic digestion using an ensemble decision-making framework composed of weak learner data mining models. The framework utilises simple but practical features such as waste composition, added water and feeding volume to predict biogas yield and to generate an optimised weekly operation pattern to maximise biogas production and minimise operational costs. The effectiveness of this framework is validated through a real-world case study conducted in the UK.

Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining.

This study presents a novel approach for urban flood forecasting in drainage systems using a dynamic ensemble-based data mining model which has yet to be utilised properly in this context. The proposed method incorporates an event identification technique and rainfall feature extraction to develop weak learner data mining models. These models are then stacked to create a time-series ensemble model using a decision tree algorithm and confusion matrix-based blending method.

A critical review for the impact of anaerobic digestion on the sustainable development goals.

Anaerobic Digestion (AD) technology emerges as a viable solution for managing municipal organic waste, offering pollution reduction and the generation of biogas and fertilisers. This study reviews the research works for the advancements in AD implementation to effectively impact the UN Sustainable Development Goals (SDGs). Furthermore, the study critically analyses responsible waste management that contributes to health and safety, elevating quality of life in both rural and urban areas and, finally, creates a map of AD outputs onto all 17 SDGs.

Development of an artificial intelligence-based framework for biogas generation from a micro anaerobic digestion plant.

Despite the advantages of the Anaerobic Digestion (AD) technology for organic waste management, low system performance in biogas production negatively affects the wide spread of this technology. This paper develops a new artificial intelligence-based framework to predict and optimise the biogas generated from a micro-AD plant. The framework comprises some main steps including data collection and imputation, recurrent neural network/ Non-Linear Autoregressive Exogenous (NARX) model, shuffled frog leaping algorithm (SFLA) optimisation model and sensitivity analysis.