Forecasting nitrous oxide emissions from a full-scale wastewater treatment plant using LSTM-based deep learning models.

Nitrous oxide (NO) emissions from wastewater treatment plants (WWTPs) exhibit significant seasonal variability, making accurate predictions with conventional biokinetic models difficult due to complex and poorly understood biochemical processes. This study addresses these challenges by exploring data-driven alternatives, using long short-term memory (LSTM) based encoder-decoder models as basis. The models were developed for future integration into a model predictive control framework, aiming to reduce NO emissions by forecasting these over varying prediction horizons.

Enhancing inflow and infiltration detection in urban sewer networks with a new deterministic sensor placement method.

The inflow and infiltration (I&I) is an issue for many urban sewer networks (USNs), which can significantly affect system functioning. Placing sensors within the USNs is a typical approach to detect large I&I event, but deploying a limited number of sensors while achieving maximum detection reliability is challenging. While some methods are available for sensor placement, they are generally heuristic search-based methods (HSBMs) and hence the resultant sensor placement strategies (SPSs) are variable over different algorithm runs or parameterizations.

Addressing data limitations in leakage detection of water distribution systems: Data creation, data requirement reduction, and knowledge transfer.

Leakage in water distribution systems is a significant problem worldwide, leading to wastage of water resources, compromised water quality and excess energy consumption. Leakage detection is essential to reduce the duration of leaks and data-driven methods are increasingly being used for this purpose. However, these models are data hungry and available observed data, especially leakage data, is limited in most cases.

Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks.

Storm water systems (SWSs) are essential infrastructure providing multiple services including environmental protection and flood prevention. Typically, utility companies rely on computer simulators to properly design, operate, and manage SWSs. However, multiple applications in SWSs are highly time-consuming.

Detecting floating litter in freshwater bodies with semi-supervised deep learning.

Researchers and practitioners have extensively utilized supervised Deep Learning methods to quantify floating litter in rivers and canals. These methods require the availability of large amount of labeled data for training. The labeling work is expensive and laborious, resulting in small open datasets available in the field compared to the comprehensive datasets for computer vision, e.

Human health risk assessment framework for new water resource recovery-based bio-composite materials.

A new type of bio-composite material is being produced from water-recovered resources such as cellulose fibres from wastewater, calcite from the drinking water softening process, and grass and reed from waterboard sites. These raw materials may be contaminated with pathogens and chemicals such as , heavy metals, and resin compounds. A novel risk assessment framework is proposed here, addressing human health risks during the production of new bio-composite materials.

Environmental risk assessment related to using resource recovery-based bio-composite materials in the aquatic environment with new laboratory leaching test data.

The concept of circular economy, aiming at increasing the sustainability of products and services in the water and other sectors, is gaining momentum worldwide. Driven by this concept, novel bio-composite materials produced by recovering resources from different parts of the water cycle are now manufactured in The Netherlands. The new materials are used for different products such as canal bank protection elements, as an alternative to similar elements made of hardwood.

Limitations of a biokinetic model to predict the seasonal variations of nitrous oxide emissions from a full-scale wastewater treatment plant.

A biokinetic model based on BioWin's Activated Sludge Digestion Model (ASDM) coupled with a nitrous oxide (NO) model was setup and calibrated for a full-scale wastewater treatment plant (WWTP) Amsterdam West, in the Netherlands. The model was calibrated using one year of continuous data to predict the seasonal variations of NO emissions in the gaseous phase. This, according to our best knowledge, is the most complete full-scale data set used to date for this purpose.

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 new approach to circularity assessment for a sustainable water sector: Accounting for environmental functional flows and losses.

Resource recovery solutions can reduce the water sector's resource use intensity. With many such solutions being proposed, an assessment method for effective decision-making is needed. The water sector predominantly deals with biogeochemical resources (e.

Predictive heuristic control: Inferring risks from heterogeneous nowcast accuracy.

Urban Drainage Systems can cause ecological and public health issues by releasing untreated contaminated water into the environment. Real-time control (RTC), augmented with rainfall nowcast, can effectively reduce these pollution loads. This research aims to identify key dynamics in the nowcast accuracies and relate those to the performance of nowcast-informed rule-based (RB)-RTC procedures.

Deep learning for detecting macroplastic litter in water bodies: A review.

Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items >5 mm) in water is essential to estimate the quantities, compositions and sources, identify emerging trends, and design preventive measures or mitigation strategies. In recent years, researchers have demonstrated the potential of computer vision (CV) techniques based on deep learning (DL) for automated detection of macroplastic litter in water bodies.

