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Water
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Water Quality Optimization
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Water Quality Optimization

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Quality and Contamination".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 11068

Special Issue Editors

Dr. George Besseris

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of West Attica, Athens, Greece
Interests: mechanical processes; optimization; chemical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Triantafyllos Kaloudis

E-Mail Website
Guest Editor
Athens Water Supply & Sewerage Company, EYDAP SA, 11146 Athens, Greece
Interests: Cyanotoxins; cyanobacterial metabolites; cyanobacterial blooms; detection/determination of cyanotoxins; mass spectrometry; water treatment; advanced oxidation processes; environmental chemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The availability of water hinges upon a certain water quality level that needs to be reached in order to render water suitable for consumption, farming, and other usages valuable to humans. The accessibility of water is related to ‘Goal 6’ of the United Nations Sustainable Development Goals. For this Special Issue of Water, entitled ‘Water Quality Optimization’, we solicit articles that place emphasis on novel methods that can help us to improve water quality. Therefore, the scope of this Special Issue is broad enough to include new optimization techniques that provide decision-making support to water professionals who seek to screen and optimize any type of water operation, including desalination and wastewater treatment. Such techniques may be novel mathematical or computational models that provide robust and convenient solutions to complex water problems. Contributions on state-of-the-art computational methods are also welcome as long as they are accompanied by a detailed case study in improving a unique water process. Moreover, technological advances that may help us to improve water quality by introducing new knowledge into water chemical systems are also relevant to this Special Issue.

Editor: Dr. George Besseris

Water is probably the most precious resource on the planet. Improvement of water quality is vital to public health and well-being and the protection of the environment. Recent advances in optimization techniques can greatly benefit water utilities by improving the effectiveness and efficiency of processes; however, they have a rather slow uptake by the water sector. The main aim of this Special Issue is to promote the application of novel and advanced optimization techniques in water-related processes that target high-quality water. We welcome original research papers on water-related processes, including drinking water and wastewater treatment (experimental–laboratory, pilot, or actual-scale), the design and development of materials and components for water treatment, improvements to the monitoring of water quality, and methods of analysis.

Editor: Dr. Triantafyllos Kaloudis

Dr. George Besseris
Dr. Triantafyllos Kaloudis
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

  • water quality
  • water quality optimization
  • water qualimetrics
  • statistical/empirical techniques for water quality improvement
  • case studies in water quality improvement
  • artificial intelligence, evolutionary computation and swarm intelligence in environmental aquametrics
  • optimizing desalination processes
  • optimizing wastewater treatment processes
  • drinking water/wastewater treatment

Published Papers (5 papers)

