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Monitoring, Modeling, and Automation of Water and Wastewater Processes

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 5132

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Special Issue Editors

Dr. Edoardo Bertone

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Guest Editor

Senior Lecturer, School of Engineering and Built Environment, Cities Research Institute & Australian Rivers Institute Griffith University, Gold Coast Campus, Gold Coast, QLD 4222, Australia
Interests: water quality monitoring and modelling; water treatment optimisation
Special Issues, Collections and Topics in MDPI journals

Dr. Benny Zuse Rousso

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Guest Editor

Research Fellow, School of Engineering, Deakin University, Waurn Ponds 3220, Australia
Interests: water quality monitoring and modelling; wastewater treatment

Special Issue Information

Dear Colleagues,

A safe and reliable water supply and wastewater disposal during challenging future scenarios demand innovation in current engineering practices. Improved automation and control of processes can be achieved by modernising water monitoring and by adopting relevant digital solutions such as machine learning and modelling.

Climate change, extreme weather and population growth have impacted many sectors of our society, including significant challenges for the water supply and wastewater treatment industry. One of the most cost effective and proactive ways to optimise current water and wastewater management practices is to improve how water is monitored throughout the system, i.e., from catchment to final disposal in the environment. In the last few decades, several new sensing technologies have become available to water authorities, allowing high-frequency and remote data acquisition of critical parameters (e.g., water quality, hydraulics).

In addition to a more comprehensive day-to-day monitoring, the acquisition of large amounts of data from such a new generation of sensors (e.g., in situ high-frequency, satellite) creates an opportunity for machine learning, and more broadly artificial intelligence, to transform raw data into useful information by unveiling concealed patterns and relationships. This information can help to predict and automate several processes, leading to optimised treatment, supply and disposal of water. However, the limited expertise in the industry in calibrating, maintaining, operating, modelling and integrating alternative sources of data has so far hindered a wider implementation of state-of-the-art research, which if adopted would result in benefits across all three sustainability pillars, i.e., social, economic and environmental.

In this context, this Special Issue focuses on promoting recent practical advancements on using non-traditional sources of data (e.g., high-frequency and remote sensing) to achieve automation and/or optimisation of water/wastewater processes. We welcome submissions outlining: applications of digital twins in water and wastewater conveyance and treatment facilities; the enhancement of reliability, robustness and application of high-frequency (e.g., optical, acoustics) water sensors; and the development of relevant data-driven/hybrid predictive models (e.g., early warning systems, water and wastewater processes optimisation).

Dr. Edoardo Bertone
Dr. Benny Zuse Rousso
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

data driven modelling
digital twin
high-frequency monitoring
water quality
water treatment
hydraulics
water and wastewater networks

Published Papers (4 papers)

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Research

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16 pages, 4803 KiB

Open AccessArticle

Comparing Darcy’s Law and the Brinkman Equation for Numerical Simulations of Saltwater Intrusion

by Jingwei Yao and Hong Zhang

Sustainability 2023, 15(18), 13287; https://doi.org/10.3390/su151813287 - 05 Sep 2023

Cited by 1 | Viewed by 939

Abstract

Saltwater intrusion into coastal aquifers presents a significant global challenge to fresh groundwater resources. Numerical modelling represents a valuable tool to study this phenomenon. Darcy’s Law is widely applied to groundwater studies and is extended into the Brinkman Equation to account for kinetic dissipations due to viscous shear. However, their comparative performance and accuracy in density-driven flows remain unclear. To determine the circumstances where the Brinkman Equation is required, numerical simulations with both models were implemented in hypothetical coastal aquifer scenarios. The results revealed that the largest discrepancies between the two models occur inside the dispersion zone during the break-through period, with concentration differences of up to 2.5%. The mixing of freshwater and saltwater induces rapid density and velocity variations. Brinkman’s viscous term moderates the rate of change and decreases the intrusion length by up to 6.1 m in a 180 m intrusion case. Furthermore, higher permeability and a lower recharge rate both strengthen the viscous effects in most sandy coastal aquifers. The Brinkman Equation excels at capturing intricate flow patterns with large variations. Therefore, it is necessary to be employed for studies on freshwater–saltwater interfaces and other similar conditions including groundwater–surface water interfaces, non-isothermal flows, and complex geological conditions. Full article

(This article belongs to the Special Issue Monitoring, Modeling, and Automation of Water and Wastewater Processes)

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14 pages, 2176 KiB

Open AccessArticle

Probabilistic Prediction of Satellite-Derived Water Quality for a Drinking Water Reservoir

by Edoardo Bertone and Sara Peters Hughes

Sustainability 2023, 15(14), 11302; https://doi.org/10.3390/su151411302 - 20 Jul 2023