Modelling the performance of an integrated fixed-film activated sludge (IFAS) system: a systematic approach to automated calibration.

IFAS systems are inherently complex due to the hybrid use of both suspended and attached bacterial colonies for the purpose of pollutant degradation as part of wastewater treatment. This poses challenges when attempting to represent these systems mathematically due to the vast number of parameters involved. Besides becoming convoluted, large effort will be incurred during model calibration.

A review of serious games for urban water management decisions: current gaps and future research directions.

Urban water management (UWM) is a complex problem characterized by multiple alternatives, conflicting objectives, and multiple uncertainties about key drivers like climate change, population growth, and increasing urbanization. Serious games are becoming a popular means to support decision-makers who are responsible for the planning and management of urban water systems. This is evident in the increasing number of articles about serious games in recent years.

Towards the long term implementation of real time control of combined sewer systems: a review of performance and influencing factors.

Real Time Control (RTC) is widely accepted as a cost-effective way to operate urban drainage systems (UDS) effectively. However, what factors influence RTC efficacy and how this might change in the long term remains largely unknown. This paper reviews the literature to understand what these factors likely are, and how they can be assessed in the future.

Foul sewer model development using geotagged information and smart water meter data.

Hydraulic modeling of a foul sewer system (FSS) enables a better understanding of the behavior of the system and its effective management. However, there is generally a lack of sufficient field measurement data for FSS model development due to the low number of in-situ sensors for data collection. To this end, this study proposes a new method to develop FSS models based on geotagged information and water consumption data from smart water meters that are readily available.

Water quality modeling in sewer networks: Review and future research directions.

Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs.

Developing a fuzzy logic-based risk assessment for groundwater contamination from well integrity failure during hydraulic fracturing.

Recent natural gas development by means of hydraulic fracturing requires a detailed risk analysis to eliminate or mitigate damage to the natural environment. Such geo-energy related subsurface activities involve complex engineering processes and uncertain data, making comprehensive, quantitative risk assessments a challenge to develop. This research seeks to develop a risk framework utilising data for quantitative numerical analysis and expert knowledge for qualitative analysis in the form of fuzzy logic, focusing on hydraulically fractured wells during the well stimulation stage applied to scenarios in the UK and Canada.

A novel machine learning application: Water quality resilience prediction Model.

Resilience-informed water quality management embraces the growing environmental challenges and provides greater accuracy by unpacking the systems' characteristics in response to failure conditions in order to identify more effective opportunities for intervention. Assessing the resilience of water quality requires complex analysis of influential parameters which can be challenging, time consuming and costly to compute. It may also require building detailed conceptual and/or physically process-based models that are difficult to build, calibrate and validate.

Predicting non-deposition sediment transport in sewer pipes using Random forest.

Sediment transport in sewers has been extensively studied in the past. This paper aims to propose a new method for predicting the self-cleansing velocity required to avoid permanent deposition of material in sewer pipes. The new Random Forest (RF) based model was implemented using experimental data collected from the literature.

Real-time foul sewer hydraulic modelling driven by water consumption data from water distribution systems.

Real-time hydraulic modelling can be used to address a wide range of issues in a foul sewer system and hence can help improve its daily operation and maintenance. However, the current bottleneck within real-time FSS modelling is the lack of spatio-temporal inflow data. To address the problem, this paper proposes a new method to develop real-time FSS models driven by water consumption data from associated water distribution systems (WDSs) as they often have a proportionally larger number of sensors.

Using Complex Network Analysis for Optimization of Water Distribution Networks.

The optimization of water networks supports the decision-making process by identifying the optimal trade-off between costs and performance (e.g., resilience and leakage).

Non-deposition self-cleansing models for large sewer pipes.

Multiple models from the literature and experimental datasets have been developed and collected to predict sediment transport in sewers. However, all these models were developed for smaller sewer pipes, i.e.

A statistical analysis on the effect of preceding dry weather on sewer blockages in South Wales.

Water and sewerage companies (WaSC) in the UK are under increasing pressures to improve customer satisfaction. The biggest cause for customer dissatisfaction in the wastewater sector is a service failure caused by a blockage. There is therefore a need to understand the factors which influence blockage processes in order to prevent them.

Predicting impacts of water conservation with a stochastic sewer model.

Population growth and climate change put a strain on water resources; hence, there are growing initiatives to reduce water use. Reducing household water use will likely reduce sewer input. This work demonstrates the use of a stochastic sewer model to quantify the effect water conservation has on sewer hydraulics and wastewater concentration.