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Research

26 pages, 4758 KiB  
Article
Databionic Swarm Intelligence to Screen Wastewater Recycling Quality with Factorial and Hyper-Parameter Non-Linear Orthogonal Mini-Datasets
by George Besseris
Water 2022, 14(13), 1990; https://0-doi-org.brum.beds.ac.uk/10.3390/w14131990 - 21 Jun 2022
Viewed by 1349
Abstract
Electrodialysis (ED) may be designed to enhance wastewater recycling efficiency for crop irrigation in areas where water distribution is otherwise inaccessible. ED process controls are difficult to manage because the ED cells need to be custom-built to meet local requirements, and the wastewater [...] Read more.
Electrodialysis (ED) may be designed to enhance wastewater recycling efficiency for crop irrigation in areas where water distribution is otherwise inaccessible. ED process controls are difficult to manage because the ED cells need to be custom-built to meet local requirements, and the wastewater influx often has heterogeneous ionic properties. Besides the underlying complex chemical phenomena, recycling screening is a challenge to engineering because the number of experimental trials must be maintained low in order to be timely and cost-effective. A new data-centric approach is presented that screens three water quality indices against four ED-process-controlling factors for a wastewater recycling application in agricultural development. The implemented unsupervised solver must: (1) be fine-tuned for optimal deployment and (2) screen the ED trials for effect potency. The databionic swarm intelligence classifier is employed to cluster the L9(34) OA mini-dataset of: (1) the removed Na+ content, (2) the sodium adsorption ratio (SAR) and (3) the soluble Na+ percentage. From an information viewpoint, the proviso for the factor profiler is that it should be apt to detect strength and curvature effects against not-computable uncertainty. The strength hierarchy was analyzed for the four ED-process-controlling factors: (1) the dilute flow, (2) the cathode flow, (3) the anode flow and (4) the voltage rate. The new approach matches two sequences for similarities, according to: (1) the classified cluster identification string and (2) the pre-defined OA factorial setting string. Internal cluster validity is checked by the Dunn and Davies–Bouldin Indices, after completing a hyper-parameter L8(4122) OA screening. The three selected hyper-parameters (distance measure, structure type and position type) created negligible variability. The dilute flow was found to regulate the overall ED-based separation performance. The results agree with other recent statistical/algorithmic studies through external validation. In conclusion, statistical/algorithmic freeware (R-packages) may be effective in resolving quality multi-indexed screening tasks of intricate non-linear mini-OA-datasets. Full article
(This article belongs to the Special Issue Water Quality Optimization)
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15 pages, 3365 KiB  
Article
Wastewater Quality Screening Using Affinity Propagation Clustering and Entropic Methods for Small Saturated Nonlinear Orthogonal Datasets
by George Besseris
Water 2022, 14(8), 1238; https://0-doi-org.brum.beds.ac.uk/10.3390/w14081238 - 12 Apr 2022
Cited by 3 | Viewed by 1379
Abstract
Wastewater recycling efficiency improvement is vital to arid regions, where crop irrigation is imperative. Analyzing small, unreplicated–saturated, multiresponse, multifactorial datasets from novel wastewater electrodialysis (ED) applications requires specialized screening/optimization techniques. A new approach is proposed to glean information from structured Taguchi-type sampling schemes [...] Read more.
Wastewater recycling efficiency improvement is vital to arid regions, where crop irrigation is imperative. Analyzing small, unreplicated–saturated, multiresponse, multifactorial datasets from novel wastewater electrodialysis (ED) applications requires specialized screening/optimization techniques. A new approach is proposed to glean information from structured Taguchi-type sampling schemes (nonlinear fractional factorial designs) in the case that direct uncertainty quantification is not computable. It uses a double information analysis–affinity propagation clustering and entropy to simultaneously discern strong effects and curvature type while profiling multiple water-quality characteristics. Three water quality indices, which are calculated from real ED process experiments, are analyzed by examining the hierarchical behavior of four controlling factors: (1) the dilute flow, (2) the cathode flow, (3) the anode flow, and (4) the voltage rate. The three water quality indices are: the removed sodium content, the sodium adsorption ratio, and the soluble sodium percentage. The factor that influences the overall wastewater separation ED performance is the dilute flow, according to both analyses’ versions. It caused the maximum contrast difference in the heatmap visualization, and it minimized the relative information entropy at the two operating end points. The results are confirmed with a second published independent dataset. Furthermore, the final outcome is scrutinized and found to agree with other published classification and nonparametric screening solutions. A combination of modern classification and simple entropic methods which are offered through freeware R-packages might be effective for testing high-complexity ‘small-and-dense’ nonlinear OA datasets, highlighting an obfuscated experimental uncertainty. Full article
(This article belongs to the Special Issue Water Quality Optimization)
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16 pages, 8707 KiB  
Article
Micro-Clustering and Rank-Learning Profiling of a Small Water-Quality Multi-Index Dataset to Improve a Recycling Process
by George Besseris