Cited by 2 | Viewed by 855

Abstract

A Bayesian network-based modelling framework was proposed to predict the probability of exceeding critical thresholds for chlorophyll-a and turbidity in an Australian subtropical drinking water reservoir, based on Sentinel-2 data and prior knowledge. The model was trained with quasi-synchronous historical in situ and satellite data for 2018–2023 and achieved satisfactory accuracy (Brier score < 0.27 for all models) despite limited poor water quality events in the final dataset. The graphical output of the model (posterior probability maps of high turbidity or chlorophyll-a) provides an effective means for the user to evaluate both the prediction, and the uncertainty behind the predictions in a single map. This avoids loss of trust in the model and can trigger spatially targeted data collection in order to reduce uncertainty. Future work will focus on refining the modelling methodology and its automation, as well as including other data such as in situ high-frequency sensors. Full article

(This article belongs to the Special Issue Monitoring, Modeling, and Automation of Water and Wastewater Processes)

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13 pages, 805 KiB

Open AccessArticle

Machine Learning for Water Quality Assessment Based on Macrophyte Presence

by Ivana Krtolica, Dragan Savić, Bojana Bajić and Snežana Radulović

Sustainability 2023, 15(1), 522; https://doi.org/10.3390/su15010522 - 28 Dec 2022

Cited by 2 | Viewed by 1640

Abstract

The ecological state of the Danube River, as the world’s most international river basin, will always be the focus of scientists in the field of ecology and environmental engineering. The concentration of orthophosphate anions in the river is one of the main indicators of the ecological state, i.e., water quality and level of eutrophication. The sedentary nature and ability to survive in river sections, combined with the presence of high levels of orthophosphate anions, make macrophytes an appropriate biological parameter for in situ prediction of in-river monitoring processes. However, a preliminary literature review identified a lack of comprehensive analysis that can enable the prediction of the ecological state of rivers using biological parameters as the input to machine learning (ML) techniques. This work focuses on comparing eight state-of-the-art ML classification models developed for this task. The data were collected at 68 sampling sites on both river sides. The predictive models use macrophyte presence scores as input variables, and classes of the ecological state of the Danube River based on orthophosphate anions, converted into a binary scale, as outputs. The results of the predictive model comparisons show that support vector machines and tree-based models provided the best prediction capabilities. They are also a low-cost and sustainable solution to assess the ecological state of the rivers. Full article

(This article belongs to the Special Issue Monitoring, Modeling, and Automation of Water and Wastewater Processes)

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Other

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16 pages, 977 KiB

Open AccessSystematic Review

In-Lake Mechanisms for Manganese Control—A Systematic Literature Review

by Christina Semasinghe and Benny Zuse Rousso

Sustainability 2023, 15(11), 8785; https://doi.org/10.3390/su15118785 - 29 May 2023

Viewed by 1095

Abstract

An elevated concentration of manganese (Mn) in lakes is a common water quality problem faced by many water utilities. Thermal stratification is a natural phenomenon that influences the Mn concentration in lakes and can exacerbate Mn accumulation in surface water without external loading sources. While several treatment methods can be utilized to treat Mn in drinking water treatment plants, in-lake control mechanisms can be a proactive and efficient strategy for Mn control by reducing water treatment complexities and costs. Despite previous research pointing to the benefits of in-lake Mn control, the feasibility and effectiveness of the various in-lake Mn control mechanisms in lakes in different environmental conditions remains unclear. To identify and consolidate the existing research on the topic, a comprehensive, systematic literature review (SLR) was conducted. The SLR identified case studies of in-lake Mn control mechanisms in thermally stratified lakes. The identified case studies were grouped into three categories based on their goals: identification of Mn behaviour in lakes, built engineering implementations and digital solutions for process optimization and anticipation. It is critical that a site-specific understanding of Mn dynamics is obtained before implementing any built or digital solutions, because lake specific dynamics can significantly impact a solution’s performance. While most reviewed mechanisms were successful in decreasing high Mn concentrations, a lack of financial and environmental cost–benefit analyses for most in-lake Mn control mechanisms was observed, which is crucial for their adoption by water authorities. The rationale of this SLR provides a summary of the benefits and limitations of the most common in-lake Mn control mechanisms, the enabling the conditions for their implementation, and the knowledge gaps and future direction for research on the topic, being valuable to support informed decision-making by water authorities managing waterbodies with high Mn concentrations. Full article

(This article belongs to the Special Issue Monitoring, Modeling, and Automation of Water and Wastewater Processes)

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