Water

Journal Browser

► Journal Browser

Special Issue Editors

Dr. Mihaela Timofti

E-Mail Website
Guest Editor

1. Department of Chemistry, Physiscs and Environment, Dunarea de Jos University of Galati, Galati, Roamnia
2. REXDAN Research Infrastructure of UDJ Galati, Galati, Romania
Interests: surface water pollution; ground water pollution; wastewater treatment; environmental pollution; soil pollution

Dr. Ashwani Kumar Tiwari

E-Mail Website
Guest Editor

School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110 067, India
Interests: water resources management and GIS; hydrogeochemistry; pollution of water resources by geogenic and anthropogenic activities; groundwater-seawater interaction; aquifer vulnerability; water quality and health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the current context of the potable water crisis, the need to treat wastewater such that it can be reused in various fields of activity is becoming increasingly evident. Before wastewater can enter treatment stations, it is necessary to conduct quality analysis that presupposes, in most cases, the use of polluting chemicals. For this reason, the mathematical modeling of wastewater analysis and/or treatment processes could be an eco-friendly analysis and control solution for both monitoring and treatment processes.

Therefore, this Special Issue aims to highlight original research and review articles on Mathematical Modelling and Model Analysis for Wastewater Treatment.

Manuscripts should consider innovative and integrated research on mathematical modelling and model analysis applied to wastewater analysis.

I look forward to receiving your contributions.

Dr. Mihaela Timofti
Dr. Ashwani Kumar Tiwari
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. Water 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 2600 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

wastewater
wastewater treatment
mathematical modelling
model analysis

Published Papers (2 papers)

Download All Papers

Research

14 pages, 1990 KiB

Open AccessArticle

Machine Learning Techniques in Dosing Coagulants and Biopolymers for Treating Leachate Generated in Landfills

by Carlos Matovelle, María Quinteros and Diego Heras

Water 2023, 15(24), 4200; https://doi.org/10.3390/w15244200 - 5 Dec 2023

Cited by 1 | Viewed by 1173

Abstract

The leachate discharges generated in sanitary landfills contain many pollutants that are harmful to the environment; treatments are scarce and should be carried out better. The use of coagulation–flocculation processes has been one of the most widely used, but due to the complexity of the characterization of the leachate, the dosing strategy of coagulants and biopolymers needs to be clarified. Therefore, the present study was carried out to determine the doses of coagulants and biopolymers suitable for coagulation–flocculation processes in the treatment of leachates using computational models of machine learning techniques such as artificial neural networks (ANNs); these allow for decreasing the operations of the tests of jars in the laboratory, optimizing resources. Through laboratory experimentation, there are real results of the effectiveness of applying biopolymers in leachate treatments at different concentration levels. The laboratory results were taken as input variables for the algorithms used; after the validation and calibration process, we proceeded to estimate predicted data with the computational model, obtaining predictions of optimal doses for treatment with high statistical adjustment indicators. It is verified that the applied coagulation–flocculation treatments reduce the turbidity values in the leachate and contaminants associated with suspended solids. In this way, the jar tests are optimized so that the operational costs decrease without affecting the results of adequate dosing. Full article

(This article belongs to the Special Issue Mathematical Modelling and Model Analysis for Wastewater Treatment)

► Show Figures

Figure 1

23 pages, 1912 KiB

Open AccessArticle

Unlocking the Potential of Wastewater Treatment: Machine Learning Based Energy Consumption Prediction

by Yasminah Alali, Fouzi Harrou and Ying Sun

Water 2023, 15(13), 2349; https://doi.org/10.3390/w15132349 - 25 Jun 2023

Cited by 11 | Viewed by 2618

Abstract

Wastewater treatment plants (WWTPs) are energy-intensive facilities that fulfill stringent effluent quality norms. Energy consumption prediction in WWTPs is crucial for cost savings, process optimization, compliance with regulations, and reducing the carbon footprint. This paper evaluates and compares a set of 23 candidate machine-learning models to predict WWTP energy consumption using actual data from the Melbourne WWTP. To this end, Bayesian optimization has been applied to calibrate the investigated machine learning models. Random Forest and XGBoost (eXtreme Gradient Boosting) were applied to assess how the incorporated features influenced the energy consumption prediction. In addition, this study investigated the consideration of information from past data in improving prediction accuracy by incorporating time-lagged measurements. Results showed that the dynamic models using time-lagged data outperformed the static and reduced machine learning models. The study shows that including lagged measurements in the model improves prediction accuracy, and the results indicate that the dynamic K-nearest neighbors model dominates state-of-the-art methods by reaching promising energy consumption predictions. Full article

(This article belongs to the Special Issue Mathematical Modelling and Model Analysis for Wastewater Treatment)

► Show Figures