Water 2021, 13(18), 2469; https://0-doi-org.brum.beds.ac.uk/10.3390/w13182469 - 08 Sep 2021
Cited by 4 | Viewed by 2067
Abstract
The efficiency improvement of wastewater recycling has been prioritized by ‘Goal 6’ of the United Nations Sustainable Development initiative. A methodology is developed to synchronously profile multiple water-quality indices of a wastewater electrodialysis (ED) process. The non-linear multifactorial screener is exclusively synthesized by [...] Read more.
The efficiency improvement of wastewater recycling has been prioritized by ‘Goal 6’ of the United Nations Sustainable Development initiative. A methodology is developed to synchronously profile multiple water-quality indices of a wastewater electrodialysis (ED) process. The non-linear multifactorial screener is exclusively synthesized by assembling proper R-based statistical freeware routines. In sync with current trends, the new methodology promotes convenient, open and rapid implementation. The new proposal unites the ‘small-and-fast’ data-sampling features of the fractional multifactorial designs to the downsizing, by microclustering, of the multiple water quality indices—using optimized silhouette-based classification. The non-linear multifactorial profiling process is catalyzed by the ‘ordinalization’ of the regular nominal nature of the resulting optimum clusters. A bump chart screening virtually eliminates weak performances. A follow-up application of the ordinal regression succeeds in assigning statistical significance to the resultant factorial potency. The rank-learning aptitude of the new profiler is tested and confirmed on recently published wastewater ED-datasets. The small ED-datasets attest to the usefulness to convert limited data in real world applications, wherever there is a necessity to improve the quality status of water for agricultural irrigation in arid areas. The predictions have been compared with other techniques and found to be agreeable. Full article
(This article belongs to the Special Issue Water Quality Optimization)
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24 pages, 5085 KiB  
Article
Low Cost Activated Carbon for Removal of NOM and DBPs: Optimization and Comparison
by Hoda Tafvizi, Shakhawat Chowdhury and Tahir Husain
Water 2021, 13(16), 2244; https://0-doi-org.brum.beds.ac.uk/10.3390/w13162244 - 17 Aug 2021
Cited by 6 | Viewed by 2550
Abstract
Higher concentrations of disinfection byproducts (DBPs) in small water systems have been a challenge. Adsorption by tailored activated carbon (AC), developed from waste materials of a pulp and paper company using optimization of chemical activation by nitric acid followed by physical activation and [...] Read more.
Higher concentrations of disinfection byproducts (DBPs) in small water systems have been a challenge. Adsorption by tailored activated carbon (AC), developed from waste materials of a pulp and paper company using optimization of chemical activation by nitric acid followed by physical activation and metal coating, was tested for the removal of natural organic matter from water using synthetic and natural water. AC was coated with aluminum and iron salts in a ratio of 0.25 to 10.0% of metal: AC (wt:wt%). The optimization of dosage, pH, and time was performed to achieve the highest adsorption capacity. The modified AC of 0.75% Fe-AC and 1.0% Al-AC showed 35–44% improvement in DOC removal from natural water. An enhancement of 40.7% in THMs removal and 77.1% in HAAs removal, compared to non-modified, AC were achieved. The pseudo-second order was the best fitted kinetic model for DOC removal, representing a physiochemical mechanism of adsorption. Full article
(This article belongs to the Special Issue Water Quality Optimization)
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14 pages, 1364 KiB  
Article
Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case
by Elizaveta Yudina, Anna Petrovskaia, Dmitrii Shadrin, Polina Tregubova, Elizaveta Chernova, Mariia Pukalchik and Ivan Oseledets
Water 2021, 13(7), 888; https://0-doi-org.brum.beds.ac.uk/10.3390/w13070888 - 24 Mar 2021
Cited by 8 | Viewed by 2086
Abstract
Currently many countries are struggling to rationalize water quality monitoring stations which is caused by economic demand. Though this process is essential indeed, the exact elements of the system to be optimized without a subsequent quality and accuracy loss still remain obscure. Therefore, [...] Read more.
Currently many countries are struggling to rationalize water quality monitoring stations which is caused by economic demand. Though this process is essential indeed, the exact elements of the system to be optimized without a subsequent quality and accuracy loss still remain obscure. Therefore, accurate historical data on groundwater pollution is required to detect and monitor considerable environmental impacts. To collect such data appropriate sampling and assessment methodologies with an optimum spatial distribution augmented should be exploited. Thus, the configuration of water monitoring sampling points and the number of the points required are now considered as a fundamental optimization challenge. The paper offers and tests metaheuristic approaches for optimization of monitoring procedure and multi-factors assessment of water quality in “New Moscow” area. It is shown that the considered algorithms allow us to reduce the size of the training sample set, so that the number of points for monitoring water quality in the area can be halved. Moreover, reducing the dataset size improved the quality of prediction by 20%. The obtained results convincingly demonstrate that the proposed algorithms dramatically decrease the total cost of analysis without dampening the quality of monitoring and could be recommended for optimization purposes. Full article
(This article belongs to the Special Issue Water Quality Optimization)
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